Operator Schemas#
This file is automatically generated from the def files via this script. Do not modify directly and instead edit operator definitions.
For an operator input/output’s differentiability, it can be differentiable, non-differentiable, or undefined. If a variable’s differentiability is not specified, that variable has undefined differentiability.
ai.onnx (default)#
Operator |
Since version |
|
|---|---|---|
Scatter (deprecated) |
||
Upsample (deprecated) |
||
Function |
Since version |
Function version |
15 |
||
17 |
||
15 |
||
12 |
||
18 |
||
13 |
||
11 |
||
18 |
||
16 |
||
18 |
||
17 |
||
17 |
||
18 |
||
14 |
||
17, 18 |
||
16 |
||
16 |
||
13, 18 |
||
13, 18 |
||
18 |
||
13 |
||
16 |
||
11 |
||
18 |
||
18 |
||
18 |
||
18 |
||
18 |
||
18 |
||
18 |
||
17 |
||
18 |
||
13, 18 |
||
13 |
||
1 |
||
18 |
||
18 |
ai.onnx.preview.training#
Operator |
Since version |
|
|---|---|---|
ai.onnx (default)#
Abs#
Absolute takes one input data (Tensor
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
abs
node = onnx.helper.make_node(
"Abs",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = abs(x)
expect(node, inputs=[x], outputs=[y], name="test_abs")
Sample Implementation#
Abs
# SPDX-License-Identifier: Apache-2.0
import numpy as np
def abs(input: np.ndarray) -> np.ndarray:
return np.abs(input)
Acos#
Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The arccosine of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
acos
node = onnx.helper.make_node(
"Acos",
inputs=["x"],
outputs=["y"],
)
x = np.array([-0.5, 0, 0.5]).astype(np.float32)
y = np.arccos(x)
expect(node, inputs=[x], outputs=[y], name="test_acos_example")
x = np.random.rand(3, 4, 5).astype(np.float32)
y = np.arccos(x)
expect(node, inputs=[x], outputs=[y], name="test_acos")
Acosh#
Calculates the hyperbolic arccosine of the given input tensor element-wise.
Version#
This version of the operator has been available since version 9 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The hyperbolic arccosine values of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
acosh
node = onnx.helper.make_node(
"Acosh",
inputs=["x"],
outputs=["y"],
)
x = np.array([10, np.e, 1]).astype(np.float32)
y = np.arccosh(x) # expected output [2.99322295, 1.65745449, 0.]
expect(node, inputs=[x], outputs=[y], name="test_acosh_example")
x = np.random.uniform(1.0, 10.0, (3, 4, 5)).astype(np.float32)
y = np.arccosh(x)
expect(node, inputs=[x], outputs=[y], name="test_acosh")
Add#
Performs element-wise binary addition (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Inputs#
- A (differentiable) : T
- First operand.
- B (differentiable) : T
- Second operand.
Outputs#
- C (differentiable) : T
- Result, has same element type as two inputs
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
add
node = onnx.helper.make_node(
"Add",
inputs=["x", "y"],
outputs=["sum"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
expect(node, inputs=[x, y], outputs=[x + y], name="test_add")
add_broadcast
node = onnx.helper.make_node(
"Add",
inputs=["x", "y"],
outputs=["sum"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
expect(node, inputs=[x, y], outputs=[x + y], name="test_add_bcast")
add_uint8
node = onnx.helper.make_node(
"Add",
inputs=["x", "y"],
outputs=["sum"],
)
x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint8")
And#
Returns the tensor resulted from performing the and logical operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Other versions of this operator: 1
Inputs#
- A (non-differentiable) : T
- First input operand for the logical operator.
- B (non-differentiable) : T
- Second input operand for the logical operator.
Outputs#
- C (non-differentiable) : T1
- Result tensor.
Type Constraints#
- T : tensor(bool)
- Constrain input to boolean tensor.
- T1 : tensor(bool)
- Constrain output to boolean tensor.
Examples#
and
node = onnx.helper.make_node(
"And",
inputs=["x", "y"],
outputs=["and"],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(bool)
y = (np.random.randn(3, 4) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and2d")
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(3, 4, 5) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and3d")
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and4d")
and_broadcast
node = onnx.helper.make_node(
"And",
inputs=["x", "y"],
outputs=["and"],
)
# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(5) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v1d")
# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(4, 5) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v2d")
# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(5, 6) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v2d")
# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(4, 5, 6) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v3d")
# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
z = np.logical_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v4d")
ArgMax#
Computes the indices of the max elements of the input tensor’s element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the max is selected if the max appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- axis : int (default is 0)
- The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- select_last_index : int (default is 0)
- Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).
Inputs#
- data (non-differentiable) : T
- An input tensor.
Outputs#
- reduced (non-differentiable) : tensor(int64)
- Reduced output tensor with integer data type.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
default_axes_keepdims
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
keepdims = 1
node = onnx.helper.make_node(
"ArgMax", inputs=["data"], outputs=["result"], keepdims=keepdims
)
# result: [[1, 1]]
result = argmax_use_numpy(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_default_axis_example",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [1, 3, 4]
result = argmax_use_numpy(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_default_axis_random",
)
default_axes_keepdims_select_last_index
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
keepdims = 1
node = onnx.helper.make_node(
"ArgMax",
inputs=["data"],
outputs=["result"],
keepdims=keepdims,
select_last_index=True,
)
# result: [[1, 1]]
result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_default_axis_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [1, 3, 4]
result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_default_axis_random_select_last_index",
)
keepdims
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
node = onnx.helper.make_node(
"ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# result: [[0], [1]]
result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node, inputs=[data], outputs=[result], name="test_argmax_keepdims_example"
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 1, 4]
result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node, inputs=[data], outputs=[result], name="test_argmax_keepdims_random"
)
keepdims_select_last_index
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
node = onnx.helper.make_node(
"ArgMax",
inputs=["data"],
outputs=["result"],
axis=axis,
keepdims=keepdims,
select_last_index=True,
)
# result: [[1], [1]]
result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_keepdims_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 1, 4]
result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_keepdims_random_select_last_index",
)
negative_axis_keepdims
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
node = onnx.helper.make_node(
"ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# result: [[0], [1]]
result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_negative_axis_keepdims_example",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 3, 1]
result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_negative_axis_keepdims_random",
)
negative_axis_keepdims_select_last_index
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
node = onnx.helper.make_node(
"ArgMax",
inputs=["data"],
outputs=["result"],
axis=axis,
keepdims=keepdims,
select_last_index=True,
)
# result: [[1], [1]]
result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_negative_axis_keepdims_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 3, 1]
result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_negative_axis_keepdims_random_select_last_index",
)
no_keepdims
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
node = onnx.helper.make_node(
"ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# result: [0, 1]
result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_no_keepdims_example",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 4]
result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_random"
)
no_keepdims_select_last_index
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
node = onnx.helper.make_node(
"ArgMax",
inputs=["data"],
outputs=["result"],
axis=axis,
keepdims=keepdims,
select_last_index=True,
)
# result: [1, 1]
result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_no_keepdims_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 4]
result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmax_no_keepdims_random_select_last_index",
)
ArgMin#
Computes the indices of the min elements of the input tensor’s element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the min is selected if the min appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- axis : int (default is 0)
- The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- select_last_index : int (default is 0)
- Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).
Inputs#
- data (non-differentiable) : T
- An input tensor.
Outputs#
- reduced (non-differentiable) : tensor(int64)
- Reduced output tensor with integer data type.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
default_axes_keepdims
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
keepdims = 1
node = onnx.helper.make_node(
"ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims
)
# The content of result is : [[0], [0]]
result = argmin_use_numpy(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_default_axis_example",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [1, 3, 4]
result = argmin_use_numpy(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_default_axis_random",
)
default_axes_keepdims_select_last_index
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
keepdims = 1
node = onnx.helper.make_node(
"ArgMin",
inputs=["data"],
outputs=["result"],
keepdims=keepdims,
select_last_index=True,
)
# result: [[0, 0]]
result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_default_axis_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [1, 3, 4]
result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_default_axis_random_select_last_index",
)
keepdims
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
node = onnx.helper.make_node(
"ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# The content of result is : [[1], [0]]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example"
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 1, 4]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random"
)
keepdims_select_last_index
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
node = onnx.helper.make_node(
"ArgMin",
inputs=["data"],
outputs=["result"],
axis=axis,
keepdims=keepdims,
select_last_index=True,
)
# result: [[1], [0]]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_keepdims_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 1, 4]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_keepdims_random_select_last_index",
)
negative_axis_keepdims
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
node = onnx.helper.make_node(
"ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# The content of result is : [[1], [0]]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_negative_axis_keepdims_example",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 3, 1]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_negative_axis_keepdims_random",
)
negative_axis_keepdims_select_last_index
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
node = onnx.helper.make_node(
"ArgMin",
inputs=["data"],
outputs=["result"],
axis=axis,
keepdims=keepdims,
select_last_index=True,
)
# result: [[1], [0]]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_negative_axis_keepdims_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 3, 1]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_negative_axis_keepdims_random_select_last_index",
)
no_keepdims
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
node = onnx.helper.make_node(
"ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# The content of result is : [[1, 0]]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_no_keepdims_example",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 4]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random"
)
no_keepdims_select_last_index
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
node = onnx.helper.make_node(
"ArgMin",
inputs=["data"],
outputs=["result"],
axis=axis,
keepdims=keepdims,
select_last_index=True,
)
# result: [[1, 0]]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_no_keepdims_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 4]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_no_keepdims_random_select_last_index",
)
Asin#
Calculates the arcsine (inverse of sine) of the given input tensor, element-wise.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The arcsine of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
asin
node = onnx.helper.make_node(
"Asin",
inputs=["x"],
outputs=["y"],
)
x = np.array([-0.5, 0, 0.5]).astype(np.float32)
y = np.arcsin(x)
expect(node, inputs=[x], outputs=[y], name="test_asin_example")
x = np.random.rand(3, 4, 5).astype(np.float32)
y = np.arcsin(x)
expect(node, inputs=[x], outputs=[y], name="test_asin")
Asinh#
Calculates the hyperbolic arcsine of the given input tensor element-wise.
Version#
This version of the operator has been available since version 9 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The hyperbolic arcsine values of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
asinh
node = onnx.helper.make_node(
"Asinh",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.arcsinh(x) # expected output [-0.88137358, 0., 0.88137358]
expect(node, inputs=[x], outputs=[y], name="test_asinh_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.arcsinh(x)
expect(node, inputs=[x], outputs=[y], name="test_asinh")
Atan#
Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The arctangent of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
atan
node = onnx.helper.make_node(
"Atan",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.arctan(x)
expect(node, inputs=[x], outputs=[y], name="test_atan_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.arctan(x)
expect(node, inputs=[x], outputs=[y], name="test_atan")
Atanh#
Calculates the hyperbolic arctangent of the given input tensor element-wise.
Version#
This version of the operator has been available since version 9 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The hyperbolic arctangent values of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
atanh
node = onnx.helper.make_node(
"Atanh",
inputs=["x"],
outputs=["y"],
)
x = np.array([-0.5, 0, 0.5]).astype(np.float32)
y = np.arctanh(x) # expected output [-0.54930615, 0., 0.54930615]
expect(node, inputs=[x], outputs=[y], name="test_atanh_example")
x = np.random.uniform(0.0, 1.0, (3, 4, 5)).astype(np.float32)
y = np.arctanh(x)
expect(node, inputs=[x], outputs=[y], name="test_atanh")
AveragePool#
AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following:
output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
or
output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
if ceil_mode is enabled
* pad_shape[i] is sum of pads along axis i
auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
And pad shape will be following if SAME_UPPER or SAME_LOWER:
pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Attributes#
- auto_pad : string (default is NOTSET)
- auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
- ceil_mode : int (default is 0)
- Whether to use ceil or floor (default) to compute the output shape.
- count_include_pad : int (default is 0)
- Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.
- kernel_shape : list of ints (required)
- The size of the kernel along each axis.
- pads : list of ints
- Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
- strides : list of ints
- Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs#
- X (differentiable) : T
- Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
Outputs#
- Y (differentiable) : T
- Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
averagepool_1d_default
"""
input_shape: [1, 3, 32]
output_shape: [1, 3, 31]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2],
)
x = np.random.randn(1, 3, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = [2]
strides = [1]
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_1d_default")
averagepool_2d_ceil
"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[2, 2],
ceil_mode=True,
)
x = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
]
).astype(np.float32)
y = np.array([[[[6, 7.5], [12, 13.5]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil")
averagepool_2d_default
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 31, 31]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_default")
averagepool_2d_pads
"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2],
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = 2
pad_top = 2
pad_right = 2
pad_left = 2
pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
out_shape = get_output_shape(
"VALID", np.add(x_shape[2:], pad_shape), kernel_shape, strides
)
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads")
averagepool_2d_pads_count_include_pad
"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2],
count_include_pad=1,
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = 2
pad_top = 2
pad_right = 2
pad_left = 2
pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
out_shape = get_output_shape(
"VALID", np.add(x_shape[2:], pad_shape), kernel_shape, strides
)
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=0,
)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
pad_shape,
"AVG",
count_include_pad=1,
)
expect(
node,
inputs=[x],
outputs=[y],
name="test_averagepool_2d_pads_count_include_pad",
)
averagepool_2d_precomputed_pads
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array(
[
[
[
[7, 7.5, 8, 8.5, 9],
[9.5, 10, 10.5, 11, 11.5],
[12, 12.5, 13, 13.5, 14],
[14.5, 15, 15.5, 16, 16.5],
[17, 17.5, 18, 18.5, 19],
]
]
]
).astype(np.float32)
expect(
node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads"
)
averagepool_2d_precomputed_pads_count_include_pad
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
count_include_pad=1,
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array(
[
[
[
[2.5200, 3.6000, 4.8000, 4.0800, 3.2400],
[4.5600, 6.4000, 8.4000, 7.0400, 5.5200],
[7.2000, 10.0000, 13.0000, 10.8000, 8.4000],
[6.9600, 9.6000, 12.4000, 10.2400, 7.9200],
[6.1200, 8.4000, 10.8000, 8.8800, 6.8400],
]
]
]
).astype(np.float32)
expect(
node,
inputs=[x],
outputs=[y],
name="test_averagepool_2d_precomputed_pads_count_include_pad",
)
averagepool_2d_precomputed_same_upper
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 3, 3]
pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[2, 2],
auto_pad="SAME_UPPER",
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[4, 5.5, 7], [11.5, 13, 14.5], [19, 20.5, 22]]]]).astype(
np.float32
)
expect(
node,
inputs=[x],
outputs=[y],
name="test_averagepool_2d_precomputed_same_upper",
)
averagepool_2d_precomputed_strides
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[4, 6], [14, 16]]]]).astype(np.float32)
expect(
node,
inputs=[x],
outputs=[y],
name="test_averagepool_2d_precomputed_strides",
)
averagepool_2d_same_lower
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_LOWER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape("SAME_LOWER", x_shape[2:], kernel_shape, strides)
pad_shape = get_pad_shape(
"SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_bottom = pad_shape[0] // 2
pad_top = pad_shape[0] - pad_bottom
pad_right = pad_shape[1] // 2
pad_left = pad_shape[1] - pad_right
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_lower")
averagepool_2d_same_upper
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_UPPER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape("SAME_UPPER", x_shape[2:], kernel_shape, strides)
pad_shape = get_pad_shape(
"SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_top = pad_shape[0] // 2
pad_bottom = pad_shape[0] - pad_top
pad_left = pad_shape[1] // 2
pad_right = pad_shape[1] - pad_left
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_upper")
averagepool_2d_strides
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 10, 10]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
strides=[3, 3],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (5, 5)
strides = (3, 3)
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_strides")
averagepool_3d_default
"""
input_shape: [1, 3, 32, 32, 32]
output_shape: [1, 3, 31, 31, 31]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2, 2],
)
x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = [2, 2, 2]
strides = [1, 1, 1]
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_3d_default")
BatchNormalization#
Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs ‘X’, ‘scale’, ‘B’, ‘input_mean’ and ‘input_var’. Note that ‘input_mean’ and ‘input_var’ are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below:
Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False)
When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True:
running_mean = input_mean * momentum + current_mean * (1 - momentum)
running_var = input_var * momentum + current_var * (1 - momentum)
Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B
where:
current_mean = ReduceMean(X, axis=all_except_channel_index)
current_var = ReduceVar(X, axis=all_except_channel_index)
Notice that ReduceVar refers to the population variance, and it equals to
sum(sqrd(x_i - x_avg)) / N
where N is the population size (this formula does not use sample size N - 1).
The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs.
When training_mode=False:
Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B
For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * … * Dn) before a BatchNormalization Op. This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Version#
This version of the operator has been available since version 15 of the default ONNX operator set.
Attributes#
- epsilon : float (default is 1e-05)
- The epsilon value to use to avoid division by zero.
- momentum : float (default is 0.9)
- Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).
- training_mode : int (default is 0)
- If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.
Inputs#
- X (differentiable) : T
- Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1
- scale (differentiable) : T1
- Scale tensor of shape (C).
- B (differentiable) : T1
- Bias tensor of shape (C).
- input_mean (differentiable) : T2
- running (training) or estimated (testing) mean tensor of shape (C).
- input_var (differentiable) : T2
- running (training) or estimated (testing) variance tensor of shape (C).
Outputs (1 - 3)#
- Y (differentiable) : T
- The output tensor of the same shape as X
- running_mean (optional, non-differentiable) : T2
- The running mean after the BatchNormalization operator.
- running_var (optional, non-differentiable) : T2
- The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
- T1 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain scale and bias types to float tensors.
- T2 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain mean and variance types to float tensors.
Examples#
batchnormalization
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
mean = np.random.randn(3).astype(np.float32)
var = np.random.rand(3).astype(np.float32)
y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32)
node = onnx.helper.make_node(
"BatchNormalization",
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y"],
)
# output size: (2, 3, 4, 5)
expect(
node,
inputs=[x, s, bias, mean, var],
outputs=[y],
name="test_batchnorm_example",
)
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
mean = np.random.randn(3).astype(np.float32)
var = np.random.rand(3).astype(np.float32)
epsilon = 1e-2
y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(np.float32)
node = onnx.helper.make_node(
"BatchNormalization",
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y"],
epsilon=epsilon,
)
# output size: (2, 3, 4, 5)
expect(
node,
inputs=[x, s, bias, mean, var],
outputs=[y],
name="test_batchnorm_epsilon",
)
train
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
mean = np.random.randn(3).astype(np.float32)
var = np.random.rand(3).astype(np.float32)
# using np.bool(1) while generating test data with "'bool' object has no attribute 'dtype'"
# working around by using np.byte(1).astype(bool)
training_mode = 1
y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var)
node = onnx.helper.make_node(
"BatchNormalization",
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y", "output_mean", "output_var"],
training_mode=training_mode,
)
# output size: (2, 3, 4, 5)
expect(
node,
inputs=[x, s, bias, mean, var],
outputs=[y, output_mean, output_var],
name="test_batchnorm_example_training_mode",
)
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
mean = np.random.randn(3).astype(np.float32)
var = np.random.rand(3).astype(np.float32)
training_mode = 1
momentum = 0.9
epsilon = 1e-2
y, output_mean, output_var = _batchnorm_training_mode(
x, s, bias, mean, var, momentum, epsilon
)
node = onnx.helper.make_node(
"BatchNormalization",
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y", "output_mean", "output_var"],
epsilon=epsilon,
training_mode=training_mode,
)
# output size: (2, 3, 4, 5)
expect(
node,
inputs=[x, s, bias, mean, var],
outputs=[y, output_mean, output_var],
name="test_batchnorm_epsilon_training_mode",
)
Bernoulli#
Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number, where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p).
This operator is non-deterministic and may not produce the same values in different implementations (even if a seed is specified).
Version#
This version of the operator has been available since version 15 of the default ONNX operator set.
Attributes#
- dtype : int
- The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.
- seed : float
- (Optional) Seed to the random generator, if not specified we will auto generate one.
Inputs#
- input : T1
- All values in input have to be in the range:[0, 1].
Outputs#
- output : T2
- The returned output tensor only has values 0 or 1, same shape as input tensor.
Type Constraints#
- T1 : tensor(float16), tensor(float), tensor(double)
- Constrain input types to float tensors.
- T2 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bool)
- Constrain output types to all numeric tensors and bool tensors.
Examples#
bernoulli_with_dtype
node = onnx.helper.make_node(
"Bernoulli",
inputs=["x"],
outputs=["y"],
dtype=onnx.TensorProto.DOUBLE,
)
x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y = bernoulli_reference_implementation(x, np.float64)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli_double")
bernoulli_with_seed
seed = np.float(0)
node = onnx.helper.make_node(
"Bernoulli",
inputs=["x"],
outputs=["y"],
seed=seed,
)
x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y = bernoulli_reference_implementation(x, np.float32)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli_seed")
bernoulli_without_dtype
node = onnx.helper.make_node(
"Bernoulli",
inputs=["x"],
outputs=["y"],
)
x = np.random.uniform(0.0, 1.0, 10).astype(np.float)
y = bernoulli_reference_implementation(x, np.float)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli")
BitShift#
Bitwise shift operator performs element-wise operation. For each input element, if the attribute “direction” is “RIGHT”, this operator moves its binary representation toward the right side so that the input value is effectively decreased. If the attribute “direction” is “LEFT”, bits of binary representation moves toward the left side, which results the increase of its actual value. The input X is the tensor to be shifted and another input Y specifies the amounts of shifting. For example, if “direction” is “Right”, X is [1, 4], and S is [1, 1], the corresponding output Z would be [0, 2]. If “direction” is “LEFT” with X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8].
Because this operator supports Numpy-style broadcasting, X’s and Y’s shapes are not necessarily identical. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Attributes#
- direction : string (required)
- Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).
Inputs#
- X (non-differentiable) : T
- First operand, input to be shifted.
- Y (non-differentiable) : T
- Second operand, amounts of shift.
Outputs#
- Z (non-differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64)
- Constrain input and output types to integer tensors.
Examples#
left_unit16
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
)
x = np.array([16, 4, 1]).astype(np.uint16)
y = np.array([1, 2, 3]).astype(np.uint16)
z = x << y # expected output [32, 16, 8]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint16")
left_unit32
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
)
x = np.array([16, 4, 1]).astype(np.uint32)
y = np.array([1, 2, 3]).astype(np.uint32)
z = x << y # expected output [32, 16, 8]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint32")
left_unit64
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
)
x = np.array([16, 4, 1]).astype(np.uint64)
y = np.array([1, 2, 3]).astype(np.uint64)
z = x << y # expected output [32, 16, 8]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint64")
left_unit8
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
)
x = np.array([16, 4, 1]).astype(np.uint8)
y = np.array([1, 2, 3]).astype(np.uint8)
z = x << y # expected output [32, 16, 8]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint8")
right_unit16
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
)
x = np.array([16, 4, 1]).astype(np.uint16)
y = np.array([1, 2, 3]).astype(np.uint16)
z = x >> y # expected output [8, 1, 0]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint16")
right_unit32
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
)
x = np.array([16, 4, 1]).astype(np.uint32)
y = np.array([1, 2, 3]).astype(np.uint32)
z = x >> y # expected output [8, 1, 0]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint32")
right_unit64
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
)
x = np.array([16, 4, 1]).astype(np.uint64)
y = np.array([1, 2, 3]).astype(np.uint64)
z = x >> y # expected output [8, 1, 0]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint64")
right_unit8
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
)
x = np.array([16, 4, 1]).astype(np.uint8)
y = np.array([1, 2, 3]).astype(np.uint8)
z = x >> y # expected output [8, 1, 0]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint8")
BitwiseAnd#
Returns the tensor resulting from performing the bitwise and operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Inputs#
- A (non-differentiable) : T
- First input operand for the bitwise operator.
- B (non-differentiable) : T
- Second input operand for the bitwise operator.
Outputs#
- C (non-differentiable) : T
- Result tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
- Constrain input to integer tensors.
Examples#
bitwiseand
node = onnx.helper.make_node(
"BitwiseAnd",
inputs=["x", "y"],
outputs=["bitwiseand"],
)
# 2d
x = np.random.randn(3, 4).astype(np.int32)
y = np.random.randn(3, 4).astype(np.int32)
z = np.bitwise_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i32_2d")
# 3d
x = np.random.randn(3, 4, 5).astype(np.int16)
y = np.random.randn(3, 4, 5).astype(np.int16)
z = np.bitwise_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i16_3d")
bitwiseand_broadcast
node = onnx.helper.make_node(
"BitwiseAnd",
inputs=["x", "y"],
outputs=["bitwiseand"],
)
# 3d vs 1d
x = np.random.randn(3, 4, 5).astype(np.uint64)
y = np.random.randn(5).astype(np.uint64)
z = np.bitwise_and(x, y)
expect(
node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui64_bcast_3v1d"
)
# 4d vs 3d
x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
y = np.random.randn(4, 5, 6).astype(np.uint8)
z = np.bitwise_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui8_bcast_4v3d")
BitwiseNot#
Returns the bitwise not of the input tensor element-wise.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Inputs#
- X (non-differentiable) : T
- Input tensor
Outputs#
- Y (non-differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
- Constrain input/output to integer tensors.
Examples#
bitwisenot
node = onnx.helper.make_node(
"BitwiseNot",
inputs=["x"],
outputs=["bitwise_not"],
)
# 2d
x = np.random.randn(3, 4).astype(np.int32)
y = np.bitwise_not(x)
expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_2d")
# 3d
x = np.random.randn(3, 4, 5).astype(np.uint16)
y = np.bitwise_not(x)
expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_3d")
# 4d
x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
y = np.bitwise_not(x)
expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_4d")
BitwiseOr#
Returns the tensor resulting from performing the bitwise or operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Inputs#
- A (non-differentiable) : T
- First input operand for the bitwise operator.
- B (non-differentiable) : T
- Second input operand for the bitwise operator.
Outputs#
- C (non-differentiable) : T
- Result tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
- Constrain input to integer tensors.
Examples#
bitwiseor
node = onnx.helper.make_node(
"BitwiseOr",
inputs=["x", "y"],
outputs=["bitwiseor"],
)
# 2d
x = np.random.randn(3, 4).astype(np.int32)
y = np.random.randn(3, 4).astype(np.int32)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i32_2d")
# 4d
x = np.random.randn(3, 4, 5, 6).astype(np.int8)
y = np.random.randn(3, 4, 5, 6).astype(np.int8)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i16_4d")
bitwiseor_broadcast
node = onnx.helper.make_node(
"BitwiseOr",
inputs=["x", "y"],
outputs=["bitwiseor"],
)
# 3d vs 1d
x = np.random.randn(3, 4, 5).astype(np.uint64)
y = np.random.randn(5).astype(np.uint64)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui64_bcast_3v1d")
# 4d vs 3d
x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
y = np.random.randn(4, 5, 6).astype(np.uint8)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui8_bcast_4v3d")
BitwiseXor#
Returns the tensor resulting from performing the bitwise xor operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Inputs#
- A (non-differentiable) : T
- First input operand for the bitwise operator.
- B (non-differentiable) : T
- Second input operand for the bitwise operator.
Outputs#
- C (non-differentiable) : T
- Result tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64)
- Constrain input to integer tensors.
Examples#
bitwiseor_broadcast
node = onnx.helper.make_node(
"BitwiseXor",
inputs=["x", "y"],
outputs=["bitwisexor"],
)
# 3d vs 1d
x = np.random.randn(3, 4, 5).astype(np.uint64)
y = np.random.randn(5).astype(np.uint64)
z = np.bitwise_xor(x, y)
expect(
node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui64_bcast_3v1d"
)
# 4d vs 3d
x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
y = np.random.randn(4, 5, 6).astype(np.uint8)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui8_bcast_4v3d")
bitwisexor
node = onnx.helper.make_node(
"BitwiseXor",
inputs=["x", "y"],
outputs=["bitwisexor"],
)
# 2d
x = np.random.randn(3, 4).astype(np.int32)
y = np.random.randn(3, 4).astype(np.int32)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i32_2d")
# 3d
x = np.random.randn(3, 4, 5).astype(np.int16)
y = np.random.randn(3, 4, 5).astype(np.int16)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i16_3d")
BlackmanWindow#
Generates a Blackman window as described in the paper https://ieeexplore.ieee.org/document/1455106.
Version#
This version of the operator has been available since version 17 of the default ONNX operator set.
Attributes#
- output_datatype : int (default is 1)
- The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT.
- periodic : int (default is 1)
- If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1.
Inputs#
- size (non-differentiable) : T1
- A scalar value indicating the length of the window.
Outputs#
- output (non-differentiable) : T2
- A Blackman window with length: size. The output has the shape: [size].
Type Constraints#
- T1 : tensor(int32), tensor(int64)
- Constrain the input size to int64_t.
- T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain output types to numeric tensors.
Examples#
blackmanwindow
# Test periodic window
node = onnx.helper.make_node(
"BlackmanWindow",
inputs=["x"],
outputs=["y"],
)
size = np.int32(10)
a0 = 0.42
a1 = -0.5
a2 = 0.08
y = a0
y += a1 * np.cos(2 * 3.1415 * np.arange(0, size, 1, dtype=np.float32) / size)
y += a2 * np.cos(4 * 3.1415 * np.arange(0, size, 1, dtype=np.float32) / size)
expect(node, inputs=[size], outputs=[y], name="test_blackmanwindow")
# Test symmetric window
node = onnx.helper.make_node(
"BlackmanWindow", inputs=["x"], outputs=["y"], periodic=0
)
size = np.int32(10)
a0 = 0.42
a1 = -0.5
a2 = 0.08
y = a0
y += a1 * np.cos(
2 * 3.1415 * np.arange(0, size, 1, dtype=np.float32) / (size - 1)
)
y += a2 * np.cos(
4 * 3.1415 * np.arange(0, size, 1, dtype=np.float32) / (size - 1)
)
expect(node, inputs=[size], outputs=[y], name="test_blackmanwindow_symmetric")
Cast#
The operator casts the elements of a given input tensor to a data type specified by the ‘to’ argument and returns an output tensor of the same size in the converted type. The ‘to’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message.
Casting from string tensor in plain (e.g., “3.14” and “1000”) and scientific numeric representations (e.g., “1e-5” and “1E8”) to float types is supported. For example, converting string “100.5” to an integer may result 100. There are some string literals reserved for special floating-point values; “+INF” (and “INF”), “-INF”, and “NaN” are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match “+INF” in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to “INF” and “NaN”. When casting from numeric tensors to string tensors, plain floating-point representation (such as “314.15926”) would be used. Converting non-numerical-literal string such as “Hello World!” is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as “2.718”, to INT is an undefined behavior.
Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can’t be stored in the targeted type.
In more detail, the conversion among numerical types should follow these rules:
Casting from floating point to:
floating point: +/- infinity if OOR (out of range).
fixed point: undefined if OOR.
bool: +/- 0.0 to False; all else to True.
Casting from fixed point to:
floating point: +/- infinity if OOR. (+ infinity in the case of uint)
fixed point: when OOR, discard higher bits and reinterpret (with respect to two’s complement representation for signed types). For example, 200 (int16) -> -56 (int8).
bool: zero to False; nonzero to True.
Casting from bool to:
floating point:
{1.0, 0.0}.fixed point:
{1, 0}.bool: no change.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- to : int (required)
- The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
Inputs#
- input (differentiable) : T1
- Input tensor to be cast.
Outputs#
- output (differentiable) : T2
- Output tensor with the same shape as input with type specified by the 'to' argument
Type Constraints#
- T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)
- Constrain input types. Casting from complex is not supported.
- T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)
- Constrain output types. Casting to complex is not supported.
Examples#
cast
shape = (3, 4)
test_cases = [
("FLOAT", "FLOAT16"),
("FLOAT", "DOUBLE"),
("FLOAT16", "FLOAT"),
("FLOAT16", "DOUBLE"),
("DOUBLE", "FLOAT"),
("DOUBLE", "FLOAT16"),
("FLOAT", "STRING"),
("STRING", "FLOAT"),
("FLOAT", "BFLOAT16"),
("BFLOAT16", "FLOAT"),
]
for from_type, to_type in test_cases:
input_type_proto = None
output_type_proto = None
if "BFLOAT16" == from_type or "BFLOAT16" == to_type:
np_fp32 = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.816468",
"0.21087195",
"0.7229038",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.float32,
)
little_endisan = sys.byteorder == "little"
np_uint16_view = np_fp32.view(dtype=np.uint16)
np_bfp16 = (
np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
)
if "BFLOAT16" == to_type:
assert from_type == "FLOAT"
input = np_fp32.reshape([3, 4])
output = np_bfp16.reshape([3, 4])
input_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.FLOAT), input.shape
)
output_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.BFLOAT16), output.shape
)
else:
assert to_type == "FLOAT"
input = np_bfp16.reshape([3, 4])
# convert bfloat to FLOAT
np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
if little_endisan:
np_fp32_zeros[1::2] = np_bfp16
else:
np_fp32_zeros[0::2] = np_bfp16
np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
output = np_fp32_from_bfloat.reshape([3, 4])
input_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.BFLOAT16), input.shape
)
output_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.FLOAT), output.shape
)
elif "STRING" != from_type:
input = np.random.random_sample(shape).astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, from_type))
)
if "STRING" == to_type:
# Converting input to str, then give it object dtype for generating script
ss = []
for i in input.flatten():
s = str(i).encode("utf-8")
su = s.decode("utf-8")
ss.append(su)
output = np.array(ss).astype(object).reshape([3, 4])
else:
output = input.astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
)
else:
input = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.816468",
"0.21087195",
"0.7229038",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.dtype(object),
).reshape([3, 4])
output = input.astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
)
node = onnx.helper.make_node(
"Cast",
inputs=["input"],
outputs=["output"],
to=getattr(TensorProto, to_type),
)
if input_type_proto and output_type_proto:
expect(
node,
inputs=[input],
outputs=[output],
name="test_cast_" + from_type + "_to_" + to_type,
input_type_protos=[input_type_proto],
output_type_protos=[output_type_proto],
)
else:
expect(
node,
inputs=[input],
outputs=[output],
name="test_cast_" + from_type + "_to_" + to_type,
)
CastLike#
The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details.
Version#
This version of the operator has been available since version 15 of the default ONNX operator set.
Inputs#
- input (differentiable) : T1
- Input tensor to be cast.
- target_type (non-differentiable) : T2
- The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.
Outputs#
- output (differentiable) : T2
- Output tensor produced by casting the first input tensor to have the same type as the second input tensor.
Type Constraints#
- T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)
- Constrain input types. Casting from complex is not supported.
- T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool), tensor(string), tensor(bfloat16)
- Constrain output types. Casting to complex is not supported.
Examples#
castlike
shape = (3, 4)
test_cases = [
("FLOAT", "FLOAT16"),
("FLOAT", "DOUBLE"),
("FLOAT16", "FLOAT"),
("FLOAT16", "DOUBLE"),
("DOUBLE", "FLOAT"),
("DOUBLE", "FLOAT16"),
("FLOAT", "STRING"),
("STRING", "FLOAT"),
("FLOAT", "BFLOAT16"),
("BFLOAT16", "FLOAT"),
]
for from_type, to_type in test_cases:
input_type_proto = None
output_type_proto = None
if "BFLOAT16" == from_type or "BFLOAT16" == to_type:
np_fp32 = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.816468",
"0.21087195",
"0.7229038",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.float32,
)
little_endisan = sys.byteorder == "little"
np_uint16_view = np_fp32.view(dtype=np.uint16)
np_bfp16 = (
np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
)
if "BFLOAT16" == to_type:
assert from_type == "FLOAT"
input = np_fp32.reshape([3, 4])
output = np_bfp16.reshape([3, 4])
input_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.FLOAT), input.shape
)
output_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.BFLOAT16), output.shape
)
else:
assert to_type == "FLOAT"
input = np_bfp16.reshape([3, 4])
# convert bfloat to FLOAT
np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
if little_endisan:
np_fp32_zeros[1::2] = np_bfp16
else:
np_fp32_zeros[0::2] = np_bfp16
np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
output = np_fp32_from_bfloat.reshape([3, 4])
input_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.BFLOAT16), input.shape
)
output_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.FLOAT), output.shape
)
elif "STRING" != from_type:
input = np.random.random_sample(shape).astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, from_type))
)
if "STRING" == to_type:
# Converting input to str, then give it np.object dtype for generating script
ss = []
for i in input.flatten():
s = str(i).encode("utf-8")
su = s.decode("utf-8")
ss.append(su)
output = np.array(ss).astype(np.object).reshape([3, 4])
else:
output = input.astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
)
else:
input = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.816468",
"0.21087195",
"0.7229038",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.dtype(np.object),
).reshape([3, 4])
output = input.astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
)
like = output.flatten()[0:1]
node = onnx.helper.make_node(
"CastLike",
inputs=["input", "like"],
outputs=["output"],
)
if input_type_proto and output_type_proto:
expect(
node,
inputs=[input, like],
outputs=[output],
name="test_castlike_" + from_type + "_to_" + to_type,
input_type_protos=[input_type_proto, output_type_proto],
output_type_protos=[output_type_proto],
)
else:
expect(
node,
inputs=[input, like],
outputs=[output],
name="test_castlike_" + from_type + "_to_" + to_type,
)
Ceil#
Ceil takes one input data (Tensor
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- X (non-differentiable) : T
- Input tensor
Outputs#
- Y (non-differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
ceil
node = onnx.helper.make_node(
"Ceil",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1.5, 1.2]).astype(np.float32)
y = np.ceil(x) # expected output [-1., 2.]
expect(node, inputs=[x], outputs=[y], name="test_ceil_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.ceil(x)
expect(node, inputs=[x], outputs=[y], name="test_ceil")
Celu#
Continuously Differentiable Exponential Linear Units: Perform the linear unit element-wise on the input tensor X using formula:
max(0,x) + min(0,alpha*(exp(x/alpha)-1))
Version#
This version of the operator has been available since version 12 of the default ONNX operator set.
Attributes#
- alpha : float (default is 1.0)
- The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float)
- Constrain input and output types to float32 tensors.
Examples#
celu
alpha = 2.0
node = onnx.helper.make_node(
"Celu",
inputs=["X"],
outputs=["Y"],
alpha=alpha,
)
input_data = np.array(
[
[
[[0.8439683], [0.5665144], [0.05836735]],
[[0.02916367], [0.12964272], [0.5060197]],
[[0.79538304], [0.9411346], [0.9546573]],
],
[
[[0.17730942], [0.46192095], [0.26480448]],
[[0.6746842], [0.01665257], [0.62473077]],
[[0.9240844], [0.9722341], [0.11965699]],
],
[
[[0.41356155], [0.9129373], [0.59330076]],
[[0.81929934], [0.7862604], [0.11799799]],
[[0.69248444], [0.54119414], [0.07513223]],
],
],
dtype=np.float32,
)
# Calculate expected output data
positive_input = np.maximum(0, input_data)
negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1))
expected_output = positive_input + negative_input
expect(node, inputs=[input_data], outputs=[expected_output], name="test_celu")
CenterCropPad#
Center crop or pad an input to given dimensions.
The crop/pad dimensions can be specified for a subset of the axes. Non-specified dimensions will not be
cropped or padded.
If the input dimensions are bigger than the crop shape, a centered cropping window is extracted from the input. If the input dimensions are smaller than the crop shape, the input is padded on each side equally, so that the input is centered in the output.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- axes : list of ints
- If provided, it specifies a subset of axes that 'shape' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.
Inputs#
- input_data (differentiable) : T
- Input to extract the centered crop from.
- shape (non-differentiable) : Tind
- 1-D tensor representing the cropping window dimensions.
Outputs#
- output_data (differentiable) : T
- Output data.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
- Tind : tensor(int32), tensor(int64)
- Constrain indices to integer types
Examples#
center_crop_pad_crop
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
)
# First dim is even diff, second is uneven
x = np.random.randn(20, 10, 3).astype(np.float32)
shape = np.array([10, 7, 3], dtype=np.int64)
y = x[5:15, 1:8, :]
expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop")
center_crop_pad_crop_and_pad
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
)
# Cropping on first dim, padding on second, third stays the same
x = np.random.randn(20, 8, 3).astype(np.float32)
shape = np.array([10, 10, 3], dtype=np.int64)
y = np.zeros([10, 10, 3], dtype=np.float32)
y[:, 1:9, :] = x[5:15, :, :]
expect(
node,
inputs=[x, shape],
outputs=[y],
name="test_center_crop_pad_crop_and_pad",
)
center_crop_pad_crop_axes_chw
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
axes=[1, 2],
)
# Cropping on second dim, padding on third, first stays the same
x = np.random.randn(3, 20, 8).astype(np.float32)
shape = np.array([10, 9], dtype=np.int64)
y = np.zeros([3, 10, 9], dtype=np.float32)
y[:, :, :8] = x[:, 5:15, :]
expect(
node,
inputs=[x, shape],
outputs=[y],
name="test_center_crop_pad_crop_axes_chw",
)
center_crop_pad_crop_axes_hwc
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
axes=[0, 1],
)
# Cropping on first dim, padding on second, third stays the same
x = np.random.randn(20, 8, 3).astype(np.float32)
shape = np.array([10, 9], dtype=np.int64)
y = np.zeros([10, 9, 3], dtype=np.float32)
y[:, :8, :] = x[5:15, :, :]
expect(
node,
inputs=[x, shape],
outputs=[y],
name="test_center_crop_pad_crop_axes_hwc",
)
center_crop_pad_pad
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
)
# First dim is even diff, second is uneven
x = np.random.randn(10, 7, 3).astype(np.float32)
shape = np.array([20, 10, 3], dtype=np.int64)
y = np.zeros([20, 10, 3], dtype=np.float32)
y[5:15, 1:8, :] = x
expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_pad")
Clip#
Clip operator limits the given input within an interval. The interval is specified by the inputs ‘min’ and ‘max’. They default to numeric_limits::lowest() and numeric_limits::max(), respectively.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs (1 - 3)#
- input (differentiable) : T
- Input tensor whose elements to be clipped
- min (optional, non-differentiable) : T
- Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).
- max (optional, non-differentiable) : T
- Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).
Outputs#
- output (differentiable) : T
- Output tensor with clipped input elements
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
clip
node = onnx.helper.make_node(
"Clip",
inputs=["x", "min", "max"],
outputs=["y"],
)
x = np.array([-2, 0, 2]).astype(np.float32)
min_val = np.float32(-1)
max_val = np.float32(1)
y = np.clip(x, min_val, max_val) # expected output [-1., 0., 1.]
expect(
node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_example"
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, min_val, max_val)
expect(node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip")
node = onnx.helper.make_node(
"Clip",
inputs=["x", "min", "max"],
outputs=["y"],
)
min_val = np.float32(-5)
max_val = np.float32(5)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.array([-1, 0, 1]).astype(np.float32)
expect(
node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_inbounds"
)
x = np.array([-6, 0, 6]).astype(np.float32)
y = np.array([-5, 0, 5]).astype(np.float32)
expect(
node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_outbounds"
)
x = np.array([-1, 0, 6]).astype(np.float32)
y = np.array([-1, 0, 5]).astype(np.float32)
expect(
node,
inputs=[x, min_val, max_val],
outputs=[y],
name="test_clip_splitbounds",
)
clip_default
node = onnx.helper.make_node(
"Clip",
inputs=["x", "min"],
outputs=["y"],
)
min_val = np.float32(0)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, min_val, np.inf)
expect(node, inputs=[x, min_val], outputs=[y], name="test_clip_default_min")
no_min = "" # optional input, not supplied
node = onnx.helper.make_node(
"Clip",
inputs=["x", no_min, "max"],
outputs=["y"],
)
max_val = np.float32(0)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, -np.inf, max_val)
expect(node, inputs=[x, max_val], outputs=[y], name="test_clip_default_max")
no_max = "" # optional input, not supplied
node = onnx.helper.make_node(
"Clip",
inputs=["x", no_min, no_max],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.array([-1, 0, 1]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_clip_default_inbounds")
clip_default_int8
node = onnx.helper.make_node(
"Clip",
inputs=["x", "min"],
outputs=["y"],
)
min_val = np.int8(0)
x = np.random.randn(3, 4, 5).astype(np.int8)
y = np.clip(x, min_val, np.iinfo(np.int8).max)
expect(
node, inputs=[x, min_val], outputs=[y], name="test_clip_default_int8_min"
)
no_min = "" # optional input, not supplied
node = onnx.helper.make_node(
"Clip",
inputs=["x", no_min, "max"],
outputs=["y"],
)
max_val = np.int8(0)
x = np.random.randn(3, 4, 5).astype(np.int8)
y = np.clip(x, np.iinfo(np.int8).min, max_val)
expect(
node, inputs=[x, max_val], outputs=[y], name="test_clip_default_int8_max"
)
no_max = "" # optional input, not supplied
node = onnx.helper.make_node(
"Clip",
inputs=["x", no_min, no_max],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.int8)
y = np.array([-1, 0, 1]).astype(np.int8)
expect(node, inputs=[x], outputs=[y], name="test_clip_default_int8_inbounds")
Col2Im#
The operator rearranges column blocks back into a multidimensional image
Col2Im behaves similarly to PyTorch’s fold https://pytorch.org/docs/stable/generated/torch.nn.Fold.html, but it only supports batched multi-dimensional image tensors. Another implementation in Python with N-dimension support can be found at https://github.com/f-dangel/unfoldNd/.
NOTE: Although specifying image_shape looks redundant because it could be calculated from convolution formulas, it is required as input for more advanced scenarios as explained at PyTorch’s implementation (https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Col2Im.cpp#L10)
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- dilations : list of ints
- 1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.
- pads : list of ints
- 1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.
- strides : list of ints
- 1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs#
- input (differentiable) : T
- Input data tensor to be rearranged from column blocks back into an image. This is a 3-dimensional tensor containing [N, C * n-ary-product(block_shape), L], where N is batch dimension, C is image channel dimension and L is number of blocks.The blocks are enumerated in increasing lexicographic-order of their indices.For example, with an image-size 10*20 and block-size 9*18, there would be 2*3 blocks, enumerated in the order block(0, 0), block(0, 1), block(0, 2), block(1, 0), block(1, 1), block(1, 2).
- image_shape (non-differentiable) : tensor(int64)
- The shape of the spatial dimensions of the image after rearranging the column blocks.This is a 1-dimensional tensor with size of at least 2, containing the value [H_img, W_img] for a 2-D image or [dim_i1, dim_i2, ..., dim_iN] for a N-D image.
- block_shape (non-differentiable) : tensor(int64)
- The shape of the block to apply on the input.This is a 1-dimensional tensor of size of at least 2, containing the value [H_block, W_block] for a 2-D image or [dim_b1, dim_b2, ..., dim_bN] for a N-D block.This is the block-shape before dilation is applied to it.
Outputs#
- output (differentiable) : T
- Output tensor produced by rearranging blocks into an image.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all numeric tensor types.
