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| }, |
| "cases": [ |
| { |
| "name": "linear_align_corners_exact_subnormal_pixel_gpu_gap", |
| "skipGpu": { |
| "category": "permanent", |
| "reason": "Portable WGSL floating-point semantics do not guarantee preservation of the subnormal values required by this fixture. Backend evidence: WebGPU f32 flushes subnormals to zero (Metal/Dawn FTZ), so a positive-subnormal payload (1e-40) carried through an exact-pixel linear resize cannot survive on the GPU; CPU-reference-only." |
| }, |
| "provenance": { |
| "notes": "Linear align_corners resize to a singleton spatial output samples the exact top-left source pixel, so a positive subnormal payload should survive." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 2], |
| "data": { "kind": "values", "values": [1e-40, 0.0, 0.0, 0.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "nearest_half_pixel_2x_f32", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_floor" |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 4] } } |
| }, |
| { |
| "name": "linear_align_corners_f32", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 3, 3] } }, |
| "tolerance": 0.000001 |
| }, |
| { |
| "name": "linear_f16", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 1, 4, 4] } }, |
| "tolerance": 0.002 |
| }, |
| { |
| "name": "nearest_round_prefer_ceil_ties", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "asymmetric", |
| "nearest_mode": "round_prefer_ceil" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 4], |
| "data": { "kind": "values", "values": [10.0, 20.0, 30.0, 40.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "nearest_round_prefer_floor_near_half_scale", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestUpSampleTest", |
| "notes": "Valid near-half source coordinate: output column 1 maps to 0.5000006, which is above the round_prefer_floor tie and should select input column 1." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_floor", |
| "scales": [1, 1, 1, 1.499999] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 1, 2], "data": { "kind": "values", "values": [10.0, 20.0] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 2], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [10.0, 20.0] } |
| } |
| } |
| }, |
| { |
| "name": "nearest_asymmetric_inferred_scale_half_tie", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpNearestDownSampleTest_tf", |
| "notes": "With sizes-driven 7-to-2 downsampling, output column 1 maps to exactly 3.5; round_prefer_ceil must choose source column 4, not an f32-rounded column 3." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "asymmetric", |
| "nearest_mode": "round_prefer_ceil" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 7], |
| "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 2], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [0.0, 4.0] } |
| } |
| } |
| }, |
| { |
| "name": "nearest_floor_align_corners_ort_4x4_to_8x8", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "align_corners", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 8, 8], "tolerance": 0 } }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestUpSample_Floor_Align_Corners" |
| } |
| }, |
| { |
| "name": "nearest_round_prefer_ceil_half_pixel_ort_2x2_to_7x8", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_ceil" |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 7, 8], "tolerance": 0 } }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestUpSample_RoundPreferCeil_HalfPixel_2x2to7x8" |
| } |
| }, |
| { |
| "name": "nearest_round_prefer_ceil_half_pixel_tie", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_ceil" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 20], |
| "data": { |
| "kind": "values", |
| "values": [0.0, 0.052631579, 0.105263158, 0.157894737, 0.210526316, 0.263157895, 0.315789474, 0.368421053, 0.421052632, 0.473684211, 0.526315789, 0.578947368, 0.631578947, 0.684210526, 0.736842105, 0.789473684, 0.842105263, 0.894736842, 0.947368421, 1.0] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 6], "tolerance": 0.000001 } }, |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestUpSample_RoundPreferCeil_HalfPixel_GH28291_Regression" |
| } |
| }, |
| { |
| "name": "linear_pytorch_half_pixel_downsample_ort", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearDownSampleTest_2DBilinear_pytorch_half_pixel", |
| "notes": "Projects ORT's rank-2 PyTorch half-pixel downsample into rank-4 NCHW form." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "pytorch_half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 2, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "linear_align_corners_non_square_multichannel", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 2, 3], |
| "data": { "kind": "values", "values": [1.0, 2.0, 4.0, 8.0, 16.0, 32.0, -1.0, -2.0, -4.0, -8.0, -16.0, -32.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 4, 5], "tolerance": 0.000003 } } |
| }, |
| { |
| "name": "nearest_ceil_asymmetric_downsample", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "ceil" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 7], |
| "data": { "kind": "values", "values": [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_linear_downsample_half_scale_2x4_to_1x2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearDownSampleTest_4DBilinear1" |
| }, |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_linear_downsample_explicit_scale_point6_2x4_to_1x2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearDownSampleTest_4DBilinear", |
| "notes": "ORT supplies explicit spatial scales of 0.6; output shape alone would imply 0.5, so this catches accidental scale inference." |
| }, |
| "attrs": { "mode": "linear", "scales": [1, 1, 0.6, 0.6] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 2], |
| "tolerance": 0.000001, |
| "data": { "kind": "values", "values": [2.6666665, 4.3333331] } |
| } |
| } |
| }, |
| { |
| "name": "ort_linear_downsample_odd_third_3x6_to_1x2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearDownSampleTest_4DBilinear1_OddNumber" |
| }, |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 6], |
| "data": { |
| "kind": "values", |
| "values": { "$ref": "#/fixtureArrays/ort_linear_downsample_odd_third_3x6_to_1x2_input_x" } |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_linear_align_corners_sizes_2x4_to_1x2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearDownSampleTest_4DBilinear_align_corners_sizes" |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_nearest_downsample_default_half_pixel_2x4_to_1x2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestDownSampleTest" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_nearest_downsample_with_sizes_2x4_to_1x3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestDownSampleTest_WithSizes" |
| }, |
| "attrs": { "mode": "nearest" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_nearest_ceil_sizes_2x2_to_7x8", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestUpSampleTest_WithSizes_CeilMode" |
| }, |
| "attrs": { "mode": "nearest", "nearest_mode": "ceil" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 7, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_nearest_ceil_sizes_rank5_2x2_to_7x8", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestUpSample5dTest_WithSizes_CeilMode", |
| "notes": "A rank-5 NCDHW tensor exercises ceil-mode nearest resizing from explicit sizes." |
| }, |
| "attrs": { "mode": "nearest", "nearest_mode": "ceil" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 2, 2], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 7, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_nearest_asymmetric_rank5_scale_1p5", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestUpSampleTest_5D_CudaRegression_Optimized3DMapping", |
| "notes": "Covers rank-5 nearest-neighbor upsampling by 1.5 with asymmetric coordinates." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "asymmetric", |
| "nearest_mode": "floor", |
| "scales": [1, 1, 1.5, 1.5, 1.5] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 2, 2], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3, 3], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 3.0, 3.0, 4.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 3.0, 3.0, 4.0, 5.0, 5.0, 6.0, 5.0, 5.0, 6.0, 7.0, 7.0, 8.0] |
| }, |
| "tolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "ort_nearest_asymmetric_rank5_downsample_0p5", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestDownSampleTest_5D_CudaRegression_Optimized3DMapping", |
| "notes": "Covers rank-5 nearest-neighbor downsampling by 0.5 with asymmetric coordinates." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "asymmetric", |
| "nearest_mode": "floor", |
| "scales": [1, 1, 0.5, 0.5, 0.5] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4, 4], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 2.0, 3.0, 4.0, 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, 60.0, 61.0, 62.0, 63.0, 64.0] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 2, 2], |
| "data": { "kind": "values", "values": [1.0, 3.0, 9.0, 11.0, 33.0, 35.0, 41.0, 43.0] }, |
| "tolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "ort_linear_asymmetric_upsample_batch2_2x2_to_4x8", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearUpSampleTest_4DBilinear_asymmetric_scales" |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "asymmetric" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 2], |
| "data": { "kind": "values", "values": [1.0, 3.0, 4.0, 8.0, 6.0, 2.0, 7.0, 11.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 1, 4, 8], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_linear_noop_scales_batch2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearScalesNoOpTest" |
| }, |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 1, 2, 2], |
| "data": { "kind": "values", "values": [1.0, 3.0, 4.0, 8.0, 6.0, 2.0, 7.0, 11.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 1, 2, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_nearest_round_prefer_ceil_half_pixel_26_to_64", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestUpSample_RoundPreferCeil_HalfPixel" |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_ceil" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 26], |
| "data": { |
| "kind": "values", |
| "values": [0.0, 1.0, 2.0, 3.0, 4.0, 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] |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 64], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_linear_5d_trilinear_pytorch_half_pixel", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearUpSampleTest_5DTrilinear_pytorch_half_pixel", |
| "notes": "Valid NCDHW trilinear Resize case from ORT." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "pytorch_half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 2, 1, 2], |
| "data": { "kind": "values", "values": [1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 4, 2, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_linear_align_corners_rank2_projection_2x2_to_4x8", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearUpSampleTest_2DBilinear_align_corners", |
| "notes": "Projects ORT's rank-2 align-corners bilinear upsample into rank-4 NCHW form." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 3.0, 4.0, 8.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 8], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_nearest_asymmetric_floor_2x_2x2_to_4x4", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpNearestUpSample_Nearest2xOptimization_Scales" |
