# Test Coverage Report (ONNX Core Operators)
## Outlines
* [Node Test Coverage](#node-test-coverage)
* [Model Test Coverage](#model-test-coverage)
* [Overall Test Coverage](#overall-test-coverage)
# Node Test Coverage
## Summary
Node tests have covered 170/185 (91.89%, 5 generators excluded) common operators.
Node tests have covered 0/0 (N/A) experimental operators.
* [Covered Common Operators](#covered-common-operators)
* [No Cover Common Operators](#no-cover-common-operators)
* [Covered Experimental Operators](#covered-experimental-operators)
* [No Cover Experimental Operators](#no-cover-experimental-operators)
## 💚Covered Common Operators
### Abs
There are 1 test cases, listed as following:
abs
```python
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")
```
### Acos
There are 1 test cases, listed as following:
acos
```python
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
There are 1 test cases, listed as following:
acosh
```python
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")
```
### Adagrad
There are 2 test cases, listed as following:
adagrad
```python
# 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
```python
# 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)
],
)
```
### Adam
There are 2 test cases, listed as following:
adam
```python
# 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
```python
# 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)
],
)
```
### Add
There are 3 test cases, listed as following:
add
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
and
```python
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
```python
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
There are 8 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 8 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
asin
```python
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
There are 1 test cases, listed as following:
asinh
```python
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
There are 1 test cases, listed as following:
atan
```python
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
There are 1 test cases, listed as following:
atanh
```python
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
There are 13 test cases, listed as following:
averagepool_1d_default
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
There are 2 test cases, listed as following:
batchnormalization
```python
# 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
```python
# 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
There are 3 test cases, listed as following:
bernoulli_with_dtype
```python
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
```python
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
```python
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
There are 8 test cases, listed as following:
left_unit16
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
bitwiseand
```python
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
```python
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
There are 1 test cases, listed as following:
bitwisenot
```python
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
There are 2 test cases, listed as following:
bitwiseor
```python
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
```python
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
There are 2 test cases, listed as following:
bitwiseor_broadcast
```python
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
```python
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
There are 1 test cases, listed as following:
blackmanwindow
```python
# 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
There are 1 test cases, listed as following:
cast
```python
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
There are 1 test cases, listed as following:
castlike
```python
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
There are 1 test cases, listed as following:
ceil
```python
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
There are 1 test cases, listed as following:
celu
```python
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
There are 5 test cases, listed as following:
center_crop_pad_crop
```python
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
```python
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
```python
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
```python
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
```python
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
There are 3 test cases, listed as following:
clip
```python
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
```python
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
```python
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
There are 5 test cases, listed as following:
col2im
```python
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
```python
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
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
compress_0
```python
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
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
concat
```python
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)),
)
```
### Constant
There are 1 test cases, listed as following:
constant
```python
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
There are 3 test cases, listed as following:
float_ones
```python
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
```python
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
```python
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
There are 3 test cases, listed as following:
conv
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
with_padding
```python
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
```python
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
There are 7 test cases, listed as following:
convtranspose
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
cos
```python
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
There are 1 test cases, listed as following:
cosh
```python
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
There are 7 test cases, listed as following:
cumsum_1d
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
dft
```python
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
There are 2 test cases, listed as following:
crd_mode_example
```python
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
```python
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
There are 2 test cases, listed as following:
axis
```python
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
```python
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
There are 2 test cases, listed as following:
2d
```python
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
```python
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
There are 2 test cases, listed as following:
div
```python
