com.ms.internal.nhwc - Conv#
Conv - 11#
Version
name: Conv (GitHub)
domain: com.ms.internal.nhwc
since_version: 11
function:
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 11 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 output_shape[i] = ceil(input_shape[i] / strides[i]) for each axis i. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
dilations - INTS : dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.
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.
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. If not present, the stride defaults is 1 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], # (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
import numpy as np
import onnx
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
import numpy as np
import onnx
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")