| }, |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_linear_half_pixel_symmetric_downsample_scales", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpHalfPixelSymmetricDownSample_ver19", |
| "notes": "ONNX scales input represented by `scales` adaptation so half_pixel_symmetric can use fractional output width." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel_symmetric", "scales": [1, 1, 1, 0.6] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 1, 4], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 2], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "ort_linear_half_pixel_symmetric_upsample_scales", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpHalfPixelSymmetricUpSample_ver19", |
| "notes": "ONNX scales input represented by `scales` adaptation so half_pixel_symmetric can use fractional output width." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "half_pixel_symmetric", |
| "scales": [1, 1, 2.3, 2.94] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 5], "tolerance": 0.00001 } } |
| }, |
| { |
| "name": "onnx_backend_resize_downsample_scales_linear", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_downsample_scales_linear", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 2], "tolerance": 0.0001 } }, |
| "attrs": { "mode": "linear" } |
| }, |
| { |
| "name": "onnx_backend_resize_downsample_scales_linear_align_corners", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_downsample_scales_linear_align_corners", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 2], "tolerance": 0.0001 } }, |
| "attrs": { "coordinate_transformation_mode": "align_corners", "mode": "linear" } |
| }, |
| { |
| "name": "onnx_backend_resize_downsample_scales_nearest", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_downsample_scales_nearest", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 2], "tolerance": 0.0001 } }, |
| "attrs": { "mode": "nearest" } |
| }, |
| { |
| "name": "onnx_backend_resize_downsample_sizes_linear_pytorch_half_pixel", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_downsample_sizes_linear_pytorch_half_pixel", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 3, 1], "tolerance": 0.0001 } }, |
| "attrs": { "coordinate_transformation_mode": "pytorch_half_pixel", "mode": "linear" } |
| }, |
| { |
| "name": "onnx_backend_resize_downsample_sizes_nearest", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_downsample_sizes_nearest", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 3], "tolerance": 0.0001 } }, |
| "attrs": { "mode": "nearest" } |
| }, |
| { |
| "name": "onnx_backend_resize_upsample_scales_linear", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_upsample_scales_linear", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 4], "tolerance": 0.0001 } }, |
| "attrs": { "mode": "linear" } |
| }, |
| { |
| "name": "onnx_backend_resize_upsample_scales_linear_align_corners", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_upsample_scales_linear_align_corners", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 4], "tolerance": 0.0001 } }, |
| "attrs": { "coordinate_transformation_mode": "align_corners", "mode": "linear" } |
| }, |
| { |
| "name": "onnx_backend_resize_upsample_scales_nearest", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_upsample_scales_nearest", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 6], "tolerance": 0.0001 } }, |
| "attrs": { "mode": "nearest" } |
| }, |
| { |
| "name": "onnx_backend_resize_upsample_sizes_nearest", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_upsample_sizes_nearest", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 7, 8], "tolerance": 0.0001 } }, |
| "attrs": { "mode": "nearest" } |
| }, |
| { |
| "name": "onnx_backend_resize_upsample_sizes_nearest_ceil_half_pixel", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_upsample_sizes_nearest_ceil_half_pixel", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 8, 8], "tolerance": 0.0001 } }, |
| "attrs": { "coordinate_transformation_mode": "half_pixel", "mode": "nearest", "nearest_mode": "ceil" } |
| }, |
| { |
| "name": "onnx_backend_resize_upsample_sizes_nearest_floor_align_corners", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_upsample_sizes_nearest_floor_align_corners", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 8, 8], "tolerance": 0.0001 } }, |
| "attrs": { "coordinate_transformation_mode": "align_corners", "mode": "nearest", "nearest_mode": "floor" } |
| }, |
| { |
| "name": "onnx_backend_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric", |
| "notes": "The fixture represents the official scales or sizes input through the declared output shape." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 8, 8], "tolerance": 0.0001 } }, |
| "attrs": { |
| "coordinate_transformation_mode": "asymmetric", |
| "mode": "nearest", |
| "nearest_mode": "round_prefer_ceil" |
| } |
| }, |
| { |
| "name": "onnx_backend_resize_upsample_scales_nearest_axes_2_3_shape", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_resize_upsample_scales_nearest_axes_2_3", |
| "notes": "Official axes/scales inputs map to the full NCHW output shape; test_resize_upsample_scales_nearest_axes_3_2 and ORT ResizeOpNearestUpSampleTest map to this same request." |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 6], "tolerance": 0 } }, |
| "attrs": { "mode": "nearest" } |
| }, |
| { |
| "name": "ort_cubic_half_pixel_2x2_to_4x4", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpCubicUpSampleTest", |
| "notes": "Compact cubic half-pixel case using cubic_coeff_a=-0.75, derived from ONNX Runtime's CPU provider." |
| }, |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "half_pixel", "cubic_coeff_a": -0.75 }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { |
| "kind": "values", |
| "values": [0.68359375, 1.015625, 1.5625, 1.89453125, 1.34765625, 1.6796875, 2.2265625, 2.55859375, 2.44140625, 2.7734375, 3.3203125, 3.65234375, 3.10546875, 3.4375, 3.984375, 4.31640625] |
| }, |
| "tolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_cubic_downsample_coeff_minus_half", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpCubicDownSampleTest_coeff", |
| "notes": "Valid cubic downsample with cubic_coeff_a=-0.5." |
| }, |
| "attrs": { "mode": "cubic", "cubic_coeff_a": -0.5, "scales": [1, 1, 0.8, 0.8] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3], |
| "data": { |
| "kind": "values", |
| "values": [1.38574, 2.68359, 4.00684, 6.57715, 7.875, 9.19824, 11.8701, 13.168, 14.4912] |
| }, |
| "tolerance": 0.0001 |
| } |
| } |
| }, |
| { |
| "name": "ort_cubic_pytorch_half_pixel_boundary_clamp", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpCubicDownSample_PytorchHalfPixel_GH28292_SpecDifference", |
| "notes": "Covers ONNX cubic boundary clamping with pytorch_half_pixel coordinates." |
| }, |
| "attrs": { |
| "mode": "cubic", |
| "coordinate_transformation_mode": "pytorch_half_pixel", |
| "cubic_coeff_a": -0.5, |
| "antialias": 0, |
| "scales": [1, 1, 0.5, 0.5] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 8, 8], |
| "data": { |
| "kind": "values", |
| "values": [0.0, 0.015873016, 0.031746034, 0.04761905, 0.06349207, 0.07936508, 0.0952381, 0.11111111, 0.12698413, 0.14285715, 0.15873016, 0.17460318, 0.1904762, 0.20634921, 0.22222222, 0.23809524, 0.25396827, 0.26984128, 0.2857143, 0.3015873, 0.31746033, 0.33333334, 0.34920636, 0.36507937, 0.3809524, 0.3968254, 0.41269842, 0.42857143, 0.44444445, 0.46031746, 0.47619048, 0.4920635, 0.50793654, 0.52380955, 0.53968257, 0.5555556, 0.5714286, 0.5873016, 0.6031746, 0.61904764, 0.63492066, 0.6507937, 0.6666667, 0.6825397, 0.6984127, 0.71428573, 0.73015875, 0.74603176, 0.7619048, 0.7777778, 0.7936508, 0.8095238, 0.82539684, 0.84126985, 0.85714287, 0.8730159, 0.8888889, 0.9047619, 0.9206349, 0.93650794, 0.95238096, 0.96825397, 0.984127, 1.0] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { |
| "kind": "values", |
| "values": [0.0625, 0.0952381, 0.12698413, 0.15972222, 0.32440478, 0.35714287, 0.3888889, 0.42162699, 0.57837301, 0.61111116, 0.64285719, 0.67559528, 0.84027779, 0.87301588, 0.90476191, 0.9375] |
| }, |
| "tolerance": 0.0001, |
| "relTolerance": 0.0001 |
| } |
| } |
| }, |
| { |
| "name": "ort_cubic_align_corners_floor_nchw", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.NoAntialias_AlignCorners_Cubic_Floor_NCHW", |
| "notes": "Valid cubic align_corners fixture from ORT." |
| }, |
| "attrs": { |
| "mode": "cubic", |
| "coordinate_transformation_mode": "align_corners", |
| "cubic_coeff_a": -0.75, |
| "antialias": 0, |
| "exclude_outside": 0, |
| "extrapolation_value": 0, |
| "nearest_mode": "floor" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 3, 4], |
| "data": { |
| "kind": "values", |
| "values": { "$ref": "#/fixtureArrays/ort_cubic_align_corners_floor_nchw_input_x" } |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 2, 6, 8], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 1.34111, 1.80029, 2.32945, 2.67055, 3.19971, 3.65889, 4.0, 2.264, 2.60511, 3.06429, 3.59345, 3.93455, 4.46371, 4.92289, 5.264, 3.912, 4.25311, 4.71229, 5.24145, 5.58256, 6.11171, 6.5709, 6.912, 6.088, 6.42911, 6.88829, 7.41745, 7.75856, 8.28771, 8.7469, 9.08801, 7.736, 8.07711, 8.53629, 9.06545, 9.40655, 9.93571, 10.3949, 10.736, 9.0, 9.34111, 9.80029, 10.3295, 10.6706, 11.1997, 11.6589, 12.0, 13.0, 13.3411, 13.8003, 14.3295, 14.6706, 15.1997, 15.6589, 16.0, 14.264, 14.6051, 15.0643, 15.5934, 15.9346, 16.4637, 16.9229, 17.264, 15.912, 16.2531, 16.7123, 17.2415, 17.5826, 18.1117, 18.5709, 18.912, 18.088, 18.4291, 18.8883, 19.4175, 19.7586, 20.2877, 20.7469, 21.088, 19.736, 20.0771, 20.5363, 21.0654, 21.4066, 21.9357, 22.3949, 22.736, 21.0, 21.3411, 21.8003, 22.3295, 22.6706, 23.1997, 23.6589, 24.0] |
| }, |
| "tolerance": 0.0001, |
| "relTolerance": 0.0001 |
| } |
| } |
| }, |
| { |
| "name": "ort_cubic_align_corners_floor_nhwc", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_sample_test_gen.py", |
| "test": "ResizeOpTest.NoAntialias_AlignCorners_Cubic_Floor_NHWC", |
| "notes": "Covers rank-4 cubic align_corners with NHWC-style height and width resize axes, using pinned ORT expectations." |
| }, |
| "attrs": { |
| "mode": "cubic", |
| "coordinate_transformation_mode": "align_corners", |
| "cubic_coeff_a": -0.75, |
| "antialias": 0, |
| "exclude_outside": 0, |
| "extrapolation_value": 0, |
| "nearest_mode": "floor" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 4, 2], |
| "data": { |
| "kind": "values", |
| "values": { "$ref": "#/fixtureArrays/ort_cubic_align_corners_floor_nchw_input_x" } |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 6, 8, 2], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 2.0, 1.6822, 2.6822, 2.6006, 3.6006, 3.6589, 4.6589, 4.3411, 5.3411, 5.3994, 6.3994, 6.3178, 7.3178, 7.0, 8.0, 3.528, 4.528, 4.2102, 5.2102, 5.1286, 6.1286, 6.1869, 7.1869, 6.8691, 7.8691, 7.9274, 8.9274, 8.8458, 9.8458, 9.528, 10.528, 6.824, 7.824, 7.5062, 8.5062, 8.4246, 9.4246, 9.4829, 10.4829, 10.1651, 11.1651, 11.2234, 12.2234, 12.1418, 13.1418, 12.824, 13.824, 11.176, 12.176, 11.8582, 12.8582, 12.7766, 13.7766, 13.8349, 14.8349, 14.5171, 15.5171, 15.5754, 16.5754, 16.4938, 17.4938, 17.176, 18.176, 14.472, 15.472, 15.1542, 16.1542, 16.0726, 17.0726, 17.1309, 18.1309, 17.8131, 18.8131, 18.8714, 19.8714, 19.7898, 20.7898, 20.472, 21.472, 17.0, 18.0, 17.6822, 18.6822, 18.6006, 19.6006, 19.6589, 20.6589, 20.3411, 21.3411, 21.3994, 22.3994, 22.3178, 23.3178, 23.0, 24.0] |
| }, |
| "tolerance": 0.0001, |
| "relTolerance": 0.0001 |
| } |
| } |
| }, |
| { |