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
```python
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
There are 12 test cases, listed as following:
default
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
dynamicquantizelinear
```python
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
There are 5 test cases, listed as following:
einsum_batch_diagonal
```python
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
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
elu
```python
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
```python
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
There are 2 test cases, listed as following:
equal
```python
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
```python
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
There are 1 test cases, listed as following:
erf
```python
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
There are 1 test cases, listed as following:
exp
```python
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
There are 2 test cases, listed as following:
dim_changed
```python
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
```python
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
There are 3 test cases, listed as following:
populate_off_main_diagonal
```python
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
```python
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
```python
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
There are 3 test cases, listed as following:
flatten
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
floor
```python
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
There are 4 test cases, listed as following:
batchwise
```python
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
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
gather_0
```python
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
```python
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
```python
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
```python
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
There are 3 test cases, listed as following:
gather_elements_0
```python
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
```python
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
```python
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
There are 3 test cases, listed as following:
float32
```python
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
```python
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
```python
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
There are 11 test cases, listed as following:
all_attributes
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
globalaveragepool
```python
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
```python
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")
```
### GlobalMaxPool
There are 2 test cases, listed as following:
globalmaxpool
```python
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
```python
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")
```
### Gradient
There are 2 test cases, listed as following:
gradient_scalar_add
```python
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
```python
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",
)
```
### Greater
There are 4 test cases, listed as following:
greater
```python
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
```python
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
```python
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
```python
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")
```
### GridSample
There are 3 test cases, listed as following:
gridsample
```python
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
```python
# 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
```python
# 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
There are 1 test cases, listed as following:
groupnormalization
```python
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
There are 1 test cases, listed as following:
hammingwindow
```python
# 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
There are 1 test cases, listed as following:
hannwindow
```python
# 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
There are 2 test cases, listed as following:
hardsigmoid
```python
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
```python
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
There are 1 test cases, listed as following:
hardswish
```python
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
There are 2 test cases, listed as following:
hardmax
```python
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
```python
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
There are 3 test cases, listed as following:
identity
```python
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
```python
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
```python
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
There are 3 test cases, listed as following:
if
```python
# 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
```python
# 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
```python
# 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
There are 1 test cases, listed as following:
instancenormalization
```python
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
There are 3 test cases, listed as following:
infinity
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
isnan
```python
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
There are 2 test cases, listed as following:
default
```python
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
```python
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
There are 4 test cases, listed as following:
batchwise
```python
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
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
d
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
leakyrelu
```python
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
```python
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
There are 4 test cases, listed as following:
less
```python
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
```python
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
```python
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
```python
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")
```
### Log
There are 1 test cases, listed as following:
log
```python
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
There are 2 test cases, listed as following:
logsoftmax
```python
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
```python
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
There are 3 test cases, listed as following:
loop_11
```python
# 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
```python
# 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
```python
