{ "op": "ai.onnx.Pad", "fixtureArrays": { "onnx_backend_constant_pad_input_data": [1.764052391052246, 0.40015721321105957, 0.978738009929657, 2.2408931255340576, 1.8675580024719238, -0.9772778749465942, 0.9500884413719177, -0.15135720372200012, -0.10321885347366333, 0.4105985164642334, 0.14404356479644775, 1.4542734622955322, 0.7610377073287964, 0.12167501449584961, 0.44386324286460876, 0.3336743414402008, 1.4940791130065918, -0.2051582634449005, 0.3130677044391632, -0.8540957570075989, -2.5529897212982178, 0.653618574142456, 0.8644362092018127, -0.7421650290489197, 2.269754648208618, -1.4543657302856445, 0.04575851559638977, -0.18718385696411133, 1.5327792167663574, 1.4693588018417358, 0.154947429895401, 0.37816253304481506, -0.8877857327461243, -1.980796456336975, -0.34791216254234314, 0.15634897351264954, 1.2302906513214111, 1.202379822731018, -0.38732680678367615, -0.302302747964859, -1.0485529899597168, -1.420017957687378, -1.7062702178955078, 1.950775384902954, -0.5096521973609924, -0.4380742907524109, -1.2527953386306763, 0.7774903774261475, -1.6138978004455566, -0.21274028718471527, -0.8954665660858154, 0.38690251111984253, -0.5108051300048828, -1.18063223361969, -0.02818222902715206, 0.4283318817615509, 0.06651721894741058, 0.30247190594673157, -0.6343221068382263, -0.3627411723136902] }, "cases": [ { "name": "rank2_constant", "attrs": { "pads": [1, 2, 1, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 5] } } }, { "name": "rank4_f16", "attrs": { "pads": [0, 0, 1, 1, 0, 0, 1, 1] }, "inputs": { "data": { "dtype": "float16", "shape": [1, 1, 2, 2] } }, "outputs": { "output": { "dtype": "float16", "shape": [1, 1, 4, 4] } }, "tolerance": 0.001 }, { "name": "ort_value_input_constant_1d", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_1D", "notes": "Uses Pad's optional `constant_value` input. Pads remain the synthesized attrs.pads initializer; the constant_value_input route consumes the `constant_value` tensor directly." }, "attrs": { "mode": "constant", "pads": [1, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 2.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [123.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [5], "tolerance": 0 } } }, { "name": "value_input_float16_constant_1d", "attrs": { "mode": "constant", "pads": [1, 2] }, "inputs": { "data": { "dtype": "float16", "shape": [2], "data": { "kind": "values", "values": [1.5, -2.0] } }, "constant_value": { "dtype": "float16", "shape": [], "data": { "kind": "values", "values": [-3.5] } } }, "outputs": { "output": { "dtype": "float16", "shape": [5], "tolerance": 0, "data": { "kind": "values", "values": [-3.5, 1.5, -2.0, -3.5, -3.5] } } } }, { "name": "value_input_int8_constant_1d", "attrs": { "mode": "constant", "pads": [1, 1] }, "inputs": { "data": { "dtype": "int8", "shape": [2], "data": { "kind": "values", "values": [-128, 127] } }, "constant_value": { "dtype": "int8", "shape": [], "data": { "kind": "values", "values": [-7] } } }, "outputs": { "output": { "dtype": "int8", "shape": [4], "tolerance": 0, "data": { "kind": "values", "values": [-7, -128, 127, -7] } } } }, { "name": "value_input_uint8_constant_1d", "attrs": { "mode": "constant", "pads": [1, 1] }, "inputs": { "data": { "dtype": "uint8", "shape": [2], "data": { "kind": "values", "values": [0, 255] } }, "constant_value": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [173] } } }, "outputs": { "output": { "dtype": "uint8", "shape": [4], "tolerance": 0, "data": { "kind": "values", "values": [173, 0, 255, 173] } } } }, { "name": "value_input_bool_constant_1d", "attrs": { "mode": "constant", "pads": [1, 1] }, "inputs": { "data": { "dtype": "bool", "shape": [2], "data": { "kind": "values", "values": [0, 1] } }, "constant_value": { "dtype": "bool", "shape": [], "data": { "kind": "values", "values": [1] } } }, "outputs": { "output": { "dtype": "bool", "shape": [4], "tolerance": 0, "data": { "kind": "values", "values": [1, 0, 1, 1] } } } }, { "name": "ort_int32_value_input_constant_1d", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_1D", "notes": "Typed ORT int32 case using Pad's optional `constant_value` input. Pads remain attrs.pads; the constant_value_input route consumes the `constant_value` tensor directly." }, "attrs": { "mode": "constant", "pads": [1, 2] }, "inputs": { "data": { "dtype": "int32", "shape": [2], "data": { "kind": "values", "values": [1, 2] } }, "constant_value": { "dtype": "int32", "shape": [], "data": { "kind": "values", "values": [123] } } }, "outputs": { "output": { "dtype": "int32", "shape": [5], "tolerance": 0 } } }, { "name": "ort_bool_constant_pad_axis1", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.BoolType" }, "attrs": { "mode": "constant", "pads": [0, 2, 0, 0] }, "inputs": { "data": { "dtype": "bool", "shape": [3, 2], "data": { "kind": "values", "values": [1, 0, 1, 0, 1, 0] } }, "constant_value": { "dtype": "bool", "shape": [], "data": { "kind": "values", "values": [1] } } }, "outputs": { "output": { "dtype": "bool", "shape": [3, 4], "tolerance": 