Examples#
col2im
input = np.array(
[
[
[1.0, 6.0, 11.0, 16.0, 21.0], # (1, 5, 5)
[2.0, 7.0, 12.0, 17.0, 22.0],
[3.0, 8.0, 13.0, 18.0, 23.0],
[4.0, 9.0, 14.0, 19.0, 24.0],
[5.0, 0.0, 15.0, 20.0, 25.0],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([1, 5]).astype(np.int64)
node = onnx.helper.make_node(
"Col2Im", ["input", "image_shape", "block_shape"], ["output"]
)
output = np.array(
[
[
[
[1.0, 2.0, 3.0, 4.0, 5.0], # (1, 1, 5, 5)
[6.0, 7.0, 8.0, 9.0, 0.0],
[11.0, 12.0, 13.0, 14.0, 15.0],
[16.0, 17.0, 18.0, 19.0, 20.0],
[21.0, 22.0, 23.0, 24.0, 25.0],
]
]
]
).astype(np.float32)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im",
)
col2im_5d
input = np.array(
[
[
[1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56], # (1, 10, 12)
[2, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, 57],
[3, 8, 13, 18, 23, 28, 33, 38, 43, 48, 53, 58],
[4, 9, 14, 19, 24, 29, 34, 39, 44, 49, 54, 59],
[5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60],
[61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116],
[62, 67, 72, 77, 82, 87, 92, 97, 102, 107, 112, 117],
[63, 68, 73, 78, 83, 88, 93, 98, 103, 108, 113, 118],
[64, 69, 74, 79, 84, 89, 94, 99, 104, 109, 114, 119],
[65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120],
]
]
).astype(np.float32)
image_shape = np.array([3, 4, 5]).astype(np.int64)
block_shape = np.array([1, 1, 5]).astype(np.int64)
output = np.array(
[
[
[
[
[1, 2, 3, 4, 5], # (1, 2, 3, 4, 5)
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
],
[
[21, 22, 23, 24, 25],
[26, 27, 28, 29, 30],
[31, 32, 33, 34, 35],
[36, 37, 38, 39, 40],
],
[
[41, 42, 43, 44, 45],
[46, 47, 48, 49, 50],
[51, 52, 53, 54, 55],
[56, 57, 58, 59, 60],
],
],
[
[
[61, 62, 63, 64, 65],
[66, 67, 68, 69, 70],
[71, 72, 73, 74, 75],
[76, 77, 78, 79, 80],
],
[
[81, 82, 83, 84, 85],
[86, 87, 88, 89, 90],
[91, 92, 93, 94, 95],
[96, 97, 98, 99, 100],
],
[
[101, 102, 103, 104, 105],
[106, 107, 108, 109, 110],
[111, 112, 113, 114, 115],
[116, 117, 118, 119, 120],
],
],
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"Col2Im", ["input", "image_shape", "block_shape"], ["output"]
)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im_5d",
)
col2im_dilations
input = np.array(
[
[
[1.0, 5.0, 9.0, 13.0, 17], # (1, 4, 5)
[2.0, 6.0, 10.0, 14.0, 18],
[3.0, 7.0, 11.0, 15.0, 19],
[4.0, 8.0, 12.0, 16.0, 20],
]
]
).astype(np.float32)
image_shape = np.array([6, 6]).astype(np.int64)
block_shape = np.array([2, 2]).astype(np.int64)
output = np.array(
[
[
[
[1.0, 0.0, 0.0, 0.0, 0.0, 2.0], # (1, 1, 6, 6)
[8.0, 0.0, 0.0, 0.0, 0.0, 10.0],
[16.0, 0.0, 0.0, 0.0, 0.0, 18.0],
[24.0, 0.0, 0.0, 0.0, 0.0, 26.0],
[32.0, 0.0, 0.0, 0.0, 0.0, 34.0],
[19.0, 0.0, 0.0, 0.0, 0.0, 20.0],
]
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"Col2Im",
["input", "image_shape", "block_shape"],
["output"],
dilations=[1, 5],
)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im_dilations",
)
col2im_pads
input = np.array(
[
[
[
1.0,
6.0,
11.0,
16.0,
21.0,
26,
31,
36,
41,
46,
51,
56,
61,
66,
71,
], # (1, 5, 15)
[
2.0,
7.0,
12.0,
17.0,
22.0,
27,
32,
37,
42,
47,
52,
57,
62,
67,
72,
],
[
3.0,
8.0,
13.0,
18.0,
23.0,
28,
33,
38,
43,
48,
53,
58,
63,
68,
73,
],
[
4.0,
9.0,
14.0,
19.0,
24.0,
29,
34,
39,
44,
49,
54,
59,
64,
69,
74,
],
[
5.0,
10.0,
15.0,
20.0,
25.0,
30,
35,
40,
45,
50,
55,
60,
65,
70,
75,
],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([1, 5]).astype(np.int64)
output = np.array(
[
[
[
[8.0, 21.0, 24.0, 27.0, 14.0], # (1, 1, 5, 5)
[38.0, 66.0, 69.0, 72.0, 54.0],
[68.0, 111.0, 114.0, 117.0, 84.0],
[98.0, 156.0, 159.0, 162.0, 114.0],
[128.0, 201.0, 204.0, 207.0, 144.0],
]
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"Col2Im",
["input", "image_shape", "block_shape"],
["output"],
pads=[0, 1, 0, 1],
)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im_pads",
)
col2im_strides
input = np.array(
[
[
[0.0, 0.0, 0.0, 0.0], # (1, 9, 4)
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 0.0],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([3, 3]).astype(np.int64)
output = np.array(
[
[
[
[0.0, 1.0, 1.0, 1.0, 1.0], # (1, 1, 5, 5)
[1.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 2.0, 1.0, 2.0, 1.0],
[1.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 1.0, 0.0],
]
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"Col2Im",
["input", "image_shape", "block_shape"],
["output"],
strides=[2, 2],
)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im_strides",
)
Compress#
Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected. Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Other versions of this operator: 9
Attributes#
- axis : int
- (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
Inputs#
- input (differentiable) : T
- Tensor of rank r >= 1.
- condition (non-differentiable) : T1
- Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.
Outputs#
- output (differentiable) : T
- Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
- T1 : tensor(bool)
- Constrain to boolean tensors.
Examples#
compress_0
node = onnx.helper.make_node(
"Compress",
inputs=["input", "condition"],
outputs=["output"],
axis=0,
)
input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
condition = np.array([0, 1, 1])
output = np.compress(condition, input, axis=0)
# print(output)
# [[ 3. 4.]
# [ 5. 6.]]
expect(
node,
inputs=[input, condition.astype(bool)],
outputs=[output],
name="test_compress_0",
)
compress_1
node = onnx.helper.make_node(
"Compress",
inputs=["input", "condition"],
outputs=["output"],
axis=1,
)
input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
condition = np.array([0, 1])
output = np.compress(condition, input, axis=1)
# print(output)
# [[ 2.]
# [ 4.]
# [ 6.]]
expect(
node,
inputs=[input, condition.astype(bool)],
outputs=[output],
name="test_compress_1",
)
compress_default_axis
node = onnx.helper.make_node(
"Compress",
inputs=["input", "condition"],
outputs=["output"],
)
input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
condition = np.array([0, 1, 0, 0, 1])
output = np.compress(condition, input)
# print(output)
# [ 2., 5.]
expect(
node,
inputs=[input, condition.astype(bool)],
outputs=[output],
name="test_compress_default_axis",
)
compress_negative_axis
node = onnx.helper.make_node(
"Compress",
inputs=["input", "condition"],
outputs=["output"],
axis=-1,
)
input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
condition = np.array([0, 1])
output = np.compress(condition, input, axis=-1)
# print(output)
# [[ 2.]
# [ 4.]
# [ 6.]]
expect(
node,
inputs=[input, condition.astype(bool)],
outputs=[output],
name="test_compress_negative_axis",
)
Concat#
Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- axis : int (required)
- Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..
Inputs (1 - ∞)#
- inputs (variadic, differentiable) : T
- List of tensors for concatenation
Outputs#
- concat_result (differentiable) : T
- Concatenated tensor
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain output types to any tensor type.
Examples#
concat
test_cases: Dict[str, Sequence[Any]] = {
"1d": ([1, 2], [3, 4]),
"2d": ([[1, 2], [3, 4]], [[5, 6], [7, 8]]),
"3d": (
[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
[[[9, 10], [11, 12]], [[13, 14], [15, 16]]],
),
}
for test_case, values_ in test_cases.items():
values = [np.asarray(v, dtype=np.float32) for v in values_]
for i in range(len(values[0].shape)):
in_args = ["value" + str(k) for k in range(len(values))]
node = onnx.helper.make_node(
"Concat", inputs=[s for s in in_args], outputs=["output"], axis=i
)
output = np.concatenate(values, i)
expect(
node,
inputs=[v for v in values],
outputs=[output],
name="test_concat_" + test_case + "_axis_" + str(i),
)
for i in range(-len(values[0].shape), 0):
in_args = ["value" + str(k) for k in range(len(values))]
node = onnx.helper.make_node(
"Concat", inputs=[s for s in in_args], outputs=["output"], axis=i
)
output = np.concatenate(values, i)
expect(
node,
inputs=[v for v in values],
outputs=[output],
name="test_concat_" + test_case + "_axis_negative_" + str(abs(i)),
)
ConcatFromSequence#
Concatenate a sequence of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on. By default ‘new_axis’ is 0, the behavior is similar to numpy.concatenate. When ‘new_axis’ is 1, the behavior is similar to numpy.stack.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Attributes#
- axis : int (required)
- Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`.
- new_axis : int (default is 0)
- Insert and concatenate on a new axis or not, default 0 means do not insert new axis.
Inputs#
- input_sequence : S
- Sequence of tensors for concatenation
Outputs#
- concat_result : T
- Concatenated tensor
Type Constraints#
- S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain input types to any tensor type.
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain output types to any tensor type.
Constant#
This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- sparse_value : sparse_tensor
- The value for the elements of the output tensor in sparse format.
- value : tensor
- The value for the elements of the output tensor.
- value_float : float
- The value for the sole element for the scalar, float32, output tensor.
- value_floats : list of floats
- The values for the elements for the 1D, float32, output tensor.
- value_int : int
- The value for the sole element for the scalar, int64, output tensor.
- value_ints : list of ints
- The values for the elements for the 1D, int64, output tensor.
- value_string : string
- The value for the sole element for the scalar, UTF-8 string, output tensor.
- value_strings : list of strings
- The values for the elements for the 1D, UTF-8 string, output tensor.
Inputs#
Outputs#
- output : T
- Output tensor containing the same value of the provided tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Examples#
constant
values = np.random.randn(5, 5).astype(np.float32)
node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["values"],
value=onnx.helper.make_tensor(
name="const_tensor",
data_type=onnx.TensorProto.FLOAT,
dims=values.shape,
vals=values.flatten().astype(float),
),
)
expect(node, inputs=[], outputs=[values], name="test_constant")
ConstantOfShape#
Generate a tensor with given value and shape.
Version#
This version of the operator has been available since version 9 of the default ONNX operator set.
Attributes#
- value : tensor
- (Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32
Inputs#
- input : T1
- 1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.
Outputs#
- output : T2
- Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.
Type Constraints#
- T1 : tensor(int64)
- Constrain input types.
- T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
- Constrain output types to be numerics.
Examples#
float_ones
x = np.array([4, 3, 2]).astype(np.int64)
tensor_value = onnx.helper.make_tensor(
"value", onnx.TensorProto.FLOAT, [1], [1]
)
node = onnx.helper.make_node(
"ConstantOfShape",
inputs=["x"],
outputs=["y"],
value=tensor_value,
)
y = np.ones(x, dtype=np.float32)
expect(node, inputs=[x], outputs=[y], name="test_constantofshape_float_ones")
int32_shape_zero
x = np.array(
[
0,
]
).astype(np.int64)
tensor_value = onnx.helper.make_tensor(
"value", onnx.TensorProto.INT32, [1], [0]
)
node = onnx.helper.make_node(
"ConstantOfShape",
inputs=["x"],
outputs=["y"],
value=tensor_value,
)
y = np.zeros(x, dtype=np.int32)
expect(
node, inputs=[x], outputs=[y], name="test_constantofshape_int_shape_zero"
)
int32_zeros
x = np.array([10, 6]).astype(np.int64)
tensor_value = onnx.helper.make_tensor(
"value", onnx.TensorProto.INT32, [1], [0]
)
node = onnx.helper.make_node(
"ConstantOfShape",
inputs=["x"],
outputs=["y"],
value=tensor_value,
)
y = np.zeros(x, dtype=np.int32)
expect(node, inputs=[x], outputs=[y], name="test_constantofshape_int_zeros")
Conv#
The convolution operator consumes an input tensor and a filter, and computes the output.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- auto_pad : string (default is NOTSET)
- auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
- dilations : list of ints
- dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.
- group : int (default is 1)
- number of groups input channels and output channels are divided into.
- kernel_shape : list of ints
- The shape of the convolution kernel. If not present, should be inferred from input W.
- pads : list of ints
- Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
- strides : list of ints
- Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.
Inputs (2 - 3)#
- X (differentiable) : T
- Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
- W (differentiable) : T
- The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.
- B (optional, differentiable) : T
- Optional 1D bias to be added to the convolution, has size of M.
Outputs#
- Y (differentiable) : T
- Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
conv
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
]
]
]
).astype(np.float32)
W = np.array(
[
[
[
[1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
# Convolution with padding
node_with_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
# Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
pads=[1, 1, 1, 1],
)
y_with_padding = np.array(
[
[
[
[12.0, 21.0, 27.0, 33.0, 24.0], # (1, 1, 5, 5) output tensor
[33.0, 54.0, 63.0, 72.0, 51.0],
[63.0, 99.0, 108.0, 117.0, 81.0],
[93.0, 144.0, 153.0, 162.0, 111.0],
[72.0, 111.0, 117.0, 123.0, 84.0],
]
]
]
).astype(np.float32)
expect(
node_with_padding,
inputs=[x, W],
outputs=[y_with_padding],
name="test_basic_conv_with_padding",
)
# Convolution without padding
node_without_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
# Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
pads=[0, 0, 0, 0],
)
y_without_padding = np.array(
[
[
[
[54.0, 63.0, 72.0], # (1, 1, 3, 3) output tensor
[99.0, 108.0, 117.0],
[144.0, 153.0, 162.0],
]
]
]
).astype(np.float32)
expect(
node_without_padding,
inputs=[x, W],
outputs=[y_without_padding],
name="test_basic_conv_without_padding",
)
conv_with_autopad_same
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
]
]
]
).astype(np.float32)
W = np.array(
[
[
[
[1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
# Convolution with auto_pad='SAME_LOWER' and strides=2
node = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
auto_pad="SAME_LOWER",
kernel_shape=[3, 3],
strides=[2, 2],
)
y = np.array(
[[[[12.0, 27.0, 24.0], [63.0, 108.0, 81.0], [72.0, 117.0, 84.0]]]]
).astype(np.float32)
expect(node, inputs=[x, W], outputs=[y], name="test_conv_with_autopad_same")
conv_with_strides
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 7, 5) input tensor
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
[25.0, 26.0, 27.0, 28.0, 29.0],
[30.0, 31.0, 32.0, 33.0, 34.0],
]
]
]
).astype(np.float32)
W = np.array(
[
[
[
[1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
# Convolution with strides=2 and padding
node_with_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[
2,
2,
], # Default values for other attributes: dilations=[1, 1], groups=1
)
y_with_padding = np.array(
[
[
[
[12.0, 27.0, 24.0], # (1, 1, 4, 3) output tensor
[63.0, 108.0, 81.0],
[123.0, 198.0, 141.0],
[112.0, 177.0, 124.0],
]
]
]
).astype(np.float32)
expect(
node_with_padding,
inputs=[x, W],
outputs=[y_with_padding],
name="test_conv_with_strides_padding",
)
# Convolution with strides=2 and no padding
node_without_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[0, 0, 0, 0],
strides=[
2,
2,
], # Default values for other attributes: dilations=[1, 1], groups=1
)
y_without_padding = np.array(
[
[
[
[54.0, 72.0], # (1, 1, 3, 2) output tensor
[144.0, 162.0],
[234.0, 252.0],
]
]
]
).astype(np.float32)
expect(
node_without_padding,
inputs=[x, W],
outputs=[y_without_padding],
name="test_conv_with_strides_no_padding",
)
# Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor)
node_with_asymmetric_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[1, 0, 1, 0],
strides=[
2,
2,
], # Default values for other attributes: dilations=[1, 1], groups=1
)
y_with_asymmetric_padding = np.array(
[
[
[
[21.0, 33.0], # (1, 1, 4, 2) output tensor
[99.0, 117.0],
[189.0, 207.0],
[171.0, 183.0],
]
]
]
).astype(np.float32)
expect(
node_with_asymmetric_padding,
inputs=[x, W],
outputs=[y_with_asymmetric_padding],
name="test_conv_with_strides_and_asymmetric_padding",
)
ConvInteger#
The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point, and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
Version#
This version of the operator has been available since version 10 of the default ONNX operator set.
Attributes#
- auto_pad : string (default is NOTSET)
- auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
- dilations : list of ints
- dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.
- group : int (default is 1)
- number of groups input channels and output channels are divided into. default is 1.
- kernel_shape : list of ints
- The shape of the convolution kernel. If not present, should be inferred from input 'w'.
- pads : list of ints
- Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.
- strides : list of ints
- Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.
Inputs (2 - 4)#
- x : T1
- Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
- w : T2
- The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL.
- x_zero_point (optional) : T1
- Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.
- w_zero_point (optional) : T2
- Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)
Outputs#
- y : T3
- Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
Type Constraints#
- T1 : tensor(int8), tensor(uint8)
- Constrain input x and its zero point data type to 8-bit integer tensor.
- T2 : tensor(int8), tensor(uint8)
- Constrain input w and its zero point data type to 8-bit integer tensor.
- T3 : tensor(int32)
- Constrain output y data type to 32-bit integer tensor.
Examples#
with_padding
x = (
np.array([2, 3, 4, 5, 6, 7, 8, 9, 10])
.astype(np.uint8)
.reshape((1, 1, 3, 3))
)
x_zero_point = np.uint8(1)
w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
y = (
np.array([1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9])
.astype(np.int32)
.reshape((1, 1, 4, 4))
)
# ConvInteger with padding
convinteger_node_with_padding = onnx.helper.make_node(
"ConvInteger",
inputs=["x", "w", "x_zero_point"],
outputs=["y"],
pads=[1, 1, 1, 1],
)
expect(
convinteger_node_with_padding,
inputs=[x, w, x_zero_point],
outputs=[y],
name="test_convinteger_with_padding",
)
without_padding
x = (
np.array([2, 3, 4, 5, 6, 7, 8, 9, 10])
.astype(np.uint8)
.reshape((1, 1, 3, 3))
)
x_zero_point = np.uint8(1)
w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2)
# ConvInteger without padding
convinteger_node = onnx.helper.make_node(
"ConvInteger", inputs=["x", "w", "x_zero_point"], outputs=["y"]
)
expect(
convinteger_node,
inputs=[x, w, x_zero_point],
outputs=[y],
name="test_convinteger_without_padding",
)
ConvTranspose#
The convolution transpose operator consumes an input tensor and a filter, and computes the output.
If the pads parameter is provided the shape of the output is calculated via the following equation:
output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i]
output_shape can also be explicitly specified in which case pads values are auto generated using these equations:
total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]
If (auto_pads == SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- auto_pad : string (default is NOTSET)
- auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
- dilations : list of ints
- dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.
- group : int (default is 1)
- number of groups input channels and output channels are divided into.
- kernel_shape : list of ints
- The shape of the convolution kernel. If not present, should be inferred from input W.
- output_padding : list of ints
- Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.
- output_shape : list of ints
- The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads
- pads : list of ints
- Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
- strides : list of ints
- Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs (2 - 3)#
- X (differentiable) : T
- Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)
- W (differentiable) : T
- The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
- B (optional, differentiable) : T
- Optional 1D bias to be added to the convolution, has size of M.
Outputs#
- Y (differentiable) : T
- Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
convtranspose
x = np.array(
[[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
W = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
y = np.array(
[
[
[
[0.0, 1.0, 3.0, 3.0, 2.0], # (1, 2, 5, 5)
[3.0, 8.0, 15.0, 12.0, 7.0],
[9.0, 21.0, 36.0, 27.0, 15.0],
[9.0, 20.0, 33.0, 24.0, 13.0],
[6.0, 13.0, 21.0, 15.0, 8.0],
],
[
[0.0, 1.0, 3.0, 3.0, 2.0],
[3.0, 8.0, 15.0, 12.0, 7.0],
[9.0, 21.0, 36.0, 27.0, 15.0],
[9.0, 20.0, 33.0, 24.0, 13.0],
[6.0, 13.0, 21.0, 15.0, 8.0],
],
]
]
).astype(np.float32)
expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose")
convtranspose_1d
x = np.array([[[0.0, 1.0, 2.0]]]).astype(np.float32) # (1, 1, 3)
W = np.array([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]).astype( # (1, 2, 3)
np.float32
)
node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
y = np.array(
[[[0.0, 1.0, 3.0, 3.0, 2.0], [0.0, 1.0, 3.0, 3.0, 2.0]]] # (1, 2, 5)
).astype(np.float32)
expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_1d")
convtranspose_3d
x = np.array(
[
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 3, 4, 5)
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
],
[
[20.0, 21.0, 22.0, 23.0, 24.0],
[25.0, 26.0, 27.0, 28.0, 29.0],
[30.0, 31.0, 32.0, 33.0, 34.0],
[35.0, 36.0, 37.0, 38.0, 39.0],
],
[
[40.0, 41.0, 42.0, 43.0, 44.0],
[45.0, 46.0, 47.0, 48.0, 49.0],
[50.0, 51.0, 52.0, 53.0, 54.0],
[55.0, 56.0, 57.0, 58.0, 59.0],
],
]
]
]
).astype(np.float32)
W = np.array(
[
[
[
[
[1.0, 1.0, 1.0], # (1, 2, 3, 3, 3)
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
],
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
],
]
]
).astype(np.float32)
node = onnx.helper.make_node("ConvTranspose", ["X", "W"], ["Y"])
y = np.array(
[
[
[
[
[0.0, 1.0, 3.0, 6.0, 9.0, 7.0, 4.0], # (1, 2, 5, 6, 7)
[5.0, 12.0, 21.0, 27.0, 33.0, 24.0, 13.0],
[15.0, 33.0, 54.0, 63.0, 72.0, 51.0, 27.0],
[30.0, 63.0, 99.0, 108.0, 117.0, 81.0, 42.0],
[25.0, 52.0, 81.0, 87.0, 93.0, 64.0, 33.0],
[15.0, 31.0, 48.0, 51.0, 54.0, 37.0, 19.0],
],
[
[20.0, 42.0, 66.0, 72.0, 78.0, 54.0, 28.0],
[50.0, 104.0, 162.0, 174.0, 186.0, 128.0, 66.0],
[90.0, 186.0, 288.0, 306.0, 324.0, 222.0, 114.0],
[120.0, 246.0, 378.0, 396.0, 414.0, 282.0, 144.0],
[90.0, 184.0, 282.0, 294.0, 306.0, 208.0, 106.0],
[50.0, 102.0, 156.0, 162.0, 168.0, 114.0, 58.0],
],
[
[60.0, 123.0, 189.0, 198.0, 207.0, 141.0, 72.0],
[135.0, 276.0, 423.0, 441.0, 459.0, 312.0, 159.0],
[225.0, 459.0, 702.0, 729.0, 756.0, 513.0, 261.0],
[270.0, 549.0, 837.0, 864.0, 891.0, 603.0, 306.0],
[195.0, 396.0, 603.0, 621.0, 639.0, 432.0, 219.0],
[105.0, 213.0, 324.0, 333.0, 342.0, 231.0, 117.0],
],
[
[60.0, 122.0, 186.0, 192.0, 198.0, 134.0, 68.0],
[130.0, 264.0, 402.0, 414.0, 426.0, 288.0, 146.0],
[210.0, 426.0, 648.0, 666.0, 684.0, 462.0, 234.0],
[240.0, 486.0, 738.0, 756.0, 774.0, 522.0, 264.0],
[170.0, 344.0, 522.0, 534.0, 546.0, 368.0, 186.0],
[90.0, 182.0, 276.0, 282.0, 288.0, 194.0, 98.0],
],
[
[40.0, 81.0, 123.0, 126.0, 129.0, 87.0, 44.0],
[85.0, 172.0, 261.0, 267.0, 273.0, 184.0, 93.0],
[135.0, 273.0, 414.0, 423.0, 432.0, 291.0, 147.0],
[150.0, 303.0, 459.0, 468.0, 477.0, 321.0, 162.0],
[105.0, 212.0, 321.0, 327.0, 333.0, 224.0, 113.0],
[55.0, 111.0, 168.0, 171.0, 174.0, 117.0, 59.0],
],
],
[
[
[0.0, 1.0, 3.0, 6.0, 9.0, 7.0, 4.0],
[5.0, 12.0, 21.0, 27.0, 33.0, 24.0, 13.0],
[15.0, 33.0, 54.0, 63.0, 72.0, 51.0, 27.0],
[30.0, 63.0, 99.0, 108.0, 117.0, 81.0, 42.0],
[25.0, 52.0, 81.0, 87.0, 93.0, 64.0, 33.0],
[15.0, 31.0, 48.0, 51.0, 54.0, 37.0, 19.0],
],
[
[20.0, 42.0, 66.0, 72.0, 78.0, 54.0, 28.0],
[50.0, 104.0, 162.0, 174.0, 186.0, 128.0, 66.0],
[90.0, 186.0, 288.0, 306.0, 324.0, 222.0, 114.0],
[120.0, 246.0, 378.0, 396.0, 414.0, 282.0, 144.0],
[90.0, 184.0, 282.0, 294.0, 306.0, 208.0, 106.0],
[50.0, 102.0, 156.0, 162.0, 168.0, 114.0, 58.0],
],
[
[60.0, 123.0, 189.0, 198.0, 207.0, 141.0, 72.0],
[135.0, 276.0, 423.0, 441.0, 459.0, 312.0, 159.0],
[225.0, 459.0, 702.0, 729.0, 756.0, 513.0, 261.0],
[270.0, 549.0, 837.0, 864.0, 891.0, 603.0, 306.0],
[195.0, 396.0, 603.0, 621.0, 639.0, 432.0, 219.0],
[105.0, 213.0, 324.0, 333.0, 342.0, 231.0, 117.0],
],
[
[60.0, 122.0, 186.0, 192.0, 198.0, 134.0, 68.0],
[130.0, 264.0, 402.0, 414.0, 426.0, 288.0, 146.0],
[210.0, 426.0, 648.0, 666.0, 684.0, 462.0, 234.0],
[240.0, 486.0, 738.0, 756.0, 774.0, 522.0, 264.0],
[170.0, 344.0, 522.0, 534.0, 546.0, 368.0, 186.0],
[90.0, 182.0, 276.0, 282.0, 288.0, 194.0, 98.0],
],
[
[40.0, 81.0, 123.0, 126.0, 129.0, 87.0, 44.0],
[85.0, 172.0, 261.0, 267.0, 273.0, 184.0, 93.0],
[135.0, 273.0, 414.0, 423.0, 432.0, 291.0, 147.0],
[150.0, 303.0, 459.0, 468.0, 477.0, 321.0, 162.0],
[105.0, 212.0, 321.0, 327.0, 333.0, 224.0, 113.0],
[55.0, 111.0, 168.0, 171.0, 174.0, 117.0, 59.0],
],
],
]
]
).astype(np.float32)
expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_3d")
convtranspose_attributes
x = np.array(
[[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
W = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
y = np.array(
[
[
[
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0], # (1, 2, 10, 8)
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0],
[3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0],
[3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0],
[3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0],
[6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0],
[6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0],
[6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
],
[
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0],
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 0.0],
[3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0],
[3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0],
[3.0, 3.0, 7.0, 4.0, 9.0, 5.0, 5.0, 0.0],
[6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0],
[6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0],
[6.0, 6.0, 13.0, 7.0, 15.0, 8.0, 8.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
],
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], output_shape=[10, 8]
)
expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_output_shape")
node = onnx.helper.make_node(
"ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], output_padding=[1, 1]
)
expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_pad")
node = onnx.helper.make_node(
"ConvTranspose",
["X", "W"],
["Y"],
name="test",
strides=[3, 2],
output_shape=[10, 8],
kernel_shape=[3, 3],
output_padding=[1, 1],
)
expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_kernel_shape")
convtranspose_autopad_same
x = np.array(
[[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
W = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"ConvTranspose", ["X", "W"], ["Y"], auto_pad="SAME_UPPER", strides=[2, 2]
)
y = np.array(
[
[
[
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0],
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0],
[3.0, 3.0, 8.0, 5.0, 12.0, 7.0],
[3.0, 3.0, 7.0, 4.0, 9.0, 5.0],
[9.0, 9.0, 20.0, 11.0, 24.0, 13.0],
[6.0, 6.0, 13.0, 7.0, 15.0, 8.0],
],
[
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0],
[0.0, 0.0, 1.0, 1.0, 3.0, 2.0],
[3.0, 3.0, 8.0, 5.0, 12.0, 7.0],
[3.0, 3.0, 7.0, 4.0, 9.0, 5.0],
[9.0, 9.0, 20.0, 11.0, 24.0, 13.0],
[6.0, 6.0, 13.0, 7.0, 15.0, 8.0],
],
]
]
).astype(np.float32)
expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_autopad_same")
convtranspose_dilations
x = np.array(
[[[[3.0, 8.0, 1.0], [9.0, 5.0, 7.0], [3.0, 2.0, 6.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
W = np.array([[[[7.0, 2.0], [1.0, 9.0]]]]).astype(np.float32) # (1, 1, 2, 2)
node = onnx.helper.make_node(
"ConvTranspose", ["X", "W"], ["Y"], dilations=[2, 2]
)
y = np.array(
[
[
[
[21.0, 56.0, 13.0, 16.0, 2.0], # [1, 1, 5, 5]
[63.0, 35.0, 67.0, 10.0, 14.0],
[24.0, 22.0, 76.0, 76.0, 21.0],
[9.0, 5.0, 88.0, 45.0, 63.0],
[3.0, 2.0, 33.0, 18.0, 54.0],
]
]
]
).astype(np.float32)
expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_dilations")
convtranspose_pads
x = np.array(
[[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
W = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"ConvTranspose", ["X", "W"], ["Y"], strides=[3, 2], pads=[1, 2, 1, 2]
)
y = np.array(
[
[
[
[1.0, 1.0, 3.0], # (1, 2, 7, 3)
[1.0, 1.0, 3.0],
[7.0, 4.0, 9.0],
[7.0, 4.0, 9.0],
[7.0, 4.0, 9.0],
[13.0, 7.0, 15.0],
[13.0, 7.0, 15.0],
],
[
[1.0, 1.0, 3.0],
[1.0, 1.0, 3.0],
[7.0, 4.0, 9.0],
[7.0, 4.0, 9.0],
[7.0, 4.0, 9.0],
[13.0, 7.0, 15.0],
[13.0, 7.0, 15.0],
],
]
]
).astype(np.float32)
expect(node, inputs=[x, W], outputs=[y], name="test_convtranspose_pads")
Cos#
Calculates the cosine of the given input tensor, element-wise.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The cosine of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
cos
node = onnx.helper.make_node(
"Cos",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.cos(x)
expect(node, inputs=[x], outputs=[y], name="test_cos_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.cos(x)
expect(node, inputs=[x], outputs=[y], name="test_cos")
Cosh#
Calculates the hyperbolic cosine of the given input tensor element-wise.
Version#
This version of the operator has been available since version 9 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The hyperbolic cosine values of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
cosh
node = onnx.helper.make_node(
"Cosh",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.cosh(x) # expected output [1.54308069, 1., 1.54308069]
expect(node, inputs=[x], outputs=[y], name="test_cosh_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.cosh(x)
expect(node, inputs=[x], outputs=[y], name="test_cosh")
CumSum#
Performs cumulative sum of the input elements along the given axis.
By default, it will do the sum inclusively meaning the first element is copied as is.
Through an exclusive attribute, this behavior can change to exclude the first element.
It can also perform summation in the opposite direction of the axis. For that, set reverse attribute to 1.
Example:
input_x = [1, 2, 3]
axis=0
output = [1, 3, 6]
exclusive=1
output = [0, 1, 3]
exclusive=0
reverse=1
output = [6, 5, 3]
exclusive=1
reverse=1
output = [5, 3, 0]
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Other versions of this operator: 11
Attributes#
- exclusive : int (default is 0)
- If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.
- reverse : int (default is 0)
- If set to 1 will perform the sums in reverse direction.
Inputs#
- x (differentiable) : T
- An input tensor that is to be processed.
- axis (non-differentiable) : T2
- A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.
Outputs#
- y (differentiable) : T
- Output tensor of the same type as 'x' with cumulative sums of the x's elements
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
- T2 : tensor(int32), tensor(int64)
- axis tensor can be int32 or int64 only
Examples#
cumsum_1d
node = onnx.helper.make_node("CumSum", inputs=["x", "axis"], outputs=["y"])
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64)
axis = np.int32(0)
y = np.array([1.0, 3.0, 6.0, 10.0, 15.0]).astype(np.float64)
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d")
cumsum_1d_exclusive
node = onnx.helper.make_node(
"CumSum", inputs=["x", "axis"], outputs=["y"], exclusive=1
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64)
axis = np.int32(0)
y = np.array([0.0, 1.0, 3.0, 6.0, 10.0]).astype(np.float64)
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_exclusive")
cumsum_1d_reverse
node = onnx.helper.make_node(
"CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64)
axis = np.int32(0)
y = np.array([15.0, 14.0, 12.0, 9.0, 5.0]).astype(np.float64)
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse")
cumsum_1d_reverse_exclusive
node = onnx.helper.make_node(
"CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1, exclusive=1
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64)
axis = np.int32(0)
y = np.array([14.0, 12.0, 9.0, 5.0, 0.0]).astype(np.float64)
expect(
node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse_exclusive"
)
cumsum_2d_axis_0
node = onnx.helper.make_node(
"CumSum",
inputs=["x", "axis"],
outputs=["y"],
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3))
axis = np.int32(0)
y = np.array([1.0, 2.0, 3.0, 5.0, 7.0, 9.0]).astype(np.float64).reshape((2, 3))
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_0")
cumsum_2d_axis_1
node = onnx.helper.make_node(
"CumSum",
inputs=["x", "axis"],
outputs=["y"],
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3))
axis = np.int32(1)
y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3))
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_1")
cumsum_2d_negative_axis
node = onnx.helper.make_node(
"CumSum",
inputs=["x", "axis"],
outputs=["y"],
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3))
axis = np.int32(-1)
y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3))
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_negative_axis")
DFT#
Computes the discrete Fourier transform of input.
Version#
This version of the operator has been available since version 17 of the default ONNX operator set.
Attributes#
- axis : int (default is 1)
- The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index.
- inverse : int (default is 0)
- Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.
- onesided : int (default is 0)
- If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.
Inputs (1 - 2)#
- input (non-differentiable) : T1
- For real input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][1]. For complex input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. The first dimension is the batch dimension. The following N dimentions correspond to the signal's dimensions. The final dimension represents the real and imaginary parts of the value in that order.
- dft_length (optional, non-differentiable) : T2
- The length of the signal.If greater than the axis dimension, the signal will be zero-padded up to dft_length. If less than the axis dimension, only the first dft_length values will be used as the signal. It's an optional value.
Outputs#
- output : T1
- The Fourier Transform of the input vector.If onesided is 0, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=0 and onesided is 1, the following shape is expected: [batch_idx][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]. If axis=1 and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][floor(signal_dim2/2)+1]...[signal_dimN][2]. If axis=N-1 and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]. The signal_dim at the specified axis is equal to the dft_length.
Type Constraints#
- T1 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
- T2 : tensor(int32), tensor(int64)
- Constrain scalar length types to int64_t.
Examples#
dft
node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=1)
x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
y = np.fft.fft(x, axis=0)
x = x.reshape(1, 10, 10, 1)
y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
expect(node, inputs=[x], outputs=[y], name="test_dft")
node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=2)
x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
y = np.fft.fft(x, axis=1)
x = x.reshape(1, 10, 10, 1)
y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
expect(node, inputs=[x], outputs=[y], name="test_dft_axis")
node = onnx.helper.make_node(
"DFT", inputs=["x"], outputs=["y"], inverse=1, axis=1
)
x = np.arange(0, 100, dtype=np.complex64).reshape(
10,
10,
)
y = np.fft.ifft(x, axis=0)
x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
expect(node, inputs=[x], outputs=[y], name="test_dft_inverse")
DepthToSpace#
DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
the input tensor where values from the depth dimension are moved in spatial blocks to the height
and width dimensions. By default, mode = DCR.
In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
following order: depth, column, and then row. The output y is computed from the input x as below:
b, c, h, w = x.shape
tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below:
b, c, h, w = x.shape
tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- blocksize : int (required)
- Blocks of [blocksize, blocksize] are moved.
- mode : string (default is DCR)
- DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.
Inputs#
- input (differentiable) : T
- Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
Outputs#
- output (differentiable) : T
- Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Examples#
crd_mode_example
node = onnx.helper.make_node(
"DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="CRD"
)
# (1, 8, 2, 3) input tensor
x = np.array(
[
[
[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]],
[[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]],
[[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]],
[[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]],
[[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]],
[[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]],
[[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]],
[[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]],
]
]
).astype(np.float32)
# (1, 2, 4, 6) output tensor
y = np.array(
[
[
[
[0.0, 9.0, 1.0, 10.0, 2.0, 11.0],
[18.0, 27.0, 19.0, 28.0, 20.0, 29.0],
[3.0, 12.0, 4.0, 13.0, 5.0, 14.0],
[21.0, 30.0, 22.0, 31.0, 23.0, 32.0],
],
[
[36.0, 45.0, 37.0, 46.0, 38.0, 47.0],
[54.0, 63.0, 55.0, 64.0, 56.0, 65.0],
[39.0, 48.0, 40.0, 49.0, 41.0, 50.0],
[57.0, 66.0, 58.0, 67.0, 59.0, 68.0],
],
]
]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_depthtospace_crd_mode_example")
default_mode_example
node = onnx.helper.make_node(
"DepthToSpace", inputs=["x"], outputs=["y"], blocksize=2, mode="DCR"
)
# (1, 8, 2, 3) input tensor
x = np.array(
[
[
[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]],
[[9.0, 10.0, 11.0], [12.0, 13.0, 14.0]],
[[18.0, 19.0, 20.0], [21.0, 22.0, 23.0]],
[[27.0, 28.0, 29.0], [30.0, 31.0, 32.0]],
[[36.0, 37.0, 38.0], [39.0, 40.0, 41.0]],
[[45.0, 46.0, 47.0], [48.0, 49.0, 50.0]],
[[54.0, 55.0, 56.0], [57.0, 58.0, 59.0]],
[[63.0, 64.0, 65.0], [66.0, 67.0, 68.0]],
]
]
).astype(np.float32)
# (1, 2, 4, 6) output tensor
y = np.array(
[
[
[
[0.0, 18.0, 1.0, 19.0, 2.0, 20.0],
[36.0, 54.0, 37.0, 55.0, 38.0, 56.0],
[3.0, 21.0, 4.0, 22.0, 5.0, 23.0],
[39.0, 57.0, 40.0, 58.0, 41.0, 59.0],
],
[
[9.0, 27.0, 10.0, 28.0, 11.0, 29.0],
[45.0, 63.0, 46.0, 64.0, 47.0, 65.0],
[12.0, 30.0, 13.0, 31.0, 14.0, 32.0],
[48.0, 66.0, 49.0, 67.0, 50.0, 68.0],
],
]
]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_depthtospace_example")
DequantizeLinear#
The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is y = (x - x_zero_point) * x_scale. ‘x_scale’ and ‘x_zero_point’ must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. ‘x_zero_point’ and ‘x’ must have same type. ‘x’ and ‘y’ must have same shape. In the case of dequantizing int32, there’s no zero point (zero point is supposed to be 0).
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 10
Attributes#
- axis : int (default is 1)
- (Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
Inputs (2 - 3)#
- x : T
- N-D quantized input tensor to be de-quantized.
- x_scale : tensor(float)
- Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.
- x_zero_point (optional) : T
- Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.
Outputs#
- y : tensor(float)
- N-D full precision output tensor. It has same shape as input 'x'.
Type Constraints#
- T : tensor(int8), tensor(uint8), tensor(int32)
- Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.
Examples#
axis
node = onnx.helper.make_node(
"DequantizeLinear",
inputs=["x", "x_scale", "x_zero_point"],
outputs=["y"],
)
# 1-D tensor zero point and scale of size equal to axis 1 of the input tensor
x = np.array(
[
[
[[3, 89], [34, 200], [74, 59]],
[[5, 24], [24, 87], [32, 13]],
[[245, 99], [4, 142], [121, 102]],
],
],
dtype=np.uint8,
)
x_scale = np.array([2, 4, 5], dtype=np.float32)
x_zero_point = np.array([84, 24, 196], dtype=np.uint8)
y = (
x.astype(np.float32) - x_zero_point.reshape(1, 3, 1, 1).astype(np.float32)
) * x_scale.reshape(1, 3, 1, 1)
expect(
node,
inputs=[x, x_scale, x_zero_point],
outputs=[y],
name="test_dequantizelinear_axis",
)
dequantizelinear
node = onnx.helper.make_node(
"DequantizeLinear",
inputs=["x", "x_scale", "x_zero_point"],
outputs=["y"],
)
# scalar zero point and scale
x = np.array([0, 3, 128, 255]).astype(np.uint8)
x_scale = np.float32(2)
x_zero_point = np.uint8(128)
y = np.array([-256, -250, 0, 254], dtype=np.float32)
expect(
node,
inputs=[x, x_scale, x_zero_point],
outputs=[y],
name="test_dequantizelinear",
)
Det#
Det calculates determinant of a square matrix or batches of square matrices.
Det takes one input tensor of shape [*, M, M], where * is zero or more batch dimensions,
and the inner-most 2 dimensions form square matrices.
The output is a tensor of shape [*], containing the determinants of all input submatrices.
e.g., When the input is 2-D, the output is a scalar(shape is empty: []).
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to floating-point tensors.
Examples#
2d
node = onnx.helper.make_node(
"Det",
inputs=["x"],
outputs=["y"],
)
x = np.arange(4).reshape(2, 2).astype(np.float32)
y = np.linalg.det(x) # expect -2
expect(node, inputs=[x], outputs=[y], name="test_det_2d")
nd
node = onnx.helper.make_node(
"Det",
inputs=["x"],
outputs=["y"],
)
x = np.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]).astype(
np.float32
)
y = np.linalg.det(x) # expect array([-2., -3., -8.])
expect(node, inputs=[x], outputs=[y], name="test_det_nd")
Div#
Performs element-wise binary division (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Inputs#
- A (differentiable) : T
- First operand.
- B (differentiable) : T
- Second operand.
Outputs#
- C (differentiable) : T
- Result, has same element type as two inputs
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
div
node = onnx.helper.make_node(
"Div",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([3, 4]).astype(np.float32)
y = np.array([1, 2]).astype(np.float32)
z = x / y # expected output [3., 2.]
expect(node, inputs=[x, y], outputs=[z], name="test_div_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0
z = x / y
expect(node, inputs=[x, y], outputs=[z], name="test_div")
x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1
z = x // y
expect(node, inputs=[x, y], outputs=[z], name="test_div_uint8")
div_broadcast
node = onnx.helper.make_node(
"Div",
inputs=["x", "y"],
outputs=["z"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.rand(5).astype(np.float32) + 1.0
z = x / y
expect(node, inputs=[x, y], outputs=[z], name="test_div_bcast")
Dropout#
Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs,
output (floating-point tensor) and mask (optional Tensor<bool>). If training_mode is true then the output Y will be a random dropout;
Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode,
the user can simply not pass training_mode input or set it to false.
output = scale * data * mask,
where
scale = 1. / (1. - ratio).
This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- seed : int
- (Optional) Seed to the random generator, if not specified we will auto generate one.
Inputs (1 - 3)#
- data (differentiable) : T
- The input data as Tensor.
- ratio (optional, non-differentiable) : T1
- The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.
- training_mode (optional, non-differentiable) : T2
- If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.
Outputs (1 - 2)#
- output (differentiable) : T
- The output.
- mask (optional, non-differentiable) : T2
- The output mask.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
- T1 : tensor(float16), tensor(float), tensor(double)
- Constrain input 'ratio' types to float tensors.
- T2 : tensor(bool)
- Constrain output 'mask' types to boolean tensors.
Examples#
default
seed = np.int64(0)
node = onnx.helper.make_node("Dropout", inputs=["x"], outputs=["y"], seed=seed)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = dropout(x)
expect(node, inputs=[x], outputs=[y], name="test_dropout_default")
default_mask
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y, z = dropout(x, return_mask=True)
expect(node, inputs=[x], outputs=[y, z], name="test_dropout_default_mask")
default_mask_ratio
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r"], outputs=["y", "z"], seed=seed
)
r = np.float32(0.1)
x = np.random.randn(3, 4, 5).astype(np.float32)
y, z = dropout(x, r, return_mask=True)
expect(
node, inputs=[x, r], outputs=[y, z], name="test_dropout_default_mask_ratio"
)
default_old
node = onnx.helper.make_node(
"Dropout",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = x
expect(
node,
inputs=[x],
outputs=[y],
name="test_dropout_default_old",
opset_imports=[helper.make_opsetid("", 11)],
)
default_ratio
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r"], outputs=["y"], seed=seed
)
r = np.float32(0.1)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = dropout(x, r)
expect(node, inputs=[x, r], outputs=[y], name="test_dropout_default_ratio")
random_old
node = onnx.helper.make_node(
"Dropout",
inputs=["x"],
outputs=["y"],
ratio=0.2,
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = x
expect(
node,
inputs=[x],
outputs=[y],
name="test_dropout_random_old",
opset_imports=[helper.make_opsetid("", 11)],
)
training
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.75)
t = np.bool_(True)
y = dropout(x, r, training_mode=t)
expect(node, inputs=[x, r, t], outputs=[y], name="test_training_dropout")
training_default
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.5)
t = np.bool_(True)
y = dropout(x, r, training_mode=t)
expect(
node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_default"
)
training_default_ratio_mask
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.5)
t = np.bool_(True)
y, z = dropout(x, r, training_mode=t, return_mask=True)
expect(
node,
inputs=[x, r, t],
outputs=[y, z],
name="test_training_dropout_default_mask",
)
training_default_zero_ratio
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.0)
t = np.bool_(True)
y = dropout(x, r, training_mode=t)
expect(
node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_zero_ratio"
)
training_default_zero_ratio_mask
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.0)
t = np.bool_(True)
y, z = dropout(x, r, training_mode=t, return_mask=True)
expect(
node,
inputs=[x, r, t],
outputs=[y, z],
name="test_training_dropout_zero_ratio_mask",
)
training_ratio_mask
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.75)
t = np.bool_(True)
y, z = dropout(x, r, training_mode=t, return_mask=True)
expect(
node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_mask"
)
DynamicQuantizeLinear#
A Function to fuse calculation for Scale, Zero Point and FP32->8Bit convertion of FP32 Input data. Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input. Scale is calculated as:
y_scale = (max(x) - min(x))/(qmax - qmin)
* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
* data range is adjusted to include 0.
Zero point is calculated as:
intermediate_zero_point = qmin - min(x)/y_scale
y_zero_point = cast(round(saturate(itermediate_zero_point)))
* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
* rounding to nearest ties to even.
Data quantization formula is:
y = saturate (round (x / y_scale) + y_zero_point)
* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
* rounding to nearest ties to even.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Inputs#
- x : T1
- Input tensor
Outputs#
- y : T2
- Quantized output tensor
- y_scale : tensor(float)
- Output scale. It's a scalar, which means a per-tensor/layer quantization.
- y_zero_point : T2
- Output zero point. It's a scalar, which means a per-tensor/layer quantization.
Type Constraints#
- T1 : tensor(float)
- Constrain 'x' to float tensor.
- T2 : tensor(uint8)
- Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.
Examples#
dynamicquantizelinear
node = onnx.helper.make_node(
"DynamicQuantizeLinear",
inputs=["x"],
outputs=["y", "y_scale", "y_zero_point"],
)
# expected scale 0.0196078438 and zero point 153
X = np.array([0, 2, -3, -2.5, 1.34, 0.5]).astype(np.float32)
x_min = np.minimum(0, np.min(X))
x_max = np.maximum(0, np.max(X))
Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255]
Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
expect(
node,
inputs=[X],
outputs=[Y, Y_Scale, Y_ZeroPoint],
name="test_dynamicquantizelinear",
)
# expected scale 0.0156862754 and zero point 255
X = np.array([-1.0, -2.1, -1.3, -2.5, -3.34, -4.0]).astype(np.float32)
x_min = np.minimum(0, np.min(X))
x_max = np.maximum(0, np.max(X))
Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255]
Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
expect(
node,
inputs=[X],
outputs=[Y, Y_Scale, Y_ZeroPoint],
name="test_dynamicquantizelinear_max_adjusted",
)
X = (
np.array([1, 2.1, 1.3, 2.5, 3.34, 4.0, 1.5, 2.6, 3.9, 4.0, 3.0, 2.345])
.astype(np.float32)
.reshape((3, 4))
)
# expected scale 0.0156862754 and zero point 0
x_min = np.minimum(0, np.min(X))
x_max = np.maximum(0, np.max(X))
Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255]
Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
expect(
node,
inputs=[X],
outputs=[Y, Y_Scale, Y_ZeroPoint],
name="test_dynamicquantizelinear_min_adjusted",
)
Einsum#
An einsum of the form term1, term2 -> output-term produces an output tensor using the following equation
output[output-term] = reduce-sum( input1[term1] * input2[term] )
where the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2) that do not occur in the output-term.
The Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation convention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to an operand tensor, and the characters within the terms correspond to operands dimensions.
This sequence may be followed by “->” to separate the left and right hand side of the equation. If the equation contains “->” followed by the right-hand side, the explicit (not classical) form of the Einstein summation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases, output indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the equation.
When a dimension character is repeated in the left-hand side, it represents summation along the dimension.
The equation may contain ellipsis (”…”) to enable broadcasting. Ellipsis must indicate a fixed number of dimensions. Specifically, every occurrence of ellipsis in the equation must represent the same number of dimensions. The right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the beginning of the output. The equation string may contain space (U+0020) character.
Version#
This version of the operator has been available since version 12 of the default ONNX operator set.
Attributes#
- equation : string (required)
- Einsum expression string.
Inputs (1 - ∞)#
- Inputs (variadic, differentiable) : T
- Operands
Outputs#
- Output (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to all numerical tensor types.
Examples#
einsum_batch_diagonal
Eqn = "...ii ->...i"
node = onnx.helper.make_node(
"Einsum", inputs=["x"], outputs=["y"], equation=Eqn
)
X = np.random.randn(3, 5, 5)
Z = einsum_reference_implementation(Eqn, (X,))
expect(node, inputs=[X], outputs=[Z], name="test_einsum_batch_diagonal")
einsum_batch_matmul
Eqn = "bij, bjk -> bik"
node = onnx.helper.make_node(
"Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn
)
X = np.random.randn(5, 2, 3)
Y = np.random.randn(5, 3, 4)
Z = einsum_reference_implementation(Eqn, (X, Y))
expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_batch_matmul")
einsum_inner_prod
Eqn = "i,i"
node = onnx.helper.make_node(
"Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn
)
X = np.random.randn(5)
Y = np.random.randn(5)
Z = einsum_reference_implementation(Eqn, (X, Y))
expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_inner_prod")
einsum_sum
Eqn = "ij->i"
node = onnx.helper.make_node(
"Einsum", inputs=["x"], outputs=["y"], equation=Eqn
)
X = np.random.randn(3, 4)
Z = einsum_reference_implementation(Eqn, (X,))
expect(node, inputs=[X], outputs=[Z], name="test_einsum_sum")
einsum_transpose
Eqn = "ij->ji"
node = onnx.helper.make_node(
"Einsum", inputs=["x"], outputs=["y"], equation=Eqn
)
X = np.random.randn(3, 4)
Y = einsum_reference_implementation(Eqn, (X,))
expect(node, inputs=[X], outputs=[Y], name="test_einsum_transpose")
Elu#
Elu takes one input data (Tensorf(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0., is applied to the tensor elementwise.
