# 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 ConvTranspose(Base): @staticmethod def export() -> None: 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") @staticmethod def export_convtranspose_1d() -> None: 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") @staticmethod def export_convtranspose_3d() -> None: 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") @staticmethod def export_convtranspose_attributes() -> None: 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") @staticmethod def export_convtranspose_pads() -> None: 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") @staticmethod def export_convtranspose_dilations() -> None: 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") @staticmethod def export_convtranspose_autopad_same() -> None: 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")