0, "data": { "kind": "values", "values": [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0] } } } }, { "name": "ort_constant_pad_axes_out_of_order_rank4", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantPadAxesOutOfOrder", "notes": "ORT supplies axes=[3,2] with pads=[1,0,1,0]; this framework represents the equivalent full-rank pads attribute." }, "attrs": { "pads": [0, 0, 0, 1, 0, 0, 0, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 2, 2, 2], "data": { "kind": "values", "values": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 2, 4], "tolerance": 0, "data": { "kind": "values", "values": [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0] } } } }, { "name": "ort_constant_pad_axes_int32_axis1_axis3", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantPadAxes", "notes": "ORT supplies axes=[1,3] with pads=[0,1,0,1]; this framework represents the equivalent full-rank pads attribute." }, "attrs": { "pads": [0, 0, 0, 1, 0, 0, 0, 1] }, "inputs": { "data": { "dtype": "int32", "shape": [1, 2, 2, 2], "data": { "kind": "values", "values": [1, 1, 1, 1, 1, 1, 1, 1] } } }, "outputs": { "output": { "dtype": "int32", "shape": [1, 2, 2, 4], "tolerance": 0 } } }, { "name": "ort_constant_pad_axes_last_two_both_sides", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantPadAxesTest2", "notes": "ORT supplies axes=[2,3] with pads=[1,1,1,1]; this framework represents the equivalent full-rank pads attribute." }, "attrs": { "pads": [0, 0, 1, 1, 0, 0, 1, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 2, 2, 2], "data": { "kind": "values", "values": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 4, 4], "tolerance": 0 } } }, { "name": "ort_constant_pad_full_rank_without_axes", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantPadAxesTest4", "notes": "ORT ConstantPadNegativeAxes, ConstantPadAxesTest1/Test3, and ConstantPadAxesWithOneDimensionSpecified project to this same full-rank pads request." }, "attrs": { "pads": [0, 0, 0, 1, 0, 0, 0, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 2, 2, 2], "data": { "kind": "values", "values": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 2, 4], "tolerance": 0 } } }, { "name": "rank5_constant", "attrs": { "pads": [0, 1, 0, 1, 0, 0, 0, 1, 0, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 2, 1, 2, 1], "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.19 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-2.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 3, 2, 3, 2], "tolerance": 0.000001 } } }, { "name": "rank6_constant", "attrs": { "pads": [0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 2, 1, 2, 1, 2], "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.19 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [7.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 3, 2, 2, 2, 3], "tolerance": 0.000001 } } }, { "name": "backend_spec_example_axis1_zero", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Spec_Example" }, "attrs": { "pads": [0, 2, 0, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.000001 } } }, { "name": "ort_constant_1d", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_1D" }, "attrs": { "pads": [1, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 2.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [123.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [5], "tolerance": 0.000001 } } }, { "name": "ort_constant_1d_zero_pads", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_1D_Zero" }, "attrs": { "pads": [0, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [2], "data": { "kind": "values", "values": [1.0, 2.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [123.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2], "tolerance": 0.000001 } } }, { "name": "ort_constant_2d", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_2D" }, "attrs": { "pads": [1, 2, 1, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [11.0, 21.0, 12.0, 22.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [123.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 6], "tolerance": 0.000001 } } }, { "name": "ort_edge_2d_axis1", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Edge_1D" }, "attrs": { "pads": [0, 2, 0, 1], "mode": "edge" }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [3, 5], "tolerance": 0.000001 } } }, { "name": "ort_reflect_2d_axis1", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Reflect_1D" }, "attrs": { "pads": [0, 1, 0, 1], "mode": "reflect" }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.000001 } } }, { "name": "ort_wrap_2d_axis1", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Wrap_1D" }, "attrs": { "pads": [0, 1, 0, 1], "mode": "wrap" }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.000001 } } }, { "name": "ort_wrap_1d_upper_pad_greater_than_input", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Wrap_WebGpu_PadGreaterThanInputDimension", "notes": "CPU-valid upper-padding projection of ORT's WebGPU regression." }, "attrs": { "pads": [0, 