# 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 from onnx.reference.ops.op_resize import _cubic_coeffs as cubic_coeffs from onnx.reference.ops.op_resize import ( _cubic_coeffs_antialias as cubic_coeffs_antialias, ) from onnx.reference.ops.op_resize import _interpolate_nd as interpolate_nd from onnx.reference.ops.op_resize import _linear_coeffs as linear_coeffs from onnx.reference.ops.op_resize import ( _linear_coeffs_antialias as linear_coeffs_antialias, ) from onnx.reference.ops.op_resize import _nearest_coeffs as nearest_coeffs class Resize(Base): @staticmethod def export_resize_upsample_scales_nearest() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest", ) @staticmethod def export_resize_downsample_scales_nearest() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[1. 3.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_nearest", ) @staticmethod def export_resize_upsample_sizes_nearest() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 7, 8], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest", ) @staticmethod def export_resize_downsample_sizes_nearest() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 1, 3], dtype=np.int64) # [[[[1. 2. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest", ) @staticmethod def export_resize_upsample_scales_linear() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[1. 1.25 1.75 2. ] # [1.5 1.75 2.25 2.5 ] # [2.5 2.75 3.25 3.5 ] # [3. 3.25 3.75 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear", ) @staticmethod def export_resize_upsample_scales_linear_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[1. 1.33333333 1.66666667 2. ] # [1.66666667 2. 2.33333333 2.66666667] # [2.33333333 2.66666667 3. 3.33333333] # [3. 3.33333333 3.66666667 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear_align_corners", ) @staticmethod def export_resize_downsample_scales_linear() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[2.6666665 4.3333331]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear", ) @staticmethod def export_resize_downsample_scales_linear_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[1. 3.142857]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_align_corners", ) @staticmethod def export_resize_upsample_scales_cubic() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 0.47265625 0.76953125 1.24609375 1.875 2.28125 # 2.91015625 3.38671875 3.68359375] # [ 1.66015625 1.95703125 2.43359375 3.0625 3.46875 # 4.09765625 4.57421875 4.87109375] # [ 3.56640625 3.86328125 4.33984375 4.96875 5.375 # 6.00390625 6.48046875 6.77734375] # [ 6.08203125 6.37890625 6.85546875 7.484375 7.890625 # 8.51953125 8.99609375 9.29296875] # [ 7.70703125 8.00390625 8.48046875 9.109375 9.515625 # 10.14453125 10.62109375 10.91796875] # [10.22265625 10.51953125 10.99609375 11.625 12.03125 # 12.66015625 13.13671875 13.43359375] # [12.12890625 12.42578125 12.90234375 13.53125 13.9375 # 14.56640625 15.04296875 15.33984375] # [13.31640625 13.61328125 14.08984375 14.71875 15.125 # 15.75390625 16.23046875 16.52734375]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic", ) @staticmethod def export_resize_upsample_scales_cubic_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 1. 1.34110787 1.80029155 2.32944606 2.67055394 # 3.19970845 3.65889213 4. ] # [ 2.36443149 2.70553936 3.16472303 3.69387755 4.03498542 # 4.56413994 5.02332362 5.36443149] # [ 4.20116618 4.54227405 5.00145773 5.53061224 5.87172012 # 6.40087464 6.86005831 7.20116618] # [ 6.31778426 6.65889213 7.1180758 7.64723032 7.98833819 # 8.51749271 8.97667638 9.31778426] # [ 7.68221574 8.02332362 8.48250729 9.01166181 9.35276968 # 9.8819242 10.34110787 10.68221574] # [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776 # 11.99854227 12.45772595 12.79883382] # [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245 # 13.83527697 14.29446064 14.63556851] # [13. 13.34110787 13.80029155 14.32944606 14.67055394 # 15.19970845 15.65889213 16. ]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_align_corners", ) @staticmethod def export_resize_downsample_scales_cubic() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1.47119141 2.78125 4.08251953] # [ 6.71142578 8.02148438 9.32275391] # [11.91650391 13.2265625 14.52783203]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic", ) @staticmethod def export_resize_downsample_scales_cubic_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="align_corners", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1. 