Version#
This version of the operator has been available since version 6 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- alpha : float (default is 1.0)
- Coefficient of ELU.
Inputs#
- X (differentiable) : T
- 1D input tensor
Outputs#
- Y (differentiable) : T
- 1D output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
elu
node = onnx.helper.make_node("Elu", inputs=["x"], outputs=["y"], alpha=2.0)
x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-1.2642411, 0., 1.]
y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
expect(node, inputs=[x], outputs=[y], name="test_elu_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
expect(node, inputs=[x], outputs=[y], name="test_elu")
elu_default
default_alpha = 1.0
node = onnx.helper.make_node(
"Elu",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha
expect(node, inputs=[x], outputs=[y], name="test_elu_default")
Equal#
Returns the tensor resulted from performing the equal logical operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- A (non-differentiable) : T
- First input operand for the logical operator.
- B (non-differentiable) : T
- Second input operand for the logical operator.
Outputs#
- C (non-differentiable) : T1
- Result tensor.
Type Constraints#
- T : tensor(bool), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input types to all numeric tensors.
- T1 : tensor(bool)
- Constrain output to boolean tensor.
Examples#
equal
node = onnx.helper.make_node(
"Equal",
inputs=["x", "y"],
outputs=["z"],
)
x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
y = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
z = np.equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_equal")
equal_broadcast
node = onnx.helper.make_node(
"Equal",
inputs=["x", "y"],
outputs=["z"],
)
x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
y = (np.random.randn(5) * 10).astype(np.int32)
z = np.equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_equal_bcast")
Erf#
Computes the error function of the given input tensor element-wise.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 9
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The error function of the input tensor computed element-wise. It has the same shape and type of the input.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
erf
node = onnx.helper.make_node(
"Erf",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
y = np.vectorize(math.erf)(x).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_erf")
Exp#
Calculates the exponential of the given input tensor, element-wise.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The exponential of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
exp
node = onnx.helper.make_node(
"Exp",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.exp(x) # expected output [0.36787945, 1., 2.71828175]
expect(node, inputs=[x], outputs=[y], name="test_exp_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.exp(x)
expect(node, inputs=[x], outputs=[y], name="test_exp")
Expand#
Broadcast the input tensor following the given shape and the broadcast rule. The broadcast rule is similar to numpy.array(input) * numpy.ones(shape): Dimensions are right alignment; Two corresponding dimensions must have the same value, or one of them is equal to 1. Also, this operator is similar to numpy.broadcast_to(input, shape), but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size(). It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1, or the shape.ndim < input.shape.ndim.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 8
Inputs#
- input (differentiable) : T
- Input tensor
- shape (non-differentiable) : tensor(int64)
- A 1-D tensor indicates the shape you want to expand to, following the broadcast rule
Outputs#
- output (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensors.
Examples#
dim_changed
node = onnx.helper.make_node(
"Expand",
inputs=["data", "new_shape"],
outputs=["expanded"],
)
shape = [3, 1]
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[1.], [2.], [3.]]
new_shape = [2, 1, 6]
expanded = data * np.ones(new_shape, dtype=np.float32)
# print(expanded)
# [[[1., 1., 1., 1., 1., 1.],
# [2., 2., 2., 2., 2., 2.],
# [3., 3., 3., 3., 3., 3.]],
#
# [[1., 1., 1., 1., 1., 1.],
# [2., 2., 2., 2., 2., 2.],
# [3., 3., 3., 3., 3., 3.]]]
new_shape = np.array(new_shape, dtype=np.int64)
expect(
node,
inputs=[data, new_shape],
outputs=[expanded],
name="test_expand_dim_changed",
)
dim_unchanged
node = onnx.helper.make_node(
"Expand",
inputs=["data", "new_shape"],
outputs=["expanded"],
)
shape = [3, 1]
new_shape = [3, 4]
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[1.], [2.], [3.]]
expanded = np.tile(data, 4)
# print(expanded)
# [[1., 1., 1., 1.],
# [2., 2., 2., 2.],
# [3., 3., 3., 3.]]
new_shape = np.array(new_shape, dtype=np.int64)
expect(
node,
inputs=[data, new_shape],
outputs=[expanded],
name="test_expand_dim_unchanged",
)
EyeLike#
Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the same as the input tensor. The data type can be specified by the ‘dtype’ argument. If ‘dtype’ is not specified, then the type of input tensor is used. By default, the main diagonal is populated with ones, but attribute ‘k’ can be used to populate upper or lower diagonals. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message and be valid as an output type.
Version#
This version of the operator has been available since version 9 of the default ONNX operator set.
Attributes#
- dtype : int
- (Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.
- k : int (default is 0)
- (Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.
Inputs#
- input : T1
- 2D input tensor to copy shape, and optionally, type information from.
Outputs#
- output : T2
- Output tensor, same shape as input tensor T1.
Type Constraints#
- T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
- Constrain input types. Strings and complex are not supported.
- T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool)
- Constrain output types. Strings and complex are not supported.
Examples#
populate_off_main_diagonal
shape = (4, 5)
off_diagonal_offset = 1
node = onnx.helper.make_node(
"EyeLike",
inputs=["x"],
outputs=["y"],
k=off_diagonal_offset,
dtype=onnx.TensorProto.FLOAT,
)
x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32)
expect(
node,
inputs=[x],
outputs=[y],
name="test_eyelike_populate_off_main_diagonal",
)
with_dtype
shape = (3, 4)
node = onnx.helper.make_node(
"EyeLike",
inputs=["x"],
outputs=["y"],
dtype=onnx.TensorProto.DOUBLE,
)
x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], dtype=np.float64)
expect(node, inputs=[x], outputs=[y], name="test_eyelike_with_dtype")
without_dtype
shape = (4, 4)
node = onnx.helper.make_node(
"EyeLike",
inputs=["x"],
outputs=["y"],
)
x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], dtype=np.int32)
expect(node, inputs=[x], outputs=[y], name="test_eyelike_without_dtype")
Flatten#
Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, … d_n) then the output will have shape (d_0 X d_1 … d_(axis-1), d_axis X d_(axis+1) … X dn).
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- axis : int (default is 1)
- Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).
Inputs#
- input (differentiable) : T
- A tensor of rank >= axis.
Outputs#
- output (differentiable) : T
- A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output to all tensor types.
Examples#
flatten
shape = (2, 3, 4, 5)
a = np.random.random_sample(shape).astype(np.float32)
for i in range(len(shape)):
node = onnx.helper.make_node(
"Flatten",
inputs=["a"],
outputs=["b"],
axis=i,
)
new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1)
b = np.reshape(a, new_shape)
expect(node, inputs=[a], outputs=[b], name="test_flatten_axis" + str(i))
flatten_negative_axis
shape = (2, 3, 4, 5)
a = np.random.random_sample(shape).astype(np.float32)
for i in range(-len(shape), 0):
node = onnx.helper.make_node(
"Flatten",
inputs=["a"],
outputs=["b"],
axis=i,
)
new_shape = (np.prod(shape[0:i]).astype(int), -1)
b = np.reshape(a, new_shape)
expect(
node,
inputs=[a],
outputs=[b],
name="test_flatten_negative_axis" + str(abs(i)),
)
flatten_with_default_axis
node = onnx.helper.make_node(
"Flatten",
inputs=["a"],
outputs=["b"], # Default value for axis: axis=1
)
shape = (5, 4, 3, 2)
a = np.random.random_sample(shape).astype(np.float32)
new_shape = (5, 24)
b = np.reshape(a, new_shape)
expect(node, inputs=[a], outputs=[b], name="test_flatten_default_axis")
Floor#
Floor takes one input data (Tensor
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- X (non-differentiable) : T
- Input tensor
Outputs#
- Y (non-differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
floor
node = onnx.helper.make_node(
"Floor",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1.5, 1.2, 2]).astype(np.float32)
y = np.floor(x) # expected output [-2., 1., 2.]
expect(node, inputs=[x], outputs=[y], name="test_floor_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.floor(x)
expect(node, inputs=[x], outputs=[y], name="test_floor")
GRU#
Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN.
Notations:
X - input tensor
z - update gate
r - reset gate
h - hidden gate
t - time step (t-1 means previous time step)
W[zrh] - W parameter weight matrix for update, reset, and hidden gates
R[zrh] - R recurrence weight matrix for update, reset, and hidden gates
Wb[zrh] - W bias vectors for update, reset, and hidden gates
Rb[zrh] - R bias vectors for update, reset, and hidden gates
WB[zrh] - W parameter weight matrix for backward update, reset, and hidden gates
RB[zrh] - R recurrence weight matrix for backward update, reset, and hidden gates
WBb[zrh] - W bias vectors for backward update, reset, and hidden gates
RBb[zrh] - R bias vectors for backward update, reset, and hidden gates
H - Hidden state
num_directions - 2 if direction == bidirectional else 1
Activation functions:
Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha*Tanh(beta*x)
HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
Elu(x) - x if x >= 0 else alpha*(e^x - 1)
Softsign(x) - x/(1 + |x|)
Softplus(x) - log(1 + e^x)
Equations (Default: f=Sigmoid, g=Tanh):
- zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
- rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
- ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
- ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0
- Ht = (1 - zt) (.) ht + zt (.) Ht-1
This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Attributes#
- activation_alpha : list of floats
- Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
- activation_beta : list of floats
- Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
- activations : list of strings
- A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
- clip : float
- Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
- direction : string (default is forward)
- Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
- hidden_size : int
- Number of neurons in the hidden layer
- layout : int (default is 0)
- The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].
- linear_before_reset : int (default is 0)
- When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.
Inputs (3 - 6)#
- X (differentiable) : T
- The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
- W (differentiable) : T
- The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.
- R (differentiable) : T
- The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.
- B (optional, differentiable) : T
- The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0
- sequence_lens (optional, non-differentiable) : T1
- Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
- initial_h (optional, non-differentiable) : T
- Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
Outputs (0 - 2)#
- Y (optional, differentiable) : T
- A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
- Y_h (optional, differentiable) : T
- The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
- T1 : tensor(int32)
- Constrain seq_lens to integer tensor.
Examples#
batchwise
input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 6
number_of_gates = 3
weight_scale = 0.2
layout = 1
node = onnx.helper.make_node(
"GRU",
inputs=["X", "W", "R"],
outputs=["Y", "Y_h"],
hidden_size=hidden_size,
layout=layout,
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
gru = GRU_Helper(X=input, W=W, R=R, layout=layout)
Y, Y_h = gru.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y.astype(np.float32), Y_h.astype(np.float32)],
name="test_gru_batchwise",
)
defaults
input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 5
weight_scale = 0.1
number_of_gates = 3
node = onnx.helper.make_node(
"GRU", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
gru = GRU_Helper(X=input, W=W, R=R)
_, Y_h = gru.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y_h.astype(np.float32)],
name="test_gru_defaults",
)
initial_bias
input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype(
np.float32
)
input_size = 3
hidden_size = 3
weight_scale = 0.1
custom_bias = 0.1
number_of_gates = 3
node = onnx.helper.make_node(
"GRU",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
# Adding custom bias
W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(
np.float32
)
R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
B = np.concatenate((W_B, R_B), axis=1)
gru = GRU_Helper(X=input, W=W, R=R, B=B)
_, Y_h = gru.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_gru_with_initial_bias",
)
seq_length
input = np.array(
[
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
[[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]],
]
).astype(np.float32)
input_size = 3
hidden_size = 5
number_of_gates = 3
node = onnx.helper.make_node(
"GRU",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = np.random.randn(1, number_of_gates * hidden_size, input_size).astype(
np.float32
)
R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype(
np.float32
)
# Adding custom bias
W_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
R_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
B = np.concatenate((W_B, R_B), axis=1)
gru = GRU_Helper(X=input, W=W, R=R, B=B)
_, Y_h = gru.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_gru_seq_length",
)
Gather#
Given data tensor of rank r >= 1, and indices tensor of rank q, gather
entries of the axis dimension of data (by default outer-most one as axis=0) indexed by indices, and concatenates
them in an output tensor of rank q + (r - 1).
axis = 0 :
Let k = indices[i_{0}, …, i_{q-1}] Then output[i_{0}, …, i_{q-1}, j_{0}, …, j_{r-2}] = input[k , j_{0}, …, j_{r-2}]
data = [
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
indices = [
[0, 1],
[1, 2],
]
output = [
[
[1.0, 1.2],
[2.3, 3.4],
],
[
[2.3, 3.4],
[4.5, 5.7],
],
]
axis = 1 :
Let k = indices[i_{0}, …, i_{q-1}] Then output[j_{0}, i_{0}, …, i_{q-1}, j_{1}, …, j_{r-2}] = input[j_{0}, k, j_{1}, …, j_{r-2}]
data = [
[1.0, 1.2, 1.9],
[2.3, 3.4, 3.9],
[4.5, 5.7, 5.9],
]
indices = [
[0, 2],
]
axis = 1,
output = [
[[1.0, 1.9]],
[[2.3, 3.9]],
[[4.5, 5.9]],
]
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- axis : int (default is 0)
- Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
Inputs#
- data (differentiable) : T
- Tensor of rank r >= 1.
- indices (non-differentiable) : Tind
- Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
Outputs#
- output (differentiable) : T
- Tensor of rank q + (r - 1).
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to any tensor type.
- Tind : tensor(int32), tensor(int64)
- Constrain indices to integer types
Examples#
gather_0
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=0,
)
data = np.random.randn(5, 4, 3, 2).astype(np.float32)
indices = np.array([0, 1, 3])
y = np.take(data, indices, axis=0)
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_0",
)
gather_1
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=1,
)
data = np.random.randn(5, 4, 3, 2).astype(np.float32)
indices = np.array([0, 1, 3])
y = np.take(data, indices, axis=1)
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_1",
)
gather_2d_indices
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=1,
)
data = np.random.randn(3, 3).astype(np.float32)
indices = np.array([[0, 2]])
y = np.take(data, indices, axis=1)
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_2d_indices",
)
gather_negative_indices
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=0,
)
data = np.arange(10).astype(np.float32)
indices = np.array([0, -9, -10])
y = np.take(data, indices, axis=0)
# print(y)
# [0. 1. 0.]
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_negative_indices",
)
GatherElements#
GatherElements takes two inputs data and indices of the same rank r >= 1
and an optional attribute axis that identifies an axis of data
(by default, the outer-most axis, that is axis 0). It is an indexing operation
that produces its output by indexing into the input data tensor at index
positions determined by elements of the indices tensor.
Its output shape is the same as the shape of indices and consists of one value
(gathered from the data) for each element in indices.
For instance, in the 3-D case (r = 3), the output produced is determined by the following equations:
out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,
out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,
out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,
This operator is also the inverse of ScatterElements. It is similar to Torch’s gather operation.
Example 1:
data = [
[1, 2],
[3, 4],
]
indices = [
[0, 0],
[1, 0],
]
axis = 1
output = [
[1, 1],
[4, 3],
]
Example 2:
data = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
]
indices = [
[1, 2, 0],
[2, 0, 0],
]
axis = 0
output = [
[4, 8, 3],
[7, 2, 3],
]
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 11
Attributes#
- axis : int (default is 0)
- Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
Inputs#
- data (differentiable) : T
- Tensor of rank r >= 1.
- indices (non-differentiable) : Tind
- Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
Outputs#
- output (differentiable) : T
- Tensor of the same shape as indices.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to any tensor type.
- Tind : tensor(int32), tensor(int64)
- Constrain indices to integer types
Examples#
gather_elements_0
axis = 1
node = onnx.helper.make_node(
"GatherElements",
inputs=["data", "indices"],
outputs=["y"],
axis=axis,
)
data = np.array([[1, 2], [3, 4]], dtype=np.float32)
indices = np.array([[0, 0], [1, 0]], dtype=np.int32)
y = gather_elements(data, indices, axis)
# print(y) produces
# [[1, 1],
# [4, 3]]
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_elements_0",
)
gather_elements_1
axis = 0
node = onnx.helper.make_node(
"GatherElements",
inputs=["data", "indices"],
outputs=["y"],
axis=axis,
)
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
indices = np.array([[1, 2, 0], [2, 0, 0]], dtype=np.int32)
y = gather_elements(data, indices, axis)
# print(y) produces
# [[4, 8, 3],
# [7, 2, 3]]
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_elements_1",
)
gather_elements_negative_indices
axis = 0
node = onnx.helper.make_node(
"GatherElements",
inputs=["data", "indices"],
outputs=["y"],
axis=axis,
)
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
indices = np.array([[-1, -2, 0], [-2, 0, 0]], dtype=np.int32)
y = gather_elements(data, indices, axis)
# print(y) produces
# [[7, 5, 3],
# [4, 2, 3]]
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_elements_negative_indices",
)
GatherND#
Given data tensor of rank r >= 1, indices tensor of rank q >= 1, and batch_dims integer b, this operator gathers
slices of data into an output tensor of rank q + r - indices_shape[-1] - 1 - b.
indices is an q-dimensional integer tensor, best thought of as a (q-1)-dimensional tensor of index-tuples into data,
where each element defines a slice of data
batch_dims (denoted as b) is an integer indicating the number of batch dimensions, i.e the leading b number of dimensions of
data tensor and indices are representing the batches, and the gather starts from the b+1 dimension.
Some salient points about the inputs’ rank and shape:
r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks
randqThe first
bdimensions of the shape ofindicestensor anddatatensor must be equal.b < min(q, r) is to be honored.
The
indices_shape[-1]should have a value between 1 (inclusive) and rankr-b(inclusive)All values in
indicesare expected to be within bounds [-s, s-1] along axis of sizes(i.e.)-data_shape[i] <= indices[...,i] <= data_shape[i] - 1. It is an error if any of the index values are out of bounds.
The output is computed as follows:
The output tensor is obtained by mapping each index-tuple in the indices tensor to the corresponding slice of the input data.
If
indices_shape[-1] > r-b=> error conditionIf
indices_shape[-1] == r-b, since the rank ofindicesisq,indicescan be thought of asN(q-b-1)-dimensional tensors containing 1-D tensors of dimensionr-b, whereNis an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape. Let us think of each suchr-branked tensor asindices_slice. Each scalar value corresponding todata[0:b-1,indices_slice]is filled into the corresponding location of the(q-b-1)-dimensional tensor to form theoutputtensor (Example 1 below)If
indices_shape[-1] < r-b, since the rank ofindicesisq,indicescan be thought of asN(q-b-1)-dimensional tensor containing 1-D tensors of dimension< r-b. Let us think of each such tensors asindices_slice. Each tensor slice corresponding todata[0:b-1, indices_slice , :]is filled into the corresponding location of the(q-b-1)-dimensional tensor to form theoutputtensor (Examples 2, 3, 4 and 5 below)
This operator is the inverse of ScatterND.
Example 1
batch_dims = 0
data = [[0,1],[2,3]] # data_shape = [2, 2]
indices = [[0,0],[1,1]] # indices_shape = [2, 2]
output = [0,3] # output_shape = [2]
Example 2
batch_dims = 0
data = [[0,1],[2,3]] # data_shape = [2, 2]
indices = [[1],[0]] # indices_shape = [2, 1]
output = [[2,3],[0,1]] # output_shape = [2, 2]
Example 3
batch_dims = 0
data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
indices = [[0,1],[1,0]] # indices_shape = [2, 2]
output = [[2,3],[4,5]] # output_shape = [2, 2]
Example 4
batch_dims = 0
data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2]
output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2]
Example 5
batch_dims = 1
data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
indices = [[1],[0]] # indices_shape = [2, 1]
output = [[2,3],[4,5]] # output_shape = [2, 2]
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- batch_dims : int (default is 0)
- The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]
Inputs#
- data (differentiable) : T
- Tensor of rank r >= 1.
- indices (non-differentiable) : tensor(int64)
- Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
Outputs#
- output (differentiable) : T
- Tensor of rank q + r - indices_shape[-1] - 1.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to any tensor type.
Examples#
float32
node = onnx.helper.make_node(
"GatherND",
inputs=["data", "indices"],
outputs=["output"],
)
data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32)
indices = np.array([[[0, 1]], [[1, 0]]], dtype=np.int64)
output = gather_nd_impl(data, indices, 0)
expected_output = np.array([[[2, 3]], [[4, 5]]], dtype=np.float32)
assert np.array_equal(output, expected_output)
expect(
node,
inputs=[data, indices],
outputs=[output],
name="test_gathernd_example_float32",
)
int32
node = onnx.helper.make_node(
"GatherND",
inputs=["data", "indices"],
outputs=["output"],
)
data = np.array([[0, 1], [2, 3]], dtype=np.int32)
indices = np.array([[0, 0], [1, 1]], dtype=np.int64)
output = gather_nd_impl(data, indices, 0)
expected_output = np.array([0, 3], dtype=np.int32)
assert np.array_equal(output, expected_output)
expect(
node,
inputs=[data, indices],
outputs=[output],
name="test_gathernd_example_int32",
)
int32_batchdim_1
node = onnx.helper.make_node(
"GatherND",
inputs=["data", "indices"],
outputs=["output"],
batch_dims=1,
)
data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.int32)
indices = np.array([[1], [0]], dtype=np.int64)
output = gather_nd_impl(data, indices, 1)
expected_output = np.array([[2, 3], [4, 5]], dtype=np.int32)
assert np.array_equal(output, expected_output)
expect(
node,
inputs=[data, indices],
outputs=[output],
name="test_gathernd_example_int32_batch_dim1",
)
Gemm#
General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
A’ = transpose(A) if transA else A
B’ = transpose(B) if transB else B
Compute Y = alpha * A’ * B’ + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check the doc. This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- alpha : float (default is 1.0)
- Scalar multiplier for the product of input tensors A * B.
- beta : float (default is 1.0)
- Scalar multiplier for input tensor C.
- transA : int (default is 0)
- Whether A should be transposed
- transB : int (default is 0)
- Whether B should be transposed
Inputs (2 - 3)#
- A (differentiable) : T
- Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
- B (differentiable) : T
- Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
- C (optional, differentiable) : T
- Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).
Outputs#
- Y (differentiable) : T
- Output tensor of shape (M, N).
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)
- Constrain input and output types to float/int tensors.
Examples#
all_attributes
node = onnx.helper.make_node(
"Gemm",
inputs=["a", "b", "c"],
outputs=["y"],
alpha=0.25,
beta=0.35,
transA=1,
transB=1,
)
a = np.random.ranf([4, 3]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.random.ranf([1, 5]).astype(np.float32)
y = gemm_reference_implementation(
a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35
)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_all_attributes")
alpha
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.5
)
a = np.random.ranf([3, 5]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, alpha=0.5)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_alpha")
beta
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], beta=0.5
)
a = np.random.ranf([2, 7]).astype(np.float32)
b = np.random.ranf([7, 4]).astype(np.float32)
c = np.random.ranf([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, beta=0.5)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_beta")
default_matrix_bias
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 6]).astype(np.float32)
b = np.random.ranf([6, 4]).astype(np.float32)
c = np.random.ranf([3, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_matrix_bias"
)
default_no_bias
node = onnx.helper.make_node("Gemm", inputs=["a", "b"], outputs=["y"])
a = np.random.ranf([2, 10]).astype(np.float32)
b = np.random.ranf([10, 3]).astype(np.float32)
y = gemm_reference_implementation(a, b)
expect(node, inputs=[a, b], outputs=[y], name="test_gemm_default_no_bias")
default_scalar_bias
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([2, 3]).astype(np.float32)
b = np.random.ranf([3, 4]).astype(np.float32)
c = np.array(3.14).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_scalar_bias"
)
default_single_elem_vector_bias
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 7]).astype(np.float32)
b = np.random.ranf([7, 3]).astype(np.float32)
c = np.random.ranf([1]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node,
inputs=[a, b, c],
outputs=[y],
name="test_gemm_default_single_elem_vector_bias",
)
default_vector_bias
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([2, 7]).astype(np.float32)
b = np.random.ranf([7, 4]).astype(np.float32)
c = np.random.ranf([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_vector_bias"
)
default_zero_bias
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 5]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_zero_bias")
transposeA
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], transA=1
)
a = np.random.ranf([6, 3]).astype(np.float32)
b = np.random.ranf([6, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, transA=1)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeA")
transposeB
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], transB=1
)
a = np.random.ranf([3, 6]).astype(np.float32)
b = np.random.ranf([4, 6]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, transB=1)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeB")
GlobalAveragePool#
GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor.
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
Outputs#
- Y (differentiable) : T
- Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
globalaveragepool
node = onnx.helper.make_node(
"GlobalAveragePool",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(1, 3, 5, 5).astype(np.float32)
y = np.mean(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool")
globalaveragepool_precomputed
node = onnx.helper.make_node(
"GlobalAveragePool",
inputs=["x"],
outputs=["y"],
)
x = np.array(
[
[
[
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
]
]
]
).astype(np.float32)
y = np.array([[[[5]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool_precomputed")
GlobalLpPool#
GlobalLpPool consumes an input tensor X and applies lp pool pooling across the values in the same channel. This is equivalent to LpPool with kernel size equal to the spatial dimension of input tensor.
Version#
This version of the operator has been available since version 2 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- p : int (default is 2)
- p value of the Lp norm used to pool over the input data.
Inputs#
- X (differentiable) : T
- Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
Outputs#
- Y (differentiable) : T
- Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
GlobalMaxPool#
GlobalMaxPool consumes an input tensor X and applies max pooling across the values in the same channel. This is equivalent to MaxPool with kernel size equal to the spatial dimension of input tensor.
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
Outputs#
- Y (differentiable) : T
- Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
globalmaxpool
node = onnx.helper.make_node(
"GlobalMaxPool",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(1, 3, 5, 5).astype(np.float32)
y = np.max(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool")
globalmaxpool_precomputed
node = onnx.helper.make_node(
"GlobalMaxPool",
inputs=["x"],
outputs=["y"],
)
x = np.array(
[
[
[
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
]
]
]
).astype(np.float32)
y = np.array([[[[9]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool_precomputed")
Greater#
Returns the tensor resulted from performing the greater logical operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- A (non-differentiable) : T
- First input operand for the logical operator.
- B (non-differentiable) : T
- Second input operand for the logical operator.
Outputs#
- C (non-differentiable) : T1
- Result tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input types to all numeric tensors.
- T1 : tensor(bool)
- Constrain output to boolean tensor.
Examples#
greater
node = onnx.helper.make_node(
"Greater",
inputs=["x", "y"],
outputs=["greater"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = np.greater(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater")
greater
node = onnx.helper.make_node(
"GreaterOrEqual",
inputs=["x", "y"],
outputs=["greater_equal"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal")
greater_broadcast
node = onnx.helper.make_node(
"Greater",
inputs=["x", "y"],
outputs=["greater"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = np.greater(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_bcast")
greater_broadcast
node = onnx.helper.make_node(
"GreaterOrEqual",
inputs=["x", "y"],
outputs=["greater_equal"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = np.greater_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast")
GreaterOrEqual#
Returns the tensor resulted from performing the greater_equal logical operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Other versions of this operator: 12
Inputs#
- A (non-differentiable) : T
- First input operand for the logical operator.
- B (non-differentiable) : T
- Second input operand for the logical operator.
Outputs#
- C (non-differentiable) : T1
- Result tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input types to all numeric tensors.
- T1 : tensor(bool)
- Constrain output to boolean tensor.
GridSample#
Given an input X and a flow-field grid, computes the output Y using X values and pixel locations from grid.
Currently, only spatial (4-D) inputs are supported. For input X with shape (N, C, H, W) and grid with shape (N, H_out, W_out, 2),
the output Y will have shape (N, C, H_out, W_out).
The tensor X contains values at centers of square pixels in a H by W 2-dimensional image.
The tensor grid describes normalized positions where the output Y is to be computed
using a specified interpolation method (the mode) and a padding mode (for grid positions falling outside the 2-dimensional image).
Elements in grid[N, H_out, W_out] are size-2 vectors specifying positions in the 2-dimensional space of X.
They are used to interpolate output values of Y[N, C, H_out, W_out].
The GridSample operator is often used in doing grid generator and sampler in the Spatial Transformer Networks. See also in torch.nn.functional.grid_sample.
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Attributes#
- align_corners : int (default is 0)
- If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.
- mode : string (default is bilinear)
- Three interpolation modes: bilinear (default), nearest and bicubic.
- padding_mode : string (default is zeros)
- Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.
Inputs#
- X (differentiable) : T1
- 4-D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the input data.
- grid (non-differentiable) : T2
- Input offset, 4-D tensor of shape (N, H_out, W_out, 2), where H_out and W_out are the height and width of grid and output, Grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.
Outputs#
- Y (differentiable) : T1
- 4-D tensor of shape (N, C, H_out, W_out) of sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.
Type Constraints#
- T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input `X` and output `Y` types to all tensor types.
- T2 : tensor(float16), tensor(float), tensor(double)
- Constrain grid types to float tensors.
Examples#
gridsample
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="bilinear",
padding_mode="zeros",
align_corners=0,
)
# X shape, [N, C, H, W] - [1, 1, 4, 4]
X = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0],
[4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0],
[12.0, 13.0, 14.0, 15.0],
]
]
],
dtype=np.float32,
)
# Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
Grid = np.array(
[
[
[
[-1.0000, -1.0000],
[-0.6000, -1.0000],
[-0.2000, -1.0000],
[0.2000, -1.0000],
[0.6000, -1.0000],
[1.0000, -1.0000],
],
[
[-1.0000, -0.6000],
[-0.6000, -0.6000],
[-0.2000, -0.6000],
[0.2000, -0.6000],
[0.6000, -0.6000],
[1.0000, -0.6000],
],
[
[-1.0000, -0.2000],
[-0.6000, -0.2000],
[-0.2000, -0.2000],
[0.2000, -0.2000],
[0.6000, -0.2000],
[1.0000, -0.2000],
],
[
[-1.0000, 0.2000],
[-0.6000, 0.2000],
[-0.2000, 0.2000],
[0.2000, 0.2000],
[0.6000, 0.2000],
[1.0000, 0.2000],
],
[
[-1.0000, 0.6000],
[-0.6000, 0.6000],
[-0.2000, 0.6000],
[0.2000, 0.6000],
[0.6000, 0.6000],
[1.0000, 0.6000],
],
[
[-1.0000, 1.0000],
[-0.6000, 1.0000],
[-0.2000, 1.0000],
[0.2000, 1.0000],
[0.6000, 1.0000],
[1.0000, 1.0000],
],
]
],
dtype=np.float32,
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
Y = np.array(
[
[
[
[0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500],
[0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000],
[2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000],
[3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000],
[5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000],
[3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500],
]
]
],
dtype=np.float32,
)
expect(node, inputs=[X, Grid], outputs=[Y], name="test_gridsample")
gridsample_mode_aligncorners
# X shape, [N, C, H, W] - [1, 1, 3, 2]
X = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
Grid = np.array(
[
[
[
[-1.0000, -1.0000],
[-0.5000, -0.5000],
[-0.2000, -0.2000],
[0.0000, 0.0000],
],
[
[0.0000, 0.0000],
[-0.2000, -0.2000],
[0.5000, 0.5000],
[1.0000, 1.0000],
],
]
],
dtype=np.float32,
)
# setting mode = 'bilinear', default align_corners = 0
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="bilinear",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_bilinear = np.array(
[[[[0.0000, 0.5000, 1.7000, 2.5000], [2.5000, 1.7000, 4.5000, 1.2500]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_bilinear],
name="test_gridsample_bilinear",
)
# setting mode = 'bilinear', align_corners = 1
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="bilinear",
align_corners=1,
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_align_corners = np.array(
[[[[0.0000, 1.2500, 2.0000, 2.5000], [2.5000, 2.0000, 3.7500, 5.0000]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_align_corners],
name="test_gridsample_aligncorners_true",
)
# setting mode = 'nearest'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="nearest",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_nearest = np.array(
[[[[0.0, 0.0, 2.0, 2.0], [2.0, 2.0, 5.0, 0.0]]]],
dtype=np.float32,
)
expect(
node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest"
)
# setting mode = 'bicubic'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="bicubic",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_bicubic = np.array(
[[[[-0.1406, 0.3828, 1.7556, 2.9688], [2.9688, 1.7556, 5.1445, 1.3906]]]],
dtype=np.float32,
)
expect(
node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic"
)
gridsample_paddingmode
# X shape, [N, C, H, W] - [1, 1, 3, 2]
X = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
Grid = np.array(
[
[
[
[-10.0000, -10.0000],
[-5.0000, -5.0000],
[-0.2000, -0.2000],
[10.0000, 10.0000],
],
[
[10.0000, 10.0000],
[-0.2000, -0.2000],
[5.0000, 5.0000],
[10.0000, 10.0000],
],
]
],
dtype=np.float32,
)
# setting padding_mode = 'zeros'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
padding_mode="zeros",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_zeros = np.array(
[[[[0.0000, 0.0000, 1.7000, 0.0000], [0.0000, 1.7000, 0.0000, 0.0000]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_zeros],
name="test_gridsample_zeros_padding",
)
# setting padding_mode = 'border'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
padding_mode="border",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_border = np.array(
[[[[0.0000, 0.0000, 1.7000, 5.0000], [5.0000, 1.7000, 5.0000, 5.0000]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_border],
name="test_gridsample_border_padding",
)
# setting padding_mode = 'reflection'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
padding_mode="reflection",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_reflection = np.array(
[[[[2.5000, 0.0000, 1.7000, 2.5000], [2.5000, 1.7000, 5.0000, 2.5000]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_reflection],
name="test_gridsample_reflection_padding",
)
GroupNormalization#
A GroupNormalization function. Carries out group normalization as described in the paper https://arxiv.org/abs/1803.08494
This operator transforms input according to
y = scale * (x - mean) / sqrt(variance + epsilon) + bias,
where the mean and variance are computed per instance per group of channels, and
scale and bias should be specified for each group of channels. The number of
groups num_groups should be divisible by the number of channels so that there are
an equal number of channels per group.
When the number of groups is the same as the number of channels, this operator is equivalent to InstanceNormalization. When there is only one group, this operator is equivalent to LayerNormalization.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- epsilon : float (default is 1e-05)
- The epsilon value to use to avoid division by zero.
- num_groups : int (required)
- The number of groups of channels. It should be a divisor of the number of channels `C`.
Inputs#
- X (differentiable) : T
- Input data tensor. Dimensions for image cases are `(N x C x H x W)`, where `N` is the batch size, `C` is the number of channels, and `H` and `W` are the height and width of the data. Statistics are computed for every group of channels over `C`, `H`, and `W`. For non-image cases, the dimensions are in the form of `(N x C x D1 x D2 ... Dn)`.
- scale (differentiable) : T
- Scale tensor of shape `(num_groups)`.
- bias (differentiable) : T
- Bias tensor of shape `(num_groups)`.
Outputs#
- Y (differentiable) : T
- The output tensor of the same shape as `X`.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
groupnormalization
x = np.random.randn(3, 4, 2, 2).astype(np.float32)
num_groups = 2
scale = np.random.randn(num_groups).astype(np.float32)
bias = np.random.randn(num_groups).astype(np.float32)
y = _group_normalization(x, num_groups, scale, bias).astype(np.float32)
node = onnx.helper.make_node(
"GroupNormalization",
inputs=["x", "scale", "bias"],
outputs=["y"],
num_groups=num_groups,
)
expect(
node,
inputs=[x, scale, bias],
outputs=[y],
name="test_group_normalization_example",
)
x = np.random.randn(3, 4, 2, 2).astype(np.float32)
num_groups = 2
scale = np.random.randn(num_groups).astype(np.float32)
bias = np.random.randn(num_groups).astype(np.float32)
epsilon = 1e-2
y = _group_normalization(x, num_groups, scale, bias, epsilon).astype(np.float32)
node = onnx.helper.make_node(
"GroupNormalization",
inputs=["x", "scale", "bias"],
outputs=["y"],
epsilon=epsilon,
num_groups=num_groups,
)
expect(
node,
inputs=[x, scale, bias],
outputs=[y],
name="test_group_normalization_epsilon",
)
HammingWindow#
Generates a Hamming window as described in the paper https://ieeexplore.ieee.org/document/1455106.
Version#
This version of the operator has been available since version 17 of the default ONNX operator set.
Attributes#
- output_datatype : int (default is 1)
- The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT.
- periodic : int (default is 1)
- If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1.
Inputs#
- size (non-differentiable) : T1
- A scalar value indicating the length of the window.
Outputs#
- output (non-differentiable) : T2
- A Hamming window with length: size. The output has the shape: [size].
Type Constraints#
- T1 : tensor(int32), tensor(int64)
- Constrain the input size to int64_t.
- T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain output types to numeric tensors.
Examples#
hammingwindow
# Test periodic window
node = onnx.helper.make_node(
"HammingWindow",
inputs=["x"],
outputs=["y"],
)
size = np.int32(10)
a0 = 25 / 46
a1 = 1 - a0
y = a0 - a1 * np.cos(
2 * 3.1415 * np.arange(0, size, 1, dtype=np.float32) / size
)
expect(node, inputs=[size], outputs=[y], name="test_hammingwindow")
# Test symmetric window
node = onnx.helper.make_node(
"HammingWindow", inputs=["x"], outputs=["y"], periodic=0
)
size = np.int32(10)
a0 = 25 / 46
a1 = 1 - a0
y = a0 - a1 * np.cos(
2 * 3.1415 * np.arange(0, size, 1, dtype=np.float32) / (size - 1)
)
expect(node, inputs=[size], outputs=[y], name="test_hammingwindow_symmetric")
HannWindow#
Generates a Hann window as described in the paper https://ieeexplore.ieee.org/document/1455106.
Version#
This version of the operator has been available since version 17 of the default ONNX operator set.
Attributes#
- output_datatype : int (default is 1)
- The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT.
- periodic : int (default is 1)
- If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1.
Inputs#
- size (non-differentiable) : T1
- A scalar value indicating the length of the window.
Outputs#
- output (non-differentiable) : T2
- A Hann window with length: size. The output has the shape: [size].
Type Constraints#
- T1 : tensor(int32), tensor(int64)
- Constrain the input size to int64_t.
- T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain output types to numeric tensors.
Examples#
hannwindow
# Test periodic window
node = onnx.helper.make_node(
"HannWindow",
inputs=["x"],
outputs=["y"],
)
size = np.int32(10)
a0 = 0.5
a1 = 0.5
y = a0 - a1 * np.cos(
2 * 3.1415 * np.arange(0, size, 1, dtype=np.float32) / size
)
expect(node, inputs=[size], outputs=[y], name="test_hannwindow")
# Test symmetric window
node = onnx.helper.make_node(
"HannWindow", inputs=["x"], outputs=["y"], periodic=0
)
size = np.int32(10)
a0 = 0.5
a1 = 0.5
y = a0 - a1 * np.cos(
2 * 3.1415 * np.arange(0, size, 1, dtype=np.float32) / (size - 1)
)
expect(node, inputs=[size], outputs=[y], name="test_hannwindow_symmetric")
HardSigmoid#
HardSigmoid takes one input data (Tensor
Version#
This version of the operator has been available since version 6 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- alpha : float (default is 0.2)
- Value of alpha.
- beta : float (default is 0.5)
- Value of beta.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
hardsigmoid
node = onnx.helper.make_node(
"HardSigmoid", inputs=["x"], outputs=["y"], alpha=0.5, beta=0.6
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1) # expected output [0.1, 0.6, 1.]
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1)
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid")
hardsigmoid_default
default_alpha = 0.2
default_beta = 0.5
node = onnx.helper.make_node(
"HardSigmoid",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * default_alpha + default_beta, 0, 1)
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_default")
HardSwish#
HardSwish takes one input data (Tensor
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
hardswish
node = onnx.helper.make_node(
"HardSwish",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = hardswish(x)
expect(node, inputs=[x], outputs=[y], name="test_hardswish")
Hardmax#
The operator computes the hardmax values for the given input:
Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise
The “axis” attribute indicates the dimension along which Hardmax will be performed. The output tensor has the same shape and contains the Hardmax values of the corresponding input.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- axis : int (default is -1)
- Describes the dimension Hardmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
Inputs#
- input (differentiable) : T
- The input tensor of rank >= axis.
Outputs#
- output (differentiable) : T
- The output values with the same shape as the input tensor.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
hardmax
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
)
x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2], [0, 1, 2, 3]]).astype(
np.float32
)
# expect result:
# [[1. 0. 0. 0.]
# [0. 1. 0. 0.]
# [0. 0. 1. 0.]
# [0. 0. 0. 1.]]
y = hardmax(x)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_example")
# For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output
x = np.array([[3, 3, 3, 1]]).astype(np.float32)
# expect result:
# [[1, 0, 0, 0]]
y = hardmax(x)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_one_hot")
hardmax_axis
x = np.random.randn(3, 4, 5).astype(np.float32)
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
axis=0,
)
y = hardmax(x, axis=0)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_0")
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
axis=1,
)
y = hardmax(x, axis=1)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_1")
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
axis=2,
)
y = hardmax(x, axis=2)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_2")
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
axis=-1,
)
y = hardmax(x, axis=-1)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_negative_axis")
# default axis is -1
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_default_axis")
Identity#
Identity operator
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Inputs#
- input (differentiable) : V
- Input tensor
Outputs#
- output (differentiable) : V
- Tensor to copy input into.
Type Constraints#
- V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
- Constrain input and output types to all tensor, sequence, and optional types.
Examples#
identity
node = onnx.helper.make_node(
"Identity",
inputs=["x"],
outputs=["y"],
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
expect(node, inputs=[data], outputs=[data], name="test_identity")
identity_opt
ten_in_tp = onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, shape=[5]
)
seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
identity_node = onnx.helper.make_node(
"Identity", inputs=["opt_in"], outputs=["opt_out"]
)
x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
expect(
identity_node,
inputs=[x],
outputs=[x],
name="test_identity_opt",
opset_imports=[onnx.helper.make_opsetid("", 16)],
input_type_protos=[opt_in_tp],
output_type_protos=[opt_in_tp],
)
sequence
node = onnx.helper.make_node(
"Identity",
inputs=["x"],
outputs=["y"],
)
data = [
np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
),
np.array(
[
[
[
[2, 3],
[1, 5],
]
]
],
dtype=np.float32,
),
]
expect(node, inputs=[data], outputs=[data], name="test_identity_sequence")
If#
If conditional
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Attributes#
- else_branch : graph (required)
- Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
- then_branch : graph (required)
- Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.
Inputs#
- cond : B
- Condition for the if
Outputs (1 - ∞)#
- outputs (variadic, heterogeneous) : V
- Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.
Type Constraints#
- V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
- All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types
- B : tensor(bool)
- Only bool
Examples#
if
# Given a bool scalar input cond.
# return constant tensor x if cond is True, otherwise return constant tensor y.
then_out = onnx.helper.make_tensor_value_info(
"then_out", onnx.TensorProto.FLOAT, [5]
)
else_out = onnx.helper.make_tensor_value_info(
"else_out", onnx.TensorProto.FLOAT, [5]
)
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
y = np.array([5, 4, 3, 2, 1]).astype(np.float32)
then_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["then_out"],
value=onnx.numpy_helper.from_array(x),
)
else_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["else_out"],
value=onnx.numpy_helper.from_array(y),
)
then_body = onnx.helper.make_graph(
[then_const_node], "then_body", [], [then_out]
)
else_body = onnx.helper.make_graph(
[else_const_node], "else_body", [], [else_out]
)
if_node = onnx.helper.make_node(
"If",
inputs=["cond"],
outputs=["res"],
then_branch=then_body,
else_branch=else_body,
)
cond = np.array(1).astype(bool)
res = x if cond else y
expect(
if_node,
inputs=[cond],
outputs=[res],
name="test_if",
opset_imports=[onnx.helper.make_opsetid("", 11)],
)
if_optional
# Given a bool scalar input cond, return an empty optional sequence of
# tensor if True, return an optional sequence with value x
# (the input optional sequence) otherwise.
ten_in_tp = onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, shape=[5]
)
seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
then_out_tensor_tp = onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, shape=[5]
)
then_out_seq_tp = onnx.helper.make_sequence_type_proto(then_out_tensor_tp)
then_out_opt_tp = onnx.helper.make_optional_type_proto(then_out_seq_tp)
then_out = onnx.helper.make_value_info("optional_empty", then_out_opt_tp)
else_out_tensor_tp = onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, shape=[5]
)
else_out_seq_tp = onnx.helper.make_sequence_type_proto(else_out_tensor_tp)
else_out_opt_tp = onnx.helper.make_optional_type_proto(else_out_seq_tp)
else_out = onnx.helper.make_value_info("else_opt", else_out_opt_tp)
x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
cond = np.array(0).astype(bool)
res = compute_if_outputs(x, cond)
opt_empty_in = onnx.helper.make_node(
"Optional", inputs=[], outputs=["optional_empty"], type=seq_in_tp
)
then_body = onnx.helper.make_graph([opt_empty_in], "then_body", [], [then_out])
else_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.numpy_helper.from_array(x[0]),
)
else_seq_node = onnx.helper.make_node(
"SequenceConstruct", inputs=["x"], outputs=["else_seq"]
)
else_optional_seq_node = onnx.helper.make_node(
"Optional", inputs=["else_seq"], outputs=["else_opt"]
)
else_body = onnx.helper.make_graph(
[else_const_node, else_seq_node, else_optional_seq_node],
"else_body",
[],
[else_out],
)
if_node = onnx.helper.make_node(
"If",
inputs=["cond"],
outputs=["sequence"],
then_branch=then_body,
else_branch=else_body,
)
expect(
if_node,
inputs=[cond],
outputs=[res],
name="test_if_opt",
output_type_protos=[else_out_opt_tp],
opset_imports=[onnx.helper.make_opsetid("", 16)],
)
if_seq
# Given a bool scalar input cond.
# return constant sequence x if cond is True, otherwise return constant sequence y.
then_out = onnx.helper.make_tensor_sequence_value_info(
"then_out", onnx.TensorProto.FLOAT, shape=[5]
)
else_out = onnx.helper.make_tensor_sequence_value_info(
"else_out", onnx.TensorProto.FLOAT, shape=[5]
)
x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
y = [np.array([5, 4, 3, 2, 1]).astype(np.float32)]
then_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.numpy_helper.from_array(x[0]),
)
then_seq_node = onnx.helper.make_node(
"SequenceConstruct", inputs=["x"], outputs=["then_out"]
)
else_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["y"],
value=onnx.numpy_helper.from_array(y[0]),
)
else_seq_node = onnx.helper.make_node(
"SequenceConstruct", inputs=["y"], outputs=["else_out"]
)
then_body = onnx.helper.make_graph(
[then_const_node, then_seq_node], "then_body", [], [then_out]
)
else_body = onnx.helper.make_graph(
[else_const_node, else_seq_node], "else_body", [], [else_out]
)
if_node = onnx.helper.make_node(
"If",
inputs=["cond"],
outputs=["res"],
then_branch=then_body,
else_branch=else_body,
)
cond = np.array(1).astype(bool)
res = x if cond else y
expect(
if_node,
inputs=[cond],
outputs=[res],
name="test_if_seq",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
InstanceNormalization#
Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022.
y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel.
Version#
This version of the operator has been available since version 6 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- epsilon : float (default is 1e-05)
- The epsilon value to use to avoid division by zero.
Inputs#
- input (differentiable) : T
- Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
- scale (differentiable) : T
- The input 1-dimensional scale tensor of size C.
- B (differentiable) : T
- The input 1-dimensional bias tensor of size C.
Outputs#
- output (differentiable) : T
- The output tensor of the same shape as input.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
instancenormalization
def _instancenorm_test_mode(x, s, bias, epsilon=1e-5): # type: ignore
dims_x = len(x.shape)
axis = tuple(range(2, dims_x))
mean = np.mean(x, axis=axis, keepdims=True)
var = np.var(x, axis=axis, keepdims=True)
dim_ones = (1,) * (dims_x - 2)
s = s.reshape(-1, *dim_ones)
bias = bias.reshape(-1, *dim_ones)
return s * (x - mean) / np.sqrt(var + epsilon) + bias
# input size: (1, 2, 1, 3)
x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
s = np.array([1.0, 1.5]).astype(np.float32)
bias = np.array([0, 1]).astype(np.float32)
y = _instancenorm_test_mode(x, s, bias).astype(np.float32)
node = onnx.helper.make_node(
"InstanceNormalization",
inputs=["x", "s", "bias"],
outputs=["y"],
)
# output size: (1, 2, 1, 3)
expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_example")
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
epsilon = 1e-2
y = _instancenorm_test_mode(x, s, bias, epsilon).astype(np.float32)
node = onnx.helper.make_node(
"InstanceNormalization",
inputs=["x", "s", "bias"],
outputs=["y"],
epsilon=epsilon,
)
# output size: (2, 3, 4, 5)
expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_epsilon")
IsInf#
Map infinity to true and other values to false.