5], "mode": "wrap" }, "inputs": { "data": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [8], "tolerance": 0, "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0] } } } }, { "name": "ort_wrap_1d_lower_pad_greater_than_input", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Wrap_WebGpu_PadGreaterThanInputDimension", "notes": "Lower-padding projection of the same ORT WebGPU regression: the negative in-coordinate path of the wrap modulo." }, "attrs": { "pads": [5, 0], "mode": "wrap" }, "inputs": { "data": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [8], "tolerance": 0, "data": { "kind": "values", "values": [2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0] } } } }, { "name": "ort_edge_2d_full_pad", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Edge_2D" }, "attrs": { "pads": [2, 2, 2, 2], "mode": "edge" }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3], "data": { "kind": "values", "values": [11.0, 21.0, 31.0, 12.0, 22.0, 32.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [6, 7], "tolerance": 0.000001 } } }, { "name": "ort_reflect_2d_full_pad", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Reflect_2D" }, "attrs": { "pads": [2, 2, 2, 2], "mode": "reflect" }, "inputs": { "data": { "dtype": "float32", "shape": [3, 3], "data": { "kind": "values", "values": [11.0, 21.0, 31.0, 12.0, 22.0, 32.0, 13.0, 23.0, 33.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [7, 7], "tolerance": 0.000001 } } }, { "name": "ort_wrap_2d_full_pad", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Wrap_2D" }, "attrs": { "pads": [2, 2, 2, 2], "mode": "wrap" }, "inputs": { "data": { "dtype": "float32", "shape": [3, 3], "data": { "kind": "values", "values": [11.0, 21.0, 31.0, 12.0, 22.0, 32.0, 13.0, 23.0, 33.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [7, 7], "tolerance": 0.000001 } } }, { "name": "ort_wrap_negative_front_positive_back", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Wrap_NegativeFront_PositiveBack" }, "attrs": { "pads": [-3, 3], "mode": "wrap" }, "inputs": { "data": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4], "tolerance": 0 } } }, { "name": "ort_reflect_pad_equals_extent_minus_one", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Reflect_PadEqualsExtentMinus1_Succeeds" }, "attrs": { "pads": [2, 2], "mode": "reflect" }, "inputs": { "data": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [7], "tolerance": 0.000001 } } }, { "name": "constant_1d_negative_front_crop_post_pad", "attrs": { "pads": [-1, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [123.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [5], "tolerance": 0.000001 } } }, { "name": "constant_2d_negative_axis0_crop", "attrs": { "pads": [-1, 0, -1, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [3, 3], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-5.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 3], "tolerance": 0.000001 } } }, { "name": "constant_vec4_negative_trailing_crop_both_axes", "provenance": { "source": "ONNX Pad-19 specification", "test": "negative end pads crop both axes on the packed default-value path" }, "attrs": { "pads": [0, 0, -1, -1] }, "inputs": { "data": { "dtype": "float32", "shape": [3, 5], "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] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 4], "tolerance": 0 } } }, { "name": "constant_vec4_last_axis_crop_removes_every_column", "provenance": { "notes": "A positive pad on the innermost axis and a negative one that crops away every source column: the output row is entirely pad value, so the sliced width the vec4 kernel carries is zero. That width has a floor at zero precisely for this shape and no case had ever reached it. The uniform output is the assertion -- no source value may survive." }, "attrs": { "pads": [0, 8, 0, -4] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 4], "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.31, "scale": 2.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 8], "tolerance": 0 } } }, { "name": "ort_constant_2d_negative_back_pad_crop", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_2D_negative_pads_1" }, "attrs": { "pads": [1, 2, 1, -1] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3], "data": { "kind": "values", "values": [11.0, 21.0, 31.0, 12.0, 22.0, 32.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [123.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 4], "tolerance": 0.000001 } } }, { "name": "ort_constant_3d_complex", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_3D_complex" }, "attrs": { "pads": [1, 0, 0, -1, 0, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 2, 2], "data": { "kind": "values", "values": [11.0, 12.0, 21.0, 22.0, 111.0, 112.0, 121.0, 122.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 2], "tolerance": 0.000001 } } }, { "name": "constant_3d_mixed_signs_crop_and_pad", "attrs": { "pads": [0, -1, 1, 0, 1, -2] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 4], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0, 10.0, 11.0, 12.0, 13.0, 20.0, 21.0, 22.0, 23.0, 100.0, 101.0, 102.0, 103.0, 110.0, 111.0, 112.0, 113.0, 120.0, 121.0, 122.0, 123.