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Open Source Computer Vision Library
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294 lines
8.0 KiB
294 lines
8.0 KiB
// This file is part of OpenCV project. |
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// It is subject to the license terms in the LICENSE file found in the top-level directory |
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// of this distribution and at http://opencv.org/license.html. |
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// Copyright (C) 2017, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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/* |
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Test for Tensorflow models loading |
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*/ |
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#include "test_precomp.hpp" |
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#include "npy_blob.hpp" |
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#include <opencv2/core/ocl.hpp> |
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#include <opencv2/ts/ocl_test.hpp> |
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namespace cvtest |
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{ |
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using namespace cv; |
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using namespace cv::dnn; |
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template<typename TString> |
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static std::string _tf(TString filename) |
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{ |
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return (getOpenCVExtraDir() + "/dnn/") + filename; |
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} |
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TEST(Test_TensorFlow, read_inception) |
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{ |
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Net net; |
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{ |
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const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false); |
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net = readNetFromTensorflow(model); |
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ASSERT_FALSE(net.empty()); |
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} |
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Mat sample = imread(_tf("grace_hopper_227.png")); |
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ASSERT_TRUE(!sample.empty()); |
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Mat input; |
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resize(sample, input, Size(224, 224)); |
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input -= 128; // mean sub |
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Mat inputBlob = blobFromImage(input); |
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net.setInput(inputBlob, "input"); |
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Mat out = net.forward("softmax2"); |
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std::cout << out.dims << std::endl; |
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} |
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TEST(Test_TensorFlow, inception_accuracy) |
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{ |
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Net net; |
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{ |
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const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false); |
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net = readNetFromTensorflow(model); |
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ASSERT_FALSE(net.empty()); |
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} |
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Mat sample = imread(_tf("grace_hopper_227.png")); |
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ASSERT_TRUE(!sample.empty()); |
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resize(sample, sample, Size(224, 224)); |
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Mat inputBlob = blobFromImage(sample); |
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net.setInput(inputBlob, "input"); |
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Mat out = net.forward("softmax2"); |
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Mat ref = blobFromNPY(_tf("tf_inception_prob.npy")); |
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normAssert(ref, out); |
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} |
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static std::string path(const std::string& file) |
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{ |
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return findDataFile("dnn/tensorflow/" + file, false); |
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} |
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static void runTensorFlowNet(const std::string& prefix, bool hasText = false, |
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double l1 = 1e-5, double lInf = 1e-4, |
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bool memoryLoad = false) |
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{ |
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std::string netPath = path(prefix + "_net.pb"); |
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std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : ""); |
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std::string inpPath = path(prefix + "_in.npy"); |
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std::string outPath = path(prefix + "_out.npy"); |
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Net net; |
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if (memoryLoad) |
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{ |
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// Load files into a memory buffers |
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string dataModel; |
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ASSERT_TRUE(readFileInMemory(netPath, dataModel)); |
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string dataConfig; |
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if (hasText) |
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ASSERT_TRUE(readFileInMemory(netConfig, dataConfig)); |
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net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(), |
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dataConfig.c_str(), dataConfig.size()); |
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} |
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else |
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net = readNetFromTensorflow(netPath, netConfig); |
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ASSERT_FALSE(net.empty()); |
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cv::Mat input = blobFromNPY(inpPath); |
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cv::Mat target = blobFromNPY(outPath); |
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net.setInput(input); |
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cv::Mat output = net.forward(); |
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normAssert(target, output, "", l1, lInf); |
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} |
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TEST(Test_TensorFlow, conv) |
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{ |
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runTensorFlowNet("single_conv"); |
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runTensorFlowNet("atrous_conv2d_valid"); |
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runTensorFlowNet("atrous_conv2d_same"); |
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runTensorFlowNet("depthwise_conv2d"); |
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} |
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TEST(Test_TensorFlow, padding) |
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{ |
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runTensorFlowNet("padding_same"); |
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runTensorFlowNet("padding_valid"); |
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runTensorFlowNet("spatial_padding"); |
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} |
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TEST(Test_TensorFlow, eltwise_add_mul) |
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{ |
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runTensorFlowNet("eltwise_add_mul"); |
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} |
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TEST(Test_TensorFlow, pad_and_concat) |
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{ |
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runTensorFlowNet("pad_and_concat"); |
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} |