| "name": "ort_antialias_bilinear_no_exclude_outside", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.Antialias_Bilinear_No_ExcludeOutside", |
| "notes": "Uses axes-relative scales, including a negative axis, for the same 4x4 to 3x3 antialiased downsample." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "half_pixel", |
| "antialias": 1, |
| "exclude_outside": 0, |
| "axes": [2, -1], |
| "scales": [0.75, 0.75] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3], |
| "data": { |
| "kind": "values", |
| "values": [2.3636363, 3.590909, 4.818182, 7.2727275, 8.5, 9.727273, 12.181818, 13.409091, 14.636364] |
| }, |
| "tolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_antialias_bilinear_exclude_outside", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.Antialias_Bilinear_ExcludeOutside", |
| "notes": "Valid antialiased linear downsample." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "half_pixel", |
| "antialias": 1, |
| "exclude_outside": 1 |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3], |
| "data": { "kind": "values", "values": [2.5, 3.7, 4.9, 7.3, 8.5, 9.7, 12.1, 13.3, 14.5] }, |
| "tolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_nearest_uint8_2x2_to_4x4", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.NhwcResizeOpLinearDownSampleTest_4DBilinear_uint8", |
| "notes": "Compact NCHW nearest uint8 adaptation to preserve integer tensor support." |
| }, |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { "dtype": "uint8", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [10, 20, 30, 40] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "uint8", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": [10, 10, 20, 20, 10, 10, 20, 20, 30, 30, 40, 40, 30, 30, 40, 40] }, |
| "tolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "f32_antialias_linear_8x_downsample_compact", |
| "provenance": { |
| "source": "ONNX Runtime's CPU provider (opset 19)", |
| "test": "Resize linear antialias 8x downsample, scales [1,1,0.125,0.125]", |
| "notes": "Pinned values from ONNX Runtime's CPU provider for the exact `fillFloat32(0.017,0.031,0.5)` input verify an 8x antialiased linear downsample." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "half_pixel", |
| "antialias": 1, |
| "scales": [1, 1, 0.125, 0.125] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 4, 32, 32], |
| "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.031, "scale": 0.5 } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 4, 4, 4], |
| "tolerance": 0.0001, |
| "relTolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [0.14381, 0.134832, 0.122264, 0.107406, 0.010124, 0.021276, 0.032032, 0.041531, -0.058779, -0.062911, -0.066305, -0.068616, 0.049954, 0.043287, 0.034385, 0.023672, 0.033038, 0.038028, 0.042104, 0.045359, -0.060355, -0.056795, -0.052536, -0.047923, 0.018086, 0.006446, -0.005784, -0.017588, 0.145374, 0.151656, 0.15382, 0.151574, -0.103847, -0.109422, -0.113012, -0.11396, -0.011121, -0.019306, -0.02784, -0.036078, 0.072642, 0.069839, 0.065722, 0.0607, 0.041272, 0.058634, 0.073706, 0.08536, -0.05019, -0.07342, -0.095276, -0.113782, 0.074633, 0.069684, 0.062911, 0.054858, 0.004398, 0.011032, 0.017827, 0.024365, -0.069399, -0.067173, -0.06541, -0.064116] |
| } |
| } |
| } |
| }, |
| { |
| "name": "ort_nearest_int8_2x2_to_4x4", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.NhwcResizeOpLinearDownSampleTest_4DBilinear_int8", |
| "notes": "Compact NCHW nearest int8 adaptation to preserve signed integer tensor support." |
| }, |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { "dtype": "int8", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [-8, -1, 7, 12] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "int8", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": [-8, -8, -1, -1, -8, -8, -1, -1, 7, 7, 12, 12, 7, 7, 12, 12] }, |
| "tolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "ort_nhwc_linear_asymmetric_uint8_upsample", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.NhwcResizeOpLinearUpSampleTest_4DBilinear_asymmetric_uint8" |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "asymmetric" }, |
| "inputs": { |
| "x": { |
| "dtype": "uint8", |
| "shape": [2, 2, 2, 1], |
| "data": { "kind": "values", "values": [1, 3, 4, 8, 6, 2, 7, 11] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "uint8", |
| "shape": [2, 4, 8, 1], |
| "data": { |
| "kind": "values", |
| "values": [1, 1, 2, 2, 3, 3, 3, 3, 2, 3, 4, 4, 5, 5, 5, 5, 4, 5, 6, 7, 8, 8, 8, 8, 4, 5, 6, 7, 8, 8, 8, 8, 6, 5, 4, 3, 2, 2, 2, 2, 6, 6, 6, 6, 6, 6, 6, 6, 7, 8, 9, 10, 11, 11, 11, 11, 7, 8, 9, 10, 11, 11, 11, 11] |
| }, |
| "tolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "ort_nhwc_linear_asymmetric_int8_upsample", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.NhwcResizeOpLinearUpSampleTest_4DBilinear_asymmetric_int8" |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "asymmetric" }, |
| "inputs": { |
| "x": { |
| "dtype": "int8", |
| "shape": [2, 2, 2, 1], |
| "data": { "kind": "values", "values": [1, -3, -4, 8, 6, -2, -7, 11] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "int8", |
| "shape": [2, 4, 8, 1], |
| "data": { |
| "kind": "values", |
| "values": [1, 0, -1, -2, -3, -3, -3, -3, -1, 0, 0, 1, 2, 2, 2, 2, -4, -1, 2, 5, 8, 8, 8, 8, -4, -1, 2, 5, 8, 8, 8, 8, 6, 4, 2, 0, -2, -2, -2, -2, 0, 0, 2, 3, 4, 4, 4, 4, -7, -2, 2, 6, 11, 11, 11, 11, -7, -2, 2, 6, 11, 11, 11, 11] |
| }, |
| "tolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "nearest_asymmetric_floor_3x_integer_scale", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 12, 12], "tolerance": 0 } } |
| }, |
| { |
| "name": "nearest_asymmetric_floor_4x_integer_scale", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.41 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 16, 16], "tolerance": 0 } } |
| }, |
| { |
| "name": "nearest_asymmetric_floor_mixed_3x2_integer_scale", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.31 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 9, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "nearest_asymmetric_floor_nonsquare_2x_scalar_path", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 3, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.37 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 6, 10], "tolerance": 0 } } |
| }, |
| { |
| "name": "nearest_half_pixel_round_prefer_floor_3x_integer_scale", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_floor" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 6, 12], "tolerance": 0 } } |
| }, |
| { |
| "name": "nearest_half_pixel_round_prefer_ceil_2x_integer_scale", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_ceil" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 6, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "nearest_asymmetric_floor_2x_integer_scale_f16", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 1, 4, 4], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 3.0, 3.0, 4.0, 4.0] |
| }, |
| "tolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "nearest_half_pixel_default_2x_larger_grid", |
| "attrs": { "mode": "nearest" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 64, 64], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.43 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 128, 128], "tolerance": 0 } } |
| }, |
| { |
| "name": "linear_half_pixel_2x_stencil_multichannel", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 8, 16], |
| "data": { "kind": "fillFloat32", "sinStep": 0.21, "cosStep": 0.33 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 16, 32] } } |
| }, |
| { |
| "name": "linear_pytorch_half_pixel_2x_stencil", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "pytorch_half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.27, "cosStep": 0.39 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 8, 8] } } |
| }, |
| { |
| "name": "linear_asymmetric_2x_stencil", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "asymmetric" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 6], |
| "data": { "kind": "fillFloat32", "sinStep": 0.15, "cosStep": 0.47 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 8, 12] } } |
| }, |
| { |
| "name": "linear_half_pixel_2x_stencil_explicit_scales", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel", "scales": [1, 1, 2, 2] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.25, "cosStep": 0.35 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 8, 8] } } |
| }, |
| { |
| "name": "ort_linear_axes_scales_5d", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.Axes_and_Scale_18" |
| }, |
| "attrs": { |
| "mode": "linear", |
| "axes": [2, 3, 4], |
| "exclude_outside": 0, |
| "antialias": 0, |
| "scales": [0.75, 0.75, 0.75] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_5d_ramp_64" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3, 3], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_linear_axes_scales_5d_output_y" } }, |
| "tolerance": 0.00002, |
| "relTolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_linear_negative_axes_scales_5d", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.Axes_NegativeInRange_18" |
| }, |
| "attrs": { |
| "mode": "linear", |
| "axes": [-3, -2, -1], |
| "exclude_outside": 0, |
| "antialias": 0, |
| "scales": [0.75, 0.75, 0.75] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_5d_ramp_64" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3, 3], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_linear_axes_scales_5d_output_y" } }, |
| "tolerance": 0.00002, |
| "relTolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_linear_axes_sizes_5d", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.Axes_and_Size_18", |
| "notes": "The axes-specific sizes {3,3,3} are represented through the declared output shape with scale attributes omitted." |
| }, |
| "attrs": { "mode": "linear", "axes": [2, 3, 4], "exclude_outside": 0, "antialias": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_5d_ramp_64" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3, 3], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/ort_linear_axes_scales_5d_output_y" } }, |
| "tolerance": 0.00002, |
| "relTolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_antialias_trilinear_no_exclude_outside", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.Antialias_Trilinear_No_ExcludeOutside", |
| "notes": "Rank-5 NCDHW adaptation of ORT's rank-3 trilinear antialias coverage." |
| }, |
| "attrs": { "mode": "linear", "antialias": 1, "exclude_outside": 0, "scales": [1, 1, 0.75, 0.75, 0.75] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_5d_ramp_64" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3, 3], |
| "data": { |
| "kind": "values", |
| "values": [5.7272725, 6.9545455, 8.181818, 10.636364, 11.863636, 13.090909, 15.545455, 16.772728, 18.0, 25.363636, 26.59091, 27.818182, 30.272728, 31.5, 32.727272, 35.18182, 36.409092, 37.636364, 45.0, 46.227272, 47.454544, 49.909092, 51.136364, 52.363636, 54.81818, 56.045456, 57.272728] |
| }, |
| "tolerance": 0.00001, |
| "relTolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_antialias_trilinear_exclude_outside", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.Antialias_Trilinear_ExcludeOutside", |
| "notes": "Rank-5 NCDHW adaptation of ORT's rank-3 trilinear antialias coverage." |
| }, |
| "attrs": { "mode": "linear", "antialias": 1, "exclude_outside": 1, "scales": [1, 1, 0.75, 0.75, 0.75] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_5d_ramp_64" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3, 3], |
| "data": { |
| "kind": "values", |