Version#
This version of the operator has been available since version 10 of the default ONNX operator set.
Attributes#
- detect_negative : int (default is 1)
- (Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.
- detect_positive : int (default is 1)
- (Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.
Inputs#
- X (non-differentiable) : T1
- input
Outputs#
- Y (non-differentiable) : T2
- output
Type Constraints#
- T1 : tensor(float), tensor(double)
- Constrain input types to float tensors.
- T2 : tensor(bool)
- Constrain output types to boolean tensors.
Examples#
infinity
node = onnx.helper.make_node(
"IsInf",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf], dtype=np.float32)
y = np.isinf(x)
expect(node, inputs=[x], outputs=[y], name="test_isinf")
negative_infinity_only
node = onnx.helper.make_node(
"IsInf", inputs=["x"], outputs=["y"], detect_positive=0
)
x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf], dtype=np.float32)
y = np.isneginf(x)
expect(node, inputs=[x], outputs=[y], name="test_isinf_negative")
positive_infinity_only
node = onnx.helper.make_node(
"IsInf", inputs=["x"], outputs=["y"], detect_negative=0
)
x = np.array([-1.7, np.nan, np.inf, 3.6, np.NINF, np.inf], dtype=np.float32)
y = np.isposinf(x)
expect(node, inputs=[x], outputs=[y], name="test_isinf_positive")
IsNaN#
Returns which elements of the input are NaN.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 9
Inputs#
- X (non-differentiable) : T1
- input
Outputs#
- Y (non-differentiable) : T2
- output
Type Constraints#
- T1 : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input types to float tensors.
- T2 : tensor(bool)
- Constrain output types to boolean tensors.
Examples#
isnan
node = onnx.helper.make_node(
"IsNaN",
inputs=["x"],
outputs=["y"],
)
x = np.array([3.0, np.nan, 4.0, np.nan], dtype=np.float32)
y = np.isnan(x)
expect(node, inputs=[x], outputs=[y], name="test_isnan")
LRN#
Local Response Normalization proposed in the AlexNet paper. It normalizes over local input regions. The local region is defined across the channels. For an element X[n, c, d1, …, dk] in a tensor of shape (N x C x D1 x D2, …, Dk), its region is {X[n, i, d1, …, dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.
square_sum[n, c, d1, …, dk] = sum(X[n, i, d1, …, dk] ^ 2), where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).
Y[n, c, d1, …, dk] = X[n, c, d1, …, dk] / (bias + alpha / size * square_sum[n, c, d1, …, dk] ) ^ beta
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- alpha : float (default is 0.0001)
- Scaling parameter.
- beta : float (default is 0.75)
- The exponent.
- bias : float (default is 1.0)
- size : int (required)
- The number of channels to sum over
Inputs#
- X (differentiable) : T
- Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
Outputs#
- Y (differentiable) : T
- Output tensor, which has the shape and type as input tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
default
alpha = 0.0001
beta = 0.75
bias = 1.0
nsize = 3
node = onnx.helper.make_node("LRN", inputs=["x"], outputs=["y"], size=3)
x = np.random.randn(5, 5, 5, 5).astype(np.float32)
square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
for n, c, h, w in np.ndindex(x.shape):
square_sum[n, c, h, w] = sum(
x[
n,
max(0, c - int(math.floor((nsize - 1) / 2))) : min(
5, c + int(math.ceil((nsize - 1) / 2)) + 1
),
h,
w,
]
** 2
)
y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
expect(node, inputs=[x], outputs=[y], name="test_lrn_default")
lrn
alpha = 0.0002
beta = 0.5
bias = 2.0
nsize = 3
node = onnx.helper.make_node(
"LRN",
inputs=["x"],
outputs=["y"],
alpha=alpha,
beta=beta,
bias=bias,
size=nsize,
)
x = np.random.randn(5, 5, 5, 5).astype(np.float32)
square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
for n, c, h, w in np.ndindex(x.shape):
square_sum[n, c, h, w] = sum(
x[
n,
max(0, c - int(math.floor((nsize - 1) / 2))) : min(
5, c + int(math.ceil((nsize - 1) / 2)) + 1
),
h,
w,
]
** 2
)
y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
expect(node, inputs=[x], outputs=[y], name="test_lrn")
LSTM#
Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN.
Notations:
X - input tensor
i - input gate
o - output gate
f - forget gate
c - cell gate
t - time step (t-1 means previous time step)
W[iofc] - W parameter weight matrix for input, output, forget, and cell gates
R[iofc] - R recurrence weight matrix for input, output, forget, and cell gates
Wb[iofc] - W bias vectors for input, output, forget, and cell gates
Rb[iofc] - R bias vectors for input, output, forget, and cell gates
P[iof] - P peephole weight vector for input, output, and forget gates
WB[iofc] - W parameter weight matrix for backward input, output, forget, and cell gates
RB[iofc] - R recurrence weight matrix for backward input, output, forget, and cell gates
WBb[iofc] - W bias vectors for backward input, output, forget, and cell gates
RBb[iofc] - R bias vectors for backward input, output, forget, and cell gates
PB[iof] - P peephole weight vector for backward input, output, and forget gates
H - Hidden state
num_directions - 2 if direction == bidirectional else 1
Activation functions:
Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha*Tanh(beta*x)
HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
Elu(x) - x if x >= 0 else alpha*(e^x - 1)
Softsign(x) - x/(1 + |x|)
Softplus(x) - log(1 + e^x)
Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
- it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
- ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
- ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
- Ct = ft (.) Ct-1 + it (.) ct
- ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
- Ht = ot (.) h(Ct)
This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Attributes#
- activation_alpha : list of floats
- Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
- activation_beta : list of floats
- Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
- activations : list of strings
- A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
- clip : float
- Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
- direction : string (default is forward)
- Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
- hidden_size : int
- Number of neurons in the hidden layer
- input_forget : int (default is 0)
- Couple the input and forget gates if 1.
- layout : int (default is 0)
- The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].
Inputs (3 - 8)#
- X (differentiable) : T
- The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
- W (differentiable) : T
- The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.
- R (differentiable) : T
- The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.
- B (optional, differentiable) : T
- The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.
- sequence_lens (optional, non-differentiable) : T1
- Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
- initial_h (optional, non-differentiable) : T
- Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
- initial_c (optional, non-differentiable) : T
- Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
- P (optional, differentiable) : T
- The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.
Outputs (0 - 3)#
- Y (optional, differentiable) : T
- A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
- Y_h (optional, differentiable) : T
- The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
- Y_c (optional, differentiable) : T
- The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
- T1 : tensor(int32)
- Constrain seq_lens to integer tensor.
Examples#
batchwise
input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 7
weight_scale = 0.3
number_of_gates = 4
layout = 1
node = onnx.helper.make_node(
"LSTM",
inputs=["X", "W", "R"],
outputs=["Y", "Y_h"],
hidden_size=hidden_size,
layout=layout,
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
lstm = LSTM_Helper(X=input, W=W, R=R, layout=layout)
Y, Y_h = lstm.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y.astype(np.float32), Y_h.astype(np.float32)],
name="test_lstm_batchwise",
)
defaults
input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 3
weight_scale = 0.1
number_of_gates = 4
node = onnx.helper.make_node(
"LSTM", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
lstm = LSTM_Helper(X=input, W=W, R=R)
_, Y_h = lstm.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y_h.astype(np.float32)],
name="test_lstm_defaults",
)
initial_bias
input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype(
np.float32
)
input_size = 3
hidden_size = 4
weight_scale = 0.1
custom_bias = 0.1
number_of_gates = 4
node = onnx.helper.make_node(
"LSTM",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
# Adding custom bias
W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(
np.float32
)
R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
B = np.concatenate((W_B, R_B), 1)
lstm = LSTM_Helper(X=input, W=W, R=R, B=B)
_, Y_h = lstm.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_lstm_with_initial_bias",
)
peepholes
input = np.array([[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]).astype(
np.float32
)
input_size = 4
hidden_size = 3
weight_scale = 0.1
number_of_gates = 4
number_of_peepholes = 3
node = onnx.helper.make_node(
"LSTM",
inputs=["X", "W", "R", "B", "sequence_lens", "initial_h", "initial_c", "P"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
# Initializing Inputs
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
B = np.zeros((1, 2 * number_of_gates * hidden_size)).astype(np.float32)
seq_lens = np.repeat(input.shape[0], input.shape[1]).astype(np.int32)
init_h = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
init_c = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
P = weight_scale * np.ones((1, number_of_peepholes * hidden_size)).astype(
np.float32
)
lstm = LSTM_Helper(
X=input, W=W, R=R, B=B, P=P, initial_c=init_c, initial_h=init_h
)
_, Y_h = lstm.step()
expect(
node,
inputs=[input, W, R, B, seq_lens, init_h, init_c, P],
outputs=[Y_h.astype(np.float32)],
name="test_lstm_with_peepholes",
)
LayerNormalization#
This is layer normalization defined in ONNX as function.
The overall computation can be split into two stages.
The first stage is standardization, which makes the
normalized elements have zero mean and unit variances.
The computation required by standardization can be
described by the following equations.
Mean = ReduceMean<axes=normalized_axes>(X) D = Sub(X, Mean) DD = Mul(D, D) Var = ReduceMean<axes=normalized_axes>(DD) VarEps = Add(Var, epsilon) StdDev = Sqrt(VarEps) InvStdDev = Reciprocal(StdDev) Normalized = Mul(D, InvStdDev)
where normalized_axes is [axis, ..., rank of X - 1].
The variables Var and StdDev stand for variance and
standard deviation, respectively. The second output is
Mean and the last one is InvStdDev.
Depending on stash_type attribute, the actual computation
must happen in different floating-point precision.
For example, if stash_type is 1, this operator casts
all input variables to 32-bit float, perform the computation, and
finally cast Normalized back to the original type of X.
The second stage then scales and shifts the outcome of the
first stage using
NormalizedScaled = Mul(Normalized, Scale) Y = Add(NormalizedScaled, B)
The second stage doesn’t depends on stash_type.
All equations are in this syntax.
The same variable (i.e., input, output, and attribute) uses
the same name in the equations above and this operator’s definition.
Let d[i] indicate the i-th dimension of X.
If X’s shape is [d[0], ..., d[axis-1], d[axis], ..., d[rank-1]],
the shape of Mean and InvStdDev is [d[0], ..., d[axis-1], 1, ..., 1].
Y and X have the same shape.
Version#
This version of the operator has been available since version 17 of the default ONNX operator set.
Attributes#
- axis : int (default is -1)
- The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. Negative value means counting dimensions from the back.
- epsilon : float (default is 1e-05)
- The epsilon value to use to avoid division by zero.
- stash_type : int (default is 1)
- Type of Mean and InvStdDev. This also specifies stage one's computation precision.
Inputs (2 - 3)#
- X : T
- Tensor to be normalized.
- Scale : T
- Scale tensor.
- B (optional) : T
- Bias tensor.
Outputs (1 - 3)#
- Y : T
- Normalized tensor.
- Mean (optional) : U
- Saved mean used during training to speed up gradient computation
- InvStdDev (optional) : U
- Saved inverse standard deviation used during training to speed up gradient computation.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input types and output Y type to float tensors.
- U : tensor(float), tensor(bfloat16)
- Type of Mean and InvStdDev tensors.
Examples#
d
X = np.random.randn(3, 4).astype(np.float32)
def case(axis: int) -> None:
normalized_shape = calculate_normalized_shape(X.shape, axis)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis)
node = onnx.helper.make_node(
"LayerNormalization",
inputs=["X", "W", "B"],
outputs=["Y", "Mean", "InvStdDev"],
axis=axis,
)
if axis < 0:
name = f"test_layer_normalization_2d_axis_negative_{-axis}"
else:
name = f"test_layer_normalization_2d_axis{axis}"
expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name)
for i in range(len(X.shape)):
case(i)
case(i - len(X.shape))
d_epsilon
epsilon = 1e-1
X = np.random.randn(2, 3, 5).astype(np.float32)
def case(axis: int) -> None:
normalized_shape = calculate_normalized_shape(X.shape, axis)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis, epsilon)
node = onnx.helper.make_node(
"LayerNormalization",
inputs=["X", "W", "B"],
outputs=["Y", "Mean", "InvStdDev"],
axis=axis,
epsilon=epsilon,
)
if axis < 0:
name = f"test_layer_normalization_3d_axis_negative_{-axis}_epsilon"
else:
name = f"test_layer_normalization_3d_axis{axis}_epsilon"
expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name)
for i in range(len(X.shape)):
case(i)
case(i - len(X.shape))
default_axis
X = np.random.randn(2, 3, 4, 5).astype(np.float32)
# Default axis in LayerNormalization is -1.
normalized_shape = calculate_normalized_shape(X.shape, -1)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
# Axis is default to -1 in the reference implementation.
Y, mean, inv_std_dev = _layer_normalization(X, W, B)
# Not specifying axis attribute means -1.
node = onnx.helper.make_node(
"LayerNormalization",
inputs=["X", "W", "B"],
outputs=["Y", "Mean", "InvStdDev"],
)
expect(
node,
inputs=[X, W, B],
outputs=[Y, mean, inv_std_dev],
name="test_layer_normalization_default_axis",
)
layernormalization
X = np.random.randn(2, 3, 4, 5).astype(np.float32)
def case(axis: int) -> None:
normalized_shape = calculate_normalized_shape(X.shape, axis)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
node = onnx.helper.make_node(
"LayerNormalization",
inputs=["X", "W", "B"],
outputs=["Y", "Mean", "InvStdDev"],
axis=axis,
)
if axis < 0:
name = f"test_layer_normalization_4d_axis_negative_{-axis}"
else:
name = f"test_layer_normalization_4d_axis{axis}"
expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name)
for i in range(len(X.shape)):
case(i)
case(i - len(X.shape))
LeakyRelu#
LeakyRelu takes input data (Tensorf(x) = alpha * x for x < 0,
f(x) = x for x >= 0, is applied to the data tensor elementwise.
History
Version 16 adds bfloat16 to the types allowed.
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Attributes#
- alpha : float (default is 0.01)
- Coefficient of leakage.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
leakyrelu
node = onnx.helper.make_node(
"LeakyRelu", inputs=["x"], outputs=["y"], alpha=0.1
)
x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-0.1, 0., 1.]
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
expect(node, inputs=[x], outputs=[y], name="test_leakyrelu")
leakyrelu_default
default_alpha = 0.01
node = onnx.helper.make_node(
"LeakyRelu",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * default_alpha
expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_default")
Less#
Returns the tensor resulted from performing the less logical operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- A (non-differentiable) : T
- First input operand for the logical operator.
- B (non-differentiable) : T
- Second input operand for the logical operator.
Outputs#
- C (non-differentiable) : T1
- Result tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input types to all numeric tensors.
- T1 : tensor(bool)
- Constrain output to boolean tensor.
Examples#
less
node = onnx.helper.make_node(
"Less",
inputs=["x", "y"],
outputs=["less"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = np.less(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_less")
less
node = onnx.helper.make_node(
"LessOrEqual",
inputs=["x", "y"],
outputs=["less_equal"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = np.less_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_less_equal")
less_broadcast
node = onnx.helper.make_node(
"Less",
inputs=["x", "y"],
outputs=["less"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = np.less(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_less_bcast")
less_broadcast
node = onnx.helper.make_node(
"LessOrEqual",
inputs=["x", "y"],
outputs=["less_equal"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = np.less_equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_bcast")
LessOrEqual#
Returns the tensor resulted from performing the less_equal logical operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Other versions of this operator: 12
Inputs#
- A (non-differentiable) : T
- First input operand for the logical operator.
- B (non-differentiable) : T
- Second input operand for the logical operator.
Outputs#
- C (non-differentiable) : T1
- Result tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input types to all numeric tensors.
- T1 : tensor(bool)
- Constrain output to boolean tensor.
Log#
Calculates the natural log of the given input tensor, element-wise.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The natural log of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
log
node = onnx.helper.make_node(
"Log",
inputs=["x"],
outputs=["y"],
)
x = np.array([1, 10]).astype(np.float32)
y = np.log(x) # expected output [0., 2.30258512]
expect(node, inputs=[x], outputs=[y], name="test_log_example")
x = np.exp(np.random.randn(3, 4, 5).astype(np.float32))
y = np.log(x)
expect(node, inputs=[x], outputs=[y], name="test_log")
LogSoftmax#
The operator computes the log of softmax values for the given input:
LogSoftmax(input, axis) = Log(Softmax(input, axis=axis))
The “axis” attribute indicates the dimension along which LogSoftmax will be performed. The output tensor has the same shape and contains the LogSoftmax values of the corresponding input.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- axis : int (default is -1)
- Describes the dimension LogSoftmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
Inputs#
- input (differentiable) : T
- The input tensor of rank >= axis.
Outputs#
- output (differentiable) : T
- The output values with the same shape as the input tensor.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
logsoftmax
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
)
x = np.array([[-1, 0, 1]]).astype(np.float32)
# expected output
# [[-2.4076061 -1.407606 -0.407606 ]]
y = logsoftmax(x)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_example_1")
logsoftmax_axis
x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32)
# expected output
# [[-3.4401896 -2.4401896 -1.4401896 -0.44018966]
# [-3.4401896 -2.4401896 -1.4401896 -0.44018966]]
y = logsoftmax(x)
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_large_number")
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
axis=0,
)
y = logsoftmax(x, axis=0)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_0")
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
axis=1,
)
y = logsoftmax(x, axis=1)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_1")
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
axis=2,
)
y = logsoftmax(x, axis=2)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_2")
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
axis=-1,
)
y = logsoftmax(x, axis=-1)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_negative_axis")
# default axis is -1
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_default_axis")
Loop#
Generic Looping construct. This loop has multiple termination conditions:
Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M.
Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not.
This table summarizes the operating modes of this operator with equivalent C-style code:
Operator inputs defined as (max_trip_count, condition_var).
input ("", ""):
for (int i=0; ; ++i) {
cond = ... // Note this value is ignored, but is required in the body
}
input ("", cond) // Note this is analogous to a while loop
bool cond = ...;
for (int i=0; cond; ++i) {
cond = ...;
}
input ("", 1) // Note this is analogous to a do-while loop
bool cond = true
for (int i=0; cond; ++i) {
cond = ...;
}
input (trip_count, "") // Note this is analogous to a for loop
int trip_count = ...
for (int i=0; i < trip_count; ++i) {
cond = ...; // ignored
}
input (trip_count, cond)
int trip_count = ...;
bool cond = ...;
for (int i=0; i < trip_count && cond; ++i) {
cond = ...;
}
Sample usage - cond as well as trip count
graph predict-net {
%a = Constant[value = <Scalar Tensor [3]>]()
%b = Constant[value = <Scalar Tensor [6]>]()
%keepgoing = Constant[value = <Scalar Tensor [1]>]()
%max_trip_count = Constant[value = <Scalar Tensor [10]>]()
%keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
return
}
graph body-net (
%i[INT32, scalar] // iteration number
%keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
%b_in[INT32, scalar] // incoming value of loop-carried-dependency b
) {
%my_local = Add(%a, %b_in)
%b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
%keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
%user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
return %keepgoing_out, %b_out, %user_defined_val
}
Sample equivalent C code
{
/* User-defined code (enclosing scope) */
int a = 3, b = 6;
bool keepgoing = true; // Analogous to input cond
/* End user-defined code */
/* Implicitly-defined code */
const int max_trip_count = 10; // Analogous to input M
int user_defined_vals[]; // Imagine this is resizable
/* End implicitly-defined code */
/* initialize loop-carried variables and scan-output variables */
bool keepgoing_out = keepgoing
int b_out = b
for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
/* Implicitly-defined code: bind actual parameter values
to formal parameter variables of loop-body */
bool keepgoing_in = keepgoing_out;
bool b_in = b_out;
/* User-defined code (loop body) */
int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
b_out = a - b_in;
keepgoing_out = my_local > b_out;
user_defined_val = b_in + b_in; // b_in and b_out are different variables
/* End user-defined code */
/* Implicitly defined-code */
user_defined_vals[i] = user_defined_val // accumulate scan-output values
}
// int t = my_local; // Can't do this. my_local is not accessible here.
// The values below are bound to the output variables of the loop and therefore accessible
// b_out; user_defined_vals; keepgoing_out;
}
There are several things of note in this code snippet:
Values from the enclosing scope (i.e. variable “a” here) are in scope and can be referenced in the inputs of the loop.
Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration.
Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop.
Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above.
Note that the semantics of this op support “diagonal” or “wavefront” execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer).
The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Attributes#
- body : graph (required)
- The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
Inputs (2 - ∞)#
- M (optional) : I
- A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
- cond (optional) : B
- A boolean termination condition. Optional. Pass empty string to skip.
- v_initial (variadic, heterogeneous) : V
- The initial values of any loop-carried dependencies (values that change across loop iterations)
Outputs (1 - ∞)#
- v_final_and_scan_outputs (variadic, heterogeneous) : V
- Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
Type Constraints#
- V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(bfloat16)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128)), optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(bfloat16))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(bfloat16)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
- All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types
- I : tensor(int64)
- tensor of int64, which should be a scalar.
- B : tensor(bool)
- tensor of bool, which should be a scalar.
Examples#
loop_11
# Given a tensor x of values [x1, ..., xN], and initial tensor y
# sum up its elements using a scan
# returning the final state (y+x1+x2+...+xN) as well the scan_output
# [y+x1, y+x1+x2, ..., y+x1+x2+...+xN]
y_in = onnx.helper.make_tensor_value_info("y_in", onnx.TensorProto.FLOAT, [1])
y_out = onnx.helper.make_tensor_value_info("y_out", onnx.TensorProto.FLOAT, [1])
scan_out = onnx.helper.make_tensor_value_info(
"scan_out", onnx.TensorProto.FLOAT, [1]
)
cond_in = onnx.helper.make_tensor_value_info(
"cond_in", onnx.TensorProto.BOOL, []
)
cond_out = onnx.helper.make_tensor_value_info(
"cond_out", onnx.TensorProto.BOOL, []
)
iter_count = onnx.helper.make_tensor_value_info(
"iter_count", onnx.TensorProto.INT64, []
)
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
y = np.array([-2]).astype(np.float32)
x_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.helper.make_tensor(
name="const_tensor_x",
data_type=onnx.TensorProto.FLOAT,
dims=x.shape,
vals=x.flatten().astype(float),
),
)
one_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["one"],
value=onnx.helper.make_tensor(
name="const_tensor_one",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[1],
),
)
i_add_node = onnx.helper.make_node(
"Add", inputs=["iter_count", "one"], outputs=["end"]
)
start_unsqueeze_node = onnx.helper.make_node(
"Unsqueeze", inputs=["iter_count"], outputs=["slice_start"], axes=[0]
)
end_unsqueeze_node = onnx.helper.make_node(
"Unsqueeze", inputs=["end"], outputs=["slice_end"], axes=[0]
)
slice_node = onnx.helper.make_node(
"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
)
y_add_node = onnx.helper.make_node(
"Add", inputs=["y_in", "slice_out"], outputs=["y_out"]
)
identity_node = onnx.helper.make_node(
"Identity", inputs=["cond_in"], outputs=["cond_out"]
)
scan_identity_node = onnx.helper.make_node(
"Identity", inputs=["y_out"], outputs=["scan_out"]
)
loop_body = onnx.helper.make_graph(
[
identity_node,
x_const_node,
one_const_node,
i_add_node,
start_unsqueeze_node,
end_unsqueeze_node,
slice_node,
y_add_node,
scan_identity_node,
],
"loop_body",
[iter_count, cond_in, y_in],
[cond_out, y_out, scan_out],
)
node = onnx.helper.make_node(
"Loop",
inputs=["trip_count", "cond", "y"],
outputs=["res_y", "res_scan"],
body=loop_body,
)
trip_count = np.array(5).astype(np.int64)
res_y = np.array([13]).astype(np.float32)
cond = np.array(1).astype(bool)
res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1))
expect(
node,
inputs=[trip_count, cond, y],
outputs=[res_y, res_scan],
name="test_loop11",
opset_imports=[onnx.helper.make_opsetid("", 11)],
)
loop_13
# Given a tensor x of values [x1, ..., xN],
# Return a sequence of tensors of
# [[x1], [x1, x2], ..., [x1, ..., xN]]
seq_in = onnx.helper.make_tensor_sequence_value_info(
"seq_in", onnx.TensorProto.FLOAT, None
)
seq_out = onnx.helper.make_tensor_sequence_value_info(
"seq_out", onnx.TensorProto.FLOAT, None
)
cond_in = onnx.helper.make_tensor_value_info(
"cond_in", onnx.TensorProto.BOOL, []
)
cond_out = onnx.helper.make_tensor_value_info(
"cond_out", onnx.TensorProto.BOOL, []
)
iter_count = onnx.helper.make_tensor_value_info(
"iter_count", onnx.TensorProto.INT64, []
)
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
x_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.helper.make_tensor(
name="const_tensor_x",
data_type=onnx.TensorProto.FLOAT,
dims=x.shape,
vals=x.flatten().astype(float),
),
)
one_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["one"],
value=onnx.helper.make_tensor(
name="const_tensor_one",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[1],
),
)
zero_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["slice_start"],
value=onnx.helper.make_tensor(
name="const_tensor_zero",
data_type=onnx.TensorProto.INT64,
dims=(1,),
vals=[0],
),
)
axes_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["axes"],
value=onnx.helper.make_tensor(
name="const_tensor_axes",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[0],
),
)
add_node = onnx.helper.make_node(
"Add", inputs=["iter_count", "one"], outputs=["end"]
)
end_unsqueeze_node = onnx.helper.make_node(
"Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
)
slice_node = onnx.helper.make_node(
"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
)
insert_node = onnx.helper.make_node(
"SequenceInsert", inputs=["seq_in", "slice_out"], outputs=["seq_out"]
)
identity_node = onnx.helper.make_node(
"Identity", inputs=["cond_in"], outputs=["cond_out"]
)
loop_body = onnx.helper.make_graph(
[
identity_node,
x_const_node,
one_const_node,
zero_const_node,
add_node,
axes_node,
end_unsqueeze_node,
slice_node,
insert_node,
],
"loop_body",
[iter_count, cond_in, seq_in],
[cond_out, seq_out],
)
node = onnx.helper.make_node(
"Loop",
inputs=["trip_count", "cond", "seq_empty"],
outputs=["seq_res"],
body=loop_body,
)
trip_count = np.array(5).astype(np.int64)
seq_empty: List[Any] = []
seq_res = [x[: int(i)] for i in x]
cond = np.array(1).astype(bool)
expect(
node,
inputs=[trip_count, cond, seq_empty],
outputs=[seq_res],
name="test_loop13_seq",
opset_imports=[onnx.helper.make_opsetid("", 13)],
input_type_protos=[
onnx.helper.make_tensor_type_proto(
onnx.TensorProto.INT64, trip_count.shape
),
onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
),
],
)
loop_16_none
# Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0],
# Return a concatenated sequence of tensors of
# [x0, [x1], [x1, x2], ..., [x1, ..., xN]]
ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
opt_in = onnx.helper.make_value_info("opt_seq_in", opt_in_tp)
seq_out = onnx.helper.make_tensor_sequence_value_info(
"seq_out", onnx.TensorProto.FLOAT, []
)
cond_in = onnx.helper.make_tensor_value_info(
"cond_in", onnx.TensorProto.BOOL, []
)
cond_out = onnx.helper.make_tensor_value_info(
"cond_out", onnx.TensorProto.BOOL, []
)
iter_count = onnx.helper.make_tensor_value_info(
"iter_count", onnx.TensorProto.INT64, []
)
x0 = np.array(0).astype(np.float32)
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
optional_has_elem_node = onnx.helper.make_node(
"OptionalHasElement", inputs=["opt_seq_in"], outputs=["optional_has_elem"]
)
optional_is_none = onnx.helper.make_node(
"Not", inputs=["optional_has_elem"], outputs=["optional_is_none"]
)
optional_get_elem = onnx.helper.make_node(
"OptionalGetElement", inputs=["opt_seq_in"], outputs=["seq_in"]
)
constant_in = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["constant_in"],
value=onnx.helper.make_tensor(
name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=(), vals=[0]
),
)
seq_const_in = onnx.helper.make_node(
"SequenceConstruct", inputs=["constant_in"], outputs=["init_seq_in"]
)
then_seq_out = onnx.helper.make_tensor_sequence_value_info(
"init_seq_in", onnx.TensorProto.FLOAT, []
)
then_body = onnx.helper.make_graph(
[constant_in, seq_const_in], "then_body", [], [then_seq_out]
)
else_seq_out = onnx.helper.make_tensor_sequence_value_info(
"seq_in", onnx.TensorProto.FLOAT, []
)
else_body = onnx.helper.make_graph(
[optional_get_elem], "else_body", [], [else_seq_out]
)
if_node = onnx.helper.make_node(
"If",
inputs=["optional_is_none"],
outputs=["sequence"],
then_branch=then_body,
else_branch=else_body,
)
x_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.helper.make_tensor(
name="const_tensor_x",
data_type=onnx.TensorProto.FLOAT,
dims=x.shape,
vals=x.flatten().astype(float),
),
)
one_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["one"],
value=onnx.helper.make_tensor(
name="const_tensor_one",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[1],
),
)
zero_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["slice_start"],
value=onnx.helper.make_tensor(
name="const_tensor_zero",
data_type=onnx.TensorProto.INT64,
dims=(1,),
vals=[0],
),
)
axes_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["axes"],
value=onnx.helper.make_tensor(
name="const_tensor_axes",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[0],
),
)
add_node = onnx.helper.make_node(
"Add", inputs=["iter_count", "one"], outputs=["end"]
)
end_unsqueeze_node = onnx.helper.make_node(
"Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
)
slice_node = onnx.helper.make_node(
"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
)
insert_node = onnx.helper.make_node(
"SequenceInsert", inputs=["sequence", "slice_out"], outputs=["seq_out"]
)
identity_node = onnx.helper.make_node(
"Identity", inputs=["cond_in"], outputs=["cond_out"]
)
loop_body = onnx.helper.make_graph(
[
identity_node,
optional_has_elem_node,
optional_is_none,
if_node,
x_const_node,
one_const_node,
zero_const_node,
add_node,
axes_node,
end_unsqueeze_node,
slice_node,
insert_node,
],
"loop_body",
[iter_count, cond_in, opt_in],
[cond_out, seq_out],
)
node = onnx.helper.make_node(
"Loop",
inputs=["trip_count", "cond", "opt_seq"],
outputs=["seq_res"],
body=loop_body,
)
trip_count = np.array(5).astype(np.int64)
cond = np.array(1).astype(bool)
seq_res = compute_loop_outputs(x, [x0], trip_count)
opt_seq_in: List[Any] = [x0]
expect(
node,
inputs=[trip_count, cond, opt_seq_in],
outputs=[seq_res],
name="test_loop16_seq_none",
opset_imports=[onnx.helper.make_opsetid("", 16)],
input_type_protos=[
onnx.helper.make_tensor_type_proto(
onnx.TensorProto.INT64, trip_count.shape
),
onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
opt_in_tp,
],
)
LpNormalization#
Given a matrix, apply Lp-normalization along the provided axis.
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Attributes#
- axis : int (default is -1)
- The axis on which to apply normalization, -1 mean last axis.
- p : int (default is 2)
- The order of the normalization, only 1 or 2 are supported.
Inputs#
- input (differentiable) : T
- Input matrix
Outputs#
- output (differentiable) : T
- Matrix after normalization
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
LpPool#
LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Attributes#
- auto_pad : string (default is NOTSET)
- auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
- kernel_shape : list of ints (required)
- The size of the kernel along each axis.
- p : int (default is 2)
- p value of the Lp norm used to pool over the input data.
- pads : list of ints
- Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
- strides : list of ints
- Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs#
- X (differentiable) : T
- Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
Outputs#
- Y (differentiable) : T
- Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
MatMul#
Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- A (differentiable) : T
- N-dimensional matrix A
- B (differentiable) : T
- N-dimensional matrix B
Outputs#
- Y (differentiable) : T
- Matrix multiply results from A * B
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(bfloat16)
- Constrain input and output types to float/int tensors.
Examples#
matmul
node = onnx.helper.make_node(
"MatMul",
inputs=["a", "b"],
outputs=["c"],
)
# 2d
a = np.random.randn(3, 4).astype(np.float32)
b = np.random.randn(4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_2d")
# 3d
a = np.random.randn(2, 3, 4).astype(np.float32)
b = np.random.randn(2, 4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_3d")
# 4d
a = np.random.randn(1, 2, 3, 4).astype(np.float32)
b = np.random.randn(1, 2, 4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d")
MatMulInteger#
Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
Version#
This version of the operator has been available since version 10 of the default ONNX operator set.
Inputs (2 - 4)#
- A (non-differentiable) : T1
- N-dimensional matrix A
- B (non-differentiable) : T2
- N-dimensional matrix B
- a_zero_point (optional, non-differentiable) : T1
- Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1].
- b_zero_point (optional, non-differentiable) : T2
- Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N].
Outputs#
- Y (non-differentiable) : T3
- Matrix multiply results from A * B
Type Constraints#
- T1 : tensor(int8), tensor(uint8)
- Constrain input A data type to 8-bit integer tensor.
- T2 : tensor(int8), tensor(uint8)
- Constrain input B data type to 8-bit integer tensor.
- T3 : tensor(int32)
- Constrain output Y data type as 32-bit integer tensor.
Examples#
matmulinteger
node = onnx.helper.make_node(
"MatMulInteger",
inputs=["A", "B", "a_zero_point", "b_zero_point"],
outputs=["Y"],
)
A = np.array(
[
[11, 7, 3],
[10, 6, 2],
[9, 5, 1],
[8, 4, 0],
],
dtype=np.uint8,
)
a_zero_point = np.array([12], dtype=np.uint8)
B = np.array(
[
[1, 4],
[2, 5],
[3, 6],
],
dtype=np.uint8,
)
b_zero_point = np.array([0], dtype=np.uint8)
output = np.array(
[
[-38, -83],
[-44, -98],
[-50, -113],
[-56, -128],
],
dtype=np.int32,
)
expect(
node,
inputs=[A, B, a_zero_point, b_zero_point],
outputs=[output],
name="test_matmulinteger",
)
Max#
Element-wise max of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs (1 - ∞)#
- data_0 (variadic, differentiable) : T
- List of tensors for max.
Outputs#
- max (differentiable) : T
- Output tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to numeric tensors.
Examples#
max
data_0 = np.array([3, 2, 1]).astype(np.float32)
data_1 = np.array([1, 4, 4]).astype(np.float32)
data_2 = np.array([2, 5, 3]).astype(np.float32)
result = np.array([3, 5, 4]).astype(np.float32)
node = onnx.helper.make_node(
"Max",
inputs=["data_0", "data_1", "data_2"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1, data_2],
outputs=[result],
name="test_max_example",
)
node = onnx.helper.make_node(
"Max",
inputs=["data_0"],
outputs=["result"],
)
expect(node, inputs=[data_0], outputs=[data_0], name="test_max_one_input")
result = np.maximum(data_0, data_1)
node = onnx.helper.make_node(
"Max",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node, inputs=[data_0, data_1], outputs=[result], name="test_max_two_inputs"
)
max_all_numeric_types
for op_dtype in all_numeric_dtypes:
data_0 = np.array([3, 2, 1]).astype(op_dtype)
data_1 = np.array([1, 4, 4]).astype(op_dtype)
result = np.array([3, 4, 4]).astype(op_dtype)
node = onnx.helper.make_node(
"Max",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1],
outputs=[result],
name=f"test_max_{np.dtype(op_dtype).name}",
)
MaxPool#
MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. max pooling consisting of computing the max on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following:
output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
or
output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
if ceil_mode is enabled
* pad_shape[i] is sum of pads along axis i
auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
And pad shape will be following if SAME_UPPER or SAME_LOWER:
pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
The output of each pooling window is maximum number of elements exclude pad.
Version#
This version of the operator has been available since version 12 of the default ONNX operator set.
Attributes#
- auto_pad : string (default is NOTSET)
- auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
- ceil_mode : int (default is 0)
- Whether to use ceil or floor (default) to compute the output shape.
- dilations : list of ints
- Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.
- kernel_shape : list of ints (required)
- The size of the kernel along each axis.
- pads : list of ints
- Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
- storage_order : int (default is 0)
- The storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output.
- strides : list of ints
- Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs#
- X (differentiable) : T
- Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
Outputs (1 - 2)#
- Y (differentiable) : T
- Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used
- Indices (optional, non-differentiable) : I
- Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(uint8)
- Constrain input and output types to float and 8 bit tensors.
- I : tensor(int64)
- Constrain index tensor to int64
Examples#
maxpool_1d_default
"""
input_shape: [1, 3, 32]
output_shape: [1, 3, 31]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2],
)
x = np.random.randn(1, 3, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = [2]
strides = [1]
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_1d_default")
maxpool_2d_ceil
"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[2, 2],
ceil_mode=True,
)
x = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
]
).astype(np.float32)
y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil")
maxpool_2d_default
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 31, 31]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_default")
maxpool_2d_dilations
"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[1, 1],
dilations=[2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
]
).astype(np.float32)
y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_dilations")
maxpool_2d_pads
"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2],
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = pad_top = pad_right = pad_left = 2
pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
out_shape = get_output_shape(
"VALID", np.add(x_shape[2:], pad_shape), kernel_shape, strides
)
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_pads")
maxpool_2d_precomputed_pads
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array(
[
[
[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
]
]
]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_pads")
maxpool_2d_precomputed_same_upper
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 3, 3]
pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[2, 2],
auto_pad="SAME_UPPER",
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[7, 9, 10], [17, 19, 20], [22, 24, 25]]]]).astype(np.float32)
expect(
node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_same_upper"
)
maxpool_2d_precomputed_strides
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2]
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32)
expect(
node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_strides"
)
maxpool_2d_same_lower
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_LOWER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape("SAME_LOWER", x_shape[2:], kernel_shape, strides)
pad_shape = get_pad_shape(
"SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_bottom = pad_shape[0] // 2
pad_top = pad_shape[0] - pad_bottom
pad_right = pad_shape[1] // 2
pad_left = pad_shape[1] - pad_right
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_lower")
maxpool_2d_same_upper
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_UPPER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape("SAME_UPPER", x_shape[2:], kernel_shape, strides)
pad_shape = get_pad_shape(
"SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_top = pad_shape[0] // 2
pad_bottom = pad_shape[0] - pad_top
pad_left = pad_shape[1] // 2
pad_right = pad_shape[1] - pad_left
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_upper")
maxpool_2d_strides
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 10, 10]
"""
node = onnx.helper.make_node(
"MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3]
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (5, 5)
strides = (3, 3)
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_strides")
maxpool_2d_uint8
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.uint8)
y = np.array(
[
[
[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
]
]
]
).astype(np.uint8)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_uint8")
maxpool_3d_default
"""
input_shape: [1, 3, 32, 32, 32]
output_shape: [1, 3, 31, 31, 31]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2, 2],
)
x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = [2, 2, 2]
strides = [1, 1, 1]
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_default")
maxpool_with_argmax_2d_precomputed_pads
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y", "z"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array(
[
[
[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
]
]
]
).astype(np.float32)
z = np.array(
[
[
[
[12, 13, 14, 14, 14],
[17, 18, 19, 19, 19],
[22, 23, 24, 24, 24],
[22, 23, 24, 24, 24],
[22, 23, 24, 24, 24],
]
]
]
).astype(np.int64)
expect(
node,
inputs=[x],
outputs=[y, z],
name="test_maxpool_with_argmax_2d_precomputed_pads",
)
maxpool_with_argmax_2d_precomputed_strides
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y", "z"],
kernel_shape=[2, 2],
strides=[2, 2],
storage_order=1,
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32)
z = np.array([[[[6, 16], [8, 18]]]]).astype(np.int64)
expect(
node,
inputs=[x],
outputs=[y, z],
name="test_maxpool_with_argmax_2d_precomputed_strides",
)
MaxRoiPool#
ROI max pool consumes an input tensor X and region of interests (RoIs) to apply max pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Attributes#
- pooled_shape : list of ints (required)
- ROI pool output shape (height, width).
- spatial_scale : float (default is 1.0)
- Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.
Inputs#
- X (differentiable) : T
- Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
- rois (non-differentiable) : T
- RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].
Outputs#
- Y (differentiable) : T
- RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
MaxUnpool#
MaxUnpool essentially computes the partial inverse of the MaxPool op. The input information to this op is typically the output information from a MaxPool op. The first input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output) from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op. The third (optional) input is a tensor that specifies the output size of the unpooling operation.
MaxUnpool is intended to do ‘partial’ inverse of the MaxPool op. ‘Partial’ because all the non-maximal values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling the result of an unpooling operation should give back the original input to the unpooling op.
MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous. The third input argument, output_size, is meant to disambiguate the op and produce output tensor of known/predictable size.
In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads, which define the exact unpooling op. The attributes typically have the same values as the corrsponding pooling op that the unpooling op is trying to invert.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Other versions of this operator: 9
Attributes#
- kernel_shape : list of ints (required)
- The size of the kernel along each axis.
- pads : list of ints
- Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
- strides : list of ints
- Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs (2 - 3)#
- X (differentiable) : T1
- Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
- I (non-differentiable) : T2
- Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).
- output_shape (optional, non-differentiable) : T2
- The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.
Outputs#
- output (differentiable) : T1
- Output data tensor that contains the result of the unpooling.
Type Constraints#
- T1 : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
- T2 : tensor(int64)
- Constrain index tensor to int64
Examples#
with_output_shape
node = onnx.helper.make_node(
"MaxUnpool",
inputs=["xT", "xI", "output_shape"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[2, 2],
)
xT = np.array([[[[5, 6], [7, 8]]]], dtype=np.float32)
xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64)
output_shape = np.array((1, 1, 5, 5), dtype=np.int64)
y = np.array(
[
[
[
[0, 0, 0, 0, 0],
[0, 5, 0, 6, 0],
[0, 0, 0, 0, 0],
[0, 7, 0, 8, 0],
[0, 0, 0, 0, 0],
]
]
],
dtype=np.float32,
)
expect(
node,
inputs=[xT, xI, output_shape],
outputs=[y],
name="test_maxunpool_export_with_output_shape",
)
without_output_shape
node = onnx.helper.make_node(
"MaxUnpool",
inputs=["xT", "xI"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[2, 2],
)
xT = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64)
y = np.array(
[[[[0, 0, 0, 0], [0, 1, 0, 2], [0, 0, 0, 0], [0, 3, 0, 4]]]],
dtype=np.float32,
)
expect(
node,
inputs=[xT, xI],
outputs=[y],
name="test_maxunpool_export_without_output_shape",
)
Mean#
Element-wise mean of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs (1 - ∞)#
- data_0 (variadic, differentiable) : T
- List of tensors for mean.
Outputs#
- mean (differentiable) : T
- Output tensor.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
mean
data_0 = np.array([3, 0, 2]).astype(np.float32)
data_1 = np.array([1, 3, 4]).astype(np.float32)
data_2 = np.array([2, 6, 6]).astype(np.float32)
result = np.array([2, 3, 4]).astype(np.float32)
node = onnx.helper.make_node(
"Mean",
inputs=["data_0", "data_1", "data_2"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1, data_2],
outputs=[result],
name="test_mean_example",
)
node = onnx.helper.make_node(
"Mean",
inputs=["data_0"],
outputs=["result"],
)
expect(node, inputs=[data_0], outputs=[data_0], name="test_mean_one_input")
result = np.divide(np.add(data_0, data_1), 2.0)
node = onnx.helper.make_node(
"Mean",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node, inputs=[data_0, data_1], outputs=[result], name="test_mean_two_inputs"
)
MeanVarianceNormalization#
A MeanVarianceNormalization Function: Perform mean variance normalization
on the input tensor X using formula:
(X-EX)/sqrt(E(X-EX)^2)
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 9
Attributes#
- axes : list of ints (default is ['0', '2', '3'])
- A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
meanvariancenormalization
node = onnx.helper.make_node(
"MeanVarianceNormalization", inputs=["X"], outputs=["Y"]
)
input_data = np.array(
[
[
[[0.8439683], [0.5665144], [0.05836735]],
[[0.02916367], [0.12964272], [0.5060197]],
[[0.79538304], [0.9411346], [0.9546573]],
],
[
[[0.17730942], [0.46192095], [0.26480448]],
[[0.6746842], [0.01665257], [0.62473077]],
[[0.9240844], [0.9722341], [0.11965699]],
],
[
[[0.41356155], [0.9129373], [0.59330076]],
[[0.81929934], [0.7862604], [0.11799799]],
[[0.69248444], [0.54119414], [0.07513223]],
],
],
dtype=np.float32,
)
# Calculate expected output data
data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
data_mean_squared = np.power(data_mean, 2)
data_squared = np.power(input_data, 2)
data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1)
std = np.sqrt(data_squared_mean - data_mean_squared)
expected_output = (input_data - data_mean) / (std + 1e-9)
expect(node, inputs=[input_data], outputs=[expected_output], name="test_mvn")
MelWeightMatrix#
Generate a MelWeightMatrix that can be used to re-weight a Tensor containing a linearly sampled frequency spectra (from DFT or STFT) into num_mel_bins frequency information based on the [lower_edge_hertz, upper_edge_hertz] range on the mel scale. This function defines the mel scale in terms of a frequency in hertz according to the following formula:
mel(f) = 2595 * log10(1 + f/700)
In the returned matrix, all the triangles (filterbanks) have a peak value of 1.0.
The returned MelWeightMatrix can be used to right-multiply a spectrogram S of shape [frames, num_spectrogram_bins] of linear scale spectrum values (e.g. STFT magnitudes) to generate a “mel spectrogram” M of shape [frames, num_mel_bins].
Version#
This version of the operator has been available since version 17 of the default ONNX operator set.
Attributes#
- output_datatype : int (default is 1)
- The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT.
Inputs#
- num_mel_bins (non-differentiable) : T1
- The number of bands in the mel spectrum.
- dft_length (non-differentiable) : T1
- The size of the original DFT. The size of the original DFT is used to infer the size of the onesided DFT, which is understood to be floor(dft_length/2) + 1, i.e. the spectrogram only contains the nonredundant DFT bins.
- sample_rate (non-differentiable) : T1
- Samples per second of the input signal used to create the spectrogram. Used to figure out the frequencies corresponding to each spectrogram bin, which dictates how they are mapped into the mel scale.
- lower_edge_hertz (non-differentiable) : T2
- Lower bound on the frequencies to be included in the mel spectrum. This corresponds to the lower edge of the lowest triangular band.
- upper_edge_hertz (non-differentiable) : T2
- The desired top edge of the highest frequency band.
Outputs#
- output (non-differentiable) : T3
- The Mel Weight Matrix. The output has the shape: [floor(dft_length/2) + 1][num_mel_bins].
Type Constraints#
- T1 : tensor(int32), tensor(int64)
- Constrain to integer tensors.
- T2 : tensor(float), tensor(float16), tensor(double), tensor(bfloat16)
- Constrain to float tensors
- T3 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain to any numerical types.
Examples#
melweightmatrix
node = onnx.helper.make_node(
"MelWeightMatrix",
inputs=[
"num_mel_bins",
"dft_length",
"sample_rate",
"lower_edge_hertz",
"upper_edge_hertz",
],
outputs=["output"],
)
num_mel_bins = np.int32(8)
dft_length = np.int32(16)
sample_rate = np.int32(8192)
lower_edge_hertz = np.float32(0)
upper_edge_hertz = np.float32(8192 / 2)
num_spectrogram_bins = dft_length // 2 + 1
frequency_bins = np.arange(0, num_mel_bins + 2)
low_frequency_mel = 2595 * np.log10(1 + lower_edge_hertz / 700)
high_frequency_mel = 2595 * np.log10(1 + upper_edge_hertz / 700)
mel_step = (high_frequency_mel - low_frequency_mel) / frequency_bins.shape[0]
frequency_bins = frequency_bins * mel_step + low_frequency_mel
frequency_bins = 700 * (np.power(10, (frequency_bins / 2595)) - 1)
frequency_bins = ((dft_length + 1) * frequency_bins) // sample_rate
frequency_bins = frequency_bins.astype(int)
output = np.zeros((num_spectrogram_bins, num_mel_bins))
output.flags.writeable = True
for i in range(num_mel_bins):
lower_frequency_value = frequency_bins[i] # left
center_frequency_point = frequency_bins[i + 1] # center
higher_frequency_point = frequency_bins[i + 2] # right
low_to_center = center_frequency_point - lower_frequency_value
if low_to_center == 0:
output[center_frequency_point, i] = 1
else:
for j in range(lower_frequency_value, center_frequency_point + 1):
output[j, i] = float(j - lower_frequency_value) / float(
low_to_center
)
center_to_high = higher_frequency_point - center_frequency_point
if center_to_high > 0:
for j in range(center_frequency_point, higher_frequency_point):
output[j, i] = float(higher_frequency_point - j) / float(
center_to_high
)
# Expected output
# 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
output = output.astype(np.float32)
expect(
node,
inputs=[
num_mel_bins,
dft_length,
sample_rate,
lower_edge_hertz,
upper_edge_hertz,
],
outputs=[output],
name="test_melweightmatrix",
)
Min#
Element-wise min of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs (1 - ∞)#
- data_0 (variadic, differentiable) : T
- List of tensors for min.
