com.microsoft.nchwc - Conv#

Conv - 1 (com.microsoft.nchwc)#

Version

  • name: Conv (GitHub)

  • domain: com.microsoft.nchwc

  • since_version: 1

  • function:

  • support_level:

  • shape inference:

This version of the operator has been available since version 1 of domain com.microsoft.nchwc.

Summary

For internal use.

Attributes

  • activation:

Default value is ?.

  • activation_params:

Default value is ?.

  • auto_pad:

Default value is ?.

  • dilations:

Default value is ?.

  • group:

Default value is ?.

  • kernel_shape:

Default value is ?.

  • pads:

Default value is ?.

  • strides:

Default value is ?.

Inputs

Between 2 and 4 inputs.

  • X (heterogeneous) - T:

  • W (heterogeneous) - T:

  • B (optional, heterogeneous) - T:

  • Sum (optional, heterogeneous) - T:

Outputs

  • Y (heterogeneous) - T:

Examples

default

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_strides

x = np.array(
    [
        [
            [
                [0.0, 1.0, 2.0, 3.0, 4.0],  # (1, 1, 7, 5) input tensor
                [5.0, 6.0, 7.0, 8.0, 9.0],
                [10.0, 11.0, 12.0, 13.0, 14.0],
                [15.0, 16.0, 17.0, 18.0, 19.0],
                [20.0, 21.0, 22.0, 23.0, 24.0],
                [25.0, 26.0, 27.0, 28.0, 29.0],
                [30.0, 31.0, 32.0, 33.0, 34.0],
            ]
        ]
    ]
).astype(np.float32)
W = np.array(
    [
        [
            [
                [1.0, 1.0, 1.0],  # (1, 1, 3, 3) tensor for convolution weights
                [1.0, 1.0, 1.0],
                [1.0, 1.0, 1.0],
            ]
        ]
    ]
).astype(np.float32)

# Convolution with strides=2 and padding
node_with_padding = onnx.helper.make_node(
    "Conv",
    inputs=["x", "W"],
    outputs=["y"],
    kernel_shape=[3, 3],
    pads=[1, 1, 1, 1],
    strides=[
        2,
        2,
    ],  # Default values for other attributes: dilations=[1, 1], groups=1
)
y_with_padding = np.array(
    [
        [
            [
                [12.0, 27.0, 24.0],  # (1, 1, 4, 3) output tensor
                [63.0, 108.0, 81.0],
                [123.0, 198.0, 141.0],
                [112.0, 177.0, 124.0],
            ]
        ]
    ]
).astype(np.float32)
expect(
    node_with_padding,
    inputs=[x, W],
    outputs=[y_with_padding],
    name="test_conv_with_strides_padding",
)

# Convolution with strides=2 and no padding
node_without_padding = onnx.helper.make_node(
    "Conv",
    inputs=["x", "W"],
    outputs=["y"],
    kernel_shape=[3, 3],
    pads=[0, 0, 0, 0],
    strides=[
        2,
        2,
    ],  # Default values for other attributes: dilations=[1, 1], groups=1
)
y_without_padding = np.array(
    [
        [
            [
                [54.0, 72.0],  # (1, 1, 3, 2) output tensor
                [144.0, 162.0],
                [234.0, 252.0],
            ]
        ]
    ]
).astype(np.float32)
expect(
    node_without_padding,
    inputs=[x, W],
    outputs=[y_without_padding],
    name="test_conv_with_strides_no_padding",
)

# Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor)
node_with_asymmetric_padding = onnx.helper.make_node(
    "Conv",
    inputs=["x", "W"],
    outputs=["y"],
    kernel_shape=[3, 3],
    pads=[1, 0, 1, 0],
    strides=[
        2,
        2,
    ],  # Default values for other attributes: dilations=[1, 1], groups=1
)
y_with_asymmetric_padding = np.array(
    [
        [
            [
                [21.0, 33.0],  # (1, 1, 4, 2) output tensor
                [99.0, 117.0],
                [189.0, 207.0],
                [171.0, 183.0],
            ]
        ]
    ]
).astype(np.float32)
expect(
    node_with_asymmetric_padding,
    inputs=[x, W],
    outputs=[y_with_asymmetric_padding],
    name="test_conv_with_strides_and_asymmetric_padding",
)

_conv_with_autopad_same

x = np.array(
    [
        [
            [
                [0.0, 1.0, 2.0, 3.0, 4.0],  # (1, 1, 5, 5) input tensor
                [5.0, 6.0, 7.0, 8.0, 9.0],
                [10.0, 11.0, 12.0, 13.0, 14.0],
                [15.0, 16.0, 17.0, 18.0, 19.0],
                [20.0, 21.0, 22.0, 23.0, 24.0],
            ]
        ]
    ]
).astype(np.float32)
W = np.array(
    [
        [
            [
                [1.0, 1.0, 1.0],  # (1, 1, 3, 3) tensor for convolution weights
                [1.0, 1.0, 1.0],
                [1.0, 1.0, 1.0],
            ]
        ]
    ]
).astype(np.float32)

# Convolution with auto_pad='SAME_LOWER' and strides=2
node = onnx.helper.make_node(
    "Conv",
    inputs=["x", "W"],
    outputs=["y"],
    auto_pad="SAME_LOWER",
    kernel_shape=[3, 3],
    strides=[2, 2],
)
y = np.array(
    [[[[12.0, 27.0, 24.0], [63.0, 108.0, 81.0], [72.0, 117.0, 84.0]]]]
).astype(np.float32)
expect(node, inputs=[x, W], outputs=[y], name="test_conv_with_autopad_same")