mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-18 21:21:17 +00:00
7780fd6cd9
* fixed the missing implementation of output tensor construction based on type checking * cleanup * cleanup
437 lines
No EOL
33 KiB
C#
437 lines
No EOL
33 KiB
C#
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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using System;
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using System.IO;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Numerics.Tensors;
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using Xunit;
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using Microsoft.ML.OnnxRuntime;
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namespace Microsoft.ML.OnnxRuntime.Tests
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{
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public class InferenceTest
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{
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[Fact]
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public void CanCreateAndDisposeSessionWithModelPath()
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{
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string modelPath = Directory.GetCurrentDirectory() + @"\squeezenet.onnx";
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using (var session = new InferenceSession(modelPath))
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{
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Assert.NotNull(session);
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Assert.NotNull(session.InputMetadata);
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Assert.Equal(1, session.InputMetadata.Count); // 1 input node
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Assert.True(session.InputMetadata.ContainsKey("data_0")); // input node name
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Assert.Equal(typeof(float), session.InputMetadata["data_0"].ElementType);
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Assert.True(session.InputMetadata["data_0"].IsTensor);
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var expectedInputDimensions = new int[] { 1, 3, 224, 224 };
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Assert.Equal(expectedInputDimensions.Length, session.InputMetadata["data_0"].Dimensions.Length);
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for (int i = 0; i < expectedInputDimensions.Length; i++)
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{
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Assert.Equal(expectedInputDimensions[i], session.InputMetadata["data_0"].Dimensions[i]);
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}
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Assert.NotNull(session.OutputMetadata);
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Assert.Equal(1, session.OutputMetadata.Count); // 1 output node
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Assert.True(session.OutputMetadata.ContainsKey("softmaxout_1")); // output node name
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Assert.Equal(typeof(float), session.OutputMetadata["softmaxout_1"].ElementType);
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Assert.True(session.OutputMetadata["softmaxout_1"].IsTensor);
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var expectedOutputDimensions = new int[] { 1, 1000, 1, 1 };
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Assert.Equal(expectedOutputDimensions.Length, session.OutputMetadata["softmaxout_1"].Dimensions.Length);
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for (int i = 0; i < expectedOutputDimensions.Length; i++)
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{
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Assert.Equal(expectedOutputDimensions[i], session.OutputMetadata["softmaxout_1"].Dimensions[i]);
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}
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}
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}
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[Fact]
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private void CanRunInferenceOnAModel()
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{
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string modelPath = Directory.GetCurrentDirectory() + @"\squeezenet.onnx";
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using (var session = new InferenceSession(modelPath))
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{
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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float[] inputData = LoadTensorFromFile(@"bench.in"); // this is the data for only one input tensor for this model
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foreach (var name in inputMeta.Keys)
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{
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Assert.Equal(typeof(float), inputMeta[name].ElementType);
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Assert.True(inputMeta[name].IsTensor);
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var tensor = new DenseTensor<float>(inputData, inputMeta[name].Dimensions);
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container.Add(NamedOnnxValue.CreateFromTensor<float>(name, tensor));
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}
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// Run the inference
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var results = session.Run(container); // results is an IReadOnlyList<NamedOnnxValue> container
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Assert.Equal(1, results.Count);
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float[] expectedOutput = LoadTensorFromFile(@"bench.expected_out");
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float errorMargin = 1e-6F;
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// validate the results
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foreach (var r in results)
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{
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Assert.Equal("softmaxout_1", r.Name);
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var resultTensor = r.AsTensor<float>();
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int[] expectedDimensions = { 1, 1000, 1, 1 }; // hardcoded for now for the test data
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Assert.Equal(expectedDimensions.Length, resultTensor.Rank);
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var resultDimensions = resultTensor.Dimensions;
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for (int i = 0; i < expectedDimensions.Length; i++)
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{
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Assert.Equal(expectedDimensions[i], resultDimensions[i]);
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}
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var resultArray = r.AsTensor<float>().ToArray();
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Assert.Equal(expectedOutput.Length, resultArray.Length);
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for (int i = 0; i < expectedOutput.Length; i++)
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{
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Assert.InRange<float>(resultArray[i], expectedOutput[i] - errorMargin, expectedOutput[i] + errorMargin);
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}
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}
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}
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}
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[Fact]
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private void ThrowWrongInputName()
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{
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var tuple = OpenSessionSqueezeNet();
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var session = tuple.Item1;
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var inputData = tuple.Item2;
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var tensor = tuple.Item3;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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container.Add(NamedOnnxValue.CreateFromTensor<float>("wrong_name", tensor));
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var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
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Assert.Equal("[ErrorCode:InvalidArgument] Missing required inputs: data_0", ex.Message);
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session.Dispose();
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}
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[Fact]
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private void ThrowWrongInputType()
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{
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var tuple = OpenSessionSqueezeNet();
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var session = tuple.Item1;
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var inputData = tuple.Item2;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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int[] inputDataInt = inputData.Select(x => (int)x).ToArray();