| "values": [6.3, 7.5, 8.7, 11.1, 12.3, 13.5, 15.9, 17.1, 18.3, 25.5, 26.7, 27.9, 30.3, 31.5, 32.7, 35.1, 36.3, 37.5, 44.7, 45.9, 47.1, 49.5, 50.7, 51.9, 54.3, 55.5, 56.7] |
| }, |
| "tolerance": 0.00001, |
| "relTolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "nearest_tf_crop_and_resize_default_roi_singleton", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample.cc", |
| "test": "Resize with omitted roi input", |
| "notes": "ONNX Runtime defaults an omitted ROI to [0, 1] per axis. A singleton output samples the center of the full source interval, covering the default-ROI nearest compiler path." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "tf_crop_and_resize", |
| "nearest_mode": "round_prefer_floor" |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 4], "data": { "kind": "values", "values": [0.0, 10.0, 20.0, 30.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1], "data": { "kind": "values", "values": [10.0] } } } |
| }, |
| { |
| "name": "linear_tf_crop_and_resize_default_roi", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample.cc", |
| "test": "Resize with omitted roi input", |
| "notes": "The default full ROI maps the first and last output coordinates to the source endpoints; the middle output interpolates at source coordinate 1.5." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "tf_crop_and_resize" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 4], "data": { "kind": "values", "values": [0.0, 10.0, 20.0, 30.0] } } |
| }, |
| "outputs": { |
| "y": { "dtype": "float32", "shape": [1, 3], "data": { "kind": "values", "values": [0.0, 15.0, 30.0] } } |
| } |
| }, |
| { |
| "name": "ort_tf_crop_and_resize_rank2_roi", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearDownSampleTest_tf_crop_and_resize", |
| "notes": "Pinned ORT expected values for ROI-based tf_crop_and_resize." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "tf_crop_and_resize", "roi": [0.4, 0.6, 0.6, 0.8] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [3, 3], |
| "data": { "kind": "values", "values": [7.600004, 7.9, 8.2, 8.8, 9.1, 9.4, 10.0, 10.3, 10.6] }, |
| "tolerance": 0.00002, |
| "relTolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_tf_crop_and_resize_extrapolation_nchw", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearDownSampleTest_tf_crop_and_resize_with_extrapolation", |
| "notes": "Pinned ORT values expressed with axes-relative ROI and scales; the negative width axis also verifies axis normalization." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "tf_crop_and_resize", |
| "extrapolation_value": 10, |
| "axes": [2, -1], |
| "roi": [0.4, 0.6, 1.2, 1.7], |
| "scales": [0.8, 0.8] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3], |
| "data": { "kind": "values", "values": [7.6, 10.0, 10.0, 12.4, 10.0, 10.0, 10.0, 10.0, 10.0] }, |
| "tolerance": 0.00002, |
| "relTolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_nhwc_tf_crop_and_resize_extrapolation_uint8", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.NhwcResizeOpLinearDownSampleTest_tf_crop_and_resize_with_extrapolation_uint8", |
| "notes": "Pinned ORT NHWC integer crop-and-resize case with extrapolation." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "tf_crop_and_resize", |
| "extrapolation_value": 10, |
| "roi": [0, 0.4, 0.6, 0, 1, 1.2, 1.7, 1], |
| "scales": [1, 0.8, 0.8, 1] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "uint8", |
| "shape": [1, 4, 4, 1], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "uint8", |
| "shape": [1, 3, 3, 1], |
| "data": { "kind": "values", "values": [7, 10, 10, 12, 10, 10, 10, 10, 10] }, |
| "tolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "ort_nhwc_tf_crop_and_resize_no_extrapolation_int8", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.NhwcResizeOpLinearDownSampleTest_tf_crop_and_resize_without_extrapolation_int8", |
| "notes": "Pinned ORT NHWC signed integer crop-and-resize case where out-of-ROI samples use the integer zero value." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "tf_crop_and_resize", |
| "roi": [0, 0.4, 0.6, 0, 1, 1.2, 1.7, 1], |
| "scales": [1, 0.8, 0.8, 1] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "int8", |
| "shape": [1, 4, 4, 1], |
| "data": { "kind": "values", "values": [1, -2, 3, -4, -5, 6, -7, 8, 9, -10, 11, -12, -13, 14, -15, 16] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "int8", |
| "shape": [1, 3, 3, 1], |
| "data": { "kind": "values", "values": [-2, 0, 0, 0, 0, 0, 0, 0, 0] }, |
| "tolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "ort_linear_align_corners_scales_2x4_to_1x2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpLinearDownSampleTest_4DBilinear_align_corners", |
| "notes": "Explicit scales exercise align_corners coordinate handling independently of an inferred sizes input." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners", "scales": [1, 1, 0.6, 0.6] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 2], |
| "data": { "kind": "values", "values": [1.0, 4.0] }, |
| "tolerance": 0.000001 |
| } |
| } |
| }, |
| { |
| "name": "ort_cubic_downsample_asymmetric", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpCubicDownSampleTest_asymmetric" |
| }, |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "asymmetric", "scales": [1, 1, 0.8, 0.8] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 2.29688, 3.59375, 6.1875, 7.48438, 8.78125, 11.375, 12.6719, 13.9688] |
| }, |
| "tolerance": 0.00005 |
| } |
| } |
| }, |
| { |
| "name": "empty_input_zero_dim", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_floor" |
| }, |
| "inputs": { "x": { "dtype": "float32", "shape": [0, 1, 2, 2], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [0, 1, 2, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "linear_half_pixel_downsample_f16_vec4_feature_map", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 32, 64, 64], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 32, 32], "tolerance": 0.02, "relTolerance": 0.01 } } |
| }, |
| { |
| "name": "linear_half_pixel_exact_2x_f16_x8_route", |
| "provenance": { |
| "notes": "A non-square multichannel 2x bilinear resize checks half-pixel coordinates across row and plane boundaries." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 7, 12], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 3, 14, 24], "tolerance": 0.02, "relTolerance": 0.01 } } |
| }, |
| { |
| "name": "linear_half_pixel_upsample_f16_scalar_w_odd", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 16, 40, 40], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 80, 82], "tolerance": 0.03, "relTolerance": 0.01 } } |
| }, |
| { |
| "name": "nearest_asymmetric_floor_3x_f16_scalar_w_odd", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "asymmetric", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 8, 10, 10], |
| "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.19 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 8, 30, 30], "tolerance": 0, "relTolerance": 0 } } |
| }, |
| { |
| "name": "linear_align_corners_f16_upsample_scalar", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 4, 8, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.37 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 4, 15, 15], "tolerance": 0.02, "relTolerance": 0.01 } } |
| }, |
| { |
| "name": "empty_spatial_dim_h_zero_nearest_nchw", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_floor" |
| }, |
| "inputs": { "x": { "dtype": "float32", "shape": [1, 2, 0, 4], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 0, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "empty_spatial_dim_w_zero_linear_nchw", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { "x": { "dtype": "float32", "shape": [1, 2, 3, 0], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 6, 0], "tolerance": 0 } } |
| }, |
| { |
| "name": "nchw_uint8_linear_asymmetric_3x_fractional_round_vs_trunc", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "asymmetric" }, |
| "inputs": { |
| "x": { "dtype": "uint8", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1, 4, 7, 10] } } |
| }, |
| "outputs": { "y": { "dtype": "uint8", "shape": [1, 1, 6, 6] } } |
| }, |
| { |
| "name": "linear_align_corners_out1_axis_singleton_source_pixel", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 3, 3], |
| "data": { |
| "kind": "values", |
| "values": { "$ref": "#/fixtureArrays/ort_linear_downsample_odd_third_3x6_to_1x2_input_x" } |
| } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 1, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "linear_half_pixel_channel_quad_odd_width_tail", |
| "provenance": { |
| "source": "ONNX Resize-19 linear half-pixel semantics", |
| "notes": "Locks four-output scalar batching across odd row tails without allowing a batch to cross an NCHW row boundary." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 4, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.31, "scale": 1.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 3, 7, 9], "tolerance": 0.000001, "relTolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank7_nearest_last_axis", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "asymmetric", |
| "nearest_mode": "floor", |
| "scales": [1, 1, 1, 1, 1, 1, 2] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 1, 2, 1, 3], |
| "data": { "kind": "linspace", "start": 0.0, "end": 11.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 2, 1, 2, 1, 6], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_cubic_exclude_outside_rank2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpCubicDownSampleTest_exclude_outside", |
| "notes": "exclude_outside=1 zeroes cubic taps outside the input range and renormalizes the remaining per-axis weights; clamp-replicating the edge instead changes every border value." |
| }, |
| "attrs": { |
| "mode": "cubic", |
| "coordinate_transformation_mode": "half_pixel", |
| "cubic_coeff_a": -0.5, |
| "exclude_outside": 1, |
| "scales": [0.8, 0.8] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [3, 3], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [1.36812973, 2.66949376, 4.01334, 6.57362935, 7.875, 9.21884343, 11.94896221, 13.25033, 14.59417723] |
| } |
| } |
| } |
| }, |
| { |
| "name": "ort_cubic_exclude_outside_nchw", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.ResizeOpCubicDownSampleTest_exclude_outside", |
| "notes": "exclude_outside=1 zeroes cubic taps outside the input range and renormalizes the remaining per-axis weights; clamp-replicating the edge instead changes every border value." |
| }, |
| "attrs": { |
| "mode": "cubic", |
| "coordinate_transformation_mode": "half_pixel", |
| "cubic_coeff_a": -0.5, |
| "exclude_outside": 1, |
| "scales": [1, 1, 0.8, 0.8] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/resize_4x4_ramp" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 3], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [1.36812973, 2.66949376, 4.01334, 6.57362935, 7.875, 9.21884343, 11.94896221, 13.25033, 14.59417723] |
| } |
| } |
| } |
| }, |
| { |
| "name": "ort_cubic_antialias_nchw", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.Antialias_Bicubic_No_ExcludeOutside", |
| "notes": "antialias=1 with cubic widens the filter support to 2*filter_scale; the plain bicubic kernel must not be selected for this combination." |
| }, |
| "attrs": { |
| "mode": "cubic", |
| "antialias": 1, |
| "coordinate_transformation_mode": "half_pixel", |