Outputs#
- min (differentiable) : T
- Output tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to numeric tensors.
Examples#
min
data_0 = np.array([3, 2, 1]).astype(np.float32)
data_1 = np.array([1, 4, 4]).astype(np.float32)
data_2 = np.array([2, 5, 0]).astype(np.float32)
result = np.array([1, 2, 0]).astype(np.float32)
node = onnx.helper.make_node(
"Min",
inputs=["data_0", "data_1", "data_2"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1, data_2],
outputs=[result],
name="test_min_example",
)
node = onnx.helper.make_node(
"Min",
inputs=["data_0"],
outputs=["result"],
)
expect(node, inputs=[data_0], outputs=[data_0], name="test_min_one_input")
result = np.minimum(data_0, data_1)
node = onnx.helper.make_node(
"Min",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node, inputs=[data_0, data_1], outputs=[result], name="test_min_two_inputs"
)
min_all_numeric_types
for op_dtype in all_numeric_dtypes:
data_0 = np.array([3, 2, 1]).astype(op_dtype)
data_1 = np.array([1, 4, 4]).astype(op_dtype)
result = np.array([1, 2, 1]).astype(op_dtype)
node = onnx.helper.make_node(
"Min",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1],
outputs=[result],
name=f"test_min_{np.dtype(op_dtype).name}",
)
Mish#
Mish: A Self Regularized Non-Monotonic Neural Activation Function.
Perform the linear unit element-wise on the input tensor X using formula:
mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + e^{x}))
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input X and output types to float tensors.
Examples#
mish
node = onnx.helper.make_node("Mish", inputs=["X"], outputs=["Y"])
input_data = np.linspace(-10, 10, 10000, dtype=np.float32)
# Calculate expected output data
expected_output = input_data * np.tanh(np.log1p(np.exp(input_data)))
expect(node, inputs=[input_data], outputs=[expected_output], name="test_mish")
Mod#
Performs element-wise binary modulus (with Numpy-style broadcasting support). The sign of the remainder is the same as that of the Divisor.
Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend
(in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided.
This attribute is set to 0 by default causing the behavior to be like integer mod.
Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().
If the input type is floating point, then `fmod` attribute must be set to 1.
In case of dividend being zero, the results will be platform dependent.
This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 10
Attributes#
- fmod : int (default is 0)
- Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment
Inputs#
- A (differentiable) : T
- Dividend tensor
- B (non-differentiable) : T
- Divisor tensor
Outputs#
- C (differentiable) : T
- Remainder tensor
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
Examples#
mod_broadcast
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.arange(0, 30).reshape([3, 2, 5]).astype(np.int32)
y = np.array([7]).astype(np.int32)
z = np.mod(x, y)
# array([[[0, 1, 2, 3, 4],
# [5, 6, 0, 1, 2]],
# [[3, 4, 5, 6, 0],
# [1, 2, 3, 4, 5]],
# [[6, 0, 1, 2, 3],
# [4, 5, 6, 0, 1]]], dtype=int32)
expect(node, inputs=[x, y], outputs=[z], name="test_mod_broadcast")
mod_int64_fmod
node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
z = np.fmod(x, y) # expected output [ 0, 1, 5, 0, -1, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_int64_fmod")
mod_mixed_sign_float16
node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float16)
y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float16)
z = np.fmod(
x, y
) # expected output [-0.10156, 0.3984 , 5. , 0.10156, -0.3984 , 3.]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float16")
mod_mixed_sign_float32
node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float32)
y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float32)
z = np.fmod(
x, y
) # expected output [-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float32")
mod_mixed_sign_float64
node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float64)
y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float64)
z = np.fmod(x, y) # expected output [-0.1, 0.4, 5. , 0.1, -0.4, 3.]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float64")
mod_mixed_sign_int16
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int16)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int16)
z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int16")
mod_mixed_sign_int32
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int32)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int32)
z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int32")
mod_mixed_sign_int64
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int64")
mod_mixed_sign_int8
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int8)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int8)
z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int8")
mod_uint16
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([4, 7, 5]).astype(np.uint16)
y = np.array([2, 3, 8]).astype(np.uint16)
z = np.mod(x, y) # expected output [0, 1, 5]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint16")
mod_uint32
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([4, 7, 5]).astype(np.uint32)
y = np.array([2, 3, 8]).astype(np.uint32)
z = np.mod(x, y) # expected output [0, 1, 5]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint32")
mod_uint64
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([4, 7, 5]).astype(np.uint64)
y = np.array([2, 3, 8]).astype(np.uint64)
z = np.mod(x, y) # expected output [0, 1, 5]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint64")
mod_uint8
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([4, 7, 5]).astype(np.uint8)
y = np.array([2, 3, 8]).astype(np.uint8)
z = np.mod(x, y) # expected output [0, 1, 5]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint8")
Mul#
Performs element-wise binary multiplication (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Inputs#
- A (differentiable) : T
- First operand.
- B (differentiable) : T
- Second operand.
Outputs#
- C (differentiable) : T
- Result, has same element type as two inputs
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
mul
node = onnx.helper.make_node(
"Mul",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.float32)
z = x * y # expected output [4., 10., 18.]
expect(node, inputs=[x, y], outputs=[z], name="test_mul_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = x * y
expect(node, inputs=[x, y], outputs=[z], name="test_mul")
x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
z = x * y
expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint8")
mul_broadcast
node = onnx.helper.make_node(
"Mul",
inputs=["x", "y"],
outputs=["z"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = x * y
expect(node, inputs=[x, y], outputs=[z], name="test_mul_bcast")
Multinomial#
Generate a tensor of samples from a multinomial distribution according to the probabilities of each of the possible outcomes.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Attributes#
- dtype : int (default is 6)
- (Optional) The data type for the elements of the output tensor, if not specified, we will use int32.
- sample_size : int (default is 1)
- Number of times to sample.
- seed : float
- (Optional) Seed to the random generator, if not specified we will auto generate one.
Inputs#
- input : T1
- Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.
Outputs#
- output : T2
- Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.
Type Constraints#
- T1 : tensor(float16), tensor(float), tensor(double)
- Constrain input types to float tensors.
- T2 : tensor(int32), tensor(int64)
- Constrain output types to integral tensors.
Neg#
Neg takes one input data (Tensor
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)
- Constrain input and output types to signed numeric tensors.
Examples#
neg
node = onnx.helper.make_node(
"Neg",
inputs=["x"],
outputs=["y"],
)
x = np.array([-4, 2]).astype(np.float32)
y = np.negative(x) # expected output [4., -2.],
expect(node, inputs=[x], outputs=[y], name="test_neg_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.negative(x)
expect(node, inputs=[x], outputs=[y], name="test_neg")
NegativeLogLikelihoodLoss#
A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its “input” tensor has the shape of (N, C, d1, d2, …, dk) where k >= 0. The “input” tensor contains log-probabilities for input[n, :, d_1, d_2,…, d_k] being in a class of [0, C). The operator’s “target” input tensor has the shape of (N, d1, d2, …, dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x … x dk samples. The loss value for input[n, :, d_1, d_2,…d_k] being classified as class c = target[n][d_1][d_2]…[d_k] is computed as:
loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k].
When an optional “weight” is provided, the sample loss is calculated as:
loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c].
loss is zero for the case when target-value equals ignore_index.
loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index
If “reduction” attribute is set to “none”, the operator’s output will be the above loss with shape (N, d1, d2, …, dk). If “reduction” attribute is set to “mean” (the default attribute value), the output loss is (weight) averaged:
mean(loss), if "weight" is not provided,
or if weight is provided,
sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples.
If “reduction” attribute is set to “sum”, the output is a scalar: sum(loss).
See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss.
Example 1:
// negative log likelihood loss, "none" reduction
N, C, d1 = 2, 3, 2
input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
[[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
target = [[2, 1], [0, 2]]
loss = np.zeros((N, d1))
for n in range(N):
for d_1 in range(d1):
c = target[n][d_1]
loss[n][d_1] = -input[n][c][d_1]
// print(loss)
// [[-3. -2.]
// [-0. -2.]]
Example 2:
// weighted negative log likelihood loss, sum reduction
N, C, d1 = 2, 3, 2
input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
[[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
target = [[2, 1], [0, 2]]
weight = [0.2, 0.3, 0.1]
loss = np.zeros((N, d1))
for n in range(N):
for d_1 in range(d1):
c = target[n][d_1]
loss[n][d_1] = -input[n][c][d_1] * weight[c]
loss = np.sum(loss)
// print(loss)
// -1.1
Example 3:
// weighted negative log likelihood loss, mean reduction
N, C, d1 = 2, 3, 2
input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
[[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
target = [[2, 1], [0, 2]]
weight = [0.2, 0.3, 0.1]
loss = np.zeros((N, d1))
weight_total = 0
for n in range(N):
for d_1 in range(d1):
c = target[n][d_1]
loss[n][d_1] = -input[n][c][d_1] * weight[c]
weight_total = weight_total + weight[c]
loss = np.sum(loss) / weight_total
// print(loss)
// -1.57
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 12
Attributes#
- ignore_index : int
- Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.
- reduction : string (default is mean)
- Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.
Inputs (2 - 3)#
- input (differentiable) : T
- Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).
- target (non-differentiable) : Tind
- Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.
- weight (optional, non-differentiable) : T
- Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.
Outputs#
- loss (differentiable) : T
- The negative log likelihood loss
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input, weight, and output types to floating-point tensors.
- Tind : tensor(int32), tensor(int64)
- Constrain target to integer types
Examples#
input_shape_is_NC
reduction = "none"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C = 3, 5
np.random.seed(0)
input = np.random.rand(N, C).astype(np.float32)
target = np.random.randint(0, high=C, size=(N,)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NC",
)
input_shape_is_NCd1
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1",
)
input_shape_is_NCd1_ii
reduction = "mean"
ignore_index = np.int64(1)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
target[0][0] = np.int64(1)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1_ii",
)
input_shape_is_NCd1_mean_weight_negative_ii
reduction = "mean"
ignore_index = np.int64(-1)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1 = 3, 5, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
target[0][0] = -1
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1_mean_weight_negative_ii",
)
input_shape_is_NCd1_weight
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1_weight",
)
input_shape_is_NCd1_weight_ii
reduction = "mean"
ignore_index = np.int64(1)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
target[0][0] = np.int64(1)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1_weight_ii",
)
input_shape_is_NCd1d2
reduction = "none"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2",
)
input_shape_is_NCd1d2_no_weight_reduction_mean_ii
reduction = "mean"
ignore_index = np.int64(1)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
target[0][0][0] = np.int64(1)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_no_weight_reduction_mean_ii",
)
input_shape_is_NCd1d2_reduction_mean
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_reduction_mean",
)
input_shape_is_NCd1d2_reduction_sum
reduction = "sum"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_reduction_sum",
)
input_shape_is_NCd1d2_with_weight
reduction = "none"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_with_weight",
)
input_shape_is_NCd1d2_with_weight_reduction_mean
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_with_weight_reduction_mean",
)
input_shape_is_NCd1d2_with_weight_reduction_sum
reduction = "sum"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_with_weight_reduction_sum",
)
input_shape_is_NCd1d2_with_weight_reduction_sum_ii
reduction = "sum"
ignore_index = np.int64(0)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
target[0][0][0] = np.int64(0)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_with_weight_reduction_sum_ii",
)
input_shape_is_NCd1d2d3_none_no_weight_negative_ii
reduction = "none"
ignore_index = np.int64(-5)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
np.int64
)
target[0][0][0][0] = -5
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2d3_none_no_weight_negative_ii",
)
input_shape_is_NCd1d2d3_sum_weight_high_ii
reduction = "sum"
ignore_index = np.int64(10)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C = 3, 5
np.random.seed(0)
input = np.random.rand(N, C).astype(np.float32)
target = np.random.randint(0, high=C, size=(N)).astype(np.int64)
target[0] = 10
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2d3_sum_weight_high_ii",
)
input_shape_is_NCd1d2d3d4d5_mean_weight
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
target = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2d3d4d5_mean_weight",
)
input_shape_is_NCd1d2d3d4d5_none_no_weight
reduction = "none"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
target = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2d3d4d5_none_no_weight",
)
NonMaxSuppression#
Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Other versions of this operator: 10
Attributes#
- center_point_box : int (default is 0)
- Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.
Inputs (2 - 5)#
- boxes : tensor(float)
- An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.
- scores : tensor(float)
- An input tensor with shape [num_batches, num_classes, spatial_dimension]
- max_output_boxes_per_class (optional) : tensor(int64)
- Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.
- iou_threshold (optional) : tensor(float)
- Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.
- score_threshold (optional) : tensor(float)
- Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.
Outputs#
- selected_indices : tensor(int64)
- selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].
Type Constraints#
Examples#
nonmaxsuppression_center_point_box_format
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
center_point_box=1,
)
boxes = np.array(
[
[
[0.5, 0.5, 1.0, 1.0],
[0.5, 0.6, 1.0, 1.0],
[0.5, 0.4, 1.0, 1.0],
[0.5, 10.5, 1.0, 1.0],
[0.5, 10.6, 1.0, 1.0],
[0.5, 100.5, 1.0, 1.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_center_point_box_format",
)
nonmaxsuppression_flipped_coordinates
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[1.0, 1.0, 0.0, 0.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, 0.9, 1.0, -0.1],
[0.0, 10.0, 1.0, 11.0],
[1.0, 10.1, 0.0, 11.1],
[1.0, 101.0, 0.0, 100.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_flipped_coordinates",
)
nonmaxsuppression_identical_boxes
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
]
]
).astype(np.float32)
scores = np.array(
[[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]]]
).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_identical_boxes",
)
nonmaxsuppression_limit_output_size
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([2]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_limit_output_size",
)
nonmaxsuppression_single_box
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array([[[0.0, 0.0, 1.0, 1.0]]]).astype(np.float32)
scores = np.array([[[0.9]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_single_box",
)
nonmaxsuppression_suppress_by_IOU
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_suppress_by_IOU",
)
nonmaxsuppression_suppress_by_IOU_and_scores
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.4]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_suppress_by_IOU_and_scores",
)
nonmaxsuppression_two_batches
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
],
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
],
]
).astype(np.float32)
scores = np.array(
[[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]], [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]
).astype(np.float32)
max_output_boxes_per_class = np.array([2]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array(
[[0, 0, 3], [0, 0, 0], [1, 0, 3], [1, 0, 0]]
).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_two_batches",
)
nonmaxsuppression_two_classes
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
]
]
).astype(np.float32)
scores = np.array(
[[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3], [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]
).astype(np.float32)
max_output_boxes_per_class = np.array([2]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array(
[[0, 0, 3], [0, 0, 0], [0, 1, 3], [0, 1, 0]]
).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_two_classes",
)
NonZero#
Returns the indices of the elements that are non-zero (in row-major order - by dimension). NonZero behaves similar to numpy.nonzero: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html, but for scalar input, NonZero produces output shape (0, N) instead of (1, N), which is different from Numpy’s behavior.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 9
Inputs#
- X (non-differentiable) : T
- input
Outputs#
- Y (non-differentiable) : tensor(int64)
- output
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain to all tensor types.
Examples#
nonzero
node = onnx.helper.make_node(
"NonZero",
inputs=["condition"],
outputs=["result"],
)
condition = np.array([[1, 0], [1, 1]], dtype=bool)
result = np.array(
np.nonzero(condition), dtype=np.int64
) # expected output [[0, 1, 1], [0, 0, 1]]
expect(node, inputs=[condition], outputs=[result], name="test_nonzero_example")
Not#
Returns the negation of the input tensor element-wise.
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Inputs#
- X (non-differentiable) : T
- Input tensor
Outputs#
- Y (non-differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(bool)
- Constrain input/output to boolean tensors.
Examples#
not
node = onnx.helper.make_node(
"Not",
inputs=["x"],
outputs=["not"],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(bool)
expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_2d")
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_3d")
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_4d")
OneHot#
Produces a one-hot tensor based on inputs. The locations represented by the index values in the ‘indices’ input tensor will have ‘on_value’ and the other locations will have ‘off_value’ in the output tensor, where ‘on_value’ and ‘off_value’ are specified as part of required input argument ‘values’, which is a two-element tensor of format [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the input tensor. The additional dimension is for one-hot representation. The additional dimension will be inserted at the position specified by ‘axis’. If ‘axis’ is not specified then then additional dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional dimension is specified by required scalar input ‘depth’. The type of the output tensor is the same as the type of the ‘values’ input. Any entries in the ‘indices’ input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all ‘off_value’ values in the output tensor.
when axis = 0:
output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise.
when axis = -1:
output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Other versions of this operator: 9
Attributes#
- axis : int (default is -1)
- (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).
Inputs#
- indices (non-differentiable) : T1
- Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.
- depth (non-differentiable) : T2
- Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.
- values (non-differentiable) : T3
- Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor.
Outputs#
- output (non-differentiable) : T3
- Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.
Type Constraints#
- T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
- Constrain input to only numeric types.
- T2 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
- Constrain input to only numeric types.
- T3 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain to any tensor type.
Examples#
with_axis
axisValue = 1
on_value = 3
off_value = 1
output_type = np.float32
node = onnx.helper.make_node(
"OneHot",
inputs=["indices", "depth", "values"],
outputs=["y"],
axis=axisValue,
)
indices = np.array([[1, 9], [2, 4]], dtype=np.float32)
depth = np.float32(10)
values = np.array([off_value, on_value], dtype=output_type)
y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
y = y * (on_value - off_value) + off_value
expect(
node,
inputs=[indices, depth, values],
outputs=[y],
name="test_onehot_with_axis",
)
with_negative_axis
axisValue = -2
on_value = 3
off_value = 1
output_type = np.float32
node = onnx.helper.make_node(
"OneHot",
inputs=["indices", "depth", "values"],
outputs=["y"],
axis=axisValue,
)
indices = np.array([[1, 9], [2, 4]], dtype=np.float32)
depth = np.float32(10)
values = np.array([off_value, on_value], dtype=output_type)
y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
y = y * (on_value - off_value) + off_value
expect(
node,
inputs=[indices, depth, values],
outputs=[y],
name="test_onehot_with_negative_axis",
)
with_negative_indices
axisValue = 1
on_value = 3
off_value = 1
output_type = np.float32
node = onnx.helper.make_node(
"OneHot",
inputs=["indices", "depth", "values"],
outputs=["y"],
axis=axisValue,
)
indices = np.array([0, -7, -8], dtype=np.int64)
# print(y)
# [[3. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 3. 1. 1. 1. 1. 1. 1.]
# [1. 1. 3. 1. 1. 1. 1. 1. 1. 1.]]
depth = np.float32(10)
values = np.array([off_value, on_value], dtype=output_type)
y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
y = y * (on_value - off_value) + off_value
expect(
node,
inputs=[indices, depth, values],
outputs=[y],
name="test_onehot_negative_indices",
)
without_axis
on_value = 5
off_value = 2
output_type = np.int32
node = onnx.helper.make_node(
"OneHot", inputs=["indices", "depth", "values"], outputs=["y"]
)
indices = np.array([0, 7, 8], dtype=np.int64)
depth = np.float32(12)
values = np.array([off_value, on_value], dtype=output_type)
y = one_hot(indices, depth, dtype=output_type)
y = y * (on_value - off_value) + off_value
expect(
node,
inputs=[indices, depth, values],
outputs=[y],
name="test_onehot_without_axis",
)
Optional#
Constructs an optional-type value containing either an empty optional of a certain type specified by the attribute, or a non-empty value containing the input element.
Version#
This version of the operator has been available since version 15 of the default ONNX operator set.
Attributes#
- type : type_proto
- Type of the element in the optional output
Inputs (0 - 1)#
- input (optional) : V
- The input element.
Outputs#
- output : O
- The optional output enclosing the input element.
Type Constraints#
- V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain input type to all tensor and sequence types.
- O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128))
- Constrain output type to all optional tensor or optional sequence types.
OptionalGetElement#
If the input is a tensor or sequence type, it returns the input. If the input is an optional type, it outputs the element in the input. It is an error if the input is an empty optional-type (i.e. does not have an element) and the behavior is undefined in this case.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Other versions of this operator: 15
Inputs#
- input : O
- The optional input.
Outputs#
- output : V
- Output element in the optional input.
Type Constraints#
- O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain input type to optional tensor and optional sequence types.
- V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain output type to all tensor or sequence types.
OptionalHasElement#
Returns true if (1) the input is an optional-type and contains an element, or, (2) the input is a tensor or sequence type. If the input is not provided or is an empty optional-type, this op returns false.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Other versions of this operator: 15
Inputs (0 - 1)#
- input (optional) : O
- The optional input.
Outputs#
- output : B
- A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.
Type Constraints#
- O : optional(seq(tensor(uint8))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(int8))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(float16))), optional(seq(tensor(float))), optional(seq(tensor(double))), optional(seq(tensor(string))), optional(seq(tensor(bool))), optional(seq(tensor(complex64))), optional(seq(tensor(complex128))), optional(tensor(uint8)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(int8)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(float16)), optional(tensor(float)), optional(tensor(double)), optional(tensor(string)), optional(tensor(bool)), optional(tensor(complex64)), optional(tensor(complex128)), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain input type to optional tensor and optional sequence types.
- B : tensor(bool)
- Constrain output to a boolean tensor.
Examples#
empty
optional = None
tensor_type_proto = onnx.helper.make_tensor_type_proto(
elem_type=onnx.TensorProto.INT32, shape=[]
)
optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
# OptionalHasElement takes a tensor or optional as input
for input_type_proto in [tensor_type_proto, optional_type_proto]:
input_name_options = {
"empty": "optional_input",
"empty_no_input_name": "",
"empty_no_input": None,
}
for test_name_surfix, input_name in input_name_options.items():
if input_type_proto == tensor_type_proto and input_name:
# the input tensor cannot be empty if input name is provided.
continue
node = onnx.helper.make_node(
"OptionalHasElement",
inputs=[] if input_name is None else [input_name],
outputs=["output"],
)
output = optional_has_element_reference_implementation(optional)
test_name = (
"test_optional_has_element_"
+ test_name_surfix
+ (
"_optional_input"
if input_type_proto == optional_type_proto
else "_tensor_input"
)
)
expect(
node,
inputs=[optional] if input_name else [],
outputs=[output],
input_type_protos=[input_type_proto] if input_name else [],
name=test_name,
)
get_element_sequence
optional = [np.array([1, 2, 3, 4]).astype(np.int32)]
tensor_type_proto = onnx.helper.make_tensor_type_proto(
elem_type=onnx.TensorProto.INT32,
shape=[
4,
],
)
seq_type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto)
optional_type_proto = onnx.helper.make_optional_type_proto(seq_type_proto)
node = onnx.helper.make_node(
"OptionalGetElement", inputs=["optional_input"], outputs=["output"]
)
output = optional_get_element_reference_implementation(optional)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[optional_type_proto],
name="test_optional_get_element_optional_sequence",
)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[seq_type_proto],
name="test_optional_get_element_sequence",
)
get_element_tensor
optional = np.array([1, 2, 3, 4]).astype(np.float32)
tensor_type_proto = onnx.helper.make_tensor_type_proto(
elem_type=onnx.TensorProto.FLOAT,
shape=[
4,
],
)
optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
node = onnx.helper.make_node(
"OptionalGetElement", inputs=["optional_input"], outputs=["output"]
)
output = optional_get_element_reference_implementation(optional)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[optional_type_proto],
name="test_optional_get_element_optional_tensor",
)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[tensor_type_proto],
name="test_optional_get_element_tensor",
)
optionalhaselement
optional = np.array([1, 2, 3, 4]).astype(np.float32)
tensor_type_proto = onnx.helper.make_tensor_type_proto(
elem_type=onnx.TensorProto.FLOAT,
shape=[
4,
],
)
optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
# OptionalHasElement takes a tensor or optional as input
for input_type_protos in [tensor_type_proto, optional_type_proto]:
node = onnx.helper.make_node(
"OptionalHasElement", inputs=["optional_input"], outputs=["output"]
)
output = optional_has_element_reference_implementation(optional)
test_name = "test_optional_has_element_" + (
"optional_input"
if input_type_protos == optional_type_proto
else "tensor_input"
)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[optional_type_proto],
name=test_name,
)
Or#
Returns the tensor resulted from performing the or logical operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Other versions of this operator: 1
Inputs#
- A (non-differentiable) : T
- First input operand for the logical operator.
- B (non-differentiable) : T
- Second input operand for the logical operator.
Outputs#
- C (non-differentiable) : T1
- Result tensor.
Type Constraints#
- T : tensor(bool)
- Constrain input to boolean tensor.
- T1 : tensor(bool)
- Constrain output to boolean tensor.
Examples#
or
node = onnx.helper.make_node(
"Or",
inputs=["x", "y"],
outputs=["or"],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(bool)
y = (np.random.randn(3, 4) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or2d")
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(3, 4, 5) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or3d")
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or4d")
or_broadcast
node = onnx.helper.make_node(
"Or",
inputs=["x", "y"],
outputs=["or"],
)
# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(5) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v1d")
# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(4, 5) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v2d")
# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v2d")
# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(4, 5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v3d")
# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v4d")
PRelu#
PRelu takes input data (Tensorf(x) = slope * x for x < 0,
f(x) = x for x >= 0., is applied to the data tensor elementwise.
History
Version 16 adds bfloat16 to the types allowed. This operator supports unidirectional broadcasting (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check the doc.
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
- slope (differentiable) : T
- Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X
Outputs#
- Y (differentiable) : T
- Output tensor (same size as X)
Type Constraints#
- T : tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(uint32), tensor(uint64), tensor(int32), tensor(int64)
- Constrain input and output types to float/int tensors.
Examples#
prelu
node = onnx.helper.make_node(
"PRelu",
inputs=["x", "slope"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
slope = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_example")
prelu_broadcast
node = onnx.helper.make_node(
"PRelu",
inputs=["x", "slope"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
slope = np.random.randn(5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_broadcast")
Pad#
Given a tensor containing the data to be padded (data), a tensor containing the number of start and end pad values for axis (pads), (optionally) a mode, and (optionally) constant_value,
a padded tensor (output) is generated.
The three supported modes are (similar to corresponding modes supported by numpy.pad):
constant(default) - pads with a given constant value as specified byconstant_value(which defaults to 0, empty string, or False)reflect- pads with the reflection of the vector mirrored on the first and last values of the vector along each axisedge- pads with the edge values of array
Example 1 (constant mode):
Insert 0 pads to the beginning of the second dimension.
data =
[
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
pads = [0, 2, 0, 0]
mode = 'constant'
constant_value = 0.0
output =
[
[0.0, 0.0, 1.0, 1.2],
[0.0, 0.0, 2.3, 3.4],
[0.0, 0.0, 4.5, 5.7],
]
Example 2 (reflect mode):
data =
[
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
pads = [0, 2, 0, 0]
mode = 'reflect'
output =
[
[1.0, 1.2, 1.0, 1.2],
[2.3, 3.4, 2.3, 3.4],
[4.5, 5.7, 4.5, 5.7],
]
Example 3 (edge mode):
data =
[
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
pads = [0, 2, 0, 0]
mode = 'edge'
output =
[
[1.0, 1.0, 1.0, 1.2],
[2.3, 2.3, 2.3, 3.4],
[4.5, 4.5, 4.5, 5.7],
]
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- mode : string (default is constant)
- Supported modes: `constant`(default), `reflect`, `edge`
Inputs (2 - 4)#
- data (differentiable) : T
- Input tensor.
- pads (non-differentiable) : tensor(int64)
- Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.
- constant_value (optional, non-differentiable) : T
- (Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).
- axes (optional, non-differentiable) : Tind
- 1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).
Outputs#
- output (differentiable) : T
- Tensor after padding.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
- Tind : tensor(int32), tensor(int64)
- Constrain indices to integer types
Examples#
constant_pad
node = onnx.helper.make_node(
"Pad", inputs=["x", "pads", "value"], outputs=["y"], mode="constant"
)
x = np.random.randn(1, 3, 4, 5).astype(np.float32)
pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype(
np.int64
) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
value = np.float32(1.2)
y = pad_impl(x, pads, "constant", 1.2)
expect(node, inputs=[x, pads, value], outputs=[y], name="test_constant_pad")
constant_pad_axes
node = onnx.helper.make_node(
"Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant"
)
x = np.random.randn(1, 3, 4, 5).astype(np.float32)
pads = np.array([0, 3, 0, 4]).astype(
np.int64
) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
value = np.float32(1.2)
axes = np.array([1, 3], dtype=np.int64)
y = pad_impl(
x,
pads,
"constant",
1.2,
[1, 3],
)
expect(
node,
inputs=[x, pads, value, axes],
outputs=[y],
name="test_constant_pad_axes",
)
reflection_and_edge_pad
for mode in ["edge", "reflect"]:
node = onnx.helper.make_node(
"Pad", inputs=["x", "pads"], outputs=["y"], mode=mode
)
x = np.random.randn(1, 3, 4, 5).astype(np.int32)
pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype(
np.int64
) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
y = pad_impl(x, pads, mode)
expect(node, inputs=[x, pads], outputs=[y], name=f"test_{mode}_pad")
Pow#
Pow takes input data (Tensorf(x) = x^exponent,
is applied to the data tensor elementwise.
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 15 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- First operand, base of the exponent.
- Y (differentiable) : T1
- Second operand, power of the exponent.
Outputs#
- Z (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input X and output types to float/int tensors.
- T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input Y types to float/int tensors.
Examples#
pow
node = onnx.helper.make_node(
"Pow",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.float32)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_example")
x = np.arange(60).reshape(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = pow(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_pow")
pow_broadcast
node = onnx.helper.make_node(
"Pow",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array(2).astype(np.float32)
z = pow(x, y) # expected output [1., 4., 9.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_scalar")
node = onnx.helper.make_node(
"Pow",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
y = np.array([1, 2, 3]).astype(np.float32)
# expected output [[1, 4, 27], [4, 25, 216]]
z = pow(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_array")
types
node = onnx.helper.make_node(
"Pow",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.int64)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int64")
x = np.array([1, 2, 3]).astype(np.int64)
y = np.array([4, 5, 6]).astype(np.float32)
z = pow(x, y) # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_float32")
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.int32)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int32")
x = np.array([1, 2, 3]).astype(np.int32)
y = np.array([4, 5, 6]).astype(np.float32)
z = pow(x, y) # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_float32")
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.uint64)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint64")
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.uint32)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint32")
x = np.array([1, 2, 3]).astype(np.int64)
y = np.array([4, 5, 6]).astype(np.int64)
z = pow(x, y) # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_int64")
x = np.array([1, 2, 3]).astype(np.int32)
y = np.array([4, 5, 6]).astype(np.int32)
z = pow(x, y) # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_int32")
QLinearConv#
The convolution operator consumes a quantized input tensor, its scale and zero point, a quantized filter, its scale and zero point, and output’s scale and zero point, and computes the quantized output. Each scale and zero-point pair must have same shape. It means they must be either scalars (per tensor) or 1-D tensors (per output channel). Each input or output and its related zero point must have same type. When bias is present it must be quantized using scale = input scale * weight scale and zero point as 0.
Version#
This version of the operator has been available since version 10 of the default ONNX operator set.
Attributes#
- auto_pad : string (default is NOTSET)
- auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
- dilations : list of ints
- dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.
- group : int (default is 1)
- number of groups input channels and output channels are divided into. default is 1.
- kernel_shape : list of ints
- The shape of the convolution kernel. If not present, should be inferred from input 'w'.
- pads : list of ints
- Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.
- strides : list of ints
- Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs (8 - 9)#
- x : T1
- Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
- x_scale : tensor(float)
- Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.
- x_zero_point : T1
- Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.
- w : T2
- The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL.
- w_scale : tensor(float)
- Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).
- w_zero_point : T2
- Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).
- y_scale : tensor(float)
- Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.
- y_zero_point : T3
- Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.
- B (optional) : T4
- Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0
Outputs#
- y : T3
- Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.
Type Constraints#
- T1 : tensor(int8), tensor(uint8)
- Constrain input type to 8-bit integer tensor.
- T2 : tensor(int8), tensor(uint8)
- Constrain filter type to 8-bit integer tensor.
- T3 : tensor(int8), tensor(uint8)
- Constrain output type to 8-bit integer tensor.
- T4 : tensor(int32)
- Constrain bias type to 32-bit integer tensor.
Examples#
qlinearconv
node = onnx.helper.make_node(
"QLinearConv",
inputs=[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
outputs=["y"],
)
x = np.array(
[
[255, 174, 162, 25, 203, 168, 58],
[15, 59, 237, 95, 129, 0, 64],
[56, 242, 153, 221, 168, 12, 166],
[232, 178, 186, 195, 237, 162, 237],
[188, 39, 124, 77, 80, 102, 43],
[127, 230, 21, 83, 41, 40, 134],
[255, 154, 92, 141, 42, 148, 247],
],
dtype=np.uint8,
).reshape((1, 1, 7, 7))
x_scale = np.float32(0.00369204697)
x_zero_point = np.uint8(132)
w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
w_scale = np.array([0.00172794575], dtype=np.float32)
w_zero_point = np.array([255], dtype=np.uint8)
y_scale = np.float32(0.00162681262)
y_zero_point = np.uint8(123)
output = np.array(
[
[0, 81, 93, 230, 52, 87, 197],
[240, 196, 18, 160, 126, 255, 191],
[199, 13, 102, 34, 87, 243, 89],
[23, 77, 69, 60, 18, 93, 18],
[67, 216, 131, 178, 175, 153, 212],
[128, 25, 234, 172, 214, 215, 121],
[0, 101, 163, 114, 213, 107, 8],
],
dtype=np.uint8,
).reshape((1, 1, 7, 7))
expect(
node,
inputs=[
x,
x_scale,
x_zero_point,
w,
w_scale,
w_zero_point,
y_scale,
y_zero_point,
],
outputs=[output],
name="test_qlinearconv",
)
QLinearMatMul#
Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor (per row for ‘a’ and per column for ‘b’). Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be an M element vector [v_1, v_2, …, v_M] for per row quantization and K element vector of shape [v_1, v_2, …, v_K] for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization. Production must never overflow, and accumulation may overflow if and only if in 32 bits.
Version#
This version of the operator has been available since version 10 of the default ONNX operator set.
Inputs#
- a (non-differentiable) : T1
- N-dimensional quantized matrix a
- a_scale (non-differentiable) : tensor(float)
- scale of quantized input a
- a_zero_point (non-differentiable) : T1
- zero point of quantized input a
- b (non-differentiable) : T2
- N-dimensional quantized matrix b
- b_scale (non-differentiable) : tensor(float)
- scale of quantized input b
- b_zero_point (non-differentiable) : T2
- zero point of quantized input b
- y_scale (non-differentiable) : tensor(float)
- scale of quantized output y
- y_zero_point (non-differentiable) : T3
- zero point of quantized output y
Outputs#
- y (non-differentiable) : T3
- Quantized matrix multiply results from a * b
Type Constraints#
- T1 : tensor(int8), tensor(uint8)
- Constrain input a and its zero point data type to 8-bit integer tensor.
- T2 : tensor(int8), tensor(uint8)
- Constrain input b and its zero point data type to 8-bit integer tensor.
- T3 : tensor(int8), tensor(uint8)
- Constrain output y and its zero point data type to 8-bit integer tensor.
Examples#
qlinearmatmul
node = onnx.helper.make_node(
"QLinearMatMul",
inputs=[
"a",
"a_scale",
"a_zero_point",
"b",
"b_scale",
"b_zero_point",
"y_scale",
"y_zero_point",
],
outputs=["y"],
)
# 2D
a = np.array(
[
[208, 236, 0, 238],
[3, 214, 255, 29],
],
dtype=np.uint8,
)
a_scale = np.array([0.0066], dtype=np.float32)
a_zero_point = np.array([113], dtype=np.uint8)
b = np.array(
[[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
dtype=np.uint8,
)
b_scale = np.array([0.00705], dtype=np.float32)
b_zero_point = np.array([114], dtype=np.uint8)
y_scale = np.array([0.0107], dtype=np.float32)
y_zero_point = np.array([118], dtype=np.uint8)
output = np.array(
[
[168, 115, 255],
[1, 66, 151],
],
dtype=np.uint8,
)
expect(
node,
inputs=[
a,
a_scale,
a_zero_point,
b,
b_scale,
b_zero_point,
y_scale,
y_zero_point,
],
outputs=[output],
name="test_qlinearmatmul_2D",
)
# 3D
a = np.array(
[
[[208, 236, 0, 238], [3, 214, 255, 29]],
[[208, 236, 0, 238], [3, 214, 255, 29]],
],
dtype=np.uint8,
)
a_scale = np.array([0.0066], dtype=np.float32)
a_zero_point = np.array([113], dtype=np.uint8)
b = np.array(
[
[[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
[[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
],
dtype=np.uint8,
)
b_scale = np.array([0.00705], dtype=np.float32)
b_zero_point = np.array([114], dtype=np.uint8)
y_scale = np.array([0.0107], dtype=np.float32)
y_zero_point = np.array([118], dtype=np.uint8)
output = np.array(
[[[168, 115, 255], [1, 66, 151]], [[168, 115, 255], [1, 66, 151]]],
dtype=np.uint8,
)
expect(
node,
inputs=[
a,
a_scale,
a_zero_point,
b,
b_scale,
b_zero_point,
y_scale,
y_zero_point,
],
outputs=[output],
name="test_qlinearmatmul_3D",
)
QuantizeLinear#
The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor. The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it’s uint8, or [-128, 127] if it’s int8. For (x / y_scale), it’s rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. ‘y_zero_point’ and ‘y’ must have same type.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 10
Attributes#
- axis : int (default is 1)
- (Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
Inputs (2 - 3)#
- x : T1
- N-D full precision Input tensor to be quantized.
- y_scale : tensor(float)
- Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.
- y_zero_point (optional) : T2
- Zero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified.
Outputs#
- y : T2
- N-D quantized output tensor. It has same shape as input 'x'.
Type Constraints#
- T1 : tensor(float), tensor(int32)
- Constrain 'x' to float or int32 tensor.
- T2 : tensor(int8), tensor(uint8)
- Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.
Examples#
axis
node = onnx.helper.make_node(
"QuantizeLinear",
inputs=["x", "y_scale", "y_zero_point"],
outputs=["y"],
)
x = np.array(
[
[
[[-162, 10], [-100, 232], [-20, -50]],
[[-76, 0], [0, 252], [32, -44]],
[[245, -485], [-960, -270], [-375, -470]],
],
],
dtype=np.float32,
)
y_scale = np.array([2, 4, 5], dtype=np.float32)
y_zero_point = np.array([84, 24, 196], dtype=np.uint8)
y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype(
np.uint8
)
expect(
node,
inputs=[x, y_scale, y_zero_point],
outputs=[y],
name="test_quantizelinear_axis",
)
quantizelinear
node = onnx.helper.make_node(
"QuantizeLinear",
inputs=["x", "y_scale", "y_zero_point"],
outputs=["y"],
)
x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
y_scale = np.float32(2)
y_zero_point = np.uint8(128)
y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
expect(
node,
inputs=[x, y_scale, y_zero_point],
outputs=[y],
name="test_quantizelinear",
)
RNN#
Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN.
Notations:
X - input tensor
i - input gate
t - time step (t-1 means previous time step)
Wi - W parameter weight matrix for input gate
Ri - R recurrence weight matrix for input gate
Wbi - W parameter bias vector for input gate
Rbi - R parameter bias vector for input gate
WBi - W parameter weight matrix for backward input gate
RBi - R recurrence weight matrix for backward input gate
WBbi - WR bias vectors for backward input gate
RBbi - RR bias vectors for backward input gate
H - Hidden state
num_directions - 2 if direction == bidirectional else 1
Activation functions:
Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha*Tanh(beta*x)
HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
Elu(x) - x if x >= 0 else alpha*(e^x - 1)
Softsign(x) - x/(1 + |x|)
Softplus(x) - log(1 + e^x)
Equations (Default: f=Tanh):
- Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Attributes#
- activation_alpha : list of floats
- Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
- activation_beta : list of floats
- Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
- activations : list of strings (default is ['Tanh', 'Tanh'])
- One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.
- clip : float
- Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
- direction : string (default is forward)
- Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
- hidden_size : int
- Number of neurons in the hidden layer
- layout : int (default is 0)
- The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].
Inputs (3 - 6)#
- X (differentiable) : T
- The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
- W (differentiable) : T
- The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.
- R (differentiable) : T
- The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.
- B (optional, differentiable) : T
- The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.
- sequence_lens (optional, non-differentiable) : T1
- Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
- initial_h (optional, non-differentiable) : T
- Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
Outputs (0 - 2)#
- Y (optional, differentiable) : T
- A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`.
- Y_h (optional, differentiable) : T
- The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
- T1 : tensor(int32)
- Constrain seq_lens to integer tensor.
Examples#
batchwise
input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 4
weight_scale = 0.5
layout = 1
node = onnx.helper.make_node(
"RNN",
inputs=["X", "W", "R"],
outputs=["Y", "Y_h"],
hidden_size=hidden_size,
layout=layout,
)
W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
rnn = RNN_Helper(X=input, W=W, R=R, layout=layout)
Y, Y_h = rnn.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y.astype(np.float32), Y_h.astype(np.float32)],
name="test_simple_rnn_batchwise",
)
defaults
input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 4
weight_scale = 0.1
node = onnx.helper.make_node(
"RNN", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size
)
W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
rnn = RNN_Helper(X=input, W=W, R=R)
_, Y_h = rnn.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y_h.astype(np.float32)],
name="test_simple_rnn_defaults",
)
initial_bias
input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype(
np.float32
)
input_size = 3
hidden_size = 5
custom_bias = 0.1
weight_scale = 0.1
node = onnx.helper.make_node(
"RNN",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
# Adding custom bias
W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32)
R_B = np.zeros((1, hidden_size)).astype(np.float32)
B = np.concatenate((W_B, R_B), axis=1)
rnn = RNN_Helper(X=input, W=W, R=R, B=B)
_, Y_h = rnn.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_simple_rnn_with_initial_bias",
)
seq_length
input = np.array(
[
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
[[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]],
]
).astype(np.float32)
input_size = 3
hidden_size = 5
node = onnx.helper.make_node(
"RNN",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = np.random.randn(1, hidden_size, input_size).astype(np.float32)
R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32)
# Adding custom bias
W_B = np.random.randn(1, hidden_size).astype(np.float32)
R_B = np.random.randn(1, hidden_size).astype(np.float32)
B = np.concatenate((W_B, R_B), axis=1)
rnn = RNN_Helper(X=input, W=W, R=R, B=B)
_, Y_h = rnn.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_rnn_seq_length",
)
RandomNormal#
Generate a tensor with random values drawn from a normal distribution. The shape
of the tensor is specified by the shape argument and the parameter of the normal distribution
specified by mean and scale.
The data type is specified by the ‘dtype’ argument. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message.
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Attributes#
- dtype : int (default is 1)
- The data type for the elements of the output tensor. Default is TensorProto::FLOAT.
- mean : float (default is 0.0)
- The mean of the normal distribution.
- scale : float (default is 1.0)
- The standard deviation of the normal distribution.
- seed : float
- (Optional) Seed to the random generator, if not specified we will auto generate one.
- shape : list of ints (required)
- The shape of the output tensor.
Inputs#
Outputs#
- output : T
- Output tensor of random values drawn from normal distribution
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain output types to float tensors.
RandomNormalLike#
Generate a tensor with random values drawn from a normal distribution.
The shape of the output tensor is copied from the shape of the input tensor,
and the parameters of the normal distribution are specified by mean and scale.
The data type is specified by the ‘dtype’ argument, or copied from the input tensor if not provided. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message, and be valid as an output type.
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Attributes#
- dtype : int
- (Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
- mean : float (default is 0.0)
- The mean of the normal distribution.
- scale : float (default is 1.0)
- The standard deviation of the normal distribution.
- seed : float
- (Optional) Seed to the random generator, if not specified we will auto generate one.
Inputs#
- input : T1
- Input tensor to copy shape and optionally type information from.
Outputs#
- output : T2
- Output tensor of random values drawn from normal distribution
Type Constraints#
- T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
- T2 : tensor(float16), tensor(float), tensor(double)
- Constrain output types to float tensors.
RandomUniform#
Generate a tensor with random values drawn from a uniform distribution. The shape
of the tensor is specified by the shape argument and the range by low and high.
The data type is specified by the ‘dtype’ argument. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message.
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Attributes#
- dtype : int (default is 1)
- The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.
- high : float (default is 1.0)
- Upper boundary of the output values.
- low : float (default is 0.0)
- Lower boundary of the output values.
- seed : float
- (Optional) Seed to the random generator, if not specified we will auto generate one.
- shape : list of ints (required)
- The shape of the output tensor.
Inputs#
Outputs#
- output : T
- Output tensor of random values drawn from uniform distribution
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain output types to float tensors.
RandomUniformLike#
Generate a tensor with random values drawn from a uniform distribution.
The shape of the output tensor is copied from the shape of the input tensor,
and the parameters of the uniform distribution are specified by low and high.
The data type is specified by the ‘dtype’ argument, or copied from the input tensor if not provided. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message and be valid as an output type.
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Attributes#
- dtype : int
- (Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
- high : float (default is 1.0)
- Upper boundary of the output values.
- low : float (default is 0.0)
- Lower boundary of the output values.
- seed : float
- (Optional) Seed to the random generator, if not specified we will auto generate one.
Inputs#
- input : T1
- Input tensor to copy shape and optionally type information from.
Outputs#
- output : T2
- Output tensor of random values drawn from uniform distribution
Type Constraints#
- T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
- T2 : tensor(float16), tensor(float), tensor(double)
- Constrain output types to float tensors.
Range#
Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta
up to limit (exclusive).
The number of elements in the output of range is computed as below-
number_of_elements = max( ceil( (limit - start) / delta ) , 0 )
The pseudocode determining the contents of the output is shown below-
for(int i=0; i<number_of_elements; ++i)
{
output[i] = start + (i * delta);
}
Example 1
Inputs: start = 3, limit = 9, delta = 3
Output: [3, 6]
Example 2
Inputs: start = 10, limit = 4, delta = -2
Output: [10, 8, 6]
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Inputs#
- start : T
- Scalar. First entry for the range of output values.
- limit : T
- Scalar. Exclusive upper limit for the range of output values.
- delta : T
- Scalar. Value to step by.
Outputs#
- output : T
- A 1-D tensor with same type as the inputs containing generated range of values.
Type Constraints#
- T : tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64)
- Constrain input types to common numeric type tensors.
Examples#
range_float_type_positive_delta
node = onnx.helper.make_node(
"Range",
inputs=["start", "limit", "delta"],
outputs=["output"],
)
start = np.float32(1)
limit = np.float32(5)
delta = np.float32(2)
output = np.arange(
start, limit, delta, dtype=np.float32
) # expected output [1.0, 3.0]
expect(
node,
inputs=[start, limit, delta],
outputs=[output],
name="test_range_float_type_positive_delta",
)
range_int32_type_negative_delta
node = onnx.helper.make_node(
"Range",
inputs=["start", "limit", "delta"],
outputs=["output"],
)
start = np.int32(10)
limit = np.int32(6)
delta = np.int32(-3)
output = np.arange(
start, limit, delta, dtype=np.int32
) # expected output [10, 7]
expect(
node,
inputs=[start, limit, delta],
outputs=[output],
name="test_range_int32_type_negative_delta",
)
Reciprocal#
Reciprocal takes one input data (Tensor
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
reciprocal
node = onnx.helper.make_node(
"Reciprocal",
inputs=["x"],
outputs=["y"],
)
x = np.array([-4, 2]).astype(np.float32)
y = np.reciprocal(x) # expected output [-0.25, 0.5],
expect(node, inputs=[x], outputs=[y], name="test_reciprocal_example")
x = np.random.rand(3, 4, 5).astype(np.float32) + 0.5
y = np.reciprocal(x)
expect(node, inputs=[x], outputs=[y], name="test_reciprocal")
ReduceL1#
Computes the L1 norm of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
Examples#
default_axes_keepdims
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1)
# print(reduced)
# [[[78.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_default_axes_keepdims_random",
)
do_not_keepdims
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceL1",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[3., 7.], [11., 15.], [19., 23.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_do_not_keepdims_random",
)
keepdims
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL1",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_keep_dims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_keep_dims_random",
)
negative_axes_keepdims
shape = [3, 2, 2]
axes = np.array([-1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL1",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_negative_axes_keep_dims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_negative_axes_keep_dims_random",
)
ReduceL2#
Computes the L2 norm of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
Examples#
default_axes_keepdims
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1))
# print(reduced)
# [[[25.49509757]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_default_axes_keepdims_random",
)
do_not_keepdims
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceL2",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
# print(reduced)
# [[2.23606798, 5.],
# [7.81024968, 10.63014581],
# [13.45362405, 16.2788206]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_do_not_keepdims_random",
)
keepdims
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL2",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
# print(reduced)
# [[[2.23606798], [5.]]
# [[7.81024968], [10.63014581]]
# [[13.45362405], [16.2788206 ]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_keep_dims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_keep_dims_random",
)
negative_axes_keepdims
shape = [3, 2, 2]
axes = np.array([-1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL2",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
# print(reduced)
# [[[2.23606798], [5.]]
# [[7.81024968], [10.63014581]]
# [[13.45362405], [16.2788206 ]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_negative_axes_keep_dims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_negative_axes_keep_dims_random",
)
ReduceLogSum#
Computes the log sum of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
Examples#
keepdims
node = onnx.helper.make_node(
"ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"]
)
data = np.random.ranf([3, 4, 5]).astype(np.float32)
reduced = np.log(np.sum(data, keepdims=True))
axes = np.array([], dtype=np.int64)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_default",
)
negative_axes_keepdims
axes = np.array([-2], dtype=np.int64)
node = onnx.helper.make_node(
"ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"]
)
data = np.random.ranf([3, 4, 5]).astype(np.float32)
reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=True))
# print(reduced)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_negative_axes",
)
nokeepdims
shape = [3, 4, 5]
axes = np.array([2, 1], dtype=np.int64)
node = onnx.helper.make_node(
"ReduceLogSum",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=0,
)
data = np.random.ranf(shape).astype(np.float32)
reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_desc_axes",
)
axes = np.array([0, 1], dtype=np.int64)
node = onnx.helper.make_node(
"ReduceLogSum",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=0,
)
data = np.random.ranf(shape).astype(np.float32)
reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_asc_axes",
)
ReduceLogSumExp#
Computes the log sum exponent of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
Examples#
default_axes_keepdims
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceLogSumExp",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
)
reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1))
# print(reduced)
# [[[60.00671387]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.double)
reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_default_axes_keepdims_random",
)
do_not_keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceLogSumExp",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
# print(reduced)
# [[20., 2.31326175]
# [40.00004578, 2.31326175]
# [60.00671387, 2.31326175]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.double)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_do_not_keepdims_random",
)
keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceLogSumExp",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
# print(reduced)
# [[[20., 2.31326175]]
# [[40.00004578, 2.31326175]]
# [[60.00671387, 2.31326175]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.double)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_keepdims_random",
)
negative_axes_keepdims
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceLogSumExp",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
# print(reduced)
# [[[20., 2.31326175]]
# [[40.00004578, 2.31326175]]
# [[60.00671387, 2.31326175]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.double)
reduced = np.log(
np.sum(np.exp(data), axis=tuple(axes.tolist()), keepdims=keepdims == 1)
)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_negative_axes_keepdims_random",
)
ReduceMax#
Computes the max of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)
- Constrain input and output types to high-precision and 8 bit numeric tensors.