2.39519159 3.79038317] # [ 6.58076634 7.97595793 9.37114951] # [12.16153268 13.55672427 14.95191585]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), scale_factors=scales, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_align_corners", ) @staticmethod def export_resize_upsample_sizes_cubic() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 9, 10], dtype=np.int64) # [[[[ 0.45507922 0.64057922 0.97157922 1.42257922 1.90732922 # 2.22332922 2.70807922 3.15907922 3.49007922 3.67557922] # [ 1.39437963 1.57987963 1.91087963 2.36187963 2.84662963 # 3.16262963 3.64737963 4.09837963 4.42937963 4.61487963] # [ 2.95130693 3.13680693 3.46780693 3.91880693 4.40355693 # 4.71955693 5.20430693 5.65530693 5.98630693 6.17180693] # [ 5.20525069 5.39075069 5.72175069 6.17275069 6.65750069 # 6.97350069 7.45825069 7.90925069 8.24025069 8.42575069] # [ 6.88975 7.07525 7.40625 7.85725 8.342 # 8.658 9.14275 9.59375 9.92475 10.11025 ] # [ 8.57424931 8.75974931 9.09074931 9.54174931 10.02649931 # 10.34249931 10.82724931 11.27824931 11.60924931 11.79474931] # [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307 # 12.59644307 13.08119307 13.53219307 13.86319307 14.04869307] # [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037 # 14.15337037 14.63812037 15.08912037 15.42012037 15.60562037] # [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078 # 15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_cubic", ) @staticmethod def export_resize_downsample_sizes_cubic() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 1.63078704 3.00462963 4.37847222] # [ 7.12615741 8.5 9.87384259] # [12.62152778 13.99537037 15.36921296]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_cubic", ) # TensorFlow v1 bicubic with half_pixel_centers=True @staticmethod def export_resize_upsample_scales_cubic_A_n0p5_exclude_outside() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", cubic_coeff_a=-0.5, exclude_outside=True, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 0.55882353 0.81494204 1.35698249 1.89705882 2.39705882 # 2.93713516 3.47917561 3.73529412] # [ 1.58329755 1.83941606 2.38145651 2.92153285 3.42153285 # 3.96160918 4.50364964 4.75976814] # [ 3.75145936 4.00757787 4.54961832 5.08969466 5.58969466 # 6.12977099 6.67181144 6.92792995] # [ 5.91176471 6.16788321 6.70992366 7.25 7.75 # 8.29007634 8.83211679 9.08823529] # [ 7.91176471 8.16788321 8.70992366 9.25 9.75 # 10.29007634 10.83211679 11.08823529] # [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534 # 12.45038168 12.99242213 13.24854064] # [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715 # 14.61854349 15.16058394 15.41670245] # [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118 # 15.64301751 16.18505796 16.44117647]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.5), scale_factors=scales, exclude_outside=True, ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_A_n0p5_exclude_outside", ) @staticmethod def export_resize_downsample_scales_cubic_A_n0p5_exclude_outside() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", cubic_coeff_a=-0.5, exclude_outside=True, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32) # [[[[ 1.36812675 2.6695014 4.0133367 ] # [ 6.57362535 7.875 9.2188353 ] # [11.94896657 13.25034122 14.59417652]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.5), scale_factors=scales, exclude_outside=True, ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_A_n0p5_exclude_outside", ) # TensorFlow v1 bicubic with half_pixel_centers=False @staticmethod def export_resize_upsample_scales_cubic_asymmetric() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", coordinate_transformation_mode="asymmetric", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32) # [[[[ 1. 1.40625 2. 2.5 3. 3.59375 4. # 4.09375] # [ 2.625 3.03125 3.625 4.125 4.625 5.21875 5.625 # 5.71875] # [ 5. 5.40625 6. 6.5 7. 7.59375 8. # 8.09375] # [ 7. 7.40625 8. 8.5 9. 9.59375 10. # 10.09375] # [ 9. 9.40625 10. 10.5 11. 11.59375 12. # 12.09375] # [11.375 11.78125 12.375 12.875 13.375 13.96875 14.375 # 14.46875] # [13. 13.40625 14. 14.5 15. 15.59375 16. # 16.09375] # [13.375 13.78125 14.375 14.875 15.375 15.96875 16.375 # 16.46875]]]] output = interpolate_nd( data, lambda x, _: cubic_coeffs(x, A=-0.75), scale_factors=scales, coordinate_transformation_mode="asymmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_cubic_asymmetric", ) @staticmethod def export_resize_tf_crop_and_resize() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize", ) @staticmethod def export_resize_tf_crop_and_resize_extrapolation_value() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", extrapolation_value=10.0, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 7.6000004 10. 10. ] # [12.400001 10. 10. ] # [10. 10. 10. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, coordinate_transformation_mode="tf_crop_and_resize", extrapolation_value=10.0, ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize", ) @staticmethod def export_resize_downsample_sizes_linear_pytorch_half_pixel() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="pytorch_half_pixel", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 1], dtype=np.int64) # [[[[ 1.6666666] # [ 7. ] # [12.333333 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, coordinate_transformation_mode="pytorch_half_pixel", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_linear_pytorch_half_pixel", ) @staticmethod def export_resize_upsample_sizes_nearest_floor_align_corners() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="align_corners", nearest_mode="floor", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 1. 1. 1. 2. 2. 3. 3. 4.] # [ 5. 5. 5. 6. 6. 7. 7. 8.] # [ 5. 5. 5. 6. 6. 7. 7. 8.] # [ 9. 9. 9. 10. 10. 11. 11. 12.] # [ 9. 9. 9. 10. 10. 11. 11. 12.] # [13. 13. 13. 14. 14. 15. 15. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="floor"), output_size=sizes, coordinate_transformation_mode="align_corners", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_floor_align_corners", ) @staticmethod def export_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="round_prefer_ceil", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 2. 2. 3. 3. 4. 4. 4.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="round_prefer_ceil"), output_size=sizes, coordinate_transformation_mode="asymmetric", ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric", ) @staticmethod def export_resize_upsample_sizes_nearest_ceil_half_pixel() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", coordinate_transformation_mode="half_pixel", nearest_mode="ceil", ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 8, 8], dtype=np.int64) # [[[[ 1. 2. 2. 3. 3. 4. 4. 4.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 5. 6. 6. 7. 7. 8. 8. 8.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [ 9. 10. 10. 11. 11. 12. 12. 12.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.] # [13. 14. 14. 15. 15. 16. 16. 16.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x, mode="ceil"), output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_ceil_half_pixel", ) @staticmethod def export_resize_downsample_scales_linear_antialias() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[ 2.875 4.5 ] # [ 9.375 11. ]]]] output = interpolate_nd( data, linear_coeffs_antialias, scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_antialias", ) @staticmethod def export_resize_downsample_sizes_linear_antialias() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="linear", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 2.3636363 3.590909 4.818182 ] # [ 7.2727275 8.5 9.727273 ] # [12.181818 13.409091 14.636364 ]]]] output = interpolate_nd( data, linear_coeffs_antialias, output_size=sizes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_linear_antialias", ) @staticmethod def export_resize_downsample_scales_cubic_antialias() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="cubic", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32) # [[[[ 2.5180721 4.2858863] # [ 9.589329 11.357142 ]]]] output = interpolate_nd( data, cubic_coeffs_antialias, scale_factors=scales ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_cubic_antialias", ) @staticmethod def export_resize_downsample_sizes_cubic_antialias() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="cubic", antialias=1, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) sizes = np.array([1, 1, 3, 3], dtype=np.int64) # [[[[ 1.7750092 3.1200073 4.4650054] # [ 7.1550016 8.5 9.844998 ] # [12.534994 13.8799925 15.224991 ]]]] output = interpolate_nd(data, cubic_coeffs_antialias, output_size=sizes).astype( np.float32 ) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_cubic_antialias", ) @staticmethod def export_resize_upsample_scales_nearest_axes_2_3() -> None: axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([2.0, 3.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest_axes_2_3", ) @staticmethod def export_resize_upsample_scales_nearest_axes_3_2() -> None: axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) scales = np.array([3.0, 2.0], dtype=np.float32) # [[[[1. 1. 1. 2. 2. 2.] # [1. 1. 1. 2. 2. 2.] # [3. 3. 3. 4. 4. 