| "cubic_coeff_a": -0.75 |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 4, 6], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 2.0, 3.0, 4.0, 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] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 2, 3, 4], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [2.175381, 3.65532, 5.204702, 6.684642, 10.24537, 11.725309, 13.274693, 14.754631, 18.315359, 19.795298, 21.344679, 22.824617, 26.175383, 27.655321, 29.204706, 30.684639, 34.245377, 35.725315, 37.274696, 38.754627, 42.315361, 43.795296, 45.344681, 46.824615] |
| } |
| } |
| } |
| }, |
| { |
| "name": "ort_cubic_antialias_rank2", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/resize_op_test.cc", |
| "test": "ResizeOpTest.Antialias_Bicubic_No_ExcludeOutside", |
| "notes": "A rank-2 tensor exercises generic antialiased bicubic resizing." |
| }, |
| "attrs": { |
| "mode": "cubic", |
| "antialias": 1, |
| "coordinate_transformation_mode": "half_pixel", |
| "cubic_coeff_a": -0.75 |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [4, 6], |
| "data": { |
| "kind": "values", |
| "values": { "$ref": "#/fixtureArrays/ort_cubic_align_corners_floor_nchw_input_x" } |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [3, 4], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [2.175381, 3.65532, 5.204702, 6.684642, 10.24537, 11.725309, 13.274693, 14.754631, 18.315359, 19.795298, 21.344679, 22.824617] |
| } |
| } |
| } |
| }, |
| { |
| "name": "ort_linear_antialias_fractional_scale06", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample_antialias.h", |
| "test": "explicit-scale coordinate transform", |
| "notes": "Because floor(7*0.6)=4, the explicit 0.6 scale and inferred 4/7 shape ratio give different centers and filter scales; source coordinates must use the explicit scale." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "antialias": 1, |
| "coordinate_transformation_mode": "half_pixel", |
| "scales": [1, 1, 0.6, 0.6] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 7, 7], |
| "data": { |
| "kind": "values", |
| "values": [0.0, 1.0, 2.0, 3.0, 4.0, 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] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [3.0, 4.625, 6.375, 7.875, 14.375001, 16.0, 17.75, 19.25, 26.625, 28.250002, 30.0, 31.499998, 37.125, 38.749996, 40.499996, 41.999996] |
| } |
| } |
| } |
| }, |
| { |
| "name": "rank5_antialias_linear_fractional_scales_per_axis", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample_antialias.h", |
| "test": "rank-5 explicit-scale coordinate transform", |
| "notes": "Rank-5 linear antialias coverage with distinct fractional scales [0.6,0.7,0.8]. Each output extent truncates, so the supplied scales differ from the inferred shape ratios and any dropped or shifted axis changes the result. Pinned values follow ONNX Runtime's separable antialias algorithm in `onnxruntime/core/providers/cpu/tensor/upsample_antialias.h`, including edge-clamped taps and per-axis weight normalization." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "antialias": 1, |
| "coordinate_transformation_mode": "half_pixel", |
| "exclude_outside": 0, |
| "scales": [1, 1, 0.6, 0.7, 0.8] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 7, 5, 3], |
| "data": { |
| "kind": "values", |
| "values": [1.18, -2.64, 3.54, -0.28, -4.1, 2.08, -1.74, 4.44, 0.62, -3.2, 2.98, -0.84, -4.66, 1.52, -2.29, 3.89, 0.07, -3.75, 2.43, -1.39, 4.79, 0.97, -2.85, 3.33, -0.49, -4.31, 1.87, -1.95, 4.23, 0.41, -3.41, 2.77, -1.05, -4.87, 1.31, -2.51, 3.67, -0.15, -3.97, 2.21, -1.61, 4.57, 0.75, -3.07, 3.12, -0.7, -4.52, 1.66, -2.16, 4.02, 0.2, -3.62, 2.56, -1.26, 4.92, 1.1, -2.72, 3.46, -0.36, -4.18, 2.0, -1.82, 4.36, 0.54, -3.28, 2.9, -0.92, -4.74, 1.44, -2.38, 3.8, -0.02, -3.84, 2.35, -1.47, 4.71, 0.89, -2.93, 3.25, -0.57, -4.39, 1.79, -2.03, 4.15, 0.33, -3.49, 2.69, -1.13, -4.95, 1.23, -2.59, 3.59, -0.23, -4.05, 2.13, -1.69, 4.49, 0.67, -3.15, 3.03, -0.79, -4.61, 1.58, -2.24, 3.94] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 3, 2], |
| "tolerance": 0.0001, |
| "relTolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [0.861608, -0.57944, -0.209847, 0.732485, -1.521765, 0.617429, -1.078995, 0.120811, -0.055723, -0.187044, 1.076578, -0.13302, 0.260176, -0.213807, -0.91959, -0.214676, 0.22326, 0.241922, 1.164493, 0.039813, 0.958422, -0.624769, 0.270865, -0.932433] |
| } |
| } |
| } |
| }, |
| { |
| "name": "rank5_antialias_cubic_fractional_scales_per_axis", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample_antialias.h", |
| "test": "rank-5 explicit-scale coordinate transform, cubic filter", |
| "notes": "Cubic counterpart to the rank-5 fractional-scale case, covering the cubic route's independent scale plumbing. Depth and width truncate, so scales 0.6 and 0.8 differ from inferred shape ratios; height remains unchanged to bound the tap count. Pinned values follow the Keys-cubic separable algorithm in `onnxruntime/core/providers/cpu/tensor/upsample_antialias.h`, including edge-clamped taps and per-axis normalization." |
| }, |
| "attrs": { |
| "mode": "cubic", |
| "antialias": 1, |
| "coordinate_transformation_mode": "half_pixel", |
| "exclude_outside": 0, |
| "cubic_coeff_a": -0.75, |
| "scales": [1, 1, 0.6, 1, 0.8] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 7, 2, 3], |
| "data": { |
| "kind": "values", |
| "values": [1.18, -2.64, 3.54, -0.28, -4.1, 2.08, -1.74, 4.44, 0.62, -3.2, 2.98, -0.84, -4.66, 1.52, -2.29, 3.89, 0.07, -3.75, 2.43, -1.39, 4.79, 0.97, -2.85, 3.33, -0.49, -4.31, 1.87, -1.95, 4.23, 0.41, -3.41, 2.77, -1.05, -4.87, 1.31, -2.51, 3.67, -0.15, -3.97, 2.21, -1.61, 4.57] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 2, 2], |
| "tolerance": 0.0001, |
| "relTolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [-0.182603, 1.118934, -1.642603, -0.341066, -1.538309, 1.475514, 1.706941, -0.683816, -0.811055, -0.656666, -0.779036, 1.324795, -0.110369, -0.125005, -1.54873, 0.731214] |
| } |
| } |
| } |
| }, |
| { |
| "name": "ort_nearest_round_prefer_ceil_tie_scale03", |
| "provenance": { |
| "source": "onnxruntime/core/providers/webgpu/tensor/resize_impl.cc", |
| "test": "nearest half-tie epsilon", |
| "notes": "Output col 4 maps to x_original = 4.5/0.3 - 0.5 = 14.5 exactly in reals, but f32 division can land just below .5; round_prefer_ceil must still pick 15." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "nearest_mode": "round_prefer_ceil", |
| "coordinate_transformation_mode": "half_pixel", |
| "scales": [1, 1, 1, 0.3] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 20], |
| "data": { |
| "kind": "values", |
| "values": [0.0, 1.0, 2.0, 3.0, 4.0, 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] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 6], |
| "tolerance": 0, |
| "data": { "kind": "values", "values": [1.0, 5.0, 8.0, 11.0, 15.0, 18.0] } |
| } |
| } |
| }, |
| { |
| "name": "linear_half_pixel_2x_stencil_odd_width_scalar", |
| "provenance": { |
| "notes": "An input width of 3 produces a doubled output width of 6, which is not divisible by four. The case therefore exercises the scalar 2x linear stencil." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 3], |
| "data": { "kind": "values", "values": [1.0, 2.0, 4.0, 8.0, 16.0, 32.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 6], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank8_nearest_last_axis", |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "asymmetric", |
| "nearest_mode": "floor", |
| "scales": [1, 1, 1, 1, 1, 1, 1, 2] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 1, 2, 1, 2, 3], |
| "data": { "kind": "linspace", "start": 1.0, "end": 24.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 2, 1, 2, 1, 2, 6], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank2_nearest_half_pixel_sizes", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "Rank-2 sizes-driven half-pixel nearest resize." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel", |
| "nearest_mode": "round_prefer_floor" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank2_nearest_pytorch_half_pixel_sizes", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "The singleton first output axis exercises pytorch_half_pixel's size-one rule." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "pytorch_half_pixel", |
| "nearest_mode": "round_prefer_floor" |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank2_nearest_half_pixel_symmetric_scales", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "Fractional exact scales make the half_pixel_symmetric adjustment observable." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "half_pixel_symmetric", |
| "nearest_mode": "round_prefer_floor", |
| "scales": [0.6, 0.5] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "rank2_linear_asymmetric_scales", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "Rank-2 linear resize with exact fractional scales and asymmetric coordinates." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "asymmetric", "scales": [0.6, 0.5] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank2_linear_pytorch_half_pixel_scales", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "Exact scales combine a singleton first output axis with a non-singleton second axis." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "pytorch_half_pixel", "scales": [0.6, 0.5] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank2_linear_align_corners_scales", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "Exact scales combine align_corners' singleton and non-singleton output rules." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners", "scales": [0.6, 0.5] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank2_linear_half_pixel_symmetric_scales", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "Fractional exact scales make the half_pixel_symmetric offset observable on both axes." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel_symmetric", "scales": [0.6, 0.5] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank2_linear_align_corners_sizes", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "Sizes-driven align_corners resize combines singleton and non-singleton output axes." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank2_linear_pytorch_half_pixel_sizes", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "Sizes-driven pytorch_half_pixel resize combines singleton and non-singleton output axes." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "pytorch_half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "rank5_linear_align_corners_ncdhw", |
| "provenance": { |
| "source": "ONNX Resize-19 specification (coordinate_transformation_mode=align_corners)", |
| "test": "rank-5 NCDHW trilinear align_corners", |
| "notes": "Rank-5 align_corners uses the no-explicit-scale coordinate formula. Its three spatial axes have distinct (in-1)/(out-1) ratios: D 4->3 gives 1.5, H 2->3 gives 0.5, and W 3->4 gives 2/3, so swapping or dropping an axis changes the output." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 2, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.41, "cosStep": 0.73, "scale": 0.6 } |
| } |
| }, |
| "outputs": { |
| "y": { "dtype": "float32", "shape": [1, 1, 3, 3, 4], "tolerance": 0.00001, "relTolerance": 0.000001 } |
| } |
| }, |
| { |
| "name": "rank5_linear_half_pixel_symmetric_explicit_scales_ncdhw", |
| "provenance": { |
| "source": "ONNX Resize-19 specification (coordinate_transformation_mode=half_pixel_symmetric)", |
| "test": "rank-5 NCDHW trilinear half_pixel_symmetric with per-axis scales", |