# 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,
],
)
```
### MatMul
There are 1 test cases, listed as following:
matmul
```python
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
There are 1 test cases, listed as following:
matmulinteger
```python
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
There are 2 test cases, listed as following:
max
```python
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
```python
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
There are 15 test cases, listed as following:
maxpool_1d_default
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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
```python
"""
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",
)
```
### MaxUnpool
There are 2 test cases, listed as following:
with_output_shape
```python
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
```python
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
There are 1 test cases, listed as following:
mean
```python
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
There are 1 test cases, listed as following:
meanvariancenormalization
```python
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
There are 1 test cases, listed as following:
melweightmatrix
```python
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
There are 2 test cases, listed as following:
min
```python
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
```python
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
There are 1 test cases, listed as following:
mish
```python
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
There are 13 test cases, listed as following:
mod_broadcast
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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")
```
### Momentum
There are 3 test cases, listed as following:
momentum
```python
# 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
```python
# 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
```python
# 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)
],
)
```
### Mul
There are 2 test cases, listed as following:
mul
```python
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
```python
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")
```
### Neg
There are 1 test cases, listed as following:
neg
```python
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
There are 18 test cases, listed as following:
input_shape_is_NC
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 9 test cases, listed as following:
nonmaxsuppression_center_point_box_format
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
nonzero
```python
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
There are 1 test cases, listed as following:
not
```python
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
There are 4 test cases, listed as following:
with_axis
```python
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
```python
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
```python
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
```python
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",
)
```
### OptionalHasElement
There are 4 test cases, listed as following:
empty
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
or
```python
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
```python
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
There are 2 test cases, listed as following:
prelu
```python
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
```python
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
There are 3 test cases, listed as following:
constant_pad
```python
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
```python
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
```python
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
There are 3 test cases, listed as following:
pow
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
qlinearconv
```python
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
There are 1 test cases, listed as following:
qlinearmatmul
```python
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
There are 2 test cases, listed as following:
axis
```python
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
```python
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
There are 4 test cases, listed as following:
batchwise
```python
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
```python
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
```python
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
```python
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",
)
```
### Range
There are 2 test cases, listed as following:
range_float_type_positive_delta
```python
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
```python
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
There are 1 test cases, listed as following:
reciprocal
```python
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
There are 4 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
There are 3 test cases, listed as following:
keepdims
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
There are 5 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
```python
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
There are 4 test cases, listed as following:
default_axes_keepdims
```python
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
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
relu
```python
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
There are 2 test cases, listed as following:
allowzero
```python
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
```python
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
There are 37 test cases, listed as following:
resize_downsample_scales_cubic
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
reversesequence_batch
```python
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
```python
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
There are 2 test cases, listed as following:
roialign_aligned_false
```python
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
```python
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
There are 1 test cases, listed as following:
round
```python
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
There are 1 test cases, listed as following:
stft
```python
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
There are 2 test cases, listed as following:
scan_8
```python
# 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
```python
# 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
There are 2 test cases, listed as following:
scatter_with_axis
```python
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
```python
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
There are 6 test cases, listed as following:
scatter_elements_with_axis
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 5 test cases, listed as following:
scatternd