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [9.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 3, 3], "tolerance": 0.000001 } } }, { "name": "ort_constant_4d_crop_removes_all_source_values", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantFill_F32_RemovesAllDataOnAxis" }, "attrs": { "pads": [0, 0, -4, 0, 0, 0, 4, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 1, 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] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 1, 4, 4], "tolerance": 0 } } }, { "name": "ort_constant_3d_innermost_crop_then_post_pad", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantMode_InnermostCropThenPostPad" }, "attrs": { "pads": [1, 3, -2, -1, 0, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 5], "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] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [9.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 6, 4], "tolerance": 0.000001 } } }, { "name": "ort_constant_3d_mixed_signs_small", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantMode_MixedSigns_Small" }, "attrs": { "pads": [1, 3, -2, -1, 0, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 6, 4], "data": { "kind": "cycle", "values": [1.0, 2.0, 3.0, 4.0, 5.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [9.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 9, 3], "tolerance": 0.000001 } } }, { "name": "ort_edge_mode_extent_one_after_crop", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.EdgeMode_ExtentOne_Valid" }, "attrs": { "pads": [-3, 3], "mode": "edge" }, "inputs": { "data": { "dtype": "float32", "shape": [4], "data": { "kind": "constant", "value": 1.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [4], "tolerance": 0 } } }, { "name": "ort_edge_mode_flattened_innermost_axis", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.EdgeMode_FlattenedInnermostAxis" }, "attrs": { "pads": [0, 0, 0, 0, 0, 0, 0, 1], "mode": "edge" }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 2, 4], "data": { "kind": "linspace", "start": 0.0, "end": 47.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 3, 2, 5], "tolerance": 0 } } }, { "name": "ort_zero_dim_1d_pad_to_nonempty", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_DimWithZeroInput / 1D" }, "attrs": { "pads": [1, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2], "tolerance": 0.000001 } } }, { "name": "ort_zero_dim_1d_empty_pads", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_DimWithZeroInput / 1D empty pads" }, "attrs": { "pads": [0, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [0], "tolerance": 0.000001 } } }, { "name": "ort_zero_dim_2d_inner_pad_to_nonempty", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_DimWithZeroInput / 2D" }, "attrs": { "pads": [1, 1, 1, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 0], "data": { "kind": "values", "values": [] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 2], "tolerance": 0.000001 } } }, { "name": "ort_zero_dim_3d_middle_pad_to_nonempty_f16", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_DimWithZeroInput / 3D" }, "attrs": { "pads": [0, 1, 0, 0, 1, 0] }, "inputs": { "data": { "dtype": "float16", "shape": [2, 0, 2], "data": { "kind": "values", "values": [] } }, "constant_value": { "dtype": "float16", "shape": [], "data": { "kind": "values", "values": [1.0] } } }, "outputs": { "output": { "dtype": "float16", "shape": [2, 2, 2], "tolerance": 0.001 } } }, { "name": "ort_edge_zero_dim_unpadded_axis_stays_empty", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Edge_DimWithZeroInput", "notes": "Valid ORT edge-mode case where the zero-sized axis is not padded, so the output remains empty." }, "attrs": { "mode": "edge", "pads": [1, 0, 1, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 0], "data": { "kind": "values", "values": [] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 0], "tolerance": 0 } } }, { "name": "ort_reflect_zero_dim_unpadded_axis_stays_empty", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Reflect_DimWithZeroInput", "notes": "Valid ORT reflect-mode case where the zero-sized axis is not padded, so the output remains empty." }, "attrs": { "mode": "reflect", "pads": [1, 0, 1, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 0], "data": { "kind": "values", "values": [] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 0], "tolerance": 0 } } }, { "name": "ort_constant_2d_crop_front_axis0", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_2D_negative_pads_2" }, "attrs": { "pads": [-1, 0, 0, 0], "mode": "constant" }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3], "data": { "kind": "values", "values": [11.0, 21.0, 31.0, 12.0, 22.0, 32.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [123.