| "notes": "Rank-5 half_pixel_symmetric resize with explicit scales. Each spatial product is non-integral (3*1.4, 4*0.6, and 3*0.8), producing three distinct symmetry offsets and making a dropped or axis-shifted scale observable. This is valid ONNX opset-19 behavior: output dimensions are floor(input_dim*scale), with scales supplied by the compile-time adaptation." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "half_pixel_symmetric", |
| "scales": [1, 1, 1.4, 0.6, 0.8] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 3, 4, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.67, "cosStep": 1.13, "scale": 0.6 } |
| } |
| }, |
| "outputs": { |
| "y": { "dtype": "float32", "shape": [1, 1, 4, 2, 2], "tolerance": 0.00001, "relTolerance": 0.000001 } |
| } |
| }, |
| { |
| "name": "rank5_linear_half_pixel_f16_ncdhw", |
| "provenance": { |
| "source": "ONNX Resize-19 specification (T = tensor(float16))", |
| "test": "rank-5 NCDHW trilinear half_pixel, float16", |
| "notes": "Rank-5 NCDHW float16 uses mixed resizing: D 2->3, H 3->2, and W 2->4, so all three axes have distinct scales and directions. The tolerance covers float16 output rounding after float32 accumulation." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 1, 2, 3, 2], |
| "data": { "kind": "fillFloat32", "sinStep": 0.83, "cosStep": 1.31, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 1, 3, 2, 4], "tolerance": 0.002, "relTolerance": 0.002 } } |
| }, |
| { |
| "name": "antialias_linear_asymmetric_downsample", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample_antialias.h", |
| "test": "antialiased linear downsample, asymmetric coordinates", |
| "notes": "Covers valid ONNX linear antialiasing with asymmetric coordinates, where center=output*ratio and no half-pixel shift is applied. Different H/W ratios expose axis mix-ups, while the first output exercises edge-clamped taps. Pinned values follow the separable triangle filter in `onnxruntime/core/providers/cpu/tensor/upsample_antialias.h`; window starts remain clear of floating-point floor boundaries." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "asymmetric", |
| "antialias": 1, |
| "exclude_outside": 0 |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.91, "cosStep": 0.31, "scale": 0.6 } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 3], |
| "tolerance": 0.00001, |
| "relTolerance": 0.00001, |
| "data": { |
| "kind": "values", |
| "values": [0.369885653, 0.24363111, -0.345615536, -0.281011194, -0.20116964, 0.148209721] |
| } |
| } |
| } |
| }, |
| { |
| "name": "antialias_linear_half_pixel_f16", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample_antialias.h", |
| "test": "antialiased linear downsample, float16", |
| "notes": "Covers the float16 storage route for linear antialiasing with distinct H/W ratios. Inputs are exact multiples of 0.125, isolating tolerance to the final f32-to-f16 rounding. Pinned values follow the separable triangle filter in `onnxruntime/core/providers/cpu/tensor/upsample_antialias.h`; window starts remain clear of floating-point floor boundaries." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "half_pixel", |
| "antialias": 1, |
| "exclude_outside": 0 |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 1, 5, 3], |
| "data": { |
| "kind": "values", |
| "values": [1.25, -2.5, 0.75, 3.0, -1.875, 2.125, -0.375, 1.5, -2.75, 0.25, 2.875, -1.125, 0.625, -3.0, 1.75] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 1, 3, 2], |
| "tolerance": 0.001, |
| "relTolerance": 0.001, |
| "data": { |
| "kind": "values", |
| "values": [0.515625, 0.0885416642, 0.694444418, -0.518518507, 0.0572916679, 0.182291672] |
| } |
| } |
| } |
| }, |
| { |
| "name": "nearest_align_corners_scalar_x4_odd_width", |
| "provenance": { |
| "notes": "nearest + align_corners on the scalar-x4 path checks the no-explicit-scale coordinate formula at width 7, which is at least four but not divisible by four." |
| }, |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "align_corners", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.97, "cosStep": 1.31, "scale": 0.5, "offset": 0.5 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 8, 7], "tolerance": 0.0002 } } |
| }, |
| { |
| "name": "rank5_nearest_align_corners_ncdhw", |
| "provenance": { |
| "notes": "Rank-5 NCDHW nearest + align_corners checks the no-explicit-scale coordinate formula. D 2->4, H 4->8, and W 4->8 keep the three axes independently addressable." |
| }, |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "align_corners", "nearest_mode": "floor" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 2, 4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 1.13, "cosStep": 0.89, "scale": 0.5, "offset": 0.5 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 4, 8, 8], "tolerance": 0.0002 } } |
| }, |
| { |
| "name": "cubic_half_pixel_f16", |
| "provenance": { |
| "notes": "Float16 NCHW cubic resize doubles each spatial axis with half-pixel coordinates and coefficient -0.75. Expected values were independently derived from the bicubic formula using the same float16 input values." |
| }, |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "half_pixel", "cubic_coeff_a": -0.75 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.91, "cosStep": 1.19, "scale": 0.4, "offset": 0.5 } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 1, 8, 8], |
| "tolerance": 0.02, |
| "relTolerance": 0.01, |
| "data": { |
| "kind": "values", |
| "values": [0.595215, 0.71875, 0.922852, 1.049805, 1.040039, 0.835449, 0.544922, 0.368408, 0.371094, 0.451904, 0.583496, 0.731445, 0.815918, 0.803711, 0.696289, 0.631836, -0.012573, -0.000332, 0.013382, 0.201416, 0.448975, 0.760254, 0.957031, 1.081055, 0.224976, 0.139893, -0.00399, 0.002836, 0.14563, 0.4375, 0.717773, 0.890625, 0.966309, 0.804199, 0.541992, 0.237793, 0.061188, -0.000281, 0.098633, 0.155884, 1.098633, 1.001953, 0.849121, 0.560059, 0.30249, 0.073669, 0.009857, -0.032959, 0.559082, 0.635254, 0.762207, 0.806152, 0.74707, 0.621094, 0.494873, 0.41748, 0.23999, 0.420654, 0.716309, 0.958496, 1.015625, 0.947754, 0.781738, 0.682129] |
| } |
| } |
| } |
| }, |
| { |
| "name": "rank3_linear_half_pixel_f16_generic", |
| "provenance": { |
| "notes": "Rank 3 makes this float16 half-pixel linear resize use the generic rank-N path, including its float16 storage declarations." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 1.07, "cosStep": 0.83, "scale": 0.4, "offset": 0.5 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 4, 8], "tolerance": 0.02, "relTolerance": 0.01 } } |
| }, |
| { |
| "name": "nearest_tf_crop_and_resize_roi_axis_to_one", |
| "provenance": { |
| "notes": "Nearest-neighbor tf_crop_and_resize with an explicit ROI reduces output height to one, taking the ROI midpoint rule for that axis; width remains three and uses ordinary ROI coordinates. nearest_mode is floor, no coordinate is a rounding tie, and every selected sample lies within the input bounds." |
| }, |
| "attrs": { |
| "mode": "nearest", |
| "coordinate_transformation_mode": "tf_crop_and_resize", |
| "nearest_mode": "floor", |
| "roi": [0, 0, 0.1, 0.1, 1, 1, 0.9, 0.9] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.79, "cosStep": 1.21, "scale": 0.5, "offset": 0.5 } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 3], |
| "tolerance": 0.0002, |
| "data": { "kind": "values", "values": [-0.055154, -0.323958, 0.122715] } |
| } |
| } |
| }, |
| { |
| "name": "linear_asymmetric_exact_2x_f16_x8_route", |
| "provenance": { |
| "notes": "Exercises the float16 x8 linear stencil with asymmetric coordinates. Width 8 keeps the vectorized stencil aligned while distinct sampling positions verify the asymmetric coordinate transform." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "asymmetric" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 1, 4, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.87, "cosStep": 1.09, "scale": 0.4, "offset": 0.5 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 1, 8, 8], "tolerance": 0.02, "relTolerance": 0.01 } } |
| }, |
| { |
| "name": "antialias_linear_align_corners_axis_to_one", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample_antialias.h", |
| "test": "antialiased linear resize, align_corners coordinates", |
| "notes": "Antialiased linear resize with align_corners maps an 8-element axis to one element, taking the defined output-size-one coordinate instead of dividing by output_size - 1; the 6->3 axis uses the ordinary formula. Expected values were independently derived from the separable antialias filter." |
| }, |
| "attrs": { "mode": "linear", "antialias": 1, "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [8, 6], |
| "data": { "kind": "fillFloat32", "sinStep": 1.03, "cosStep": 0.77, "scale": 0.5, "offset": 0.5 } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 3], |
| "tolerance": 0.0001, |
| "relTolerance": 0.0001, |
| "data": { "kind": "values", "values": [0.785487, 0.321457, 0.443004] } |
| } |
| } |
| }, |
| { |
| "name": "antialias_linear_pytorch_half_pixel_axis_to_one", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample_antialias.h", |
| "test": "antialiased linear resize, pytorch_half_pixel coordinates", |
| "notes": "Antialiased linear resize with pytorch_half_pixel maps one axis from 8 to 1, where the coordinate is zero rather than a half-pixel offset; the other axis maps 6 to 3 and uses the ordinary formula. Expected values were independently derived from the separable antialias filter." |
| }, |
| "attrs": { "mode": "linear", "antialias": 1, "coordinate_transformation_mode": "pytorch_half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [8, 6], |
| "data": { "kind": "fillFloat32", "sinStep": 0.93, "cosStep": 1.27, "scale": 0.5, "offset": 0.5 } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 3], |
| "tolerance": 0.0001, |
| "relTolerance": 0.0001, |
| "data": { "kind": "values", "values": [0.693016, 0.605582, 0.168189] } |
| } |
| } |
| }, |
| { |
| "name": "antialias_linear_upsampled_axis_unit_filter_scale", |
| "provenance": { |
| "source": "onnxruntime/core/providers/cpu/tensor/upsample_antialias.h", |
| "test": "antialiased linear resize, half_pixel coordinates", |
| "notes": "Mixed-direction antialiasing upsamples axis 0 from 4 to 8, clamping its filter scale to 1, while downsampling axis 1 from 6 to 3 with filter scale 2. Pinned values follow the separable antialias filter." |
| }, |
| "attrs": { "mode": "linear", "antialias": 1, "coordinate_transformation_mode": "half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [4, 6], |
| "data": { "kind": "fillFloat32", "sinStep": 1.17, "cosStep": 0.71, "scale": 0.5, "offset": 0.5 } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [8, 3], |
| "tolerance": 0.0001, |
| "relTolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [0.595145, 0.064221, 0.678453, 0.683032, 0.080117, 0.624435, 0.858805, 0.111908, 0.516397, 0.826848, 0.241738, 0.586882, 0.587161, 0.469606, 0.83589, 0.337075, 0.525641, 0.96268, 0.076591, 0.409842, 0.967253, -0.053651, 0.351942, 0.96954] |
| } |
| } |
| } |
| }, |
| { |
| "name": "axes_scales_differ_from_output_ratio_nchw", |
| "provenance": { |
| "notes": "The opset-18 `axes` form names the two spatial dimensions and supplies scale 0.7 for each. Because 5*0.7 truncates to 3, the explicit scale differs from the inferred 3/5 shape ratio and must determine the source coordinates." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "half_pixel", |
| "axes": [2, 3], |
| "scales": [0.7, 0.7] |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 2, 5, 5], |
