EyeLike#

EyeLike - 9 #

Version

name: EyeLike (GitHub)
domain: main
since_version: 9
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 9.

Summary

Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the same as the input tensor. The data type can be specified by the ‘dtype’ argument. If ‘dtype’ is not specified, then the type of input tensor is used. By default, the main diagonal is populated with ones, but attribute ‘k’ can be used to populate upper or lower diagonals. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message and be valid as an output type.

Attributes

dtype: (Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to ‘float’.
k: (Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal. Default value is 0.

Inputs

input (heterogeneous) - T1: 2D input tensor to copy shape, and optionally, type information from.

Outputs

output (heterogeneous) - T2: Output tensor, same shape as input tensor T1.

Type Constraints

T1 in ( tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input types. Strings and complex are not supported.
T2 in ( tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain output types. Strings and complex are not supported.

Examples

_without_dtype

shape = (4, 4)
node = onnx.helper.make_node(
    "EyeLike",
    inputs=["x"],
    outputs=["y"],
)

x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], dtype=np.int32)
expect(node, inputs=[x], outputs=[y], name="test_eyelike_without_dtype")

_with_dtype

shape = (3, 4)
node = onnx.helper.make_node(
    "EyeLike",
    inputs=["x"],
    outputs=["y"],
    dtype=onnx.TensorProto.DOUBLE,
)

x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], dtype=np.float64)
expect(node, inputs=[x], outputs=[y], name="test_eyelike_with_dtype")

_populate_off_main_diagonal

shape = (4, 5)
off_diagonal_offset = 1
node = onnx.helper.make_node(
    "EyeLike",
    inputs=["x"],
    outputs=["y"],
    k=off_diagonal_offset,
    dtype=onnx.TensorProto.FLOAT,
)

x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32)
expect(
    node,
    inputs=[x],
    outputs=[y],
    name="test_eyelike_populate_off_main_diagonal",
)

EyeLike#

EyeLike - 9#

EyeLike - 9 #