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var tensor = new DenseTensor<int>(inputDataInt, inputMeta["data_0"].Dimensions);
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container.Add(NamedOnnxValue.CreateFromTensor<int>("data_0", tensor));
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var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
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Assert.Equal("[ErrorCode:InvalidArgument] Unexpected input data type. Actual: (class onnxruntime::NonOnnxType<int>) , expected: (class onnxruntime::NonOnnxType<float>)", ex.Message);
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session.Dispose();
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}
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[Fact]
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private void ThrowWrongDimensions()
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{
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var tuple = OpenSessionSqueezeNet();
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var session = tuple.Item1;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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var inputData = new float[] { 0.1f, 0.2f, 0.3f };
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var tensor = new DenseTensor<float>(inputData, new int[] { 1, 3 });
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container.Add(NamedOnnxValue.CreateFromTensor<float>("data_0", tensor));
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var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
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Assert.Equal("[ErrorCode:Fail] X num_dims does not match W num_dims. X: {1,3} W: {64,3,3,3}", ex.Message);
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session.Dispose();
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}
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[Fact]
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private void ThrowDuplicateInput()
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{
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var tuple = OpenSessionSqueezeNet();
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var session = tuple.Item1;
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var inputData = tuple.Item2;
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var tensor = tuple.Item3;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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var nov = NamedOnnxValue.CreateFromTensor<float>("data_0", tensor);
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container.Add(nov);
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container.Add(nov);
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var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
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Assert.Equal("[ErrorCode:InvalidArgument] duplicated input name", ex.Message);
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session.Dispose();
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}
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[Fact]
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private void ThrowExtraInputs()
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{
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var tuple = OpenSessionSqueezeNet();
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var session = tuple.Item1;
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var inputData = tuple.Item2;
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var tensor = tuple.Item3;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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var nov1 = NamedOnnxValue.CreateFromTensor<float>("data_0", tensor);
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var nov2 = NamedOnnxValue.CreateFromTensor<float>("extra", tensor);
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container.Add(nov1);
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container.Add(nov2);
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var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
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Assert.StartsWith("[ErrorCode:InvalidArgument] Invalid Feed Input Names: extra. Valid input names are: ", ex.Message);
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session.Dispose();
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}
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[Fact]
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private void Yunsong()
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{
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var session = new InferenceSession(@"model_181031_12.onnx");
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float[] zerof = new float[] { 0 };
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long[] zerol = new long[] { 1 };
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var data = new List<NamedOnnxValue>() {
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NamedOnnxValue.CreateFromTensor<float>("input_0_0", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_0_1", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<long>("input_1_0", new DenseTensor<long>(zerol, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<long>("input_1_1", new DenseTensor<long>(zerol, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<long>("input_1_2", new DenseTensor<long>(zerol, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<long>("input_1_3", new DenseTensor<long>(zerol, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<long>("input_1_4", new DenseTensor<long>(zerol, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_2_0", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_2_1", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_2_2", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_2_3", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_2_4", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_2_5", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<long>("input_3_0", new DenseTensor<long>(zerol, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<long>("input_3_1", new DenseTensor<long>(zerol, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_0", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_1", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_2", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_3", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_4", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_5", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_6", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_7", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_8", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_9", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_10", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_11", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_12", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_13", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_14", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_15", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_16", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_17", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_18", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_19", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_20", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_21", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_22", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_23", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_24", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_25", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_26", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_27", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_28", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_29", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_30", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_31", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_32", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_33", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_34", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_35", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_36", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_37", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_38", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_39", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_40", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_41", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_42", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_43", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_44", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_45", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_46", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_47", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_48", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_49", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_50", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_51", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_52", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_53", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_54", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_55", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_56", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_57", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_58", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_59", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_60", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_61", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_62", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_63", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_64", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_65", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_66", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_67", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_68", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_69", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_70", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_71", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_72", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_73", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_74", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_75", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_76", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_77", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_78", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_79", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_80", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_81", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_82", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_83", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_84", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_85", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_86", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_87", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_88", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_89", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_90", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_91", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_92", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_93", new DenseTensor<float>(zerof, new int[] { 1 })),
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NamedOnnxValue.CreateFromTensor<float>("input_4_94", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_95", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_96", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_97", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_98", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_99", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_100", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_101", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_102", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_103", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_104", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_105", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_106", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_107", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_108", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_109", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_110", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_111", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_112", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_113", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_114", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_115", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_116", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_117", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_118", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_119", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_120", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_121", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_122", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_123", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_124", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_125", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_126", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_127", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_128", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_129", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_130", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_131", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_132", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_133", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_134", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_135", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_136", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_137", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_138", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_139", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_140", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_141", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_142", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_143", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_144", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_145", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_146", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<float>("input_4_147", new DenseTensor<float>(zerof, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_0", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_1", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_2", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_3", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_4", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_5", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_6", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_7", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_8", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_9", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_10", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_11", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_12", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_13", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_14", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_15", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_16", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_17", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_18", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_19", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_20", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_21", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_22", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_23", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_24", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_25", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_26", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_27", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_28", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_29", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_30", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_31", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_32", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_33", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_34", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_35", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_36", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
NamedOnnxValue.CreateFromTensor<long>("input_5_37", new DenseTensor<long>(zerol, new int[] { 1 })),
|
|
};
|
|
|
|
var result = session.Run(data);
|
|
Assert.NotNull(result);
|
|
Assert.Equal(1, result.Count);
|
|
var value = result.First<NamedOnnxValue>();
|
|
Assert.Equal("label", value.Name);
|
|
Assert.NotNull(value.AsTensor<long>());
|
|
Assert.Equal(1, value.AsTensor<long>().Length);
|
|
}
|
|
|
|
|
|
|
|
static float[] LoadTensorFromFile(string filename)
|
|
{
|
|
var tensorData = new List<float>();
|
|
|
|
// read data from file
|
|
using (var inputFile = new System.IO.StreamReader(filename))
|
|
{
|
|
inputFile.ReadLine(); //skip the input name
|
|
string[] dataStr = inputFile.ReadLine().Split(new char[] { ',', '[', ']' }, StringSplitOptions.RemoveEmptyEntries);
|
|
for (int i = 0; i < dataStr.Length; i++)
|
|
{
|
|
tensorData.Add(Single.Parse(dataStr[i]));
|
|
}
|
|
}
|
|
|
|
return tensorData.ToArray();
|
|
}
|
|
|
|
static Tuple<InferenceSession, float[], DenseTensor<float>> OpenSessionSqueezeNet()
|
|
{
|
|
string modelPath = Directory.GetCurrentDirectory() + @"\squeezenet.onnx";
|
|
var session = new InferenceSession(modelPath);
|
|
float[] inputData = LoadTensorFromFile(@"bench.in");
|
|
var inputMeta = session.InputMetadata;
|
|
var tensor = new DenseTensor<float>(inputData, inputMeta["data_0"].Dimensions);
|
|
return new Tuple<InferenceSession, float[], DenseTensor<float>>(session, inputData, tensor);
|
|
}
|
|
|
|
}
|
|
} |