| { |
| "op": "ai.onnx.Dropout", |
| "fixtureArrays": { |
| "onnx_backend_input_x": [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": "float32_inference_copy", |
| "provenance": { "source": "onnxruntime/test/providers/cpu/nn/dropout_op_test.cc", "test": "Dropout.Opset10" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 5.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 2] } } |
| }, |
| { |
| "name": "ort_opset7_float32_inference_copy", |
| "provenance": { "source": "onnxruntime/test/providers/cpu/nn/dropout_op_test.cc", "test": "Dropout.Opset7" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 2] } } |
| }, |
| { |
| "name": "ort_opset13_optional_bool_mask_exact", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/nn/dropout_op_test.cc", |
| "test": "Dropout.WithOptionalOutputOpset10", |
| "notes": "Same optional-mask behavior validated through the current-schema ORT path, but with the ONNX-native bool mask dtype." |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 5.0] } } |
| }, |
| "outputs": { |
| "y": { "dtype": "float32", "shape": [2, 2] }, |
| "mask": { "dtype": "bool", "shape": [2, 2], "tolerance": 0 } |
| } |
| }, |
| { |
| "name": "float16_inference_copy", |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 2], "data": { "kind": "values", "values": [1.0, -2.0, 3.5, 5.0] } } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 2], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "float32_scalar_inference_copy", |
| "inputs": { "x": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-4.5] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "float32_nan_and_infinity_copy", |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [5], |
| "data": { "kind": "values", "values": ["NaN", "-Infinity", 0.0, "Infinity", 3.0] } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [5], "tolerance": 0.000001, "allowNaN": true } } |
| }, |
| { |
| "name": "float16_zero_sized_copy", |
| "inputs": { "x": { "dtype": "float16", "shape": [2, 0, 3], "data": { "kind": "values", "values": [] } } }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 0, 3], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "onnx_backend_default_rank3_copy", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_dropout_default", |
| "notes": "Imports the inference-copy output; optional ratio/training-mode inputs and mask output are omitted by this framework variant. The legacy test_dropout_random_old vector projects to the same inference-only request." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_x" } } |
| } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "onnx_backend_default_mask_rank3", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_dropout_default_mask", |
| "notes": "Inference mode emits the all-true ONNX bool mask." |
| }, |
| "inputs": { |
| "x": { |
| "dtype": "float32", |
| "shape": [3, 4, 5], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_x" } } |
| } |
| }, |
| "outputs": { |
| "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.000001 }, |
| "mask": { "dtype": "bool", "shape": [3, 4, 5], "tolerance": 0 } |
| } |
| }, |
| { |
| "name": "onnx_backend_default_old_copy", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_dropout_default_old", |
| "notes": "Older-schema Dropout inference-copy fixture represented with the project inference-only variant." |
| }, |
| "inputs": { "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1.0, 0.0, 1.0] } } }, |
| "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "f32_scalar_mask_path_non_aligned", |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [5], "data": { "kind": "values", "values": [1.0, -2.0, 3.5, 0.0, -0.5] } } |
| }, |
| "outputs": { |
| "y": { "dtype": "float32", "shape": [5], "tolerance": 0.000001 }, |
| "mask": { "dtype": "bool", "shape": [5], "tolerance": 0 } |
| } |
| }, |
| { |
| "name": "f16_scalar_mask_path_numel3", |
| "inputs": { "x": { "dtype": "float16", "shape": [3], "data": { "kind": "values", "values": [1.0, -2.0, 3.5] } } }, |
| "outputs": { |
| "y": { "dtype": "float16", "shape": [3], "tolerance": 0.001 }, |
| "mask": { "dtype": "bool", "shape": [3], "tolerance": 0 } |
| } |
| } |
| ] |
| } |
|
|