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 3], "tolerance": 0 } } }, { "name": "ort_constant_3d_crop_both_last_axis", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_3D_negative_pads" }, "attrs": { "pads": [0, 0, -1, 0, 0, -1], "mode": "constant" }, "inputs": { "data": { "dtype": "float32", "shape": [1, 1, 3], "data": { "kind": "values", "values": [0.0, 1.0, 2.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 1, 1], "tolerance": 0 } } }, { "name": "ort_constant_4d_negative_pads_inner_window", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_4D_negative_pads" }, "attrs": { "pads": [0, 0, -1, -3, 0, 0, -2, -4], "mode": "constant" }, "inputs": { "data": { "dtype": "float32", "shape": [1, 1, 10, 10], "data": { "kind": "linspace", "start": 0.0, "end": 99.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 1, 7, 3], "tolerance": 0 } } }, { "name": "ort_constant_fill_removes_all_data_on_axis", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantFill_F32_RemovesAllDataOnAxis" }, "attrs": { "pads": [0, 0, -4, 0, 0, 0, 4, 0], "mode": "constant" }, "inputs": { "data": { "dtype": "float32", "shape": [1, 1, 4, 4], "data": { "kind": "linspace", "start": 1.0, "end": 16.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 1, 4, 4], "tolerance": 0 } } }, { "name": "ort_constant_large_negative_pad_no_output", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantPadLargeNegativePadNoOutput", "notes": "Large positive secondary dimension is paired with a zero leading dimension, so the output has zero elements." }, "attrs": { "pads": [1, 1048576, -2, -3, 0, 1], "mode": "constant" }, "inputs": { "data": { "dtype": "float32", "shape": [2, 18, 4], "data": { "kind": "cycle", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [100.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [0, 1048594, 3], "tolerance": 0 } } }, { "name": "ort_constant_mixed_signs_small", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantMode_MixedSigns_Small" }, "attrs": { "pads": [1, 3, -2, -1, 0, 1], "mode": "constant" }, "inputs": { "data": { "dtype": "float32", "shape": [2, 6, 4], "data": { "kind": "cycle", "values": [1.0, 2.0, 3.0, 4.0, 5.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [9.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 9, 3], "tolerance": 0 } } }, { "name": "ort_constant_innermost_crop_then_post_pad", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantMode_InnermostCropThenPostPad" }, "attrs": { "pads": [1, 3, -2, -1, 0, 1], "mode": "constant" }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [9.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 6, 4], "tolerance": 0 } } }, { "name": "ort_edge_extent_one_after_crop", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.EdgeMode_ExtentOne_Valid" }, "attrs": { "pads": [-3, 3], "mode": "edge" }, "inputs": { "data": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [1.0, 1.0, 1.0, 1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4], "tolerance": 0 } } }, { "name": "ort_constant_3d_complex_crop_and_prepad", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_3D_complex" }, "attrs": { "pads": [1, 0, 0, -1, 0, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 2, 2], "data": { "kind": "values", "values": [11.0, 12.0, 21.0, 22.0, 111.0, 112.0, 121.0, 122.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [2, 2, 2], "tolerance": 0 } } }, { "name": "ort_reflect_3d_inner_no_padding", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Reflect_3D_Inner_No_Padding" }, "attrs": { "pads": [1, 1, 0, 1, 1, 0], "mode": "reflect" }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [5, 4, 5], "tolerance": 0 } } }, { "name": "ort_wrap_3d_inner_no_padding", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_wrap_3D_Inner_No_Padding" }, "attrs": { "pads": [1, 1, 0, 1, 1, 0], "mode": "wrap" }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [5, 4, 5], "tolerance": 0 } } }, { "name": "onnx_backend_constant_pad", "attrs": { "mode": "constant", "pads": [0, 0, 1, 3, 0, 0, 2, 4] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 3, 4, 5], "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_constant_pad_input_data" } } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [1.2000000476837158] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 3, 7, 12] } }, "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_constant_pad", "notes": "ONNX int64 metadata/index tensors use framework int32/uint32 slots where representable. ONNX Pad pads/value inputs represented as framework attributes." } }, { "name": "onnx_backend_constant_pad_axes", "attrs": { "mode": "constant", "pads": [0, 0, 0, 3, 0, 0, 0, 4] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 3, 4, 5], "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_constant_pad_input_data" } } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [1.2000000476837158] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 3, 4, 12] } }, "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_constant_pad_axes", "notes": "ONNX int64 metadata uses supported framework storage where representable, and Pad inputs are projected to the full-rank pads