```python
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
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
selu
```python
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
```python
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")
```
### SequenceInsert
There are 1 test cases, listed as following:
sequenceinsert
```python
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,
)
```
### SequenceMap
There are 6 test cases, listed as following:
sequence_map_add_1_sequence_1_tensor
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
shape
```python
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
There are 2 test cases, listed as following:
hard_shrink
```python
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
```python
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
There are 1 test cases, listed as following:
sigmoid
```python
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
There are 1 test cases, listed as following:
sign
```python
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
There are 1 test cases, listed as following:
sin
```python
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
There are 1 test cases, listed as following:
sinh
```python
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
There are 1 test cases, listed as following:
size
```python
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
There are 8 test cases, listed as following:
slice
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
softmax
```python
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
```python
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
There are 34 test cases, listed as following:
input_shape_is_NCd1_mean_weight_negative_ii
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
```python
# 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
There are 1 test cases, listed as following:
softplus
```python
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
There are 1 test cases, listed as following:
softsign
```python
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
There are 2 test cases, listed as following:
example
```python
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
```python
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
There are 10 test cases, listed as following:
1d_opset13
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
# 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
```python
# 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",
)
```
### Sqrt
There are 1 test cases, listed as following:
sqrt
```python
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
There are 2 test cases, listed as following:
squeeze
```python
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
```python
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
There are 6 test cases, listed as following:
monday_casesensintive_lower
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
sub
```python
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
```python
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
There are 1 test cases, listed as following:
sum
```python
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
There are 1 test cases, listed as following:
tan
```python
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
There are 1 test cases, listed as following:
tanh
```python
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
There are 7 test cases, listed as following:
tf_batch_onlybigrams_skip0
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
default
```python
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
```python
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
There are 2 test cases, listed as following:
tile
```python
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
```python
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
There are 3 test cases, listed as following:
top_k
```python
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
```python
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
```python
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
There are 2 test cases, listed as following:
all_permutations
```python
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
```python
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
There are 18 test cases, listed as following:
tril
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
There are 5 test cases, listed as following:
not_sorted_without_axis
```python
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
```python
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
```python
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
```python
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
```python
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
There are 5 test cases, listed as following:
unsqueeze_negative_axes
```python
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
```python
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
```python
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
```python
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
```python
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
There are 1 test cases, listed as following:
nearest
```python
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
There are 2 test cases, listed as following:
long
```python
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
```python
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
There are 2 test cases, listed as following:
xor
```python
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
```python
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")
```
## 💔No Cover Common Operators
### ConcatFromSequence (call for test cases)
### GlobalLpPool (call for test cases)
### GreaterOrEqual (call for test cases)
### LessOrEqual (call for test cases)
### LpNormalization (call for test cases)
### LpPool (call for test cases)
### MaxRoiPool (call for test cases)
### Multinomial (random generator operator)
### Optional (call for test cases)
### OptionalGetElement (call for test cases)
### RandomNormal (random generator operator)
### RandomNormalLike (random generator operator)
### RandomUniform (random generator operator)
### RandomUniformLike (random generator operator)
### SequenceAt (call for test cases)
### SequenceConstruct (call for test cases)
### SequenceEmpty (call for test cases)
### SequenceErase (call for test cases)
### SequenceLength (call for test cases)
### SplitToSequence (call for test cases)
## 💚Covered Experimental Operators
## 💔No Cover Experimental Operators
# Model Test Coverage
## bvlc_alexnet
bvlc_alexnet has 24 nodes. Of these, 24 are covered by node tests (100.0%)
nodes
Conv: 4 out of 6 attributes covered
auto_pad: 0
dilations: 0
group: 1
kernel_shape: 3
pads: 3
strides: 2
Dropout: 1 out of 1 attributes covered
seed: 0
Gemm: 1 out of 4 attributes covered
alpha: 0
beta: 0
transA: 0
transB: 1
