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| # Copyright (c) ONNX Project Contributors | |
| # | |
| # SPDX-License-Identifier: Apache-2.0 | |
| import numpy as np | |
| import onnx | |
| from onnx.backend.test.case.base import Base | |
| from onnx.backend.test.case.node import expect | |
| class Conv(Base): | |
| def export() -> None: | |
| 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", | |
| ) | |
| def export_conv_with_strides() -> None: | |
| 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", | |
| ) | |
| def export_conv_with_autopad_same() -> None: | |
| 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") | |