attribute. The official test_constant_pad_negative_axes fixture projects to this same request." } }, { "name": "onnx_backend_edge_pad", "attrs": { "mode": "edge", "pads": [0, 0, 1, 1, 0, 0, 1, 1] }, "inputs": { "data": { "dtype": "int32", "shape": [1, 3, 4, 5], "data": { "kind": "values", "values": [1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, -2, 0, 0, 0, 2, -1, 0, 0, 1, 1, 0, 0, 0, -1, 0, 0, 1, 1, 0, 0, -1, -1, -1, 1, 0, 0, -1, 0, -1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0] } } }, "outputs": { "output": { "dtype": "int32", "shape": [1, 3, 6, 7] } }, "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_edge_pad", "notes": "ONNX int64 metadata/index tensors use framework int32/uint32 slots where representable. ONNX Pad pads/value inputs represented as framework attributes." } }, { "name": "onnx_backend_reflect_pad", "attrs": { "mode": "reflect", "pads": [0, 0, 1, 1, 0, 0, 1, 1] }, "inputs": { "data": { "dtype": "int32", "shape": [1, 3, 4, 5], "data": { "kind": "values", "values": [0, 0, 0, -1, 0, 0, -1, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, -1, 0, 0, -1, 1, 1, 1, 0, -1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, -1, -1, 0, -1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0] } } }, "outputs": { "output": { "dtype": "int32", "shape": [1, 3, 6, 7] } }, "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reflect_pad", "notes": "ONNX int64 metadata/index tensors use framework int32/uint32 slots where representable. ONNX Pad pads/value inputs represented as framework attributes." } }, { "name": "onnx_backend_wrap_pad", "attrs": { "mode": "wrap", "pads": [0, 0, 1, 1, 0, 0, 1, 1] }, "inputs": { "data": { "dtype": "int32", "shape": [1, 3, 4, 5], "data": { "kind": "values", "values": [0, -1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, 0, 0, 0, 2, 0, 0, 1, -1, 0, 0, 1, 0, 0, 0, 0, 1, -1, -1, 0, 0, 1, 0, 0, -1, 0, -1, -1, 1, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0] } } }, "outputs": { "output": { "dtype": "int32", "shape": [1, 3, 6, 7] } }, "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_wrap_pad", "notes": "ONNX int64 metadata/index tensors use framework int32/uint32 slots where representable. ONNX Pad pads/value inputs represented as framework attributes." } }, { "name": "ort_edge_3d", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Edge_3D", "notes": "ONNX Pad pads/value inputs represented as framework attributes." }, "attrs": { "mode": "edge", "pads": [1, 2, 2, 1, 2, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 2, 3], "data": { "kind": "values", "values": [11.0, 21.0, 31.0, 12.0, 22.0, 32.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [3, 6, 7], "tolerance": 0 } } }, { "name": "ort_constant_3d_inner_no_padding", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_3D_Inner_No_Padding", "notes": "ONNX Pad pads/value inputs represented as framework attributes." }, "attrs": { "mode": "constant", "pads": [1, 1, 0, 1, 1, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [31.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [5, 4, 5], "tolerance": 0 } } }, { "name": "ort_edge_3d_inner_no_padding", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Edge_3D_Inner_No_Padding", "notes": "ONNX Pad pads/value inputs represented as framework attributes." }, "attrs": { "mode": "edge", "pads": [1, 1, 0, 1, 1, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [5, 4, 5], "tolerance": 0 } } }, { "name": "ort_edge_3d_last_pad_slice_inner_no_padding", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Edge_3D_Last_Pad_Slice_Inner_No_Padding", "notes": "ONNX Pad pads/value inputs represented as framework attributes." }, "attrs": { "mode": "edge", "pads": [1, -1, 0, 1, 1, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [5, 2, 5], "tolerance": 0 } } }, { "name": "ort_edge_3d_last_slice_inner_no_padding", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Edge_3D_Last_Slice_Inner_No_Padding", "notes": "ONNX Pad pads/value inputs represented as framework attributes." }, "attrs": { "mode": "edge", "pads": [1, -1, 0, 1, 0, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 2, 5], "tolerance": 0 } } }, { "name": "ort_reflect_3d_last_pad_slice_inner_no_padding", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Reflect_3D_Last_Pad_Slice_Inner_No_Padding", "notes": "ONNX Pad pads/value inputs represented as framework attributes." }, "attrs": { "mode": "reflect", "pads": [1, -1, 0, 1, 1, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 3, 5], "tolerance": 0 } } }, { "name": "ort_reflect_3d_last_slice_inner_no_padding", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Reflect_3D_Last_Slice_Inner_No_Padding", "notes": "ONNX Pad pads/value inputs represented as framework attributes." }, "attrs": { "mode": "reflect", "pads": [1, -1, 0, 1, 0, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 2, 5], "tolerance": 0 } } }, { "name": "ort_wrap_3d_inner_no_padding2", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_wrap_3D_Inner_No_Padding2", "notes": "ONNX