LRN: 4 out of 4 attributes covered
alpha: 1
beta: 1
bias: 1
size: 1
MaxPool: 3 out of 7 attributes covered
auto_pad: 0
ceil_mode: 0
dilations: 0
kernel_shape: 1
pads: 2
storage_order: 0
strides: 1
## densenet121
densenet121 has 910 nodes. Of these, 910 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 6 attributes covered
auto_pad: 0
ceil_mode: 0
count_include_pad: 0
kernel_shape: 1
pads: 1
strides: 1
BatchNormalization: 1 out of 3 attributes covered
epsilon: 1
momentum: 0
training_mode: 0
Concat: 1 out of 1 attributes covered
axis: 1
Conv: 4 out of 6 attributes covered
auto_pad: 0
dilations: 0
group: 1
kernel_shape: 5
pads: 4
strides: 3
Dropout: 1 out of 1 attributes covered
seed: 0
Gemm: 1 out of 4 attributes covered
alpha: 0
beta: 0
transA: 0
transB: 1
LRN: 4 out of 4 attributes covered
alpha: 1
beta: 1
bias: 1
size: 1
MaxPool: 3 out of 7 attributes covered
auto_pad: 0
ceil_mode: 0
dilations: 0
kernel_shape: 1
pads: 3
storage_order: 0
strides: 1
Unsqueeze: 1 out of 0 attributes covered
## inception_v1
inception_v1 has 144 nodes. Of these, 144 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 6 attributes covered
auto_pad: 0
ceil_mode: 0
count_include_pad: 0
kernel_shape: 2
pads: 2
strides: 2
BatchNormalization: 1 out of 3 attributes covered
epsilon: 1
momentum: 0
training_mode: 0
Concat: 1 out of 1 attributes covered
axis: 1
Conv: 4 out of 6 attributes covered
auto_pad: 0
dilations: 0
group: 1
kernel_shape: 5
pads: 4
strides: 3
Dropout: 1 out of 1 attributes covered
seed: 0
Gemm: 1 out of 4 attributes covered
alpha: 0
beta: 0
transA: 0
transB: 1
LRN: 4 out of 4 attributes covered
alpha: 1
beta: 1
bias: 1
size: 1
MaxPool: 3 out of 7 attributes covered
auto_pad: 0
ceil_mode: 0
dilations: 0
kernel_shape: 1
pads: 3
storage_order: 0
strides: 2
Unsqueeze: 1 out of 0 attributes covered
## inception_v2
inception_v2 has 509 nodes. Of these, 509 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 6 attributes covered
auto_pad: 0
ceil_mode: 0
count_include_pad: 0
kernel_shape: 3
pads: 3
strides: 2
BatchNormalization: 1 out of 3 attributes covered
epsilon: 1
momentum: 0
training_mode: 0
Concat: 1 out of 1 attributes covered
axis: 1
Conv: 4 out of 6 attributes covered
auto_pad: 0
dilations: 0
group: 1
kernel_shape: 5
pads: 4
strides: 3
Dropout: 1 out of 1 attributes covered
seed: 0
Gemm: 1 out of 4 attributes covered
alpha: 0
beta: 0
transA: 0
transB: 1
LRN: 4 out of 4 attributes covered
alpha: 1
beta: 1
bias: 1
size: 1
MaxPool: 3 out of 7 attributes covered
auto_pad: 0
ceil_mode: 0
dilations: 0
kernel_shape: 1
pads: 3
storage_order: 0
strides: 2
Unsqueeze: 1 out of 0 attributes covered
## resnet50
resnet50 has 176 nodes. Of these, 176 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 6 attributes covered
auto_pad: 0
ceil_mode: 0
count_include_pad: 0
kernel_shape: 3
pads: 3
strides: 2
BatchNormalization: 1 out of 3 attributes covered
epsilon: 2
momentum: 0
training_mode: 0
Concat: 1 out of 1 attributes covered
axis: 1
Conv: 4 out of 6 attributes covered
auto_pad: 0
dilations: 0
group: 1
kernel_shape: 5
pads: 4
strides: 3
Dropout: 1 out of 1 attributes covered
seed: 0
Gemm: 1 out of 4 attributes covered
alpha: 0
beta: 0
transA: 0
transB: 1
LRN: 4 out of 4 attributes covered
alpha: 1
beta: 1
bias: 1
size: 1
MaxPool: 3 out of 7 attributes covered
auto_pad: 0
ceil_mode: 0
dilations: 0
kernel_shape: 1
pads: 3
storage_order: 0
strides: 2
Unsqueeze: 1 out of 0 attributes covered
## shufflenet
shufflenet has 203 nodes. Of these, 203 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 6 attributes covered
auto_pad: 0
ceil_mode: 0
count_include_pad: 0
kernel_shape: 3
pads: 3
strides: 2
BatchNormalization: 1 out of 3 attributes covered
epsilon: 2
momentum: 0
training_mode: 0
Concat: 1 out of 1 attributes covered
axis: 1
Conv: 4 out of 6 attributes covered
auto_pad: 0
dilations: 0
group: 6
kernel_shape: 5
pads: 4
strides: 3
Dropout: 1 out of 1 attributes covered
seed: 0
Gemm: 1 out of 4 attributes covered
alpha: 0
beta: 0
transA: 0
transB: 1
LRN: 4 out of 4 attributes covered
alpha: 1
beta: 1
bias: 1
size: 1
MaxPool: 3 out of 7 attributes covered
auto_pad: 0
ceil_mode: 0
dilations: 0
kernel_shape: 1
pads: 3
storage_order: 0
strides: 2
Transpose: 1 out of 1 attributes covered
perm: 1
Unsqueeze: 1 out of 0 attributes covered
## squeezenet_old
squeezenet_old has 66 nodes. Of these, 66 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 6 attributes covered
auto_pad: 0
ceil_mode: 0
count_include_pad: 0
kernel_shape: 3
pads: 3
strides: 2
BatchNormalization: 1 out of 3 attributes covered
epsilon: 2
momentum: 0
training_mode: 0
Concat: 1 out of 1 attributes covered
axis: 1
Conv: 4 out of 6 attributes covered
auto_pad: 0
dilations: 0
group: 6
kernel_shape: 5
pads: 4
strides: 3
Dropout: 1 out of 1 attributes covered
seed: 0
Gemm: 1 out of 4 attributes covered
alpha: 0
beta: 0
transA: 0
transB: 1
LRN: 4 out of 4 attributes covered
alpha: 1
beta: 1
bias: 1
size: 1
MaxPool: 3 out of 7 attributes covered
auto_pad: 0
ceil_mode: 0
dilations: 0
kernel_shape: 1
pads: 3
storage_order: 0
strides: 2
Transpose: 1 out of 1 attributes covered
perm: 1
Unsqueeze: 1 out of 0 attributes covered
## vgg19
vgg19 has 46 nodes. Of these, 46 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 6 attributes covered
auto_pad: 0
ceil_mode: 0
count_include_pad: 0
kernel_shape: 3
pads: 3
strides: 2
BatchNormalization: 1 out of 3 attributes covered
epsilon: 2
momentum: 0
training_mode: 0
Concat: 1 out of 1 attributes covered
axis: 1
Conv: 4 out of 6 attributes covered
auto_pad: 0
dilations: 0
group: 6
kernel_shape: 5
pads: 4
strides: 3
Dropout: 1 out of 1 attributes covered
seed: 0
Gemm: 1 out of 4 attributes covered
alpha: 0
beta: 0
transA: 0
transB: 1
LRN: 4 out of 4 attributes covered
alpha: 1
beta: 1
bias: 1
size: 1
MaxPool: 3 out of 7 attributes covered
auto_pad: 0
ceil_mode: 0
dilations: 0
kernel_shape: 2
pads: 3
storage_order: 0
strides: 2
Transpose: 1 out of 1 attributes covered
perm: 1
Unsqueeze: 1 out of 0 attributes covered
## zfnet512
zfnet512 has 22 nodes. Of these, 22 are covered by node tests (100.0%)
nodes
AveragePool: 3 out of 6 attributes covered
auto_pad: 0
ceil_mode: 0
count_include_pad: 0
kernel_shape: 3
pads: 3
strides: 2
BatchNormalization: 1 out of 3 attributes covered
epsilon: 2
momentum: 0
training_mode: 0
Concat: 1 out of 1 attributes covered
axis: 1
Conv: 4 out of 6 attributes covered
auto_pad: 0
dilations: 0
group: 6
kernel_shape: 5
pads: 4
strides: 3
Dropout: 1 out of 1 attributes covered
seed: 0
Gemm: 1 out of 4 attributes covered
alpha: 0
beta: 0
transA: 0
transB: 1
LRN: 4 out of 4 attributes covered
alpha: 2
beta: 1
bias: 2
size: 1
MaxPool: 3 out of 7 attributes covered
auto_pad: 0
ceil_mode: 0
dilations: 0
kernel_shape: 2
pads: 3
storage_order: 0
strides: 2
Transpose: 1 out of 1 attributes covered
perm: 1
Unsqueeze: 1 out of 0 attributes covered
# Overall Test Coverage
## To be filled.