| "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13, "scale": 3.0 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 2, 3, 3], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "cubic_half_pixel_symmetric_fractional_scales", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "Cubic interpolation with the symmetric half-pixel transform. The scales make out_size / (in_size * scale) differ from one on both spatial axes, so the symmetric offset is nonzero and a plain half-pixel transform gives different coordinates." |
| }, |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "half_pixel_symmetric", "scales": [1, 1, 0.6, 0.7] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 1, 7, 5], |
| "data": { |
| "kind": "values", |
| "values": [1.0, 3.0, 2.0, 5.0, 4.0, 6.0, 2.0, 8.0, 3.0, 7.0, 4.0, 9.0, 1.0, 6.0, 2.0, 8.0, 5.0, 3.0, 7.0, 9.0, 2.0, 6.0, 4.0, 1.0, 5.0, 9.0, 3.0, 7.0, 2.0, 8.0, 5.0, 1.0, 6.0, 4.0, 3.0] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 4, 3], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [2.2176567, 5.65625, 4.4600036, 7.9989539, 0.662037, 5.4977763, 4.737444, 3.630787, 3.1282799, 3.6801203, 6.78125, 3.8350036] |
| } |
| } |
| } |
| }, |
| { |
| "name": "antialias_linear_rank1_downsample", |
| "provenance": { |
| "source": "ONNX Resize-19 specification", |
| "notes": "A batchless waveform downsampled with antialiasing. The single resized axis is the whole tensor, so the filter support is widened by the downsample ratio and every output sample averages a source window." |
| }, |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "half_pixel", "antialias": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [12], |
| "data": { "kind": "values", "values": [1.0, 4.0, 2.0, 7.0, 3.0, 9.0, 5.0, 8.0, 6.0, 2.0, 7.0, 4.0] } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [5], |
| "tolerance": 0.00001, |
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| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 6, 10, 7], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "suffix_linear_float32_c8", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 6, 10, 8], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "suffix_linear_float32_c16", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 16], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 6, 10, 16], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "suffix_linear_float16_c1", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 1], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 6, 10, 1], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "suffix_linear_float16_c3", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 3], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 6, 10, 3], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "suffix_linear_float16_c4", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 6, 10, 4], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "suffix_linear_float16_c7", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 7], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 6, 10, 7], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "suffix_linear_float16_c8", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 6, 10, 8], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "suffix_linear_float16_c16", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 16], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 6, 10, 16], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "suffix_linear_int8_c1", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { "dtype": "int8", "shape": [1, 3, 5, 1], "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } } |
| }, |
| "outputs": { "y": { "dtype": "int8", "shape": [1, 6, 10, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_int8_c3", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { "dtype": "int8", "shape": [1, 3, 5, 3], "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } } |
| }, |
| "outputs": { "y": { "dtype": "int8", "shape": [1, 6, 10, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_int8_c4", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { "dtype": "int8", "shape": [1, 3, 5, 4], "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } } |
| }, |
| "outputs": { "y": { "dtype": "int8", "shape": [1, 6, 10, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_int8_c7", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { "dtype": "int8", "shape": [1, 3, 5, 7], "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } } |
| }, |
| "outputs": { "y": { "dtype": "int8", "shape": [1, 6, 10, 7], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_int8_c8", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { "dtype": "int8", "shape": [1, 3, 5, 8], "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } } |
| }, |
| "outputs": { "y": { "dtype": "int8", "shape": [1, 6, 10, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_int8_c16", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "int8", |
| "shape": [1, 3, 5, 16], |
| "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "int8", "shape": [1, 6, 10, 16], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_uint8_c1", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "uint8", |
| "shape": [1, 3, 5, 1], |
| "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "uint8", "shape": [1, 6, 10, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_uint8_c3", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "uint8", |
| "shape": [1, 3, 5, 3], |
| "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "uint8", "shape": [1, 6, 10, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_uint8_c4", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "uint8", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "uint8", "shape": [1, 6, 10, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_uint8_c7", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "uint8", |
| "shape": [1, 3, 5, 7], |
| "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "uint8", "shape": [1, 6, 10, 7], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_uint8_c8", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "uint8", |
| "shape": [1, 3, 5, 8], |
| "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "uint8", "shape": [1, 6, 10, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_uint8_c16", |
| "attrs": { "mode": "linear" }, |
| "inputs": { |
| "x": { |
| "dtype": "uint8", |
| "shape": [1, 3, 5, 16], |
| "data": { "kind": "cycle", "values": [1, 7, 13, 3, 31, 23, 5] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "uint8", "shape": [1, 6, 10, 16], "tolerance": 0 } } |
| }, |
| { |
| "name": "suffix_linear_1axes_float32", |
| "attrs": { "mode": "linear", "exclude_outside": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 5, 4], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "suffix_linear_1axes_float16", |
| "attrs": { "mode": "linear", "exclude_outside": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 5, 4], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "suffix_linear_3axes_float32", |
| "attrs": { "mode": "linear", "exclude_outside": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 3, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 5, 5, 5, 4], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "suffix_linear_3axes_float16", |
| "attrs": { "mode": "linear", "exclude_outside": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 3, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 5, 5, 5, 4], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "suffix_linear_exclude0", |
| "attrs": { "mode": "linear", "exclude_outside": 0 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 5, 7, 8], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "suffix_cubic_1axes_float32", |
| "attrs": { "mode": "cubic", "exclude_outside": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 4], |
| "data": { |
| "kind": "values", |
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| } |
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| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 5, 4], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
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| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator; generated from the exact dtype-rounded, channel-distinct input. No GPU output is used as the reference." |
| } |
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| { |
| "name": "suffix_cubic_1axes_float16", |
| "attrs": { "mode": "cubic", "exclude_outside": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 4], |
| "data": { |
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| } |
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| "outputs": { |
| "y": { |
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| "shape": [1, 5, 4], |
| "tolerance": 0.002, |
| "data": { |
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| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator; generated from the exact dtype-rounded, channel-distinct input. No GPU output is used as the reference." |
| } |
| }, |
| { |
| "name": "suffix_cubic_3axes_float32", |
| "attrs": { "mode": "cubic", "exclude_outside": 1 }, |
| "inputs": { |
| "x": { |
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| "shape": [1, 3, 3, 3, 4], |
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| } |
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| "outputs": { |
| "y": { |
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| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator; generated from the exact dtype-rounded, channel-distinct input. No GPU output is used as the reference." |
| } |
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| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator; generated from the exact dtype-rounded, channel-distinct input. No GPU output is used as the reference." |
| } |
| }, |
| { |
| "name": "suffix_cubic_exclude0", |
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| "inputs": { |
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| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_cubic_exclude0_input_x" } } |
| } |
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| "outputs": { |
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| "dtype": "float32", |
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| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator; generated from the exact dtype-rounded, channel-distinct input. No GPU output is used as the reference." |
| } |
| }, |
| { |
| "name": "suffix_cubic_exclude1", |
| "attrs": { "mode": "cubic", "exclude_outside": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 8], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_cubic_exclude0_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 5, 7, 8], |
| "tolerance": 0.0001, |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_cubic_exclude1_output_y" } } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator; generated from the exact dtype-rounded, channel-distinct input. No GPU output is used as the reference." |
| } |
| }, |
| { |
| "name": "suffix_reversed_axes_identity", |
| "attrs": { "mode": "linear", "axes": [-1, -3, -2], "scales": [1, 2, 2] }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 6, 10, 4], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "suffix_same_extent_nonidentity_scale", |
| "attrs": { "mode": "linear", "scales": [1, 2, 2, 1.01] }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 6, 10, 4], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "suffix_crop_preserves_last_axis", |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "tf_crop_and_resize", |
| "axes": [-1, -3, -2], |