Pad pads/value inputs represented as framework attributes." }, "attrs": { "mode": "wrap", "pads": [1, 2, 0, 1, 2, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2, 5], "data": { "kind": "linspace", "start": 1.0, "end": 30.0 } } }, "outputs": { "output": { "dtype": "float32", "shape": [5, 6, 5], "tolerance": 0 } } }, { "name": "ort_int8_constant_1d_edge_values", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_1D", "notes": "Constant 1D padding with logical int8 edge values." }, "attrs": { "mode": "constant", "pads": [2, 1] }, "inputs": { "data": { "dtype": "int8", "shape": [3], "data": { "kind": "values", "values": [-128, 0, 127] } }, "constant_value": { "dtype": "int8", "shape": [], "data": { "kind": "values", "values": [-1] } } }, "outputs": { "output": { "dtype": "int8", "shape": [6], "tolerance": 0 } } }, { "name": "uint8_constant_crop_then_post_pad", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.ConstantMode_InnermostCropThenPostPad", "notes": "Crops the first column, then post-pads with a uint8 edge value." }, "attrs": { "mode": "constant", "pads": [0, -1, 0, 2] }, "inputs": { "data": { "dtype": "uint8", "shape": [2, 3], "data": { "kind": "values", "values": [0, 1, 2, 253, 254, 255] } }, "constant_value": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [255] } } }, "outputs": { "output": { "dtype": "uint8", "shape": [2, 4], "tolerance": 0 } } }, { "name": "int32_constant_value_exact_below_negative_float24", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_1D", "notes": "Signed companion for exact integer padding values: -16777217 is representable as int32 but rounds to -16777216 if routed through f32." }, "attrs": { "mode": "constant", "pads": [1, 1] }, "inputs": { "data": { "dtype": "int32", "shape": [2], "data": { "kind": "values", "values": [-123, 123] } }, "constant_value": { "dtype": "int32", "shape": [], "data": { "kind": "values", "values": [-16777217] } } }, "outputs": { "output": { "dtype": "int32", "shape": [4], "tolerance": 0, "data": { "kind": "values", "values": [-16777217, -123, 123, -16777217] } } } }, { "name": "constant_two_pass_rank4_border", "attrs": { "mode": "constant", "pads": [0, 0, 1, 2, 0, 0, 1, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 2, 6, 8], "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.23 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 8, 12], "tolerance": 0 } } }, { "name": "constant_two_pass_unaligned_interior_tail", "attrs": { "mode": "constant", "pads": [1, 0, 1, 0, 1, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 5], "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.29 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [9.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [3, 4, 8], "tolerance": 0 } } }, { "name": "constant_two_pass_crop_and_pad", "attrs": { "mode": "constant", "pads": [1, 0, -2, 0, 1, -4] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 10], "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.13 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [7.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [3, 4, 4], "tolerance": 0 } } }, { "name": "constant_two_pass_rank1_crop_unaligned", "attrs": { "mode": "constant", "pads": [3, -1] }, "inputs": { "data": { "dtype": "float32", "shape": [6], "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.07 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-2.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [8], "tolerance": 0 } } }, { "name": "constant_two_pass_f16_border", "attrs": { "mode": "constant", "pads": [0, 0, 1, 1, 0, 0, 1, 1] }, "inputs": { "data": { "dtype": "float16", "shape": [1, 1, 3, 6] }, "constant_value": { "dtype": "float16", "shape": [], "data": { "kind": "values", "values": [0.5] } } }, "outputs": { "output": { "dtype": "float16", "shape": [1, 1, 5, 8] } }, "tolerance": 0.001 }, { "name": "edge_3d_quad_inner_aligned", "attrs": { "mode": "edge", "pads": [1, 1, 1, 1, 1, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 6], "data": { "kind": "fillFloat32", "sinStep": 0.21, "cosStep": 0.17 } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 5, 8], "tolerance": 0 } } }, { "name": "reflect_3d_quad_inner_aligned", "attrs": { "mode": "reflect", "pads": [1, 1, 3, 1, 1, 3] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 6], "data": { "kind": "fillFloat32", "sinStep": 0.27, "cosStep": 0.19 } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 5, 12], "tolerance": 0 } } }, { "name": "wrap_3d_quad_inner_aligned", "attrs": { "mode": "wrap", "pads": [1, 1, 4, 1, 1, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 3, 6], "data": { "kind": "fillFloat32", "sinStep": 0.33, "cosStep": 0.11 } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 5, 12], "tolerance": 0 } } }, { "name": "int32_constant_attr_value_exact_above_float24_scalar_path_unaligned", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_1D", "notes": "Scalar-path companion to the aligned int32/uint32 large-attribute-value cases. The output's last dimension is 