Examples#
default_axes_keepdims
shape = [3, 2, 2]
axes = None
keepdims = 1
node = onnx.helper.make_node(
"ReduceMax", inputs=["data"], outputs=["reduced"], keepdims=keepdims
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_max_default_axes_keepdim_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.maximum.reduce(data, axis=axes, keepdims=keepdims == 1)
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_max_default_axes_keepdims_random",
)
do_not_keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceMax",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[20., 2.]
# [40., 2.]
# [60., 2.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_max_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_max_do_not_keepdims_random",
)
keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMax",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[20., 2.]]
# [[40., 2.]]
# [[60., 2.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_max_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_max_keepdims_random",
)
negative_axes_keepdims
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMax",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[20., 2.]]
# [[40., 2.]]
# [[60., 2.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_max_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.maximum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_max_negative_axes_keepdims_random",
)
ReduceMean#
Computes the mean of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
Examples#
default_axes_keepdims
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMean",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.mean(data, axis=None, keepdims=keepdims == 1)
# print(reduced)
# [[[18.25]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.mean(data, axis=None, keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_default_axes_keepdims_random",
)
do_not_keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceMean",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[12.5, 1.5]
# [35., 1.5]
# [57.5, 1.5]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_do_not_keepdims_random",
)
keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMean",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[12.5, 1.5]]
# [[35., 1.5]]
# [[57.5, 1.5]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_keepdims_random",
)
negative_axes_keepdims
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMean",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[12.5, 1.5]]
# [[35., 1.5]]
# [[57.5, 1.5]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_negative_axes_keepdims_random",
)
ReduceMin#
Computes the min of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8)
- Constrain input and output types to high-precision and 8 bit numeric tensors.
Examples#
default_axes_keepdims
shape = [3, 2, 2]
axes = None
keepdims = 1
node = onnx.helper.make_node(
"ReduceMin", inputs=["data"], outputs=["reduced"], keepdims=keepdims
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
# print(reduced)
# [[[1.]]]
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_min_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_min_default_axes_keepdims_random",
)
do_not_keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceMin",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[5., 1.]
# [30., 1.]
# [55., 1.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_do_not_keepdims_random",
)
keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMin",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[5., 1.]]
# [[30., 1.]]
# [[55., 1.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_keepdims_random",
)
negative_axes_keepdims
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMin",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[5., 1.]]
# [[30., 1.]]
# [[55., 1.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_negative_axes_keepdims_random",
)
ReduceProd#
Computes the product of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
Examples#
default_axes_keepdims
shape = [3, 2, 2]
axes = None
keepdims = 1
node = onnx.helper.make_node(
"ReduceProd", inputs=["data"], outputs=["reduced"], keepdims=keepdims
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
# print(reduced)
# [[[4.790016e+08]]]
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_prod_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_prod_default_axes_keepdims_random",
)
do_not_keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceProd",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[3., 8.]
# [35., 48.]
# [99., 120.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_do_not_keepdims_random",
)
keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceProd",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[3., 8.]]
# [[35., 48.]]
# [[99., 120.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_keepdims_random",
)
negative_axes_keepdims
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceProd",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[3., 8.]]
# [[35., 48.]]
# [[99., 120.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_negative_axes_keepdims_random",
)
ReduceSum#
Computes the sum of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
Examples#
default_axes_keepdims
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.sum(data, axis=None, keepdims=keepdims == 1)
# print(reduced)
# [[[78.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(data, axis=None, keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_default_axes_keepdims_random",
)
do_not_keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
# print(reduced)
# [[4., 6.]
# [12., 14.]
# [20., 22.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_do_not_keepdims_random",
)
empty_axes_input_noop
shape = [3, 2, 2]
keepdims = 1
node = onnx.helper.make_node(
"ReduceSum",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
noop_with_empty_axes=True,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
axes = np.array([], dtype=np.int64)
reduced = np.array(data)
# print(reduced)
# [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_empty_axes_input_noop_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.array(data)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_negative_axes_keepdims_random",
)
keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
# print(reduced)
# [[[4., 6.]]
# [[12., 14.]]
# [[20., 22.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_keepdims_random",
)
negative_axes_keepdims
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceSum", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
# print(reduced)
# [[[4., 6.]]
# [[12., 14.]]
# [[20., 22.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_negative_axes_keepdims_random",
)
ReduceSumSquare#
Computes the sum square of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- keepdims : int (default is 1)
- Keep the reduced dimension or not, default 1 means keep reduced dimension.
- noop_with_empty_axes : int (default is 0)
- Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.
Inputs (1 - 2)#
- data (differentiable) : T
- An input tensor.
- axes (optional, non-differentiable) : tensor(int64)
- Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- reduced (differentiable) : T
- Reduced output tensor.
Type Constraints#
- T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to high-precision numeric tensors.
Examples#
default_axes_keepdims
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceSumSquare",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.sum(np.square(data), axis=None, keepdims=keepdims == 1)
# print(reduced)
# [[[650.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_square_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(np.square(data), axis=None, keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_square_default_axes_keepdims_random",
)
do_not_keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceSumSquare",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[10., 20.]
# [74., 100.]
# [202., 244.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_square_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_square_do_not_keepdims_random",
)
keepdims
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceSumSquare",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[10., 20.]]
# [[74., 100.]]
# [[202., 244.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_square_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_square_keepdims_random",
)
negative_axes_keepdims
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceSumSquare",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[10., 20.s]]
# [[74., 100.]]
# [[202., 244.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_square_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_square_negative_axes_keepdims_random",
)
Relu#
Relu takes one input data (Tensor
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float), tensor(int32), tensor(int8), tensor(int16), tensor(int64), tensor(float16), tensor(double), tensor(bfloat16)
- Constrain input and output types to signed numeric tensors.
Examples#
relu
node = onnx.helper.make_node(
"Relu",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf)
expect(node, inputs=[x], outputs=[y], name="test_relu")
Reshape#
Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If ‘allowzero’ is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor’s shape and the output tensor’s shape are required to have the same number of elements.
If the attribute ‘allowzero’ is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely.
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Attributes#
- allowzero : int (default is 0)
- (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.
Inputs#
- data (differentiable) : T
- An input tensor.
- shape (non-differentiable) : tensor(int64)
- Specified shape for output.
Outputs#
- reshaped (differentiable) : T
- Reshaped data.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Examples#
allowzero
original_shape = [0, 3, 4]
test_cases = {
"allowzero_reordered": np.array([3, 4, 0], dtype=np.int64),
}
data = np.random.random_sample(original_shape).astype(np.float32)
for test_name, shape in test_cases.items():
node = onnx.helper.make_node(
"Reshape",
inputs=["data", "shape"],
outputs=["reshaped"],
allowzero=1, # if allowzero=1, final shape = (3, 4, 0)
# if allowzero=0, final shape = (3, 4, 4)
)
reshaped = reshape_reference_implementation(data, shape, allowzero=1)
expect(
node,
inputs=[data, shape],
outputs=[reshaped],
name="test_reshape_" + test_name,
)
reshape
original_shape = [2, 3, 4]
test_cases = {
"reordered_all_dims": np.array([4, 2, 3], dtype=np.int64),
"reordered_last_dims": np.array([2, 4, 3], dtype=np.int64),
"reduced_dims": np.array([2, 12], dtype=np.int64),
"extended_dims": np.array([2, 3, 2, 2], dtype=np.int64),
"one_dim": np.array([24], dtype=np.int64),
"negative_dim": np.array([2, -1, 2], dtype=np.int64),
"negative_extended_dims": np.array([-1, 2, 3, 4], dtype=np.int64),
"zero_dim": np.array([2, 0, 4, 1], dtype=np.int64),
"zero_and_negative_dim": np.array([2, 0, 1, -1], dtype=np.int64),
}
data = np.random.random_sample(original_shape).astype(np.float32)
for test_name, shape in test_cases.items():
node = onnx.helper.make_node(
"Reshape",
inputs=["data", "shape"],
outputs=["reshaped"],
)
reshaped = reshape_reference_implementation(data, shape)
expect(
node,
inputs=[data, shape],
outputs=[reshaped],
name="test_reshape_" + test_name,
)
Resize#
Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor.
Each dimension value of the output tensor is:
output_dimension = floor(input_dimension * (roi_end - roi_start) * scale)
if input “sizes” is not specified.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- antialias : int (default is 0)
- If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.
- axes : list of ints
- If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.
- coordinate_transformation_mode : string (default is half_pixel)
-
This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let’s describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor,
length_originalas the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input “roi”,scale = length_resized / length_original,if coordinate_transformation_mode is
"half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5if coordinate_transformation_mode is
"pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0if coordinate_transformation_mode is
"align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1)if coordinate_transformation_mode is
"asymmetric",
x_original = x_resized / scaleif coordinate_transformation_mode is
"tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). - cubic_coeff_a : float (default is -0.75)
- The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".
- exclude_outside : int (default is 0)
- If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.
- extrapolation_value : float (default is 0.0)
- When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.
- keep_aspect_ratio_policy : string (default is stretch)
-
This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when
the `scales` input is used.
Given a set of
sizes, associated with a subset ofaxes(explicitly provided or default), and assumingd = axes[i], withibeing the index of the providedsizes.If
keep_aspect_ratio_policyis"stretch", the original aspect ratio is disregarded, and the input is resized to the specified size:
out_size[d] = sizes[i]If
keep_aspect_ratio_policyis"not_larger", the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
scale = Min(sizes[i] / in_size[d])
out_size[d] = round_int(scale * in_size[i])If
keep_aspect_ratio_policyis"not_smaller", the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
scale = Max(sizes[i] / in_size[d])
out_size[d] = round_int(scale * in_size[i])For non-resizable axes (those not specified in
axes), the output size will be equal to the input size.Note:
round_intstands for computing the nearest integer value, rounding halfway cases up. - mode : string (default is nearest)
- Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).
- nearest_mode : string (default is round_prefer_floor)
- Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".
Inputs (1 - 4)#
- X (differentiable) : T1
- N-D tensor
- roi (optional, non-differentiable) : T2
- 1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"
- scales (optional, non-differentiable) : tensor(float)
- The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.
- sizes (optional, non-differentiable) : tensor(int64)
- Target size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value.The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. Only one of 'scales' and 'sizes' can be specified.
Outputs#
- Y (differentiable) : T1
- N-D tensor after resizing
Type Constraints#
- T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input 'X' and output 'Y' to all tensor types.
- T2 : tensor(float16), tensor(float), tensor(double)
- Constrain roi type to float or double.
Examples#
resize_downsample_scales_cubic
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
# [[[[ 1.47119141 2.78125 4.08251953]
# [ 6.71142578 8.02148438 9.32275391]
# [11.91650391 13.2265625 14.52783203]]]]
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_cubic",
)
resize_downsample_scales_cubic_A_n0p5_exclude_outside
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
cubic_coeff_a=-0.5,
exclude_outside=True,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
# [[[[ 1.36812675 2.6695014 4.0133367 ]
# [ 6.57362535 7.875 9.2188353 ]
# [11.94896657 13.25034122 14.59417652]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x, A=-0.5),
scale_factors=scales,
exclude_outside=True,
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_cubic_A_n0p5_exclude_outside",
)
resize_downsample_scales_cubic_align_corners
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
coordinate_transformation_mode="align_corners",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
# [[[[ 1. 2.39519159 3.79038317]
# [ 6.58076634 7.97595793 9.37114951]
# [12.16153268 13.55672427 14.95191585]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_cubic_align_corners",
)
resize_downsample_scales_cubic_antialias
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
antialias=1,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[ 2.5180721 4.2858863]
# [ 9.589329 11.357142 ]]]]
output = interpolate_nd(
data, cubic_coeffs_antialias, scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_cubic_antialias",
)
resize_downsample_scales_linear
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[2.6666665 4.3333331]]]]
output = interpolate_nd(
data, lambda x, _: linear_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_linear",
)
resize_downsample_scales_linear_align_corners
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="align_corners",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[1. 3.142857]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_linear_align_corners",
)
resize_downsample_scales_linear_antialias
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
antialias=1,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[ 2.875 4.5 ]
# [ 9.375 11. ]]]]
output = interpolate_nd(
data, linear_coeffs_antialias, scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_linear_antialias",
)
resize_downsample_scales_nearest
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[1. 3.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_nearest",
)
resize_downsample_sizes_cubic
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="cubic",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 1.63078704 3.00462963 4.37847222]
# [ 7.12615741 8.5 9.87384259]
# [12.62152778 13.99537037 15.36921296]]]]
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_cubic",
)
resize_downsample_sizes_cubic_antialias
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="cubic",
antialias=1,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 1.7750092 3.1200073 4.4650054]
# [ 7.1550016 8.5 9.844998 ]
# [12.534994 13.8799925 15.224991 ]]]]
output = interpolate_nd(data, cubic_coeffs_antialias, output_size=sizes).astype(
np.float32
)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_cubic_antialias",
)
resize_downsample_sizes_linear_antialias
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="linear",
antialias=1,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 2.3636363 3.590909 4.818182 ]
# [ 7.2727275 8.5 9.727273 ]
# [12.181818 13.409091 14.636364 ]]]]
output = interpolate_nd(
data, linear_coeffs_antialias, output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_linear_antialias",
)
resize_downsample_sizes_linear_pytorch_half_pixel
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="pytorch_half_pixel",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 1], dtype=np.int64)
# [[[[ 1.6666666]
# [ 7. ]
# [12.333333 ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
coordinate_transformation_mode="pytorch_half_pixel",
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_linear_pytorch_half_pixel",
)
resize_downsample_sizes_nearest
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 1, 3], dtype=np.int64)
# [[[[1. 2. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_nearest",
)
resize_downsample_sizes_nearest_not_larger
keep_aspect_ratio_policy = "not_larger"
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 3], dtype=np.int64) # Results in 1x2
# [[[[1. 3.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_nearest_not_larger",
)
resize_downsample_sizes_nearest_not_smaller
keep_aspect_ratio_policy = "not_smaller"
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 3], dtype=np.int64) # Results in 2x3
# [[[[1. 2. 4.]
# [5. 6. 8.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_nearest_not_smaller",
)
resize_tf_crop_and_resize
node = onnx.helper.make_node(
"Resize",
inputs=["X", "roi", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="tf_crop_and_resize",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
# Note: for some rois, the result may be different with that of TF for inaccurate floating point
roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 7.6000004 7.9 8.2 ]
# [ 8.8 9.1 9.400001 ]
# [10. 10.3 10.6 ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
coordinate_transformation_mode="tf_crop_and_resize",
).astype(np.float32)
expect(
node,
inputs=[data, roi, sizes],
outputs=[output],
name="test_resize_tf_crop_and_resize",
)
resize_tf_crop_and_resize_axes_2_3
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "roi", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="tf_crop_and_resize",
axes=axes,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
# Note: for some rois, the result may be different with that of TF for inaccurate floating point
roi = np.array([0.4, 0.6, 0.6, 0.8], dtype=np.float32)
sizes = np.array([3, 3], dtype=np.int64)
# [[[[ 7.6000004 7.9 8.2 ]
# [ 8.8 9.1 9.400001 ]
# [10. 10.3 10.6 ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
axes=axes,
coordinate_transformation_mode="tf_crop_and_resize",
).astype(np.float32)
expect(
node,
inputs=[data, roi, sizes],
outputs=[output],
name="test_resize_tf_crop_and_resize_axes_2_3",
)
resize_tf_crop_and_resize_axes_3_2
axes = [3, 2]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "roi", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="tf_crop_and_resize",
axes=axes,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
# Note: for some rois, the result may be different with that of TF for inaccurate floating point
roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32)
sizes = np.array([3, 3], dtype=np.int64)
# [[[[ 7.6000004 7.9 8.2 ]
# [ 8.8 9.1 9.400001 ]
# [10. 10.3 10.6 ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
axes=axes,
coordinate_transformation_mode="tf_crop_and_resize",
).astype(np.float32)
expect(
node,
inputs=[data, roi, sizes],
outputs=[output],
name="test_resize_tf_crop_and_resize_axes_3_2",
)
resize_tf_crop_and_resize_extrapolation_value
node = onnx.helper.make_node(
"Resize",
inputs=["X", "roi", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="tf_crop_and_resize",
extrapolation_value=10.0,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
# Note: for some rois, the result may be different with that of TF for inaccurate floating point
roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 7.6000004 10. 10. ]
# [12.400001 10. 10. ]
# [10. 10. 10. ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
coordinate_transformation_mode="tf_crop_and_resize",
extrapolation_value=10.0,
).astype(np.float32)
expect(
node,
inputs=[data, roi, sizes],
outputs=[output],
name="test_resize_tf_crop_and_resize",
)
resize_upsample_scales_cubic
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[ 0.47265625 0.76953125 1.24609375 1.875 2.28125
# 2.91015625 3.38671875 3.68359375]
# [ 1.66015625 1.95703125 2.43359375 3.0625 3.46875
# 4.09765625 4.57421875 4.87109375]
# [ 3.56640625 3.86328125 4.33984375 4.96875 5.375
# 6.00390625 6.48046875 6.77734375]
# [ 6.08203125 6.37890625 6.85546875 7.484375 7.890625
# 8.51953125 8.99609375 9.29296875]
# [ 7.70703125 8.00390625 8.48046875 9.109375 9.515625
# 10.14453125 10.62109375 10.91796875]
# [10.22265625 10.51953125 10.99609375 11.625 12.03125
# 12.66015625 13.13671875 13.43359375]
# [12.12890625 12.42578125 12.90234375 13.53125 13.9375
# 14.56640625 15.04296875 15.33984375]
# [13.31640625 13.61328125 14.08984375 14.71875 15.125
# 15.75390625 16.23046875 16.52734375]]]]
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_cubic",
)
resize_upsample_scales_cubic_A_n0p5_exclude_outside
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
cubic_coeff_a=-0.5,
exclude_outside=True,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[ 0.55882353 0.81494204 1.35698249 1.89705882 2.39705882
# 2.93713516 3.47917561 3.73529412]
# [ 1.58329755 1.83941606 2.38145651 2.92153285 3.42153285
# 3.96160918 4.50364964 4.75976814]
# [ 3.75145936 4.00757787 4.54961832 5.08969466 5.58969466
# 6.12977099 6.67181144 6.92792995]
# [ 5.91176471 6.16788321 6.70992366 7.25 7.75
# 8.29007634 8.83211679 9.08823529]
# [ 7.91176471 8.16788321 8.70992366 9.25 9.75
# 10.29007634 10.83211679 11.08823529]
# [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534
# 12.45038168 12.99242213 13.24854064]
# [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715
# 14.61854349 15.16058394 15.41670245]
# [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118
# 15.64301751 16.18505796 16.44117647]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x, A=-0.5),
scale_factors=scales,
exclude_outside=True,
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_cubic_A_n0p5_exclude_outside",
)
resize_upsample_scales_cubic_align_corners
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
coordinate_transformation_mode="align_corners",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[ 1. 1.34110787 1.80029155 2.32944606 2.67055394
# 3.19970845 3.65889213 4. ]
# [ 2.36443149 2.70553936 3.16472303 3.69387755 4.03498542
# 4.56413994 5.02332362 5.36443149]
# [ 4.20116618 4.54227405 5.00145773 5.53061224 5.87172012
# 6.40087464 6.86005831 7.20116618]
# [ 6.31778426 6.65889213 7.1180758 7.64723032 7.98833819
# 8.51749271 8.97667638 9.31778426]
# [ 7.68221574 8.02332362 8.48250729 9.01166181 9.35276968
# 9.8819242 10.34110787 10.68221574]
# [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776
# 11.99854227 12.45772595 12.79883382]
# [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245
# 13.83527697 14.29446064 14.63556851]
# [13. 13.34110787 13.80029155 14.32944606 14.67055394
# 15.19970845 15.65889213 16. ]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_cubic_align_corners",
)
resize_upsample_scales_cubic_asymmetric
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
coordinate_transformation_mode="asymmetric",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[ 1. 1.40625 2. 2.5 3. 3.59375 4.
# 4.09375]
# [ 2.625 3.03125 3.625 4.125 4.625 5.21875 5.625
# 5.71875]
# [ 5. 5.40625 6. 6.5 7. 7.59375 8.
# 8.09375]
# [ 7. 7.40625 8. 8.5 9. 9.59375 10.
# 10.09375]
# [ 9. 9.40625 10. 10.5 11. 11.59375 12.
# 12.09375]
# [11.375 11.78125 12.375 12.875 13.375 13.96875 14.375
# 14.46875]
# [13. 13.40625 14. 14.5 15. 15.59375 16.
# 16.09375]
# [13.375 13.78125 14.375 14.875 15.375 15.96875 16.375
# 16.46875]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x, A=-0.75),
scale_factors=scales,
coordinate_transformation_mode="asymmetric",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_cubic_asymmetric",
)
resize_upsample_scales_linear
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[1. 1.25 1.75 2. ]
# [1.5 1.75 2.25 2.5 ]
# [2.5 2.75 3.25 3.5 ]
# [3. 3.25 3.75 4. ]]]]
output = interpolate_nd(
data, lambda x, _: linear_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_linear",
)
resize_upsample_scales_linear_align_corners
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="align_corners",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[1. 1.33333333 1.66666667 2. ]
# [1.66666667 2. 2.33333333 2.66666667]
# [2.33333333 2.66666667 3. 3.33333333]
# [3. 3.33333333 3.66666667 4. ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_linear_align_corners",
)
resize_upsample_scales_nearest
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
# [[[[1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 2. 2. 2.]
# [3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_nearest",
)
resize_upsample_scales_nearest_axes_2_3
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="nearest",
axes=axes,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([2.0, 3.0], dtype=np.float32)
# [[[[1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 2. 2. 2.]
# [3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_nearest_axes_2_3",
)
resize_upsample_scales_nearest_axes_3_2
axes = [3, 2]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="nearest",
axes=axes,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([3.0, 2.0], dtype=np.float32)
# [[[[1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 2. 2. 2.]
# [3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_nearest_axes_3_2",
)
resize_upsample_sizes_cubic
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="cubic",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 9, 10], dtype=np.int64)
# [[[[ 0.45507922 0.64057922 0.97157922 1.42257922 1.90732922
# 2.22332922 2.70807922 3.15907922 3.49007922 3.67557922]
# [ 1.39437963 1.57987963 1.91087963 2.36187963 2.84662963
# 3.16262963 3.64737963 4.09837963 4.42937963 4.61487963]
# [ 2.95130693 3.13680693 3.46780693 3.91880693 4.40355693
# 4.71955693 5.20430693 5.65530693 5.98630693 6.17180693]
# [ 5.20525069 5.39075069 5.72175069 6.17275069 6.65750069
# 6.97350069 7.45825069 7.90925069 8.24025069 8.42575069]
# [ 6.88975 7.07525 7.40625 7.85725 8.342
# 8.658 9.14275 9.59375 9.92475 10.11025 ]
# [ 8.57424931 8.75974931 9.09074931 9.54174931 10.02649931
# 10.34249931 10.82724931 11.27824931 11.60924931 11.79474931]
# [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307
# 12.59644307 13.08119307 13.53219307 13.86319307 14.04869307]
# [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037
# 14.15337037 14.63812037 15.08912037 15.42012037 15.60562037]
# [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078
# 15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]]
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_cubic",
)
resize_upsample_sizes_nearest
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 7, 8], dtype=np.int64)
# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest",
)
resize_upsample_sizes_nearest_axes_2_3
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64)
# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_axes_2_3",
)
resize_upsample_sizes_nearest_axes_3_2
axes = [3, 2]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([8, 7], dtype=np.int64)
# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_axes_3_2",
)
resize_upsample_sizes_nearest_ceil_half_pixel
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
coordinate_transformation_mode="half_pixel",
nearest_mode="ceil",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
# [[[[ 1. 2. 2. 3. 3. 4. 4. 4.]
# [ 5. 6. 6. 7. 7. 8. 8. 8.]
# [ 5. 6. 6. 7. 7. 8. 8. 8.]
# [ 9. 10. 10. 11. 11. 12. 12. 12.]
# [ 9. 10. 10. 11. 11. 12. 12. 12.]
# [13. 14. 14. 15. 15. 16. 16. 16.]
# [13. 14. 14. 15. 15. 16. 16. 16.]
# [13. 14. 14. 15. 15. 16. 16. 16.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x, mode="ceil"), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_ceil_half_pixel",
)
resize_upsample_sizes_nearest_floor_align_corners
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
coordinate_transformation_mode="align_corners",
nearest_mode="floor",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
# [[[[ 1. 1. 1. 2. 2. 3. 3. 4.]
# [ 1. 1. 1. 2. 2. 3. 3. 4.]
# [ 1. 1. 1. 2. 2. 3. 3. 4.]
# [ 5. 5. 5. 6. 6. 7. 7. 8.]
# [ 5. 5. 5. 6. 6. 7. 7. 8.]
# [ 9. 9. 9. 10. 10. 11. 11. 12.]
# [ 9. 9. 9. 10. 10. 11. 11. 12.]
# [13. 13. 13. 14. 14. 15. 15. 16.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x, mode="floor"),
output_size=sizes,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_floor_align_corners",
)
resize_upsample_sizes_nearest_not_larger
keep_aspect_ratio_policy = "not_larger"
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64) # Results in 7x7
# [[[[1. 1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_not_larger",
)
resize_upsample_sizes_nearest_not_smaller
keep_aspect_ratio_policy = "not_smaller"
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64) # Results in 8x8
# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_not_larger",
)
resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
coordinate_transformation_mode="asymmetric",
nearest_mode="round_prefer_ceil",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
# [[[[ 1. 2. 2. 3. 3. 4. 4. 4.]
# [ 5. 6. 6. 7. 7. 8. 8. 8.]
# [ 5. 6. 6. 7. 7. 8. 8. 8.]
# [ 9. 10. 10. 11. 11. 12. 12. 12.]
# [ 9. 10. 10. 11. 11. 12. 12. 12.]
# [13. 14. 14. 15. 15. 16. 16. 16.]
# [13. 14. 14. 15. 15. 16. 16. 16.]
# [13. 14. 14. 15. 15. 16. 16. 16.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x, mode="round_prefer_ceil"),
output_size=sizes,
coordinate_transformation_mode="asymmetric",
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric",
)
ReverseSequence#
Reverse batch of sequences having different lengths specified by sequence_lens.
For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis, and copies elements whose index’s beyond sequence_lens[i] to the output. So the output slice i contains reversed sequences on the first sequence_lens[i] elements, then have original values copied for the other elements.
Example 1: input = [[0.0, 4.0, 8.0, 12.0], [1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0]] sequence_lens = [4, 3, 2, 1] time_axis = 0 batch_axis = 1
output = [[3.0, 6.0, 9.0, 12.0],
[2.0, 5.0, 8.0, 13.0],
[1.0, 4.0, 10.0, 14.0],
[0.0, 7.0, 11.0, 15.0]]
Example 2: input = [[0.0, 1.0, 2.0, 3.0 ], [4.0, 5.0, 6.0, 7.0 ], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0]] sequence_lens = [1, 2, 3, 4] time_axis = 1 batch_axis = 0
output = [[0.0, 1.0, 2.0, 3.0 ],
[5.0, 4.0, 6.0, 7.0 ],
[10.0, 9.0, 8.0, 11.0],
[15.0, 14.0, 13.0, 12.0]]
Version#
This version of the operator has been available since version 10 of the default ONNX operator set.
Attributes#
- batch_axis : int (default is 1)
- (Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.
- time_axis : int (default is 0)
- (Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.
Inputs#
- input : T
- Tensor of rank r >= 2.
- sequence_lens : tensor(int64)
- Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.
Outputs#
- Y : T
- Tensor with same shape of input.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Input and output types can be of any tensor type.
Examples#
reversesequence_batch
node = onnx.helper.make_node(
"ReverseSequence",
inputs=["x", "sequence_lens"],
outputs=["y"],
time_axis=1,
batch_axis=0,
)
x = np.array(
[
[0.0, 1.0, 2.0, 3.0],
[4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0],
[12.0, 13.0, 14.0, 15.0],
],
dtype=np.float32,
)
sequence_lens = np.array([1, 2, 3, 4], dtype=np.int64)
y = np.array(
[
[0.0, 1.0, 2.0, 3.0],
[5.0, 4.0, 6.0, 7.0],
[10.0, 9.0, 8.0, 11.0],
[15.0, 14.0, 13.0, 12.0],
],
dtype=np.float32,
)
expect(
node,
inputs=[x, sequence_lens],
outputs=[y],
name="test_reversesequence_batch",
)
reversesequence_time
node = onnx.helper.make_node(
"ReverseSequence",
inputs=["x", "sequence_lens"],
outputs=["y"],
time_axis=0,
batch_axis=1,
)
x = np.array(
[
[0.0, 4.0, 8.0, 12.0],
[1.0, 5.0, 9.0, 13.0],
[2.0, 6.0, 10.0, 14.0],
[3.0, 7.0, 11.0, 15.0],
],
dtype=np.float32,
)
sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64)
y = np.array(
[
[3.0, 6.0, 9.0, 12.0],
[2.0, 5.0, 8.0, 13.0],
[1.0, 4.0, 10.0, 14.0],
[0.0, 7.0, 11.0, 15.0],
],
dtype=np.float32,
)
expect(
node,
inputs=[x, sequence_lens],
outputs=[y],
name="test_reversesequence_time",
)
RoiAlign#
Region of Interest (RoI) align operation described in the Mask R-CNN paper. RoiAlign consumes an input tensor X and region of interests (rois) to apply pooling across each RoI; it produces a 4-D tensor of shape (num_rois, C, output_height, output_width).
RoiAlign is proposed to avoid the misalignment by removing quantizations while converting from original image into feature map and from feature map into RoI feature; in each ROI bin, the value of the sampled locations are computed directly through bilinear interpolation.
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Other versions of this operator: 10
Attributes#
- coordinate_transformation_mode : string (default is half_pixel)
- Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).
- mode : string (default is avg)
- The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.
- output_height : int (default is 1)
- default 1; Pooled output Y's height.
- output_width : int (default is 1)
- default 1; Pooled output Y's width.
- sampling_ratio : int (default is 0)
- Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.
- spatial_scale : float (default is 1.0)
- Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f.
Inputs#
- X : T1
- Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
- rois : T1
- RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.
- batch_indices : T2
- 1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.
Outputs#
- Y : T1
- RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].
Type Constraints#
- T1 : tensor(float16), tensor(float), tensor(double)
- Constrain types to float tensors.
- T2 : tensor(int64)
- Constrain types to int tensors.
Examples#
roialign_aligned_false
node = onnx.helper.make_node(
"RoiAlign",
inputs=["X", "rois", "batch_indices"],
outputs=["Y"],
spatial_scale=1.0,
output_height=5,
output_width=5,
sampling_ratio=2,
coordinate_transformation_mode="output_half_pixel",
)
X, batch_indices, rois = get_roi_align_input_values()
# (num_rois, C, output_height, output_width)
Y = np.array(
[
[
[
[0.4664, 0.4466, 0.3405, 0.5688, 0.6068],
[0.3714, 0.4296, 0.3835, 0.5562, 0.3510],
[0.2768, 0.4883, 0.5222, 0.5528, 0.4171],
[0.4713, 0.4844, 0.6904, 0.4920, 0.8774],
[0.6239, 0.7125, 0.6289, 0.3355, 0.3495],
]
],
[
[
[0.3022, 0.4305, 0.4696, 0.3978, 0.5423],
[0.3656, 0.7050, 0.5165, 0.3172, 0.7015],
[0.2912, 0.5059, 0.6476, 0.6235, 0.8299],
[0.5916, 0.7389, 0.7048, 0.8372, 0.8893],
[0.6227, 0.6153, 0.7097, 0.6154, 0.4585],
]
],
[
[
[0.2384, 0.3379, 0.3717, 0.6100, 0.7601],
[0.3767, 0.3785, 0.7147, 0.9243, 0.9727],
[0.5749, 0.5826, 0.5709, 0.7619, 0.8770],
[0.5355, 0.2566, 0.2141, 0.2796, 0.3600],
[0.4365, 0.3504, 0.2887, 0.3661, 0.2349],
]
],
],
dtype=np.float32,
)
expect(
node,
inputs=[X, rois, batch_indices],
outputs=[Y],
name="test_roialign_aligned_false",
)
roialign_aligned_true
node = onnx.helper.make_node(
"RoiAlign",
inputs=["X", "rois", "batch_indices"],
outputs=["Y"],
spatial_scale=1.0,
output_height=5,
output_width=5,
sampling_ratio=2,
coordinate_transformation_mode="half_pixel",
)
X, batch_indices, rois = get_roi_align_input_values()
# (num_rois, C, output_height, output_width)
Y = np.array(
[
[
[
[0.5178, 0.3434, 0.3229, 0.4474, 0.6344],
[0.4031, 0.5366, 0.4428, 0.4861, 0.4023],
[0.2512, 0.4002, 0.5155, 0.6954, 0.3465],
[0.3350, 0.4601, 0.5881, 0.3439, 0.6849],
[0.4932, 0.7141, 0.8217, 0.4719, 0.4039],
]
],
[
[
[0.3070, 0.2187, 0.3337, 0.4880, 0.4870],
[0.1871, 0.4914, 0.5561, 0.4192, 0.3686],
[0.1433, 0.4608, 0.5971, 0.5310, 0.4982],
[0.2788, 0.4386, 0.6022, 0.7000, 0.7524],
[0.5774, 0.7024, 0.7251, 0.7338, 0.8163],
]
],
[
[
[0.2393, 0.4075, 0.3379, 0.2525, 0.4743],
[0.3671, 0.2702, 0.4105, 0.6419, 0.8308],
[0.5556, 0.4543, 0.5564, 0.7502, 0.9300],
[0.6626, 0.5617, 0.4813, 0.4954, 0.6663],
[0.6636, 0.3721, 0.2056, 0.1928, 0.2478],
]
],
],
dtype=np.float32,
)
expect(
node,
inputs=[X, rois, batch_indices],
outputs=[Y],
name="test_roialign_aligned_true",
)
Round#
Round takes one input Tensor and rounds the values, element-wise, meaning it finds the nearest integer for each value. In case of halfs, the rule is to round them to the nearest even integer. The output tensor has the same shape and type as the input.
Examples:
round([0.9]) = [1.0]
round([2.5]) = [2.0]
round([2.3]) = [2.0]
round([1.5]) = [2.0]
round([-4.5]) = [-4.0]
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Inputs#
- X (non-differentiable) : T
- Input tensor
Outputs#
- Y (non-differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
round
node = onnx.helper.make_node(
"Round",
inputs=["x"],
outputs=["y"],
)
x = np.array(
[
0.1,
0.5,
0.9,
1.2,
1.5,
1.8,
2.3,
2.5,
2.7,
-1.1,
-1.5,
-1.9,
-2.2,
-2.5,
-2.8,
]
).astype(np.float32)
y = np.array(
[
0.0,
0.0,
1.0,
1.0,
2.0,
2.0,
2.0,
2.0,
3.0,
-1.0,
-2.0,
-2.0,
-2.0,
-2.0,
-3.0,
]
).astype(
np.float32
) # expected output
expect(node, inputs=[x], outputs=[y], name="test_round")
STFT#
Computes the Short-time Fourier Transform of the signal.
Version#
This version of the operator has been available since version 17 of the default ONNX operator set.
Attributes#
- onesided : int (default is 1)
- If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.
Inputs (2 - 4)#
- signal (non-differentiable) : T1
- Input tensor representing a real or complex valued signal. For real input, the following shape is expected: [batch_size][signal_length][1]. For complex input, the following shape is expected: [batch_size][signal_length][2], where [batch_size][signal_length][0] represents the real component and [batch_size][signal_length][1] represents the imaginary component of the signal.
- frame_step (non-differentiable) : T2
- The number of samples to step between successive DFTs.
- window (optional, non-differentiable) : T1
- A tensor representing the window that will be slid over the signal.The window must have rank 1 with shape: [window_shape]. It's an optional value.
- frame_length (optional, non-differentiable) : T2
- A scalar representing the size of the DFT. It's an optional value.
Outputs#
- output (non-differentiable) : T1
- The Short-time Fourier Transform of the signals.If onesided is 1, the output has the shape: [batch_size][frames][dft_unique_bins][2], where dft_unique_bins is frame_length // 2 + 1 (the unique components of the DFT) If onesided is 0, the output has the shape: [batch_size][frames][frame_length][2], where frame_length is the length of the DFT.
Type Constraints#
- T1 : tensor(float), tensor(float16), tensor(double), tensor(bfloat16)
- Constrain signal and output to float tensors.
- T2 : tensor(int32), tensor(int64)
- Constrain scalar length types to int64_t.
Examples#
stft
signal = np.arange(0, 128, dtype=np.float32).reshape(1, 128, 1)
length = np.array(16).astype(np.int64)
onesided_length = (length >> 1) + 1
step = np.array(8).astype(np.int64)
no_window = "" # optional input, not supplied
node = onnx.helper.make_node(
"STFT",
inputs=["signal", "frame_step", no_window, "frame_length"],
outputs=["output"],
)
nstfts = ((signal.shape[1] - length) // step) + 1
# [batch_size][frames][frame_length][2]
output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
for i in range(nstfts):
start = i * step
stop = i * step + length
complex_out = np.fft.fft(signal[0, start:stop, 0])[0:onesided_length]
output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1)
expect(node, inputs=[signal, step, length], outputs=[output], name="test_stft")
node = onnx.helper.make_node(
"STFT",
inputs=["signal", "frame_step", "window"],
outputs=["output"],
)
# Test with window
a0 = 0.5
a1 = 0.5
window = a0 + a1 * np.cos(
2 * 3.1415 * np.arange(0, length, 1, dtype=np.float32) / length
)
nstfts = 1 + (signal.shape[1] - window.shape[0]) // step
# [batch_size][frames][frame_length][2]
output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
for i in range(nstfts):
start = i * step
stop = i * step + length
complex_out = np.fft.fft(signal[0, start:stop, 0] * window)[
0:onesided_length
]
output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1)
expect(
node,
inputs=[signal, step, window],
outputs=[output],
name="test_stft_with_window",
)
Scan#
Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained.
The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation).
Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input.
The scan operation returns the final values of the state_variables as well as the scan_outputs.
The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction.
The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration.
The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero.
The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1.
Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs.
The behavior of
Scan <
num_scan_inputs = m,
body = loop-body,
scan_input_axes = [axis_1, ..., axis_m]
> (init_1, ..., init_n, scan_1, ..., scan_m)
is equivalent to the following pseudo-code:
// scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i
// scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.
sequence_length = scan_1.shape[axis_1];
// initialize state-variables
st_1 = init_1; ... st_n = init_n;
// initialize scan-output variables: [] denotes an empty tensor
scan_out_1 = []; ...; scan_out_k = [];
// identify number of iterations:
// execute loop
for (int t = 0; t < sequence_length; ++t) {
// generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor
// of rank one less than T obtained by indexing T at position t along axis k.
si_1 = scan_1<axis=axis_1>[t];
... ;
si_m = scan_m<axis=axis_m>[t];
// execute loop-body
st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)
// accumulate the scan-output elements
scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);
}
return st_1, ..., st_n, scan_out_1, ..., scan_out_k;
Sample usage: Encoding RNN using a Scan
The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes
values are computed in the outer graph, they need to be passed in as extra state_variables.
graph rnn-encoding {
%H_0 = ...
%X = ...
%Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)
return %Y, %Y_h
}
graph rnn-cell-1 (
%H_tminus1[FLOAT, tensor]
%X_t[FLOAT, tensor]
) {
%Wi = ...
%Ri = ...
%Wbi = ...
%Rbi = ...
%t1 = X_t * (Wi^T)
%t2 = H_tminus1*(Ri^T)
%t3 = Add(%t1, %t2)
%t4 = Add(%t3, %Wbi)
%t5 = Add(%t4, %Rbi)
%Ht = Tanh(%t5)
%Accumulate = Identity(%Ht)
return %Ht, %Accumulate
}
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Attributes#
- body : graph (required)
- The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.
- num_scan_inputs : int (required)
- An attribute specifying the number of scan_inputs M.
- scan_input_axes : list of ints
- An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
- scan_input_directions : list of ints
- An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.
- scan_output_axes : list of ints
- An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].
- scan_output_directions : list of ints
- An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.
Inputs (1 - ∞)#
- initial_state_and_scan_inputs (variadic, heterogeneous) : V
- Initial values of the loop's N state variables followed by M scan_inputs
Outputs (1 - ∞)#
- final_state_and_scan_outputs (variadic, heterogeneous) : V
- Final values of the loop's N state variables followed by K scan_outputs
Type Constraints#
- V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- All Tensor types
Examples#
scan_8
# Given an input sequence [x1, ..., xN], sum up its elements using a scan
# returning the final state (x1+x2+...+xN) as well the scan_output
# [x1, x1+x2, ..., x1+x2+...+xN]
#
# create graph to represent scan body
sum_in = onnx.helper.make_tensor_value_info(
"sum_in", onnx.TensorProto.FLOAT, [2]
)
next = onnx.helper.make_tensor_value_info("next", onnx.TensorProto.FLOAT, [2])
sum_out = onnx.helper.make_tensor_value_info(
"sum_out", onnx.TensorProto.FLOAT, [2]
)
scan_out = onnx.helper.make_tensor_value_info(
"scan_out", onnx.TensorProto.FLOAT, [2]
)
add_node = onnx.helper.make_node(
"Add", inputs=["sum_in", "next"], outputs=["sum_out"]
)
id_node = onnx.helper.make_node(
"Identity", inputs=["sum_out"], outputs=["scan_out"]
)
scan_body = onnx.helper.make_graph(
[add_node, id_node], "scan_body", [sum_in, next], [sum_out, scan_out]
)
# create scan op node
no_sequence_lens = "" # optional input, not supplied
node = onnx.helper.make_node(
"Scan",
inputs=[no_sequence_lens, "initial", "x"],
outputs=["y", "z"],
num_scan_inputs=1,
body=scan_body,
)
# create inputs for batch-size 1, sequence-length 3, inner dimension 2
initial = np.array([0, 0]).astype(np.float32).reshape((1, 2))
x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((1, 3, 2))
# final state computed = [1 + 3 + 5, 2 + 4 + 6]
y = np.array([9, 12]).astype(np.float32).reshape((1, 2))
# scan-output computed
z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((1, 3, 2))
expect(
node,
inputs=[initial, x],
outputs=[y, z],
name="test_scan_sum",
opset_imports=[onnx.helper.make_opsetid("", 8)],
)
scan_9
# Given an input sequence [x1, ..., xN], sum up its elements using a scan
# returning the final state (x1+x2+...+xN) as well the scan_output
# [x1, x1+x2, ..., x1+x2+...+xN]
#
# create graph to represent scan body
sum_in = onnx.helper.make_tensor_value_info(
"sum_in", onnx.TensorProto.FLOAT, [2]
)
next = onnx.helper.make_tensor_value_info("next", onnx.TensorProto.FLOAT, [2])
sum_out = onnx.helper.make_tensor_value_info(
"sum_out", onnx.TensorProto.FLOAT, [2]
)
scan_out = onnx.helper.make_tensor_value_info(
"scan_out", onnx.TensorProto.FLOAT, [2]
)
add_node = onnx.helper.make_node(
"Add", inputs=["sum_in", "next"], outputs=["sum_out"]
)
id_node = onnx.helper.make_node(
"Identity", inputs=["sum_out"], outputs=["scan_out"]
)
scan_body = onnx.helper.make_graph(
[add_node, id_node], "scan_body", [sum_in, next], [sum_out, scan_out]
)
# create scan op node
node = onnx.helper.make_node(
"Scan",
inputs=["initial", "x"],
outputs=["y", "z"],
num_scan_inputs=1,
body=scan_body,
)
# create inputs for sequence-length 3, inner dimension 2
initial = np.array([0, 0]).astype(np.float32).reshape((2,))
x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2))
# final state computed = [1 + 3 + 5, 2 + 4 + 6]
y = np.array([9, 12]).astype(np.float32).reshape((2,))
# scan-output computed
z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2))
expect(
node,
inputs=[initial, x],
outputs=[y, z],
name="test_scan9_sum",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
Scatter (deprecated)#
This operator is deprecated. Please use ScatterElements, which provides the same functionality.
Scatter takes three inputs data, updates, and indices of the same
rank r >= 1 and an optional attribute axis that identifies an axis of data
(by default, the outer-most axis, that is axis 0). The output of the operation
is produced by creating a copy of the input data, and then updating its value
to values specified by updates at specific index positions specified by
indices. Its output shape is the same as the shape of data.
For each entry in updates, the target index in data is obtained by combining
the corresponding entry in indices with the index of the entry itself: the
index-value for dimension = axis is obtained from the value of the corresponding
entry in indices and the index-value for dimension != axis is obtained from the
index of the entry itself.
For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below:
output[indices[i][j]][j] = updates[i][j] if axis = 0,
output[i][indices[i][j]] = updates[i][j] if axis = 1,
This operator is the inverse of GatherElements. It is similar to Torch’s Scatter operation.
Example 1:
data = [
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
]
indices = [
[1, 0, 2],
[0, 2, 1],
]
updates = [
[1.0, 1.1, 1.2],
[2.0, 2.1, 2.2],
]
output = [
[2.0, 1.1, 0.0]
[1.0, 0.0, 2.2]
[0.0, 2.1, 1.2]
]
Example 2:
data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
indices = [[1, 3]]
updates = [[1.1, 2.1]]
axis = 1
output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
Version#
This version of the operator has been deprecated since version 11 of the default ONNX operator set.
Other versions of this operator: 9
Examples#
scatter_with_axis
axis = 1
node = onnx.helper.make_node(
"Scatter",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, 3]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter(data, indices, updates, axis=axis)
# print(y) produces
# [[1.0, 1.1, 3.0, 2.1, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_with_axis",
opset_imports=[helper.make_opsetid("", 10)],
)
scatter_without_axis
node = onnx.helper.make_node(
"Scatter",
inputs=["data", "indices", "updates"],
outputs=["y"],
)
data = np.zeros((3, 3), dtype=np.float32)
indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
y = scatter(data, indices, updates)
# print(y) produces
# [[2.0, 1.1, 0.0],
# [1.0, 0.0, 2.2],
# [0.0, 2.1, 1.2]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_without_axis",
opset_imports=[helper.make_opsetid("", 10)],
)
ScatterElements#
ScatterElements takes three inputs data, updates, and indices of the same
rank r >= 1 and an optional attribute axis that identifies an axis of data
(by default, the outer-most axis, that is axis 0). The output of the operation
is produced by creating a copy of the input data, and then updating its value
to values specified by updates at specific index positions specified by
indices. Its output shape is the same as the shape of data.
For each entry in updates, the target index in data is obtained by combining
the corresponding entry in indices with the index of the entry itself: the
index-value for dimension = axis is obtained from the value of the corresponding
entry in indices and the index-value for dimension != axis is obtained from the
index of the entry itself.
reduction allows specification of an optional reduction operation, which is applied to all values in updates
tensor into output at the specified indices.
