com.ms.internal.nhwc - ConvTranspose#
ConvTranspose - 1#
Version
name: ConvTranspose (GitHub)
domain: com.ms.internal.nhwc
since_version: 1
function:
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 1 of domain com.ms.internal.nhwc.
Summary
Attributes
activation - STRING :
activation_params - FLOATS :
auto_pad - STRING : auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
dilations - INTS : dilation value along each spatial axis of the filter.
group - INT : number of groups input channels and output channels are divided into.
kernel_shape - INTS : The shape of the convolution kernel. If not present, should be inferred from input W.
output_padding - INTS : The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.
output_shape - INTS : The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads
pads - INTS : Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. pads format should be as follow [x1_begin, x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added at the beginning of axis i and xi_end, the number of pixels added at the end of axis i. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides - INTS : Stride along each spatial axis.
Inputs
Between 2 and 3 inputs.
X (heterogeneous) - T:
W (heterogeneous) - T:
B (optional, heterogeneous) - T:
Outputs
Y (heterogeneous) - T:
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.
Examples
default
import numpy as np
import onnx
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
import numpy as np
import onnx
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
import numpy as np
import onnx
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
import numpy as np
import onnx
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_pads
import numpy as np
import onnx
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")
_convtranspose_dilations
import numpy as np
import onnx
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_autopad_same
import numpy as np
import onnx
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")