3, which excludes the vec4 path and verifies that exact int32 attribute conversion is preserved by the scalar fallback." }, "attrs": { "mode": "constant", "pads": [1, 1] }, "inputs": { "data": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [7] } }, "constant_value": { "dtype": "int32", "shape": [], "data": { "kind": "values", "values": [16777217] } } }, "outputs": { "output": { "dtype": "int32", "shape": [3], "tolerance": 0, "data": { "kind": "values", "values": [16777217, 7, 16777217] } } } }, { "name": "uint32_value_input_exact_above_float24", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Constant_1D", "notes": "An optional uint32 value input above 2^24 must remain exact rather than round through f32." }, "attrs": { "mode": "constant", "pads": [1, 1] }, "inputs": { "data": { "dtype": "uint32", "shape": [2], "data": { "kind": "values", "values": [4000000001, 17] } }, "constant_value": { "dtype": "uint32", "shape": [], "data": { "kind": "values", "values": [16777217] } } }, "outputs": { "output": { "dtype": "uint32", "shape": [4], "tolerance": 0, "data": { "kind": "values", "values": [16777217, 4000000001, 17, 16777217] } } } }, { "name": "f16_reflect_rank4_inner_aligned", "attrs": { "mode": "reflect", "pads": [0, 0, 2, 2, 0, 0, 2, 2] }, "inputs": { "data": { "dtype": "float16", "shape": [1, 16, 4, 8] } }, "outputs": { "output": { "dtype": "float16", "shape": [1, 16, 8, 12], "tolerance": 0.003 } } }, { "name": "uint8_edge_rank2", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Edge_2D", "notes": "uint8 with a non-constant (edge) mode was untested; edge/reflect/wrap were only covered for f32/int32. Pure value-copy, exact." }, "attrs": { "mode": "edge", "pads": [1, 2, 1, 2] }, "inputs": { "data": { "dtype": "uint8", "shape": [2, 3], "data": { "kind": "values", "values": [10, 20, 30, 40, 50, 60] } } }, "outputs": { "output": { "dtype": "uint8", "shape": [4, 7], "tolerance": 0 } } }, { "name": "value_input_reflect_value_ignored", "provenance": { "source": "onnxruntime/test/providers/cpu/tensor/pad_test.cc", "test": "PadOpTest.Pad_Reflect_1D", "notes": "constant_value_input gates on all 4 modes; for reflect, map_coord never returns -1 so padValue[0] is never read. Supplies a bogus value (999) to confirm the value input is correctly ignored for non-constant modes." }, "attrs": { "mode": "reflect", "pads": [0, 1, 0, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [3, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [999.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [3, 4], "tolerance": 0 } } }, { "name": "value_input_empty_data_constant_1d", "attrs": { "mode": "constant", "pads": [1, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [123.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [3], "tolerance": 0, "data": { "kind": "values", "values": [123.0, 123.0, 123.0] } } } }, { "name": "value_input_empty_inner_data_2d_constant", "attrs": { "mode": "constant", "pads": [1, 1, 1, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [2, 0], "data": { "kind": "values", "values": [] } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [5.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4, 2], "tolerance": 0, "data": { "kind": "values", "values": [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0] } } } }, { "name": "value_input_scalar_path_2d_fold_last_row", "attrs": { "mode": "constant", "pads": [1, 0, 1, 0] }, "inputs": { "data": { "dtype": "float32", "shape": [4094, 512], "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.023 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-7.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [4096, 512], "tolerance": 0 } } }, { "name": "rank3_value_input_scalar_path_perf_compact", "provenance": { "notes": "Compact sibling for the rank-3 value-input Pad benchmark; optional `constant_value` input selects constant_value_input and the unaligned inner extent keeps scalar address handling active." }, "attrs": { "mode": "constant", "pads": [1, 0, 2, 1, 0, 2] }, "inputs": { "data": { "dtype": "float32", "shape": [4, 16, 31], "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.023 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-7.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [6, 16, 35], "tolerance": 0 } } }, { "name": "rank7_constant_last_axis", "attrs": { "mode": "constant", "pads": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 2, 1, 2, 1, 2, 3], "data": { "kind": "linspace", "start": 1.0, "end": 24.0 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 1, 2, 1, 2, 4], "tolerance": 0 } } }, { "name": "rank8_constant_last_axis", "attrs": { "mode": "constant", "pads": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] }, "inputs": { "data": { "dtype": "float32", "shape": [1, 2, 1, 2, 1, 2, 2, 3], "data": { "kind": "linspace", "start": 1.0, "end": 48.0 } }, "constant_value": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-1.0] } } }, "outputs": { "output": { "dtype": "float32", "shape": [1, 2, 1, 2, 1, 2, 2, 4], "tolerance": 0 } } } ] }