| "roi": [0, -0.2, 0.1, 1, 1.1, 0.9], |
| "extrapolation_value": -0.25 |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_crop_preserves_last_axis_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 6, 10, 4], |
| "tolerance": 0.0001, |
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| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator; generated from the exact dtype-rounded, channel-distinct input. No GPU output is used as the reference." |
| } |
| }, |
| { |
| "name": "suffix_crop_nonidentity_last_axis", |
| "provenance": { |
| "notes": "A same-extent channel ROI must sample at 0.75, 1.25, 1.75, 2.25, not preserve channels 0, 1, 2, 3. The exact dyadic reference is verified with the ONNX ReferenceEvaluator; ONNX Runtime's CPU provider ignores this channel ROI when selecting its NHWC linear path. Spatial 2-to-3 interpolation uses only dyadic weights, making the zero-tolerance reference exact through the complete accumulation." |
| }, |
| "attrs": { |
| "mode": "linear", |
| "coordinate_transformation_mode": "tf_crop_and_resize", |
| "axes": [-1, -3, -2], |
| "roi": [0.25, 0, 0, 0.75, 1, 1] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 2, 2, 4], "data": { "kind": "cycle", "values": [0.0, 1.0, 2.0, 3.0] } } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 3, 3, 4], |
| "data": { "kind": "cycle", "values": [0.75, 1.25, 1.75, 2.25] }, |
| "tolerance": 0, |
| "relTolerance": 0 |
| } |
| } |
| }, |
| { |
| "name": "suffix_singleton_resized", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "pytorch_half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 1, 4], "tolerance": 0.0001 } } |
| }, |
| { |
| "name": "vec2_linear_w5", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 5], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec2_linear_w6", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 6], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec2_linear_w7", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 7], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec2_linear_w8", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 8], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec2_linear_w10", |
| "attrs": { "mode": "linear", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 10], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec2_nearest_w5", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 5], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec2_nearest_w6", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 6], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec2_nearest_w7", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 7], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec2_nearest_w8", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 8], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "vec2_nearest_w10", |
| "attrs": { "mode": "nearest", "coordinate_transformation_mode": "align_corners" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.71, "cosStep": 0.39, "scale": 0.6 } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7, 10], "tolerance": 0.002 } } |
| }, |
| { |
| "name": "cubic_suffix_half_pixel_float32", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "half_pixel", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_crop_preserves_last_axis_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.0001, |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float32_output_y" } } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_pytorch_half_pixel_float32", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "pytorch_half_pixel", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_crop_preserves_last_axis_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.0001, |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float32_output_y" } } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_align_corners_float32", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "align_corners", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_crop_preserves_last_axis_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
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| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_asymmetric_float32", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "asymmetric", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_crop_preserves_last_axis_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
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| } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_half_pixel_symmetric_float32", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "half_pixel_symmetric", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_crop_preserves_last_axis_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.0001, |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float32_output_y" } } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_identity_float32", |
| "attrs": { "mode": "cubic" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "data": { |
| "kind": "values", |
| "values": [0.547941267490387, 0.5975310802459717, 0.359794020652771, -0.10139164328575134, -0.6067157983779907, -0.94154953956604, -0.9552274942398071, -0.6321859359741211] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [2, 4], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [0.547941267490387, 0.5975310802459717, 0.359794020652771, -0.10139164328575134, -0.6067157983779907, -0.94154953956604, -0.9552274942398071, -0.6321859359741211] |
| } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_singleton_float32", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "pytorch_half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/suffix_crop_preserves_last_axis_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float32", |
| "shape": [1, 1, 1, 4], |
| "tolerance": 0.0001, |
| "data": { |
| "kind": "values", |
| "values": [0.547941267490387, 0.5975310802459717, 0.359794020652771, -0.10139164328575134] |
| } |
| } |
| }, |
| "provenance": { |
| "notes": "ONNX Runtime's CPU provider, NHWC transposed to NCHW and back, agrees exactly with ONNX Resize-19 pytorch_half_pixel singleton coordinate zero and first-pixel cubic sampling. The ONNX ReferenceEvaluator instead uses -0.5 here and is not this fixture's reference." |
| } |
| }, |
| { |
| "name": "cubic_suffix_half_pixel_float16", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "half_pixel", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float16_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.002, |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float16_output_y" } } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_pytorch_half_pixel_float16", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "pytorch_half_pixel", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float16_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.002, |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float16_output_y" } } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_align_corners_float16", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "align_corners", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float16_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.002, |
| "data": { |
| "kind": "values", |
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| } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_asymmetric_float16", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "asymmetric", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float16_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.002, |
| "data": { |
| "kind": "values", |
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| } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_half_pixel_symmetric_float16", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "half_pixel_symmetric", "cubic_coeff_a": -0.5 }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float16_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 5, 7, 4], |
| "tolerance": 0.002, |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float16_output_y" } } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_identity_float16", |
| "attrs": { "mode": "cubic" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [2, 4], |
| "data": { |
| "kind": "values", |
| "values": [0.5478515625, 0.59765625, 0.35986328125, -0.10137939453125, -0.60693359375, -0.94140625, -0.955078125, -0.63232421875] |
| } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [2, 4], |
| "tolerance": 0.002, |
| "data": { |
| "kind": "values", |
| "values": [0.5478515625, 0.59765625, 0.35986328125, -0.10137939453125, -0.60693359375, -0.94140625, -0.955078125, -0.63232421875] |
| } |
| } |
| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_singleton_float16", |
| "attrs": { "mode": "cubic", "coordinate_transformation_mode": "pytorch_half_pixel" }, |
| "inputs": { |
| "x": { |
| "dtype": "float16", |
| "shape": [1, 3, 5, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/cubic_suffix_half_pixel_float16_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { |
| "dtype": "float16", |
| "shape": [1, 1, 1, 4], |
| "tolerance": 0.002, |
| "data": { "kind": "values", "values": [0.5478515625, 0.59765625, 0.35986328125, -0.10137939453125] } |
| } |
| }, |
| "provenance": { |
| "notes": "ONNX Runtime's CPU provider, NHWC transposed to NCHW and back, agrees exactly with ONNX Resize-19 pytorch_half_pixel singleton coordinate zero and first-pixel cubic sampling. The ONNX ReferenceEvaluator instead uses -0.5 here and is not this fixture's reference." |
| } |
| }, |
| { |
| "name": "cubic_suffix_alignment_c3", |
| "attrs": { "mode": "cubic", "exclude_outside": 1 }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [1, 3, 5, 3], |
| "data": { |
| "kind": "values", |
| "values": [0.547941267490387, 0.5975310802459717, 0.359794020652771, -0.10139164328575134, -0.6067157983779907, -0.94154953956604, -0.9552274942398071, -0.6321859359741211, -0.100829117000103, 0.4243633449077606, 0.7401348352432251, 0.7450881004333496, 0.48099780082702637, 0.10598835349082947, -0.18646365404129028, -0.2628708779811859, -0.11306791752576828, 0.14889498054981232, 0.3461253046989441, 0.3319292664527893, 0.06842120736837387, -0.34844693541526794, -0.7305420637130737, -0.8864681720733643, -0.7150757908821106, -0.25821852684020996, 0.31331953406333923, 0.7748502492904663, 0.9486299157142639, 0.7829550504684448, 0.3723013997077942, -0.09049784392118454, -0.4065987765789032, -0.4635164737701416, -0.2834371030330658, -0.005032815970480442, 0.19131122529506683, 0.1775568276643753, -0.05513086915016174, -0.38978859782218933, -0.640857994556427, -0.6474397778511047, -0.3569943308830261, 0.1418544352054596, 0.6543782353401184] |
| } |
| } |
| }, |
| "outputs": { |
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| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_alignment_c7", |
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| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
| "name": "cubic_suffix_nonidentity_scale", |
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| "outputs": { |
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| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| }, |
| { |
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| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
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| }, |
| "provenance": { |
| "notes": "Expected output from the ONNX ReferenceEvaluator, using exact dtype-rounded channel-distinct input. No GPU outputs used." |
| } |
| } |
| ] |
| } |
|
|