In cases where reduction is set to “none”, indices should not have duplicate entries: that is, if idx1 != idx2,
then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update
corresponding to the [i][j] entry is performed as below:
output[indices[i][j]][j] = updates[i][j] if axis = 0,
output[i][indices[i][j]] = updates[i][j] if axis = 1,
When reduction is set to some reduction function f, the update corresponding to the [i][j] entry is performed as below:
output[indices[i][j]][j] += f(output[indices[i][j]][j], updates[i][j]) if axis = 0,
output[i][indices[i][j]] += f(output[i][indices[i][j]], updates[i][j]) if axis = 1,
where the f is +/*/max/min as specified.
This operator is the inverse of GatherElements. It is similar to Torch’s Scatter operation.
(Opset 18 change): Adds max/min to the set of allowed reduction ops.
Example 1:
data = [
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
]
indices = [
[1, 0, 2],
[0, 2, 1],
]
updates = [
[1.0, 1.1, 1.2],
[2.0, 2.1, 2.2],
]
output = [
[2.0, 1.1, 0.0]
[1.0, 0.0, 2.2]
[0.0, 2.1, 1.2]
]
Example 2:
data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
indices = [[1, 3]]
updates = [[1.1, 2.1]]
axis = 1
output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- axis : int (default is 0)
- Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
- reduction : string (default is none)
- Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.
Inputs#
- data (differentiable) : T
- Tensor of rank r >= 1.
- indices (non-differentiable) : Tind
- Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
- updates (differentiable) : T
- Tensor of rank r >=1 (same rank and shape as indices)
Outputs#
- output (differentiable) : T
- Tensor of rank r >= 1 (same rank as input).
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Input and output types can be of any tensor type.
- Tind : tensor(int32), tensor(int64)
- Constrain indices to integer types
Examples#
scatter_elements_with_axis
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, 3]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis)
# print(y) produces
# [[1.0, 1.1, 3.0, 2.1, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_axis",
)
scatter_elements_with_duplicate_indices
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
reduction="add",
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, 1]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis, reduction="add")
# print(y) produces
# [[1.0, 5.2, 3.0, 4.0, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_duplicate_indices",
)
scatter_elements_with_negative_indices
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, -3]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis)
# print(y) produces
# [[1.0, 1.1, 2.1, 4.0, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_negative_indices",
)
scatter_elements_with_reduction_max
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
reduction="max",
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, 1]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis, reduction="max")
# print(y) produces
# [[1.0, 2.1, 3.0, 4.0, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_reduction_max",
)
scatter_elements_with_reduction_min
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
reduction="min",
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, 1]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis, reduction="min")
# print(y) produces
# [[1.0, 1.1, 3.0, 4.0, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_reduction_min",
)
scatter_elements_without_axis
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
)
data = np.zeros((3, 3), dtype=np.float32)
indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
y = scatter_elements(data, indices, updates)
# print(y) produces
# [[2.0, 1.1, 0.0],
# [1.0, 0.0, 2.2],
# [0.0, 2.1, 1.2]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_without_axis",
)
ScatterND#
ScatterND takes three inputs data tensor of rank r >= 1, indices tensor of rank q >= 1,
and updates tensor of rank q + r - indices.shape[-1] - 1. The output of the operation
is produced by creating a copy of the input data, and then updating its value to values
specified by updates at specific index positions specified by indices. Its output shape
is the same as the shape of data.
indices is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of indices.
indices is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into data.
Hence, k can be a value at most the rank of data. When k equals rank(data), each update entry specifies an
update to a single element of the tensor. When k is less than rank(data) each update entry specifies an
update to a slice of the tensor. Index values are allowed to be negative, as per the usual
convention for counting backwards from the end, but are expected in the valid range.
updates is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the
first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape.
The remaining dimensions of updates correspond to the dimensions of the
replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,
corresponding to the trailing (r-k) dimensions of data. Thus, the shape of updates
must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation
of shapes.
The output is calculated via the following equation:
output = np.copy(data)
update_indices = indices.shape[:-1]
for idx in np.ndindex(update_indices):
output[indices[idx]] = updates[idx]
The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order.
reduction allows specification of an optional reduction operation, which is applied to all values in updates
tensor into output at the specified indices.
In cases where reduction is set to “none”, indices should not have duplicate entries: that is, if idx1 != idx2,
then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order.
When reduction is set to some reduction function f, output is calculated as follows:
output = np.copy(data)
update_indices = indices.shape[:-1]
for idx in np.ndindex(update_indices):
output[indices[idx]] = f(output[indices[idx]], updates[idx])
where the f is +/*/max/min as specified.
This operator is the inverse of GatherND.
(Opset 18 change): Adds max/min to the set of allowed reduction ops.
Example 1:
data = [1, 2, 3, 4, 5, 6, 7, 8]
indices = [[4], [3], [1], [7]]
updates = [9, 10, 11, 12]
output = [1, 11, 3, 10, 9, 6, 7, 12]
Example 2:
data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
indices = [[0], [2]]
updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- reduction : string (default is none)
- Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.
Inputs#
- data (differentiable) : T
- Tensor of rank r >= 1.
- indices (non-differentiable) : tensor(int64)
- Tensor of rank q >= 1.
- updates (differentiable) : T
- Tensor of rank q + r - indices_shape[-1] - 1.
Outputs#
- output (differentiable) : T
- Tensor of rank r >= 1.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to any tensor type.
Examples#
scatternd
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [2]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates)
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd",
)
scatternd_add
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
reduction="add",
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [0]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[7, 8, 9, 10], [13, 14, 15, 16], [18, 17, 16, 15], [16, 15, 14, 13]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates, reduction="add")
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd_add",
)
scatternd_max
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
reduction="max",
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [0]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[5, 5, 5, 5], [6, 6, 7, 8], [8, 7, 7, 7], [8, 8 ,8, 8]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates, reduction="max")
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd_max",
)
scatternd_min
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
reduction="min",
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [0]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 3, 2, 1]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates, reduction="min")
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd_min",
)
scatternd_multiply
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
reduction="mul",
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [0]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[5, 10, 15, 20], [60, 72, 84, 96], [168, 147, 126, 105], [128, 96, 64, 32]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates, reduction="mul")
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd_multiply",
)
Selu#
Selu takes one input data (Tensory = gamma * (alpha * e^x - alpha) for x <= 0, y = gamma * x for x > 0,
is applied to the tensor elementwise.
Version#
This version of the operator has been available since version 6 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- alpha : float (default is 1.67326)
- Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).
- gamma : float (default is 1.0507)
- Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
selu
node = onnx.helper.make_node(
"Selu", inputs=["x"], outputs=["y"], alpha=2.0, gamma=3.0
)
x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-3.79272318, 0., 3.]
y = (
np.clip(x, 0, np.inf) * 3.0
+ (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
)
expect(node, inputs=[x], outputs=[y], name="test_selu_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = (
np.clip(x, 0, np.inf) * 3.0
+ (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
)
expect(node, inputs=[x], outputs=[y], name="test_selu")
selu_default
default_alpha = 1.67326319217681884765625
default_gamma = 1.05070102214813232421875
node = onnx.helper.make_node(
"Selu",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = (
np.clip(x, 0, np.inf) * default_gamma
+ (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha * default_gamma
)
expect(node, inputs=[x], outputs=[y], name="test_selu_default")
SequenceAt#
Outputs a tensor copy from the tensor at ‘position’ in ‘input_sequence’.
Accepted range for ‘position’ is in [-n, n - 1], where n is the number of tensors in ‘input_sequence’.
Negative value means counting positions from the back.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Inputs#
- input_sequence : S
- Input sequence.
- position : I
- Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).
Outputs#
- tensor : T
- Output tensor at the specified position in the input sequence.
Type Constraints#
- S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain to any tensor type.
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain to any tensor type.
- I : tensor(int32), tensor(int64)
- Constrain position to integral tensor. It must be a scalar(tensor of empty shape).
SequenceConstruct#
Construct a tensor sequence containing ‘inputs’ tensors. All tensors in ‘inputs’ must have the same data type.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Inputs (1 - ∞)#
- inputs (variadic) : T
- Tensors.
Outputs#
- output_sequence : S
- Sequence enclosing the input tensors.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input types to any tensor type.
- S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain output types to any tensor type.
SequenceEmpty#
Construct an empty tensor sequence, with given data type.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Attributes#
- dtype : int
- (Optional) The data type of the tensors in the output sequence. The default type is 'float'.
Inputs#
Outputs#
- output : S
- Empty sequence.
Type Constraints#
- S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain output types to any tensor type.
SequenceErase#
Outputs a tensor sequence that removes the tensor at ‘position’ from ‘input_sequence’.
Accepted range for ‘position’ is in [-n, n - 1], where n is the number of tensors in ‘input_sequence’.
Negative value means counting positions from the back.
‘position’ is optional, by default it erases the last tensor from ‘input_sequence’.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Inputs (1 - 2)#
- input_sequence : S
- Input sequence.
- position (optional) : I
- Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).
Outputs#
- output_sequence : S
- Output sequence that has the tensor at the specified position removed.
Type Constraints#
- S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain to any tensor type.
- I : tensor(int32), tensor(int64)
- Constrain position to integral tensor. It must be a scalar(tensor of empty shape).
SequenceInsert#
Outputs a tensor sequence that inserts ‘tensor’ into ‘input_sequence’ at ‘position’.
‘tensor’ must have the same data type as ‘input_sequence’.
Accepted range for ‘position’ is in [-n, n], where n is the number of tensors in ‘input_sequence’.
Negative value means counting positions from the back.
‘position’ is optional, by default it inserts ‘tensor’ to the back of ‘input_sequence’.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Inputs (2 - 3)#
- input_sequence : S
- Input sequence.
- tensor : T
- Input tensor to be inserted into the input sequence.
- position (optional) : I
- Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).
Outputs#
- output_sequence : S
- Output sequence that contains the inserted tensor at given position.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain to any tensor type.
- S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain to any tensor type.
- I : tensor(int32), tensor(int64)
- Constrain position to integral tensor. It must be a scalar(tensor of empty shape).
Examples#
sequenceinsert
test_cases = {
"at_back": [np.array([10, 11, 12]).astype(np.int64)],
"at_front": [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)],
}
sequence = [
np.array([1, 2, 3, 4]).astype(np.int64),
np.array([5, 6, 7]).astype(np.int64),
np.array([8, 9]).astype(np.int64),
]
for test_name, test_inputs in test_cases.items():
tensor = test_inputs[0].astype(np.int64)
if len(test_inputs) > 1:
node = onnx.helper.make_node(
"SequenceInsert",
inputs=["sequence", "tensor", "position"],
outputs=["output_sequence"],
)
position = test_inputs[1]
inserted = sequence_insert_reference_implementation(
sequence, tensor, position
)
expect(
node,
inputs=[sequence, tensor, position],
outputs=[inserted],
name="test_sequence_insert_" + test_name,
)
else:
node = onnx.helper.make_node(
"SequenceInsert",
inputs=["sequence", "tensor"],
outputs=["output_sequence"],
)
inserted = sequence_insert_reference_implementation(sequence, tensor)
expect(
node,
inputs=[sequence, tensor],
outputs=[inserted],
name="test_sequence_insert_" + test_name,
)
SequenceLength#
Produces a scalar(tensor of empty shape) containing the number of tensors in ‘input_sequence’.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Inputs#
- input_sequence : S
- Input sequence.
Outputs#
- length : I
- Length of input sequence. It must be a scalar(tensor of empty shape).
Type Constraints#
- S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain to any tensor type.
- I : tensor(int64)
- Constrain output to integral tensor. It must be a scalar(tensor of empty shape).
SequenceMap#
Applies a sub-graph to each sample in the input sequence(s).
Inputs can be either tensors or sequences, with the exception of the first input which must be a sequence. The length of the first input sequence will determine the number of samples in the outputs. Any other sequence inputs should have the same number of samples. The number of inputs and outputs, should match the one of the subgraph.
For each i-th element in the output, a sample will be extracted from the input sequence(s) at the i-th position and the sub-graph will be applied to it. The outputs will contain the outputs of the sub-graph for each sample, in the same order as in the input.
This operator assumes that processing each sample is independent and could executed in parallel or in any order. Users cannot expect any specific ordering in which each subgraph is computed.
Version#
This version of the operator has been available since version 17 of the default ONNX operator set.
Attributes#
- body : graph (required)
- The graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.
Inputs (1 - ∞)#
- input_sequence : S
- Input sequence.
- additional_inputs (variadic, heterogeneous) : V
- Additional inputs to the graph
Outputs (1 - ∞)#
- out_sequence (variadic, heterogeneous) : S
- Output sequence(s)
Type Constraints#
- S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain input types to any sequence type.
- V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128), seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain to any tensor or sequence type.
Examples#
sequence_map_add_1_sequence_1_tensor
body = onnx.helper.make_graph(
[onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"in0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"in1", onnx.TensorProto.FLOAT, ["N"]
),
],
[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
)
x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)]
x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y0 = [x0[i] + x1 for i in range(3)]
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
expect(
node,
inputs=[x0, x1],
outputs=[y0],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_add_1_sequence_1_tensor",
)
sequence_map_add_2_sequences
body = onnx.helper.make_graph(
[onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"in0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"in1", onnx.TensorProto.FLOAT, ["N"]
),
],
[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
)
N = [np.random.randint(1, 10) for _ in range(3)]
x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
y0 = [x0[k] + x1[k] for k in range(3)]
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
expect(
node,
inputs=[x0, x1],
outputs=[y0],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_add_2_sequences",
)
sequence_map_extract_shapes
body = onnx.helper.make_graph(
[onnx.helper.make_node("Shape", ["x"], ["shape"])],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"x", onnx.TensorProto.FLOAT, ["H", "W", "C"]
)
],
[onnx.helper.make_tensor_value_info("shape", onnx.TensorProto.INT64, [3])],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["in_seq"], outputs=["shapes"], body=body
)
shapes = [
np.array([40, 30, 3], dtype=np.int64),
np.array([20, 10, 3], dtype=np.int64),
np.array([10, 5, 3], dtype=np.int64),
]
x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes]
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, ["H", "W", "C"]
)
),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3])
),
]
expect(
node,
inputs=[x0],
outputs=[shapes],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_extract_shapes",
)
sequence_map_identity_1_sequence
body = onnx.helper.make_graph(
[onnx.helper.make_node("Identity", ["in0"], ["out0"])],
"seq_map_body",
[onnx.helper.make_tensor_value_info("in0", onnx.TensorProto.FLOAT, ["N"])],
[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["M"])],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x"], outputs=["y"], body=body
)
x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for _ in range(3)]
y = x
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
expect(
node,
inputs=[x],
outputs=[y],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_identity_1_sequence",
)
sequence_map_identity_1_sequence_1_tensor
body = onnx.helper.make_graph(
[
onnx.helper.make_node("Identity", ["in0"], ["out0"]),
onnx.helper.make_node("Identity", ["in1"], ["out1"]),
],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"in0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"in1", onnx.TensorProto.FLOAT, ["M"]
),
],
[
onnx.helper.make_tensor_value_info(
"out0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"out1", onnx.TensorProto.FLOAT, ["M"]
),
],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
)
x0 = [
np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
for _ in range(3)
]
x1 = np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
y0 = x0
y1 = [x1 for _ in range(3)]
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
),
]
expect(
node,
inputs=[x0, x1],
outputs=[y0, y1],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_identity_1_sequence_1_tensor",
)
sequence_map_identity_2_sequences
body = onnx.helper.make_graph(
[
onnx.helper.make_node("Identity", ["in0"], ["out0"]),
onnx.helper.make_node("Identity", ["in1"], ["out1"]),
],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"in0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"in1", onnx.TensorProto.FLOAT, ["M"]
),
],
[
onnx.helper.make_tensor_value_info(
"out0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"out1", onnx.TensorProto.FLOAT, ["M"]
),
],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
)
x0 = [
np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
for _ in range(3)
]
x1 = [
np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
for _ in range(3)
]
y0 = x0
y1 = x1
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
),
]
expect(
node,
inputs=[x0, x1],
outputs=[y0, y1],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_identity_2_sequences",
)
Shape#
Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor’s shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r-1], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0.
For example: Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4]
Input tensor with shape: [2, 3, 4] start: -1 Output: [4]
Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3]
Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3]
Version#
This version of the operator has been available since version 15 of the default ONNX operator set.
Attributes#
- end : int
- (Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.
- start : int (default is 0)
- (Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.
Inputs#
- data (non-differentiable) : T
- An input tensor.
Outputs#
- shape (non-differentiable) : T1
- Shape of the input tensor
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Input tensor can be of arbitrary type.
- T1 : tensor(int64)
- Constrain output to int64 tensor.
Examples#
shape
x = np.array(
[
[1, 2, 3],
[4, 5, 6],
]
).astype(np.float32)
test_shape("_example", x) # preserve names of original test cases
x = np.random.randn(3, 4, 5).astype(np.float32)
test_shape("", x) # preserve names of original test cases
test_shape("_start_1", x, start=1)
test_shape("_end_1", x, end=1)
test_shape("_start_negative_1", x, start=-1)
test_shape("_end_negative_1", x, end=-1)
test_shape("_start_1_end_negative_1", x, start=1, end=-1)
test_shape("_start_1_end_2", x, start=1, end=2)
test_shape("_clip_start", x, start=-10)
test_shape("_clip_end", x, end=10)
Shrink#
Shrink takes one input data (Tensor
Version#
This version of the operator has been available since version 9 of the default ONNX operator set.
Attributes#
- bias : float (default is 0.0)
- The bias value added to output. Default is 0.
- lambd : float (default is 0.5)
- The lambd value for the Shrink formulation. Default is 0.5.
Inputs#
- input (differentiable) : T
- The input data as Tensor.
Outputs#
- output (differentiable) : T
- The output.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
- Constrain input to only numeric types.
Examples#
hard_shrink
node = onnx.helper.make_node(
"Shrink",
inputs=["x"],
outputs=["y"],
lambd=1.5,
)
X = np.arange(-2.0, 2.1, dtype=np.float32)
Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32)
expect(node, inputs=[X], outputs=[Y], name="test_shrink_hard")
soft_shrink
node = onnx.helper.make_node(
"Shrink",
inputs=["x"],
outputs=["y"],
lambd=1.5,
bias=1.5,
)
X = np.arange(-2.0, 2.1, dtype=np.float32)
Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32)
expect(node, inputs=[X], outputs=[Y], name="test_shrink_soft")
Sigmoid#
Sigmoid takes one input data (Tensor
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
sigmoid
node = onnx.helper.make_node(
"Sigmoid",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = 1.0 / (
1.0 + np.exp(np.negative(x))
) # expected output [0.26894143, 0.5, 0.7310586]
expect(node, inputs=[x], outputs=[y], name="test_sigmoid_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = 1.0 / (1.0 + np.exp(np.negative(x)))
expect(node, inputs=[x], outputs=[y], name="test_sigmoid")
Sign#
Calculate the sign of the given input tensor element-wise. If input > 0, output 1. if input < 0, output -1. if input == 0, output 0.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 9
Inputs#
- input (non-differentiable) : T
- Input tensor
Outputs#
- output (non-differentiable) : T
- The sign of the input tensor computed element-wise. It has the same shape and type of the input.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
sign
node = onnx.helper.make_node(
"Sign",
inputs=["x"],
outputs=["y"],
)
x = np.array(range(-5, 6)).astype(np.float32)
y = np.sign(x)
expect(node, inputs=[x], outputs=[y], name="test_sign")
Sin#
Calculates the sine of the given input tensor, element-wise.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The sine of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
sin
node = onnx.helper.make_node(
"Sin",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.sin(x)
expect(node, inputs=[x], outputs=[y], name="test_sin_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.sin(x)
expect(node, inputs=[x], outputs=[y], name="test_sin")
Sinh#
Calculates the hyperbolic sine of the given input tensor element-wise.
Version#
This version of the operator has been available since version 9 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The hyperbolic sine values of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
sinh
node = onnx.helper.make_node(
"Sinh",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.sinh(x) # expected output [-1.17520118, 0., 1.17520118]
expect(node, inputs=[x], outputs=[y], name="test_sinh_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.sinh(x)
expect(node, inputs=[x], outputs=[y], name="test_sinh")
Size#
Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 1
Inputs#
- data (non-differentiable) : T
- An input tensor.
Outputs#
- size (non-differentiable) : T1
- Total number of elements of the input tensor
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Input tensor can be of arbitrary type.
- T1 : tensor(int64)
- Constrain output to int64 tensor, which should be a scalar though.
Examples#
size
node = onnx.helper.make_node(
"Size",
inputs=["x"],
outputs=["y"],
)
x = np.array(
[
[1, 2, 3],
[4, 5, 6],
]
).astype(np.float32)
y = np.array(6).astype(np.int64)
expect(node, inputs=[x], outputs=[y], name="test_size_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.array(x.size).astype(np.int64)
expect(node, inputs=[x], outputs=[y], name="test_size")
Slice#
Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding
Slice uses the starts, ends, axes and steps inputs to select a sub-tensor
of its input data tensor.
An effective start[i], end[i], and step[i] must be computed for each i
in [0, ... r-1] where r = rank(input) as follows:
If axes are omitted, they are set to [0, ..., r-1].
If steps are omitted, they are set to [1, ..., 1] of length len(starts)
The effective values are initialized as start[i] = 0, end[i] = dims[i] where
dims are the dimensions of input and step[i] = 1.
All negative elements of axes are made non-negatve by adding r to them, where
r =rank(input).
All negative values in starts[i] and ends[i] have dims[axes[i]] added to them,
where dims are the dimensions of input. Then start[axes[i]] is the adjusted
starts[i] is clamped into the range [0, dims[axes[i]]] for positive stepping
and [0, dims[axes[i]]-1] for negative stepping.
The clamping for the adjusted ends[i] depends on the sign of steps[i] and must
accommodate copying 0 through dims[axes[i]] elements, so for positive stepping
end[axes[i]] is clamped to [0, dims[axes[i]]], while for negative stepping it
is clamped to [-1, dims[axes[i]]-1].
Finally, step[axes[i]] = steps[i].
For slicing to the end of a dimension with unknown size, it is recommended to pass
in INT_MAX when slicing forward and ‘INT_MIN’ when slicing backward.
Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ]
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs (3 - 5)#
- data (differentiable) : T
- Tensor of data to extract slices from.
- starts (non-differentiable) : Tind
- 1-D tensor of starting indices of corresponding axis in `axes`
- ends (non-differentiable) : Tind
- 1-D tensor of ending indices (exclusive) of corresponding axis in `axes`
- axes (optional, non-differentiable) : Tind
- 1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated.
- steps (optional, non-differentiable) : Tind
- 1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1s.
Outputs#
- output (differentiable) : T
- Sliced data tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
- Tind : tensor(int32), tensor(int64)
- Constrain indices to integer types
Examples#
slice
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
y = x[0:3, 0:10]
starts = np.array([0, 0], dtype=np.int64)
ends = np.array([3, 10], dtype=np.int64)
axes = np.array([0, 1], dtype=np.int64)
steps = np.array([1, 1], dtype=np.int64)
expect(
node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice"
)
slice_default_axes
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0, 0, 3], dtype=np.int64)
ends = np.array([20, 10, 4], dtype=np.int64)
y = x[:, :, 3:4]
expect(
node, inputs=[x, starts, ends], outputs=[y], name="test_slice_default_axes"
)
slice_default_steps
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0, 0, 3], dtype=np.int64)
ends = np.array([20, 10, 4], dtype=np.int64)
axes = np.array([0, 1, 2], dtype=np.int64)
y = x[:, :, 3:4]
expect(
node,
inputs=[x, starts, ends, axes],
outputs=[y],
name="test_slice_default_steps",
)
slice_end_out_of_bounds
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([1], dtype=np.int64)
ends = np.array([1000], dtype=np.int64)
axes = np.array([1], dtype=np.int64)
steps = np.array([1], dtype=np.int64)
y = x[:, 1:1000]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_end_out_of_bounds",
)
slice_neg
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0], dtype=np.int64)
ends = np.array([-1], dtype=np.int64)
axes = np.array([1], dtype=np.int64)
steps = np.array([1], dtype=np.int64)
y = x[:, 0:-1]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_neg",
)
slice_neg_steps
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([20, 10, 4], dtype=np.int64)
ends = np.array([0, 0, 1], dtype=np.int64)
axes = np.array([0, 1, 2], dtype=np.int64)
steps = np.array([-1, -3, -2]).astype(np.int64)
y = x[20:0:-1, 10:0:-3, 4:1:-2]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_neg_steps",
)
slice_negative_axes
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0, 0, 3], dtype=np.int64)
ends = np.array([20, 10, 4], dtype=np.int64)
axes = np.array([0, -2, -1], dtype=np.int64)
y = x[:, :, 3:4]
expect(
node,
inputs=[x, starts, ends, axes],
outputs=[y],
name="test_slice_negative_axes",
)
slice_start_out_of_bounds
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([1000], dtype=np.int64)
ends = np.array([1000], dtype=np.int64)
axes = np.array([1], dtype=np.int64)
steps = np.array([1], dtype=np.int64)
y = x[:, 1000:1000]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_start_out_of_bounds",
)
Softmax#
The operator computes the normalized exponential values for the given input:
Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1)
The “axis” attribute indicates the dimension along which Softmax will be performed. The output tensor has the same shape and contains the Softmax values of the corresponding input.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Attributes#
- axis : int (default is -1)
- Describes the dimension Softmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
Inputs#
- input (differentiable) : T
- The input tensor of rank >= axis.
Outputs#
- output (differentiable) : T
- The output values with the same shape as the input tensor.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
softmax
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
)
x = np.array([[-1, 0, 1]]).astype(np.float32)
# expected output [[0.09003058, 0.24472848, 0.66524094]]
y = softmax(x, axis=1)
expect(node, inputs=[x], outputs=[y], name="test_softmax_example")
softmax_axis
x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32)
# expected output
# [[0.032058604 0.08714432 0.23688284 0.6439143 ]
# [0.032058604 0.08714432 0.23688284 0.6439143 ]]
y = softmax(x)
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_softmax_large_number")
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
axis=0,
)
y = softmax(x, axis=0)
expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_0")
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
axis=1,
)
y = softmax(x, axis=1)
expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_1")
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
axis=2,
)
y = softmax(x, axis=2)
expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_2")
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
axis=-1,
)
y = softmax(x, axis=-1)
expect(node, inputs=[x], outputs=[y], name="test_softmax_negative_axis")
# default axis is -1
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_softmax_default_axis")
SoftmaxCrossEntropyLoss#
Loss function that measures the softmax cross entropy between ‘scores’ and ‘labels’. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, …, l_N). If the input is N-D tensor with shape (N, C, D1, D2, …, Dk), the loss tensor L may have (N, D1, D2, …, Dk) as its shape and L[i,][j_1][j_2]…[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator.
shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,…, Dk), with K >= 1 in case of K-dimensional loss. shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,…, Dk), with K >= 1 in case of K-dimensional loss.
The loss for one sample, l_i, can caculated as follows: l[i][d1][d2]…[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes. or l[i][d1][d2]…[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if ‘weights’ is provided.
loss is zero for the case when label-value equals ignore_index. l[i][d1][d2]…[dk] = 0, when labels[n][d1][d2]…[dk] = ignore_index
where: p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]…[dk]
Finally, L is optionally reduced: If reduction = ‘none’, the output is L with shape (N, D1, D2, …, Dk). If reduction = ‘sum’, the output is scalar: Sum(L). If reduction = ‘mean’, the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W), where tensor W is of shape (N, D1, D2, …, Dk) and W[n][d1][d2]…[dk] = weights[labels[i][d1][d2]…[dk]].
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 12
Attributes#
- ignore_index : int
- Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.
- reduction : string (default is mean)
- Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.
Inputs (2 - 3)#
- scores (differentiable) : T
- The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.
- labels (non-differentiable) : Tind
- The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.
- weights (optional, non-differentiable) : T
- A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.
Outputs (1 - 2)#
- output (differentiable) : T
- Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.
- log_prob (optional, differentiable) : T
- Log probability tensor. If the output of softmax is prob, its value is log(prob).
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
- Tind : tensor(int32), tensor(int64)
- Constrain target to integer types
Examples#
input_shape_is_NCd1_mean_weight_negative_ii
reduction = "mean"
ignore_index = np.int64(-1)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1 = 3, 5, 6
np.random.seed(0)
x = np.random.rand(N, C, dim1).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
labels[0][0] = -1
weight = np.random.rand(C).astype(np.float32)
sce = softmaxcrossentropy(
x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[x, labels, weight],
outputs=[sce],
name="test_sce_NCd1_mean_weight_negative_ii",
)
input_shape_is_NCd1_mean_weight_negative_ii_log_prob
reduction = "mean"
ignore_index = np.int64(-1)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1 = 3, 5, 6
np.random.seed(0)
x = np.random.rand(N, C, dim1).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
labels[0][0] = -1
weight = np.random.rand(C).astype(np.float32)
loss, log_prob = softmaxcrossentropy(
x,
labels,
weight=weight,
reduction=reduction,
ignore_index=ignore_index,
get_log_prob=True,
)
expect(
node,
inputs=[x, labels, weight],
outputs=[loss, log_prob],
name="test_sce_NCd1_mean_weight_negative_ii_log_prob",
)
input_shape_is_NCd1d2d3_none_no_weight_negative_ii
reduction = "none"
ignore_index = np.int64(-5)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
np.int64
)
labels[0][0][0][0] = -5
sce = softmaxcrossentropy(
x, labels, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[x, labels],
outputs=[sce],
name="test_sce_NCd1d2d3_none_no_weight_negative_ii",
)
input_shape_is_NCd1d2d3_none_no_weight_negative_ii_log_prob
reduction = "none"
ignore_index = np.int64(-5)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
np.int64
)
labels[0][0][0][0] = -5
loss, log_prob = softmaxcrossentropy(
x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True
)
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob",
)
input_shape_is_NCd1d2d3_sum_weight_high_ii
reduction = "sum"
ignore_index = np.int64(10)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C = 3, 5
np.random.seed(0)
x = np.random.rand(N, C).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
labels[0] = 10
weight = np.random.rand(C).astype(np.float32)
sce = softmaxcrossentropy(
x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[x, labels, weight],
outputs=[sce],
name="test_sce_NCd1d2d3_sum_weight_high_ii",
)
input_shape_is_NCd1d2d3_sum_weight_high_ii_log_prob
reduction = "sum"
ignore_index = np.int64(10)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C = 3, 5
np.random.seed(0)
x = np.random.rand(N, C).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
labels[0] = 10
weight = np.random.rand(C).astype(np.float32)
loss, log_prob = softmaxcrossentropy(
x,
labels,
weight=weight,
reduction=reduction,
ignore_index=ignore_index,
get_log_prob=True,
)
expect(
node,
inputs=[x, labels, weight],
outputs=[loss, log_prob],
name="test_sce_NCd1d2d3_sum_weight_high_ii_log_prob",
)
input_shape_is_NCd1d2d3d4d5_mean_weight
reduction = "mean"
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
labels = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
sce = softmaxcrossentropy(x, labels, weight=weight, reduction=reduction)
expect(
node,
inputs=[x, labels, weight],
outputs=[sce],
name="test_sce_NCd1d2d3d4d5_mean_weight",
)
input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob
reduction = "mean"
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
labels = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weight, reduction=reduction, get_log_prob=True
)
expect(
node,
inputs=[x, labels, weight],
outputs=[loss, log_prob],
name="test_sce_NCd1d2d3d4d5_mean_weight_log_prob",
)
input_shape_is_NCd1d2d3d4d5_none_no_weight
reduction = "none"
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
labels = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
sce = softmaxcrossentropy(x, labels, reduction=reduction)
expect(
node,
inputs=[x, labels],
outputs=[sce],
name="test_sce_NCd1d2d3d4d5_none_no_weight",
)
input_shape_is_NCd1d2d3d4d5_none_no_weight_log_prob
reduction = "none"
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
labels = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
loss, log_prob = softmaxcrossentropy(
x, labels, reduction=reduction, get_log_prob=True
)
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_NCd1d2d3d4d5_none_no_weight_log_prob",
)
softmaxcrossentropy_mean
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels)
# Check results
expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_mean")
softmaxcrossentropy_mean_3d
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, y)
# Check results
expect(node, inputs=[x, y], outputs=[sce], name="test_sce_mean_3d")
softmaxcrossentropy_mean_3d_log_prob
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(x, y, get_log_prob=True)
# Check results
expect(
node,
inputs=[x, y],
outputs=[loss, log_prob],
name="test_sce_mean_3d_log_prob",
)
softmaxcrossentropy_mean_log_prob
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(x, labels, get_log_prob=True)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_mean_log_prob",
)
softmaxcrossentropy_mean_no_weights_ii
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
labels[0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
# Check results
expect(
node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii"
)
softmaxcrossentropy_mean_no_weights_ii_3d
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
labels[0][0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[sce],
name="test_sce_mean_no_weight_ii_3d",
)
softmaxcrossentropy_mean_no_weights_ii_3d_log_prob
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
labels[0][0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_mean_no_weight_ii_3d_log_prob",
)
softmaxcrossentropy_mean_no_weights_ii_4d
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2, 7).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
labels[0][0][0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(
x, labels, reduction=reduction, ignore_index=ignore_index
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[sce],
name="test_sce_mean_no_weight_ii_4d",
)
softmaxcrossentropy_mean_no_weights_ii_4d_log_prob
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2, 7).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
labels[0][0][0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_mean_no_weight_ii_4d_log_prob",
)
softmaxcrossentropy_mean_no_weights_ii_log_prob
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
labels[0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_mean_no_weight_ii_log_prob",
)
softmaxcrossentropy_mean_weights
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, weight=weights)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_mean_weight",
)
softmaxcrossentropy_mean_weights_ii
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(0)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
labels[0] = np.int64(0)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_mean_weight_ii",
)
softmaxcrossentropy_mean_weights_ii_3d
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(1)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
labels[0][0] = np.int64(1)
weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_mean_weight_ii_3d",
)
softmaxcrossentropy_mean_weights_ii_3d_log_prob
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(1)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
labels[0][0] = np.int64(1)
weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_mean_weight_ii_3d_log_prob",
)
softmaxcrossentropy_mean_weights_ii_4d
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2, 7).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
labels[0][0][0] = np.int64(2)
weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(
x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_mean_weight_ii_4d",
)
softmaxcrossentropy_mean_weights_ii_4d_log_prob
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2, 7).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
labels[0][0][0] = np.int64(2)
weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x,
labels,
reduction=reduction,
weight=weights,
ignore_index=ignore_index,
get_log_prob=True,
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_mean_weight_ii_4d_log_prob",
)
softmaxcrossentropy_mean_weights_ii_log_prob
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(0)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
labels[0] = np.int64(0)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_mean_weight_ii_log_prob",
)
softmaxcrossentropy_mean_weights_log_prob
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weights, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_mean_weight_log_prob",
)
softmaxcrossentropy_none
# Define operator attributes.
reduction = "none"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, reduction="none")
# Check results
expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_none")
softmaxcrossentropy_none_log_prob
# Define operator attributes.
reduction = "none"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, reduction="none", get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_none_log_prob",
)
softmaxcrossentropy_none_weights
# Define operator attributes.
reduction = "none"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, weight=weights, reduction="none")
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_none_weights",
)
softmaxcrossentropy_none_weights_log_prob
# Define operator attributes.
reduction = "none"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weights, reduction="none", get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_none_weights_log_prob",
)
softmaxcrossentropy_sum
# Define operator attributes.
reduction = "sum"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, reduction="sum")
# Check results
expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_sum")
softmaxcrossentropy_sum_log_prob
# Define operator attributes.
reduction = "sum"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, reduction="sum", get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_sum_log_prob",
)
Softplus#
Softplus takes one input data (Tensor
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- 1D input tensor
Outputs#
- Y (differentiable) : T
- 1D input tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
softplus
node = onnx.helper.make_node(
"Softplus",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.log(
np.exp(x) + 1
) # expected output [0.31326166, 0.69314718, 1.31326163]
expect(node, inputs=[x], outputs=[y], name="test_softplus_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.log(np.exp(x) + 1)
expect(node, inputs=[x], outputs=[y], name="test_softplus")
Softsign#
Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise.
Version#
This version of the operator has been available since version 1 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The softsign (x/(1+|x|)) values of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
softsign
node = onnx.helper.make_node(
"Softsign",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.array([-0.5, 0, 0.5]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_softsign_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = x / (1 + np.abs(x))
expect(node, inputs=[x], outputs=[y], name="test_softsign")
SpaceToDepth#
SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- blocksize : int (required)
- Blocks of [blocksize, blocksize] are moved.
Inputs#
- input (differentiable) : T
- Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
Outputs#
- output (differentiable) : T
- Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Examples#
example
node = onnx.helper.make_node(
"SpaceToDepth",
inputs=["x"],
outputs=["y"],
blocksize=2,
)
# (1, 1, 4, 6) input tensor
x = np.array(
[
[
[
[0, 6, 1, 7, 2, 8],
[12, 18, 13, 19, 14, 20],
[3, 9, 4, 10, 5, 11],
[15, 21, 16, 22, 17, 23],
]
]
]
).astype(np.float32)
# (1, 4, 2, 3) output tensor
y = np.array(
[
[
[[0, 1, 2], [3, 4, 5]],
[[6, 7, 8], [9, 10, 11]],
[[12, 13, 14], [15, 16, 17]],
[[18, 19, 20], [21, 22, 23]],
]
]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_spacetodepth_example")
spacetodepth
b, c, h, w = shape = (2, 2, 6, 6)
blocksize = 2
node = onnx.helper.make_node(
"SpaceToDepth",
inputs=["x"],
outputs=["y"],
blocksize=blocksize,
)
x = np.random.random_sample(shape).astype(np.float32)
tmp = np.reshape(
x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize]
)
tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize])
expect(node, inputs=[x], outputs=[y], name="test_spacetodepth")
Split#
Split a tensor into a list of tensors, along the specified ‘axis’.
Either input ‘split’ or the attribute ‘num_outputs’ should be specified, but not both.
If the attribute ‘num_outputs’ is specified, then the tensor is split into equal sized parts.
If the tensor is not evenly splittable into num_outputs, the last chunk will be smaller.
If the input ‘split’ is specified, it indicates the sizes of each output in the split.
Version#
This version of the operator has been available since version 18 of the default ONNX operator set.
Attributes#
- axis : int (default is 0)
- Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).
- num_outputs : int
- Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.
Inputs (1 - 2)#
- input (differentiable) : T
- The tensor to split
- split (optional, non-differentiable) : tensor(int64)
- Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.
Outputs (1 - ∞)#
- outputs (variadic, differentiable) : T
- One or more outputs forming list of tensors after splitting
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Examples#
1d_opset13
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=0,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_1d_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=0,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_1d_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
1d_opset18
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=0,
num_outputs=3,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_1d_opset18",
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=0,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_1d_opset18",
)
1d_uneven_split_opset18
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]).astype(np.float32)
# If axis is not specified, split is applied on default axis 0
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3", "output_4"],
num_outputs=4,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
np.array([7.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_1d_uneven_split_opset18",
)
2d_opset13
node_input = np.array(
[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]]
).astype(np.float32)
node = onnx.helper.make_node(
"Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1
)
expected_outputs = [
np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32),
np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_2d_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=1,
)
expected_outputs = [
np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32),
np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype(
np.float32
),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_2d_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
2d_opset18
node_input = np.array(
[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]]
).astype(np.float32)
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2"],
axis=1,
num_outputs=2,
)
expected_outputs = [
np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32),
np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_2d",
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=1,
)
expected_outputs = [
np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32),
np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype(
np.float32
),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_2d_opset18",
)
2d_uneven_split_opset18
node_input = np.array(
[
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0],
]
).astype(np.float32)
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=1,
num_outputs=3,
)
expected_outputs = [
np.array([[1.0, 2.0, 3.0], [9.0, 10.0, 11.0]]).astype(np.float32),
np.array([[4.0, 5.0, 6.0], [12.0, 13.0, 14.0]]).astype(np.float32),
np.array([[7.0, 8.0], [15.0, 16.0]]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_2d_uneven_split_opset18",
)
default_values_opset13
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
# If axis is not specified, split is applied on default axis 0
node = onnx.helper.make_node(
"Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"]
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_default_axis_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split", inputs=["input", "split"], outputs=["output_1", "output_2"]
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_default_axis_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
default_values_opset18
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
# If axis is not specified, split is applied on default axis 0
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
num_outputs=3,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_default_axis_opset18",
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split", inputs=["input", "split"], outputs=["output_1", "output_2"]
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_default_axis_opset18",
)
zero_size_splits_opset13
# 1-dimensional tensor with dimension_size=0
node_input = np.array([]).astype(np.float32)
# Split emtpy tensor to tensors of size zero
split = np.array([0, 0, 0]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2", "output_3"],
)
expected_outputs = [
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_zero_size_splits_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
zero_size_splits_opset18
# 1-dimensional tensor with dimension_size=0
node_input = np.array([]).astype(np.float32)
# Split emtpy tensor to tensors of size zero
split = np.array([0, 0, 0]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2", "output_3"],
)
expected_outputs = [
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_zero_size_splits_opset18",
)
SplitToSequence#
Split a tensor into a sequence of tensors, along the specified ‘axis’. Lengths of the parts can be specified using argument ‘split’. ‘split’ must contain only positive numbers. ‘split’ is either a scalar (tensor of empty shape), or a 1-D tensor. If ‘split’ is a scalar, then ‘input’ will be split into equally sized chunks(if possible). Last chunk will be smaller if the ‘input’ size along the given axis ‘axis’ is not divisible by ‘split’. Otherwise, the tensor is split into ‘size(split)’ chunks, with lengths of the parts on ‘axis’ specified in ‘split’. In this scenario, the sum of entries in ‘split’ must be equal to the dimension size of input tensor on ‘axis’.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Attributes#
- axis : int (default is 0)
- Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].
- keepdims : int (default is 1)
- Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.
Inputs (1 - 2)#
- input : T
- The tensor to split
- split (optional) : I
- Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0.
Outputs#
- output_sequence : S
- One or more outputs forming a sequence of tensors after splitting
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input types to all tensor types.
- I : tensor(int32), tensor(int64)
- Constrain split size to integral tensor.
- S : seq(tensor(uint8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(int8)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(float16)), seq(tensor(float)), seq(tensor(double)), seq(tensor(string)), seq(tensor(bool)), seq(tensor(complex64)), seq(tensor(complex128))
- Constrain output types to all tensor types.
Sqrt#
Square root takes one input data (Tensor
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
sqrt
node = onnx.helper.make_node(
"Sqrt",
inputs=["x"],
outputs=["y"],
)
x = np.array([1, 4, 9]).astype(np.float32)
y = np.sqrt(x) # expected output [1., 2., 3.]
expect(node, inputs=[x], outputs=[y], name="test_sqrt_example")
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
y = np.sqrt(x)
expect(node, inputs=[x], outputs=[y], name="test_sqrt")
Squeeze#
Remove single-dimensional entries from the shape of a tensor.
Takes an input axes with a list of axes to squeeze.
If axes is not provided, all the single dimensions will be removed from
the shape. If an axis is selected with shape entry not equal to one, an error is raised.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs (1 - 2)#
- data (differentiable) : T
- Tensors with at least max(dims) dimensions.
- axes (optional, non-differentiable) : tensor(int64)
- List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
Outputs#
- squeezed (differentiable) : T
- Reshaped tensor with same data as input.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Examples#
squeeze
node = onnx.helper.make_node(
"Squeeze",
inputs=["x", "axes"],
outputs=["y"],
)
x = np.random.randn(1, 3, 4, 5).astype(np.float32)
axes = np.array([0], dtype=np.int64)
y = np.squeeze(x, axis=0)
expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze")
squeeze_negative_axes
node = onnx.helper.make_node(
"Squeeze",
inputs=["x", "axes"],
outputs=["y"],
)
x = np.random.randn(1, 3, 1, 5).astype(np.float32)
axes = np.array([-2], dtype=np.int64)
y = np.squeeze(x, axis=-2)
expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze_negative_axes")
StringNormalizer#
StringNormalization performs string operations for basic cleaning. This operator has only one input (denoted by X) and only one output (denoted by Y). This operator first examines the elements in the X, and removes elements specified in “stopwords” attribute. After removing stop words, the intermediate result can be further lowercased, uppercased, or just returned depending the “case_change_action” attribute. This operator only accepts [C]- and [1, C]-tensor. If all elements in X are dropped, the output will be the empty value of string tensor with shape [1] if input shape is [C] and shape [1, 1] if input shape is [1, C].
Version#
This version of the operator has been available since version 10 of the default ONNX operator set.
Attributes#
- case_change_action : string (default is NONE)
- string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"
- is_case_sensitive : int (default is 0)
- Boolean. Whether the identification of stop words in X is case-sensitive. Default is false
- locale : string
- Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.
- stopwords : list of strings
- List of stop words. If not set, no word would be removed from X.
Inputs#
- X : tensor(string)
- UTF-8 strings to normalize
Outputs#
- Y : tensor(string)
- UTF-8 Normalized strings
Type Constraints#
Examples#
monday_casesensintive_lower
input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
output = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="LOWER",
is_case_sensitive=1,
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_casesensintive_lower",
)
monday_casesensintive_nochangecase
input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
output = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
is_case_sensitive=1,
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_casesensintive_nochangecase",
)
monday_casesensintive_upper
input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
output = np.array(["TUESDAY", "WEDNESDAY", "THURSDAY"]).astype(object)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="UPPER",
is_case_sensitive=1,
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_casesensintive_upper",
)
monday_empty_output
input = np.array(["monday", "monday"]).astype(object)
output = np.array([""]).astype(object)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="UPPER",
is_case_sensitive=1,
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_empty_output",
)
monday_insensintive_upper_twodim
input = (
np.array(
["Monday", "tuesday", "wednesday", "Monday", "tuesday", "wednesday"]
)
.astype(object)
.reshape([1, 6])
)
# It does upper case cecedille, accented E
# and german umlaut but fails
# with german eszett
output = (
np.array(["TUESDAY", "WEDNESDAY", "TUESDAY", "WEDNESDAY"])
.astype(object)
.reshape([1, 4])
)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="UPPER",
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_insensintive_upper_twodim",
)
nostopwords_nochangecase
input = np.array(["monday", "tuesday"]).astype(object)
output = input
# No stopwords. This is a NOOP
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
is_case_sensitive=1,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_nostopwords_nochangecase",
)
Sub#
Performs element-wise binary subtraction (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Inputs#
- A (differentiable) : T
- First operand.
- B (differentiable) : T
- Second operand.
Outputs#
- C (differentiable) : T
- Result, has same element type as two inputs
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to all numeric tensors.
Examples#
sub
node = onnx.helper.make_node(
"Sub",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([3, 2, 1]).astype(np.float32)
z = x - y # expected output [-2., 0., 2.]
expect(node, inputs=[x, y], outputs=[z], name="test_sub_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = x - y
expect(node, inputs=[x, y], outputs=[z], name="test_sub")
x = np.random.randint(12, 24, size=(3, 4, 5), dtype=np.uint8)
y = np.random.randint(12, size=(3, 4, 5), dtype=np.uint8)
z = x - y
expect(node, inputs=[x, y], outputs=[z], name="test_sub_uint8")
sub_broadcast
node = onnx.helper.make_node(
"Sub",
inputs=["x", "y"],
outputs=["z"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = x - y
expect(node, inputs=[x, y], outputs=[z], name="test_sub_bcast")
Sum#
Element-wise sum of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs (1 - ∞)#
- data_0 (variadic, differentiable) : T
- List of tensors for sum.
Outputs#
- sum (differentiable) : T
- Output tensor.
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
sum
data_0 = np.array([3, 0, 2]).astype(np.float32)
data_1 = np.array([1, 3, 4]).astype(np.float32)
data_2 = np.array([2, 6, 6]).astype(np.float32)
result = np.array([6, 9, 12]).astype(np.float32)
node = onnx.helper.make_node(
"Sum",
inputs=["data_0", "data_1", "data_2"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1, data_2],
outputs=[result],
name="test_sum_example",
)
node = onnx.helper.make_node(
"Sum",
inputs=["data_0"],
outputs=["result"],
)
expect(node, inputs=[data_0], outputs=[data_0], name="test_sum_one_input")
result = np.add(data_0, data_1)
node = onnx.helper.make_node(
"Sum",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node, inputs=[data_0, data_1], outputs=[result], name="test_sum_two_inputs"
)
Tan#
Calculates the tangent of the given input tensor, element-wise.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The tangent of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
tan
node = onnx.helper.make_node(
"Tan",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.tan(x)
expect(node, inputs=[x], outputs=[y], name="test_tan_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.tan(x)
expect(node, inputs=[x], outputs=[y], name="test_tan")
Tanh#
Calculates the hyperbolic tangent of the given input tensor element-wise.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor
Outputs#
- output (differentiable) : T
- The hyperbolic tangent values of the input tensor computed element-wise
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)
- Constrain input and output types to float tensors.