4.] # [3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_nearest_axes_3_2", ) @staticmethod def export_resize_upsample_sizes_nearest_axes_2_3() -> None: axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_axes_2_3", ) @staticmethod def export_resize_upsample_sizes_nearest_axes_3_2() -> None: axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([8, 7], dtype=np.int64) # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_axes_3_2", ) @staticmethod def export_resize_tf_crop_and_resize_axes_2_3() -> None: axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", axes=axes, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0.4, 0.6, 0.6, 0.8], dtype=np.float32) sizes = np.array([3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, axes=axes, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_axes_2_3", ) @staticmethod def export_resize_tf_crop_and_resize_axes_3_2() -> None: axes = [3, 2] node = onnx.helper.make_node( "Resize", inputs=["X", "roi", "", "sizes"], outputs=["Y"], mode="linear", coordinate_transformation_mode="tf_crop_and_resize", axes=axes, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], ] ] ], dtype=np.float32, ) # Note: for some rois, the result may be different with that of TF for inaccurate floating point roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32) sizes = np.array([3, 3], dtype=np.int64) # [[[[ 7.6000004 7.9 8.2 ] # [ 8.8 9.1 9.400001 ] # [10. 10.3 10.6 ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), output_size=sizes, roi=roi, axes=axes, coordinate_transformation_mode="tf_crop_and_resize", ).astype(np.float32) expect( node, inputs=[data, roi, sizes], outputs=[output], name="test_resize_tf_crop_and_resize_axes_3_2", ) @staticmethod def export_resize_upsample_sizes_nearest_not_larger() -> None: keep_aspect_ratio_policy = "not_larger" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # Results in 7x7 # [[[[1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_not_larger", ) @staticmethod def export_resize_upsample_sizes_nearest_not_smaller() -> None: keep_aspect_ratio_policy = "not_smaller" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2], [3, 4], ] ] ], dtype=np.float32, ) sizes = np.array([7, 8], dtype=np.int64) # Results in 8x8 # [[[[1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [1. 1. 1. 1. 2. 2. 2. 2.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.] # [3. 3. 3. 3. 4. 4. 4. 4.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_upsample_sizes_nearest_not_larger", ) @staticmethod def export_resize_downsample_sizes_nearest_not_larger() -> None: keep_aspect_ratio_policy = "not_larger" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 3], dtype=np.int64) # Results in 1x2 # [[[[1. 3.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest_not_larger", ) @staticmethod def export_resize_downsample_sizes_nearest_not_smaller() -> None: keep_aspect_ratio_policy = "not_smaller" axes = [2, 3] node = onnx.helper.make_node( "Resize", inputs=["X", "", "", "sizes"], outputs=["Y"], mode="nearest", axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ) data = np.array( [ [ [ [1, 2, 3, 4], [5, 6, 7, 8], ] ] ], dtype=np.float32, ) sizes = np.array([1, 3], dtype=np.int64) # Results in 2x3 # [[[[1. 2. 4.] # [5. 6. 8.]]]] output = interpolate_nd( data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes, keep_aspect_ratio_policy=keep_aspect_ratio_policy, ).astype(np.float32) expect( node, inputs=[data, sizes], outputs=[output], name="test_resize_downsample_sizes_nearest_not_smaller", ) @staticmethod def export_resize_downsample_scales_linear_half_pixel_symmetric() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="half_pixel_symmetric", ) data = np.array([[[[1, 2, 3, 4]]]], dtype=np.float32) scales = np.array([1.0, 1.0, 1.0, 0.6], dtype=np.float32) # [[[[1.6666667, 3.3333333]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="half_pixel_symmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_downsample_scales_linear_half_pixel_symmetric", ) @staticmethod def export_resize_upsample_scales_linear_half_pixel_symmetric() -> None: node = onnx.helper.make_node( "Resize", inputs=["X", "", "scales"], outputs=["Y"], mode="linear", coordinate_transformation_mode="half_pixel_symmetric", ) data = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32) scales = np.array([1.0, 1.0, 2.3, 2.94], dtype=np.float32) # [[[[1. , 1.15986395, 1.5 , 1.84013605, 2. ], # [1.56521738, 1.72508133, 2.06521738, 2.40535343, 2.56521738], # [2.43478262, 2.59464657, 2.93478262, 3.27491867, 3.43478262], # [3. , 3.15986395, 3.5 , 3.84013605, 4. ]]]] output = interpolate_nd( data, lambda x, _: linear_coeffs(x), scale_factors=scales, coordinate_transformation_mode="half_pixel_symmetric", ).astype(np.float32) expect( node, inputs=[data, scales], outputs=[output], name="test_resize_upsample_scales_linear_half_pixel_symmetric", )