Examples#
tanh
node = onnx.helper.make_node(
"Tanh",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.tanh(x) # expected output [-0.76159418, 0., 0.76159418]
expect(node, inputs=[x], outputs=[y], name="test_tanh_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.tanh(x)
expect(node, inputs=[x], outputs=[y], name="test_tanh")
TfIdfVectorizer#
This transform extracts n-grams from the input sequence and save them as a vector. Input can be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input. For 2-D input, the output is also a 2-D tensor whose i-th row is the n-gram representation of the i-th input row. More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1]. If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor.
In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips. If the number of skips is 2, we should skip two tokens when scanning through the original sequence. Let’s consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2. The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4]. If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28] indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively.
The output vector (denoted by Y) stores the count of each n-gram; Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping between index i and the corresponding n-gram’s output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0], ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17], respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output. Note that we may consider all skips up to S when generating the n-grams.
The examples used above are true if mode is “TF”. If mode is “IDF”, all the counts larger than 1 would be truncated to 1 and the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is “TFIDF”, this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute.
Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor. If pool_strings is set, the input must be a string tensor.
Version#
This version of the operator has been available since version 9 of the default ONNX operator set.
Attributes#
- max_gram_length : int (required)
- Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.
- max_skip_count : int (required)
- Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1
- min_gram_length : int (required)
- Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.
- mode : string (required)
- The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)
- ngram_counts : list of ints (required)
- The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.
- ngram_indexes : list of ints (required)
- list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.
- pool_int64s : list of ints
- List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.
- pool_strings : list of strings
- List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.
- weights : list of floats
- list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.
Inputs#
- X (non-differentiable) : T
- Input for n-gram extraction
Outputs#
- Y (non-differentiable) : T1
- Ngram results
Type Constraints#
- T : tensor(string), tensor(int32), tensor(int64)
- Input is ether string UTF-8 or int32/int64
- T1 : tensor(float)
- 1-D tensor of floats
Examples#
tf_batch_onlybigrams_skip0
input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
output = np.array(
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0]]
).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=0,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_batch_onlybigrams_skip0",
)
tf_batch_onlybigrams_skip5
input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
output = np.array(
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]]
).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=5,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_batch_onlybigrams_skip5",
)
tf_batch_uniandbigrams_skip5
input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
output = np.array(
[[0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0]]
).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=1,
max_gram_length=2,
max_skip_count=5,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_batch_uniandbigrams_skip5",
)
tf_only_bigrams_skip0
input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=0,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_only_bigrams_skip0",
)
tf_onlybigrams_levelempty
input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
output = np.array([1.0, 1.0, 1.0]).astype(np.float32)
ngram_counts = np.array([0, 0]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2]).astype(np.int64)
pool_int64s = np.array([5, 6, 7, 8, 6, 7]).astype( # unigrams none
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=0,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_onlybigrams_levelempty",
)
tf_onlybigrams_skip5
input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=5,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_onlybigrams_skip5",
)
tf_uniandbigrams_skip5
input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
output = np.array([0.0, 3.0, 1.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=1,
max_gram_length=2,
max_skip_count=5,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_uniandbigrams_skip5",
)
ThresholdedRelu#
ThresholdedRelu takes one input data (Tensor
Version#
This version of the operator has been available since version 10 of the default ONNX operator set.
Attributes#
- alpha : float (default is 1.0)
- Threshold value
Inputs#
- X (differentiable) : T
- Input tensor
Outputs#
- Y (differentiable) : T
- Output tensor
Type Constraints#
- T : tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
default
default_alpha = 1.0
node = onnx.helper.make_node("ThresholdedRelu", inputs=["x"], outputs=["y"])
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, default_alpha, np.inf)
y[y == default_alpha] = 0
expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_default")
thresholdedrelu
alpha = 2.0
node = onnx.helper.make_node(
"ThresholdedRelu", inputs=["x"], outputs=["y"], alpha=alpha
)
x = np.array([-1.5, 0.0, 1.2, 2.0, 2.2]).astype(np.float32)
y = np.clip(x, alpha, np.inf) # expected output [0., 0., 0., 0., 2.2]
y[y == alpha] = 0
expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, alpha, np.inf)
y[y == alpha] = 0
expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu")
Tile#
Constructs a tensor by tiling a given tensor.
This is the same as function tile in Numpy, but no broadcast.
For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- input (differentiable) : T
- Input tensor of any shape.
- repeats (non-differentiable) : T1
- 1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.
Outputs#
- output (differentiable) : T
- Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
- T1 : tensor(int64)
- Constrain repeat's type to int64 tensors.
Examples#
tile
node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"])
x = np.random.rand(2, 3, 4, 5).astype(np.float32)
repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64)
z = np.tile(x, repeats)
expect(node, inputs=[x, repeats], outputs=[z], name="test_tile")
tile_precomputed
node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"])
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
repeats = np.array([2, 2], dtype=np.int64)
z = np.array(
[[0, 1, 0, 1], [2, 3, 2, 3], [0, 1, 0, 1], [2, 3, 2, 3]], dtype=np.float32
)
expect(node, inputs=[x, repeats], outputs=[z], name="test_tile_precomputed")
TopK#
Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of shape [a_1, a_2, …, a_n, r] and integer argument k, return two outputs: -Value tensor of shape [a_1, a_2, …, a_{axis-1}, k, a_{axis+1}, … a_n] which contains the values of the top k elements along the specified axis -Index tensor of shape [a_1, a_2, …, a_{axis-1}, k, a_{axis+1}, … a_n] which contains the indices of the top k elements (original indices from the input tensor).
If “largest” is 1 (the default value) then the k largest elements are returned. If “sorted” is 1 (the default value) then the resulting k elements will be sorted. If “sorted” is 0, order of returned ‘Values’ and ‘Indices’ are undefined.
Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first.
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Attributes#
- axis : int (default is -1)
- Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
- largest : int (default is 1)
- Whether to return the top-K largest or smallest elements.
- sorted : int (default is 1)
- Whether to return the elements in sorted order.
Inputs#
- X (differentiable) : T
- Tensor of shape [a_1, a_2, ..., a_n, r]
- K (non-differentiable) : tensor(int64)
- A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve
Outputs#
- Values (differentiable) : T
- Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor
- Indices (non-differentiable) : I
- Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
- Constrain input and output types to numeric tensors.
- I : tensor(int64)
- Constrain index tensor to int64
Examples#
top_k
axis = 1
largest = 1
k = 3
node = onnx.helper.make_node(
"TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
)
X = np.array(
[
[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11],
],
dtype=np.float32,
)
K = np.array([k], dtype=np.int64)
values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
# print(values_ref)
# [[ 3. 2. 1.]
# [ 7. 6. 5.]
# [11. 10. 9.]]
# print(indices_ref)
# [[3 2 1]
# [3 2 1]
# [3 2 1]]
expect(
node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k"
)
top_k_negative_axis
axis = -1
largest = 1
k = 3
node = onnx.helper.make_node(
"TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
)
X = np.array(
[
[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11],
],
dtype=np.float32,
)
K = np.array([k], dtype=np.int64)
values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
# print(values_ref)
# [[ 3. 2. 1.]
# [ 7. 6. 5.]
# [11. 10. 9.]]
# print(indices_ref)
# [[3 2 1]
# [3 2 1]
# [3 2 1]]
expect(
node,
inputs=[X, K],
outputs=[values_ref, indices_ref],
name="test_top_k_negative_axis",
)
top_k_smallest
axis = 1
largest = 0
sorted = 1
k = 3
node = onnx.helper.make_node(
"TopK",
inputs=["x", "k"],
outputs=["values", "indices"],
axis=axis,
largest=largest,
sorted=sorted,
)
X = np.array(
[
[0, 1, 2, 3],
[4, 5, 6, 7],
[11, 10, 9, 8],
],
dtype=np.float32,
)
K = np.array([k], dtype=np.int64)
values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
# print(values_ref)
# [[ 0. 1. 2.]
# [ 4. 5. 6.]
# [ 8. 9. 10.]]
# print(indices_ref)
# [[0 1 2]
# [0 1 2]
# [3 2 1]]
expect(
node,
inputs=[X, K],
outputs=[values_ref, indices_ref],
name="test_top_k_smallest",
)
Transpose#
Transpose the input tensor similar to numpy.transpose. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3).
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Other versions of this operator: 1
Attributes#
- perm : list of ints
- A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.
Inputs#
- data (differentiable) : T
- An input tensor.
Outputs#
- transposed (differentiable) : T
- Transposed output.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Examples#
all_permutations
shape = (2, 3, 4)
data = np.random.random_sample(shape).astype(np.float32)
permutations = list(itertools.permutations(np.arange(len(shape))))
for i in range(len(permutations)):
node = onnx.helper.make_node(
"Transpose",
inputs=["data"],
outputs=["transposed"],
perm=permutations[i],
)
transposed = np.transpose(data, permutations[i])
expect(
node,
inputs=[data],
outputs=[transposed],
name="test_transpose_all_permutations_" + str(i),
)
default
shape = (2, 3, 4)
data = np.random.random_sample(shape).astype(np.float32)
node = onnx.helper.make_node(
"Transpose", inputs=["data"], outputs=["transposed"]
)
transposed = np.transpose(data)
expect(node, inputs=[data], outputs=[transposed], name="test_transpose_default")
Trilu#
Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s). The attribute “upper” determines whether the upper or lower part is retained. If set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for the “upper” attribute is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it. A negative k value retains the main diagonal and |k| diagonals below it. If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it. A negative k value excludes the main diagonal and (|k|-1) diagonals below it.
Version#
This version of the operator has been available since version 14 of the default ONNX operator set.
Attributes#
- upper : int (default is 1)
- Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.
Inputs (1 - 2)#
- input (differentiable) : T
- Input tensor of rank 2 or higher.
- k (optional, non-differentiable) : tensor(int64)
- A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.
Outputs#
- output (differentiable) : T
- Output tensor of the same type and shape as the input tensor.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Examples#
tril
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 0, 0, 0, 0],
# [1, 2, 0, 0, 0],
# [9, 4, 1, 0, 0],
# [4, 3, 4, 2, 0]]
y = tril_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name="test_tril")
tril_neg
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 0, 0, 0],
# [1, 0, 0, 0, 0],
# [9, 4, 0, 0, 0],
# [4, 3, 4, 0, 0]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_neg")
tril_one_row
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
# X:
# [[[6, 2, 4, 1, 6]],
#
# [[8, 3, 8, 7, 0]],
#
# [[2, 2, 9, 5, 9]]]
# expect result:
# [[[6, 0, 0, 0, 0]],
#
# [[8, 0, 0, 0, 0]],
#
# [[2, 0, 0, 0, 0]]]
y = tril_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name="test_tril_one_row_neg")
tril_out_neg
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-7).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_neg")
tril_out_pos
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_pos")
tril_pos
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(2).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 0, 0],
# [1, 2, 8, 6, 0],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_pos")
tril_square
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
# X:
# [[[0, 4, 3],
# [2, 0, 9],
# [8, 2, 5]],
#
# [[2, 7, 2],
# [2, 6, 0],
# [2, 6, 5]]]
# expect result:
# [[[0, 0, 0],
# [2, 0, 0],
# [8, 2, 5]],
#
# [[2, 0, 0],
# [2, 6, 0],
# [2, 6, 5]]]
y = tril_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name="test_tril_square")
tril_square_neg
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[[0, 4, 3],
# [2, 0, 9],
# [8, 2, 5]],
#
# [[2, 7, 2],
# [2, 6, 0],
# [2, 6, 5]]]
# expect result:
# [[[0, 0, 0],
# [2, 0, 0],
# [8, 2, 0]],
#
# [[0, 0, 0],
# [2, 0, 0],
# [2, 6, 0]]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_square_neg")
tril_zero
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# []
# expect result:
# []
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_zero")
triu
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [0, 2, 8, 6, 9],
# [0, 0, 0, 8, 7],
# [0, 0, 0, 2, 4]]
y = triu_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name="test_triu")
triu_neg
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [0, 4, 0, 8, 7],
# [0, 0, 4, 2, 4]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_neg")
triu_one_row
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
k = np.array(1).astype(np.int64)
# X:
# [[[1, 4, 9, 7, 1]],
#
# [[9, 2, 8, 8, 4]],
#
# [[3, 9, 7, 4, 2]]]
# expect result:
# [[[0, 4, 9, 7, 1]],
#
# [[0, 2, 8, 8, 4]],
#
# [[0, 9, 7, 4, 2]]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_one_row")
triu_out_neg_out
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-7).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_neg_out")
triu_out_pos
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_pos")
triu_pos
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(2).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 3, 7, 9],
# [0, 0, 0, 6, 9],
# [0, 0, 0, 0, 7],
# [0, 0, 0, 0, 0]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_pos")
triu_square
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
y = triu_reference_implementation(x)
# X:
# [[[4, 6, 9],
# [7, 5, 4],
# [8, 1, 2]],
#
# [[1, 4, 9],
# [9, 6, 3],
# [8, 9, 8]]]
# expect result:
# [[[4, 6, 9],
# [0, 5, 4],
# [0, 0, 2]],
#
# [[1, 4, 9],
# [0, 6, 3],
# [0, 0, 8]]]
expect(node, inputs=[x], outputs=[y], name="test_triu_square")
triu_square_neg
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[[4, 6, 9],
# [7, 5, 4],
# [8, 1, 2]],
#
# [[1, 4, 9],
# [9, 6, 3],
# [8, 9, 8]]]
# expect result:
# [[[4, 6, 9],
# [7, 5, 4],
# [0, 1, 2]],
#
# [[1, 4, 9],
# [9, 6, 3],
# [0, 9, 8]]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_square_neg")
triu_zero
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(0, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# []
# expect result:
# []
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_zero")
Unique#
Find the unique elements of a tensor. When an optional attribute ‘axis’ is provided, unique subtensors sliced along the ‘axis’ are returned. Otherwise the input tensor is flattened and unique values of the flattened tensor are returned.
This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs. The first output tensor ‘Y’ contains all unique values or subtensors of the input. The second optional output tensor ‘indices’ contains indices of ‘Y’ elements’ first occurance in ‘X’.. The third optional output tensor ‘inverse_indices’ contains, for elements of ‘X’, its corresponding indices in ‘Y’. “. The fourth optional output tensor ‘counts’ contains the count of each element of ‘Y’ in the input.
Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input.
https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html
Example 1: input_X = [2, 1, 1, 3, 4, 3] attribute_sorted = 0 attribute_axis = None output_Y = [2, 1, 3, 4] output_indices = [0, 1, 3, 4] output_inverse_indices = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1]
Example 2: input_X = [[1, 3], [2, 3]] attribute_sorted = 1 attribute_axis = None output_Y = [1, 2, 3] output_indices = [0, 2, 1] output_inverse_indices = [0, 2, 1, 2] output_counts = [1, 1, 2]
Example 3: input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]] attribute_sorted = 1 attribute_axis = 0 output_Y = [[1, 0, 0], [2, 3, 4]] output_indices = [0, 2] output_inverse_indices = [0, 0, 1] output_counts = [2, 1]
Example 4: input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]], [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]] attribute_sorted = 1 attribute_axis = 1
intermediate data are presented below for better understanding:
there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)):
A: [[1, 1], [1, 1]],
[[0, 1], [0, 1]],
[[2, 1], [2, 1]],
[[0, 1], [0, 1]].
there are 3 unique subtensors:
[[1, 1], [1, 1]],
[[0, 1], [0, 1]],
[[2, 1], [2, 1]].
sorted unique subtensors:
B: [[0, 1], [0, 1]],
[[1, 1], [1, 1]],
[[2, 1], [2, 1]].
output_Y is constructed from B:
[[[0. 1.], [1. 1.], [2. 1.]],
[[0. 1.], [1. 1.], [2. 1.]]]
output_indices is to map from B to A:
[1, 0, 2]
output_inverse_indices is to map from A to B:
[1, 0, 2, 0]
output_counts = [2 1 1]
Version#
This version of the operator has been available since version 11 of the default ONNX operator set.
Attributes#
- axis : int
- (Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
- sorted : int (default is 1)
- (Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).
Inputs#
- X (non-differentiable) : T
- A N-D input tensor that is to be processed.
Outputs (1 - 4)#
- Y (non-differentiable) : T
- A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'
- indices (optional, non-differentiable) : tensor(int64)
- A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor.
- inverse_indices (optional, non-differentiable) : tensor(int64)
- A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'.
- counts (optional, non-differentiable) : tensor(int64)
- A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Input can be of any tensor type.
Examples#
not_sorted_without_axis
node_not_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
sorted=0,
)
# numpy unique does not retain original order (it sorts the output unique values)
# https://github.com/numpy/numpy/issues/8621
# we need to recover unsorted output and indices
x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
y, indices, inverse_indices, counts = np.unique(x, True, True, True)
# prepare index mapping from sorted to unsorted
argsorted_indices = np.argsort(indices)
inverse_indices_map = {
i: si for i, si in zip(argsorted_indices, np.arange(len(argsorted_indices)))
}
indices = indices[argsorted_indices]
y = np.take(x, indices, axis=0)
inverse_indices = np.asarray(
[inverse_indices_map[i] for i in inverse_indices], dtype=np.int64
)
counts = counts[argsorted_indices]
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
# print(y)
# [2.0, 1.0, 3.0, 4.0]
# print(indices)
# [0 1 3 4]
# print(inverse_indices)
# [0, 1, 1, 2, 3, 2]
# print(counts)
# [1, 2, 2, 1]
expect(
node_not_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_not_sorted_without_axis",
)
sorted_with_axis
node_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
sorted=1,
axis=0,
)
x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=np.float32)
y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=0)
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
# print(y)
# [[1. 0. 0.]
# [2. 3. 4.]]
# print(indices)
# [0 2]
# print(inverse_indices)
# [0 0 1]
# print(counts)
# [2 1]
expect(
node_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_sorted_with_axis",
)
sorted_with_axis_3d
node_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
sorted=1,
axis=1,
)
x = np.array(
[
[[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]],
[[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]],
],
dtype=np.float32,
)
y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=1)
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
# print(y)
# [[[0. 1.]
# [1. 1.]
# [2. 1.]]
# [[0. 1.]
# [1. 1.]
# [2. 1.]]]
# print(indices)
# [1 0 2]
# print(inverse_indices)
# [1 0 2 0]
# print(counts)
# [2 1 1]
expect(
node_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_sorted_with_axis_3d",
)
sorted_with_negative_axis
node_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
sorted=1,
axis=-1,
)
x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=np.float32)
y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=-1)
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
# print(y)
# [[0. 1.]
# [0. 1.]
# [3. 2.]]
# print(indices)
# [1 0]
# print(inverse_indices)
# [1 0 0]
# print(counts)
# [2 1]
expect(
node_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_sorted_with_negative_axis",
)
sorted_without_axis
node_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
)
x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
y, indices, inverse_indices, counts = np.unique(x, True, True, True)
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
expect(
node_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_sorted_without_axis",
)
Unsqueeze#
Insert single-dimensional entries to the shape of an input tensor (data).
Takes one required input axes - which contains a list of dimension indices and this operator will insert a dimension of value 1 into the corresponding index of the output tensor (expanded).
For example:
Given an input tensor (data) of shape [3, 4, 5], then
Unsqueeze(data, axes=[0, 4]) outputs a tensor (expanded) containing same data as data but with shape [1, 3, 4, 5, 1].
The input axes should not contain any duplicate entries. It is an error if it contains duplicates.
The rank of the output tensor (output_rank) is the rank of the input tensor (data) plus the number of values in axes.
Each value in axes should be within the (inclusive) range [-output_rank , output_rank - 1].
The order of values in axes does not matter and can come in any order.
Version#
This version of the operator has been available since version 13 of the default ONNX operator set.
Inputs#
- data (differentiable) : T
- Original tensor
- axes (non-differentiable) : tensor(int64)
- List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).
Outputs#
- expanded (differentiable) : T
- Reshaped tensor with same data as input.
Type Constraints#
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Examples#
unsqueeze_negative_axes
node = onnx.helper.make_node(
"Unsqueeze",
inputs=["x", "axes"],
outputs=["y"],
)
x = np.random.randn(1, 3, 1, 5).astype(np.float32)
axes = np.array([-2]).astype(np.int64)
y = np.expand_dims(x, axis=-2)
expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_negative_axes")
unsqueeze_one_axis
x = np.random.randn(3, 4, 5).astype(np.float32)
for i in range(x.ndim):
axes = np.array([i]).astype(np.int64)
node = onnx.helper.make_node(
"Unsqueeze",
inputs=["x", "axes"],
outputs=["y"],
)
y = np.expand_dims(x, axis=i)
expect(
node,
inputs=[x, axes],
outputs=[y],
name="test_unsqueeze_axis_" + str(i),
)
unsqueeze_three_axes
x = np.random.randn(3, 4, 5).astype(np.float32)
axes = np.array([2, 4, 5]).astype(np.int64)
node = onnx.helper.make_node(
"Unsqueeze",
inputs=["x", "axes"],
outputs=["y"],
)
y = np.expand_dims(x, axis=2)
y = np.expand_dims(y, axis=4)
y = np.expand_dims(y, axis=5)
expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_three_axes")
unsqueeze_two_axes
x = np.random.randn(3, 4, 5).astype(np.float32)
axes = np.array([1, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Unsqueeze",
inputs=["x", "axes"],
outputs=["y"],
)
y = np.expand_dims(x, axis=1)
y = np.expand_dims(y, axis=4)
expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_two_axes")
unsqueeze_unsorted_axes
x = np.random.randn(3, 4, 5).astype(np.float32)
axes = np.array([5, 4, 2]).astype(np.int64)
node = onnx.helper.make_node(
"Unsqueeze",
inputs=["x", "axes"],
outputs=["y"],
)
y = np.expand_dims(x, axis=2)
y = np.expand_dims(y, axis=4)
y = np.expand_dims(y, axis=5)
expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_unsorted_axes")
Upsample (deprecated)#
Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale).
Version#
This version of the operator has been deprecated since version 10 of the default ONNX operator set.
Examples#
nearest
node = onnx.helper.make_node(
"Upsample",
inputs=["X", "scales"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
output = np.array(
[
[
[
[1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2],
[3, 3, 3, 4, 4, 4],
[3, 3, 3, 4, 4, 4],
]
]
],
dtype=np.float32,
)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_upsample_nearest",
opset_imports=[helper.make_opsetid("", 9)],
)
Where#
Return elements, either from X or Y, depending on condition. Where behaves like numpy.where with three parameters.
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
History
Version 16 adds bfloat16 to the types allowed (for the second and third parameter).
Version#
This version of the operator has been available since version 16 of the default ONNX operator set.
Other versions of this operator: 9
Inputs#
- condition (non-differentiable) : B
- When True (nonzero), yield X, otherwise yield Y
- X (differentiable) : T
- values selected at indices where condition is True
- Y (differentiable) : T
- values selected at indices where condition is False
Outputs#
- output (differentiable) : T
- Tensor of shape equal to the broadcasted shape of condition, X, and Y.
Type Constraints#
- B : tensor(bool)
- Constrain to boolean tensors.
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types (including bfloat).
Examples#
long
node = onnx.helper.make_node(
"Where",
inputs=["condition", "x", "y"],
outputs=["z"],
)
condition = np.array([[1, 0], [1, 1]], dtype=bool)
x = np.array([[1, 2], [3, 4]], dtype=np.int64)
y = np.array([[9, 8], [7, 6]], dtype=np.int64)
z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]]
expect(
node, inputs=[condition, x, y], outputs=[z], name="test_where_long_example"
)
where
node = onnx.helper.make_node(
"Where",
inputs=["condition", "x", "y"],
outputs=["z"],
)
condition = np.array([[1, 0], [1, 1]], dtype=bool)
x = np.array([[1, 2], [3, 4]], dtype=np.float32)
y = np.array([[9, 8], [7, 6]], dtype=np.float32)
z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]]
expect(node, inputs=[condition, x, y], outputs=[z], name="test_where_example")
Xor#
Returns the tensor resulted from performing the xor logical operation
elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.
Version#
This version of the operator has been available since version 7 of the default ONNX operator set.
Other versions of this operator: 1
Inputs#
- A (non-differentiable) : T
- First input operand for the logical operator.
- B (non-differentiable) : T
- Second input operand for the logical operator.
Outputs#
- C (non-differentiable) : T1
- Result tensor.
Type Constraints#
- T : tensor(bool)
- Constrain input to boolean tensor.
- T1 : tensor(bool)
- Constrain output to boolean tensor.
Examples#
xor
node = onnx.helper.make_node(
"Xor",
inputs=["x", "y"],
outputs=["xor"],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(bool)
y = (np.random.randn(3, 4) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor2d")
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(3, 4, 5) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor3d")
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor4d")
xor_broadcast
node = onnx.helper.make_node(
"Xor",
inputs=["x", "y"],
outputs=["xor"],
)
# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(5) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v1d")
# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(4, 5) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v2d")
# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(5, 6) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v2d")
# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(4, 5, 6) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v3d")
# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v4d")
ai.onnx.preview.training#
ai.onnx.preview.training.Adagrad#
Compute one iteration of ADAGRAD, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables.
Let's define the behavior of this operator. As you can imagine, ADAGRAD requires
some parameters:
- The initial learning-rate "R".
- The update count "T". That is, the number of training iterations conducted.
- A L2-norm regularization coefficient "norm_coefficient".
- A learning-rate decay factor "decay_factor".
- A small constant "epsilon" to avoid dividing-by-zero.
At each ADAGRAD iteration, the optimized tensors are moved along a direction
computed based on their estimated gradient and accumulated squared gradient. Assume
that only a single tensor "X" is updated by this operator. We need the value of "X",
its gradient "G", and its accumulated squared gradient "H". Therefore, variables in
this operator's input list are sequentially "R", "T", "X", "G", and "H". Other
parameters are given as attributes because they are usually constants. Also, the
corresponding output tensors are the new value of "X" (called "X_new"), and then
the new accumulated squared gradient (called "H_new"). Those outputs are computed
from the given inputs following the pseudo code below.
Let "+", "-", "*", and "/" are all element-wise arithmetic operations with
numpy-style broadcasting support. The pseudo code to compute those outputs is:
// Compute a scalar learning-rate factor. At the first update of X, T is generally
// 0 (0-based update index) or 1 (1-based update index).
r = R / (1 + T * decay_factor);
// Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
G_regularized = norm_coefficient * X + G;
// Compute new accumulated squared gradient.
H_new = H + G_regularized * G_regularized;
// Compute the adaptive part of per-coordinate learning rate. Note that Sqrt(...)
// computes element-wise square-root.
H_adaptive = Sqrt(H_new) + epsilon
// Compute the new value of "X".
X_new = X - r * G_regularized / H_adaptive;
If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2", the same
pseudo code may be extended to handle all tensors jointly. More specifically, we can view "X" as a
concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
be concatenated too) and then just reuse the entire pseudo code.
Note that ADAGRAD was first proposed in http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
In that reference paper, this operator is a special case of the Figure 1's composite mirror
descent update.
Version#
This version of the operator has been available since version 1 of the ‘ai.onnx.preview.training’ operator set.
Attributes#
- decay_factor : float (default is 0.0)
- The decay factor of learning rate after one update.The effective learning rate is computed by r = R / (1 + T * decay_factor). Default to 0 so that increasing update counts doesn't reduce the learning rate.
- epsilon : float (default is 0.0)
- Small scalar to avoid dividing by zero.
- norm_coefficient : float (default is 0.0)
- Regularization coefficient in 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.
Inputs (3 - ∞)#
- R : T1
- The initial learning rate.
- T : T2
- The update count of "X". It should be a scalar.
- inputs (variadic, heterogeneous) : T3
- The current values of optimized tensors, followed by their respective gradients, followed by their respective accumulated squared gradients.For example, if two tensor "X_1" and "X_2" are optimized, The input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].
Outputs (1 - ∞)#
- outputs (variadic, heterogeneous) : T3
- Updated values of optimized tensors, followed by their updated values of accumulated squared gradients. For example, if two tensor "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].
Type Constraints#
- T1 : tensor(float), tensor(double)
- Constrain input types to float scalars.
- T2 : tensor(int64)
- Constrain input types to 64-bit integer scalars.
- T3 : tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
adagrad
# Define operator attributes.
norm_coefficient = 0.001
epsilon = 1e-5
decay_factor = 0.1
# Create operator.
node = onnx.helper.make_node(
"Adagrad",
inputs=["R", "T", "X", "G", "H"],
outputs=["X_new", "H_new"],
norm_coefficient=norm_coefficient,
epsilon=epsilon,
decay_factor=decay_factor,
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x = np.array([1.0], dtype=np.float32)
g = np.array([-1.0], dtype=np.float32)
h = np.array([2.0], dtype=np.float32)
# Compute expected outputs of Adagrad.
x_new, h_new = apply_adagrad(
r, t, x, g, h, norm_coefficient, epsilon, decay_factor
)
# Check results.
expect(
node,
inputs=[r, t, x, g, h],
outputs=[x_new, h_new],
name="test_adagrad",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
adagrad_multiple
# Define operator attributes.
norm_coefficient = 0.001
epsilon = 1e-5
decay_factor = 0.1
node = onnx.helper.make_node(
"Adagrad",
inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"],
outputs=["X1_new", "X2_new", "H1_new", "H2_new"],
norm_coefficient=norm_coefficient,
epsilon=epsilon,
decay_factor=decay_factor,
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x1 = np.array([1.0], dtype=np.float32)
g1 = np.array([-1.0], dtype=np.float32)
h1 = np.array([2.0], dtype=np.float32)
x2 = np.array([1.0, 2.0], dtype=np.float32)
g2 = np.array([-1.0, -3.0], dtype=np.float32)
h2 = np.array([4.0, 1.0], dtype=np.float32)
# Compute expected outputs of Adagrad.
x1_new, h1_new = apply_adagrad(
r, t, x1, g1, h1, norm_coefficient, epsilon, decay_factor
)
x2_new, h2_new = apply_adagrad(
r, t, x2, g2, h2, norm_coefficient, epsilon, decay_factor
)
# Check results.
expect(
node,
inputs=[r, t, x1, x2, g1, g2, h1, h2],
outputs=[x1_new, x2_new, h1_new, h2_new],
name="test_adagrad_multiple",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
ai.onnx.preview.training.Adam#
Compute one iteration of Adam, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables.
Let's define the behavior of this operator. First of all, Adam requires
some parameters:
- The learning-rate "R".
- The update count "T". That is, the number of training iterations conducted.
- A L2-norm regularization coefficient "norm_coefficient".
- A small constant "epsilon" to avoid dividing-by-zero.
- Two coefficients, "alpha" and "beta".
At each Adam iteration, the optimized tensors are moved along a direction
computed based on their exponentially-averaged historical gradient and
exponentially-averaged historical squared gradient. Assume that only a tensor
"X" is being optimized. The rest of required information is
- the value of "X",
- "X"'s gradient (denoted by "G"),
- "X"'s exponentially-averaged historical gradient (denoted by "V"), and
- "X"'s exponentially-averaged historical squared gradient (denoted by "H").
Some of those parameters are passed into this operator as input tensors and others
are stored as this operator's attributes. Specifically, this operator's input tensor
list is ["R", "T", "X", "G", "V", "H"]. That is, "R" is the first input, "T" is
the second input, and so on. Other parameters are given as attributes because they
are constants. Moreover, the corresponding output tensors are
- the new value of "X" (called "X_new"),
- the new exponentially-averaged historical gradient (denoted by "V_new"), and
- the new exponentially-averaged historical squared gradient (denoted by "H_new").
Those outputs are computed following the pseudo code below.
Let "+", "-", "*", and "/" are all element-wise arithmetic operations with
numpy-style broadcasting support. The pseudo code to compute those outputs is:
// Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
G_regularized = norm_coefficient * X + G
// Update exponentially-averaged historical gradient.
V_new = alpha * V + (1 - alpha) * G_regularized
// Update exponentially-averaged historical squared gradient.
H_new = beta * H + (1 - beta) * G_regularized * G_regularized
// Compute the element-wise square-root of H_new. V_new will be element-wisely
// divided by H_sqrt for a better update direction.
H_sqrt = Sqrt(H_new) + epsilon
// Compute learning-rate. Note that "alpha**T"/"beta**T" is alpha's/beta's T-th power.
R_adjusted = T > 0 ? R * Sqrt(1 - beta**T) / (1 - alpha**T) : R
// Compute new value of "X".
X_new = X - R_adjusted * V_new / H_sqrt
// Post-update regularization.
X_final = (1 - norm_coefficient_post) * X_new
If there are multiple inputs to be optimized, the pseudo code will be applied
independently to each of them.
Version#
This version of the operator has been available since version 1 of the ‘ai.onnx.preview.training’ operator set.
Attributes#
- alpha : float (default is 0.9)
- Coefficient of previously accumulated gradient in running average. Default to 0.9.
- beta : float (default is 0.999)
- Coefficient of previously accumulated squared-gradient in running average. Default to 0.999.
- epsilon : float (default is 0.0)
- Small scalar to avoid dividing by zero.
- norm_coefficient : float (default is 0.0)
- Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.
- norm_coefficient_post : float (default is 0.0)
- Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.
Inputs (3 - ∞)#
- R : T1
- The initial learning rate.
- T : T2
- The update count of "X". It should be a scalar.
- inputs (variadic, heterogeneous) : T3
- The tensors to be optimized, followed by their respective gradients, followed by their respective accumulated gradients (aka momentum), followed by their respective accumulated squared gradients. For example, to optimize tensors "X_1" and "X_2,", the input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated gradient of "X_1", accumulated gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].
Outputs (1 - ∞)#
- outputs (variadic, heterogeneous) : T3
- New values of optimized tensors, followed by their respective new accumulated gradients, followed by their respective new accumulated squared gradients. For example, if two tensors "X_1" and "X_2" are optimized, the outputs list would be [new value of "X_1", new value of "X_2", new accumulated gradient of "X_1", new accumulated gradient of "X_2", new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].
Type Constraints#
- T1 : tensor(float), tensor(double)
- Constrain input types to float scalars.
- T2 : tensor(int64)
- Constrain input types to 64-bit integer scalars.
- T3 : tensor(float), tensor(double)
- Constrain input and output types to float tensors.
Examples#
adam
# Define operator attributes.
norm_coefficient = 0.001
alpha = 0.95
beta = 0.1
epsilon = 1e-7
# Create operator.
node = onnx.helper.make_node(
"Adam",
inputs=["R", "T", "X", "G", "V", "H"],
outputs=["X_new", "V_new", "H_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
epsilon=epsilon,
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x = np.array([1.2, 2.8], dtype=np.float32)
g = np.array([-0.94, -2.5], dtype=np.float32)
v = np.array([1.7, 3.6], dtype=np.float32)
h = np.array([0.1, 0.1], dtype=np.float32)
# Compute expected outputs of Adam.
x_new, v_new, h_new = apply_adam(
r, t, x, g, v, h, norm_coefficient, 0.0, alpha, beta, epsilon
)
# Check results.
expect(
node,
inputs=[r, t, x, g, v, h],
outputs=[x_new, v_new, h_new],
name="test_adam",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
adam_multiple
# Define operator attributes.
norm_coefficient = 0.001
alpha = 0.95
beta = 0.85
epsilon = 1e-2
node = onnx.helper.make_node(
"Adam",
inputs=["R", "T", "X1", "X2", "G1", "G2", "V1", "V2", "H1", "H2"],
outputs=["X1_new", "X2_new", "V1_new", "V2_new", "H1_new", "H2_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x1 = np.array([1.0], dtype=np.float32)
g1 = np.array([-1.0], dtype=np.float32)
v1 = np.array([2.0], dtype=np.float32)
h1 = np.array([0.5], dtype=np.float32)
x2 = np.array([1.0, 2.0], dtype=np.float32)
g2 = np.array([-1.0, -3.0], dtype=np.float32)
v2 = np.array([4.0, 1.0], dtype=np.float32)
h2 = np.array([1.0, 10.0], dtype=np.float32)
# Compute expected outputs of Adam.
x1_new, v1_new, h1_new = apply_adam(
r, t, x1, g1, v1, h1, norm_coefficient, 0.0, alpha, beta, epsilon
)
x2_new, v2_new, h2_new = apply_adam(
r, t, x2, g2, v2, h2, norm_coefficient, 0.0, alpha, beta, epsilon
)
# Check results.
expect(
node,
inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2],
outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new],
name="test_adam_multiple",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
ai.onnx.preview.training.Gradient#
Gradient operator computes the partial derivatives of a specific tensor w.r.t. some other tensors. This operator is widely used in gradient-based training algorithms. To illustrate its use, let’s consider a computation graph,
X -----.
|
v
W --> Conv --> H --> Gemm --> Y
^
|
Z
, where W and Z are trainable tensors. Note that operators’ attributes are omitted for the sake of simplicity. Let dY/dW (dY/dZ) be the gradient of Y with respect to W (Z). The user can compute gradient by inserting Gradient operator to form another graph shown below.
W --> Conv --> H --> Gemm --> Y
| ^ ^
| | |
| X Z
| | |
| | .----------'
| | | (W/Z/X is the 1st/2nd/3rd input of Gradient as shown in
| | | "xs" followed by "zs")
| v v
'---> Gradient(xs=["W", "Z"], zs=["X"], y="Y")
| |
| '-----------------------------------> dY/dW (1st output of Gradient)
|
'---------------------------------------> dY/dZ (2nd output of Gradient)
By definition, the tensor “y” is a function of independent variables in “xs” and “zs”. Since we only compute the gradient of “y” w.r.t. the differentiable variables in “xs”, this Gradient only outputs dY/dW and dY/dZ. Note that “H” cannot appear in “xs” and “zs”. The reason is that “H” can be determined by tensors “W” and “X” and therefore “H” is not an independent variable.
All outputs are optional. If needed, for example, user can assign an empty string to the 1st output name of that Gradient to skip the generation of dY/dW. Note that the concept of optional outputs can also be found in ONNX’s RNN, GRU, and LSTM.
Gradient operator can compute derivative against intermediate tensors. For example, the gradient of Y with respect to H can be done via
W --> Conv --> H --> Gemm --> Y
^ | ^
| | |
X | Z
.-------' |
| .----------'
| | (H/Z is the 1st/2nd input of Gradient as shown in "xs")
v v
Gradient(xs=["H", "Z"], y="Y")
| |
| '-----------------------------------> dY/dH (1st output of Gradient)
|
'---------------------------------------> dY/dZ (2nd output of Gradient)
It is possible to represent high-order differentiation using Gradient operators. For example, given the following linear model:
W --> Gemm --> Y --> Loss --> O
^ ^
| |
X L
To compute the 2nd order derivative of O with respect to W (denoted by d^2O/dW^2), one can do
W --> Gemm --> Y --> Loss --> O
| ^ ^
| | |
| X .------------L
| | | |
| | | v
+------+-+> Gradient(xs=["X", "W"], zs=["L"], y="O") ---> dO/dX (1st output of Gradient)
| | | |
| | | '---> dO/dW (2nd output of Gradient)
| v v
'---> Gradient(xs=["X", "W"], zs=["L"], y="dO/dW") ---> d(dO/dW)dX (1st output of
| Gradient)
|
|
'---> d^2O/dW^2 (2nd output of Gradient)
The tensors named in attributes “xs”, “zs”, and “y” define the differentiated computation graph, and the inputs to Gradient node define the values at which the gradient is computed. We can feed different tensors to the identified graph. For example, one can compute the gradient of Y with respect to H at a specific value of H, H_1, by providing that value as an input to the Gradient node.
W --> Conv --> H --> Gemm --> Y
^ ^
| |
X Z
Z_1 (2nd input of Gradient)
|
v
H_1 --> Gradient(xs=["H", "Z"], y="Y") ---> dY/dH when H = H_1 and Y = Y_1.
|
'------------------------------> dY/dZ (2nd output of Gradient)
When the inputs of Gradient are the tensors named in “xs” and “zs”, the computation can be optimized. More specifically, intermediate variables in forward pass can be reused if the gradient is computed via reverse-mode auto-differentiation.
Version#
This version of the operator has been available since version 1 of the ‘ai.onnx.preview.training’ operator set.
Attributes#
- xs : list of strings (required)
- Input tensor names of the differentiated sub-graph. It contains only the necessary differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.
- y : string (required)
- The targeted tensor. It can be viewed as the output of the differentiated function. The attribute "xs" and attribute "zs" are the minimal independent variable set that determines the value of "y".
- zs : list of strings
- Input tensor names of the differentiated sub-graph. It contains only the necessary non-differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.
Inputs (1 - ∞)#
- Inputs (variadic, heterogeneous) : T1
- The values fed into graph identified by the attributes. The i-th input is the value of the i-th tensor specified in the concatenated list of the attribute "xs" and the attribute "zs". For example, if xs=["A", "B"] and zs=["C"], the first input is used as the value of symbol "A" and the 3rd input is substituted for all the occurrences of "C".
Outputs (1 - ∞)#
- Outputs (variadic, heterogeneous) : T2
- The gradient of the tensor specified by the attribute "y" with respect to each of tensors specified in the attribute "xs". The i-th output is the gradient of "y" with respect to the i-th tensor specified in the attribute "xs".
Type Constraints#
- T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Allow outputs to be any kind of tensor.
- T2 : tensor(float16), tensor(float), tensor(double)
- Allow inputs to be any kind of floating-point tensor.
Examples#
gradient_scalar_add
add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add")
gradient_node = onnx.helper.make_node(
"Gradient",
["a", "b"],
["dc_da", "dc_db"],
name="my_gradient",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
xs=["a", "b"],
y="c",
)
a = np.array(1.0).astype(np.float32)
b = np.array(2.0).astype(np.float32)
c = a + b
# dc / da = d(a+b) / da = 1
dc_da = np.array(1).astype(np.float32)
# db / db = d(a+b) / db = 1
dc_db = np.array(1).astype(np.float32)
graph = onnx.helper.make_graph(
nodes=[add_node, gradient_node],
name="GradientOfAdd",
inputs=[
onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []),
],
outputs=[
onnx.helper.make_tensor_value_info("c", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("dc_da", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("dc_db", onnx.TensorProto.FLOAT, []),
],
)
opsets = [
onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
]
model = onnx.helper.make_model_gen_version(
graph, producer_name="backend-test", opset_imports=opsets
)
expect(
model, inputs=[a, b], outputs=[c, dc_da, dc_db], name="test_gradient_of_add"
)
gradient_scalar_add_and_mul
add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add")
mul_node = onnx.helper.make_node("Mul", ["c", "a"], ["d"], name="my_mul")
gradient_node = onnx.helper.make_node(
"Gradient",
["a", "b"],
["dd_da", "dd_db"],
name="my_gradient",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
xs=["a", "b"],
y="d",
)
a = np.array(1.0).astype(np.float32)
b = np.array(2.0).astype(np.float32)
c = a + b
# d = a * c = a * (a + b)
d = a * c
# dd / da = d(a*a+a*b) / da = 2 * a + b
dd_da = (2 * a + b).astype(np.float32)
# dd / db = d(a*a+a*b) / db = a
dd_db = a
graph = onnx.helper.make_graph(
nodes=[add_node, mul_node, gradient_node],
name="GradientOfTwoOperators",
inputs=[
onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []),
],
outputs=[
onnx.helper.make_tensor_value_info("d", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("dd_da", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("dd_db", onnx.TensorProto.FLOAT, []),
],
)
opsets = [
onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
]
model = onnx.helper.make_model_gen_version(
graph, producer_name="backend-test", opset_imports=opsets
)
expect(
model,
inputs=[a, b],
outputs=[d, dd_da, dd_db],
name="test_gradient_of_add_and_mul",
)
ai.onnx.preview.training.Momentum#
Compute one iteration of stochastic gradient update with momentum. This operator can conduct the optimization of multiple tensor variables.
Let's define the behavior of this operator. As you can imagine, SG with momentum requires
several parameters:
- The learning-rate "R".
- The update count "T". That is, the number of conducted training iterations. It should
be zero in the first training iteration.
- A L2-norm regularization coefficient "norm_coefficient".
- A decay coefficient of previous accumulated gradient (i.e., momentum) "alpha".
- The scaling coefficient of current gradient "beta".
- An attribute to choose either standard momentum or Nesterov's momentum "mode" should
be used.
For the sake of simplicity, assume that there is only one tensor (called "X") to be optimized.
Other necessary inputs are "X"'s gradient (called "G") and "X"'s momentum (called "V"). This
Momentum operator maps all these inputs to the new value of "X" (called "X_new") and its new
momentum (called "V_new").
This operator supports two different momentum algorithms. Set the attribute "mode" to
"nesterov" if Nesterov's momentum is desired. Otherwise, set the attribute "model" to
"standard" to use standard momentum. Computation details are described subsequently.
Let "+", "-", "*", and "/" are all element-wise operations with numpy-style broadcasting.
Pseudo code for SG with standard momentum:
// Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
// values of all elements in X.
G_regularized = norm_coefficient * X + G
// In the first training iteration, beta should always be 1.
beta_adjusted = T > 0 ? beta : 1
// Compute the current momentum based on previous momentum and the current gradient.
V_new = alpha * V + beta_adjusted * G_regularized
// Update X.
X_new = X - R * V_new
Pseudo code for SG with Nesterov's momentum:
// Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
// values of all elements in X.
G_regularized = norm_coefficient * X + G;
// In the first training iteration, beta should always be 1.
beta_adjusted = T > 0 ? beta : 1
// Compute the current momentum based on previous momentum and the current gradient.
V_new = alpha * V + beta_adjusted * G_regularized;
// Compute final update direction and then update X.
X_new = X - R * (G_regularized + alpha * V_new)
If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2". The same
pseudo code would be extended to handle all tensors jointly. More specifically, we can view "X" as a
concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
be concatenated too) and then our pseudo code becomes applicable.
Version#
This version of the operator has been available since version 1 of the ‘ai.onnx.preview.training’ operator set.
Attributes#
- alpha : float (required)
- The decay factor of momentum. It should be a scalar.
- beta : float (required)
- The coefficient of gradient in computing new momentum. It should be a scalar.
- mode : string (required)
- Its value should be either "nesterov" or "standard". The value "nesterov" leads to the use of Nesterov's momentum while "standard" invokes stochastic gradient method using standard momentum
- norm_coefficient : float (required)
- Coefficient of 0.5 * norm_coefficient * ||X||^2.
Inputs (3 - ∞)#
- R : T1
- The learning rate.
- T : T2
- Update count of "X". It should be a scalar.
- inputs (variadic, heterogeneous) : T3
- It sequentially contains the current values of optimized tensors, then their gradient tensors, and finally their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, The expected input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", momentum of "X_1", momentum of "X_2"].
Outputs (1 - ∞)#
- outputs (variadic, heterogeneous) : T3
- It sequentially contains the new values of optimized tensors and then the new values of their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new momentum of "X_1", new momentum of "X_2"].
Type Constraints#
- T1 : tensor(float), tensor(double)
- Constrain input types to float scalars.
- T2 : tensor(int64)
- Constrain input types to 64-bit integer scalars.
- T3 : tensor(float), tensor(double)
- Constrain input types to float tensors.
Examples#
momentum
# Define operator attributes.
norm_coefficient = 0.001
alpha = 0.95
beta = 0.1
# Create operator.
node = onnx.helper.make_node(
"Momentum",
inputs=["R", "T", "X", "G", "V"],
outputs=["X_new", "V_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
mode="standard",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x = np.array([1.2, 2.8], dtype=np.float32)
g = np.array([-0.94, -2.5], dtype=np.float32)
v = np.array([1.7, 3.6], dtype=np.float32)
# Compute expected outputs of Momentum.
x_new, v_new = apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta)
# Check results.
expect(
node,
inputs=[r, t, x, g, v],
outputs=[x_new, v_new],
name="test_momentum",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
momentum_multiple
# Define operator attributes.
norm_coefficient = 0.001
alpha = 0.95
beta = 0.85
node = onnx.helper.make_node(
"Momentum",
inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"],
outputs=["X1_new", "X2_new", "V1_new", "V2_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
mode="standard",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x1 = np.array([1.0], dtype=np.float32)
g1 = np.array([-1.0], dtype=np.float32)
v1 = np.array([2.0], dtype=np.float32)
x2 = np.array([1.0, 2.0], dtype=np.float32)
g2 = np.array([-1.0, -3.0], dtype=np.float32)
v2 = np.array([4.0, 1.0], dtype=np.float32)
# Compute expected outputs of Momentum.
x1_new, v1_new = apply_momentum(r, t, x1, g1, v1, norm_coefficient, alpha, beta)
x2_new, v2_new = apply_momentum(r, t, x2, g2, v2, norm_coefficient, alpha, beta)
# Check results.
expect(
node,
inputs=[r, t, x1, x2, g1, g2, v1, v2],
outputs=[x1_new, x2_new, v1_new, v2_new],
name="test_momentum_multiple",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
nesterov_momentum
# Define operator attributes.
norm_coefficient = 0.01
alpha = 0.95
beta = 1.0
# Create operator.
node = onnx.helper.make_node(
"Momentum",
inputs=["R", "T", "X", "G", "V"],
outputs=["X_new", "V_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
mode="nesterov",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x = np.array([1.2, 2.8], dtype=np.float32)
g = np.array([-0.94, -2.5], dtype=np.float32)
v = np.array([1.7, 3.6], dtype=np.float32)
# Compute expected outputs of Momentum.
x_new, v_new = apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta)
# Check results.
expect(
node,
inputs=[r, t, x, g, v],
outputs=[x_new, v_new],
name="test_nesterov_momentum",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)