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TEST(Test_TensorFlow, batch_norm) |
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{ |
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runTensorFlowNet("batch_norm"); |
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runTensorFlowNet("fused_batch_norm"); |
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runTensorFlowNet("batch_norm_text", true); |
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} |
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TEST(Test_TensorFlow, pooling) |
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{ |
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runTensorFlowNet("max_pool_even"); |
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runTensorFlowNet("max_pool_odd_valid"); |
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runTensorFlowNet("max_pool_odd_same"); |
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} |
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TEST(Test_TensorFlow, deconvolution) |
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{ |
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runTensorFlowNet("deconvolution"); |
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} |
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TEST(Test_TensorFlow, matmul) |
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{ |
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runTensorFlowNet("matmul"); |
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runTensorFlowNet("nhwc_reshape_matmul"); |
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runTensorFlowNet("nhwc_transpose_reshape_matmul"); |
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} |
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TEST(Test_TensorFlow, defun) |
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{ |
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runTensorFlowNet("defun_dropout"); |
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} |
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TEST(Test_TensorFlow, reshape) |
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{ |
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runTensorFlowNet("shift_reshape_no_reorder"); |
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runTensorFlowNet("reshape_reduce"); |
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runTensorFlowNet("flatten", true); |
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} |
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TEST(Test_TensorFlow, fp16) |
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{ |
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const float l1 = 1e-3; |
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const float lInf = 1e-2; |
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runTensorFlowNet("fp16_single_conv", false, l1, lInf); |
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runTensorFlowNet("fp16_deconvolution", false, l1, lInf); |
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runTensorFlowNet("fp16_max_pool_odd_same", false, l1, lInf); |
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runTensorFlowNet("fp16_padding_valid", false, l1, lInf); |
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runTensorFlowNet("fp16_eltwise_add_mul", false, l1, lInf); |
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runTensorFlowNet("fp16_max_pool_odd_valid", false, l1, lInf); |
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runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf); |
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runTensorFlowNet("fp16_max_pool_even", false, l1, lInf); |
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runTensorFlowNet("fp16_padding_same", false, l1, lInf); |
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} |
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TEST(Test_TensorFlow, quantized) |
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{ |
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runTensorFlowNet("uint8_single_conv"); |
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} |
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TEST(Test_TensorFlow, MobileNet_SSD) |
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{ |
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std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false); |
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std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false); |
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std::string imgPath = findDataFile("dnn/street.png", false); |
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Mat inp; |
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resize(imread(imgPath), inp, Size(300, 300)); |
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inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true); |
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std::vector<String> outNames(3); |
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outNames[0] = "concat"; |
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outNames[1] = "concat_1"; |
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outNames[2] = "detection_out"; |
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std::vector<Mat> target(outNames.size()); |
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for (int i = 0; i < outNames.size(); ++i) |
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{ |
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std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false); |
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target[i] = blobFromNPY(path); |
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} |
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Net net = readNetFromTensorflow(netPath, netConfig); |
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net.setInput(inp); |
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std::vector<Mat> output; |
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net.forward(output, outNames); |
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normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1)); |
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normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 2e-4); |
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normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2); |
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} |
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OCL_TEST(Test_TensorFlow, MobileNet_SSD) |
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{ |
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std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false); |
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std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false); |
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std::string imgPath = findDataFile("dnn/street.png", false); |
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Mat inp; |
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resize(imread(imgPath), inp, Size(300, 300)); |
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inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true); |
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std::vector<String> outNames(3); |
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outNames[0] = "concat"; |
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outNames[1] = "concat_1"; |
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outNames[2] = "detection_out"; |
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std::vector<Mat> target(outNames.size()); |
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for (int i = 0; i < outNames.size(); ++i) |
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{ |
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std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false); |
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target[i] = blobFromNPY(path); |
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} |
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Net net = readNetFromTensorflow(netPath, netConfig); |
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net.setPreferableBackend(DNN_BACKEND_DEFAULT); |
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net.setPreferableTarget(DNN_TARGET_OPENCL); |
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net.setInput(inp); |
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std::vector<Mat> output; |
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net.forward(output, outNames); |
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normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1)); |
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normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 2e-4); |
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normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2); |
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} |
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TEST(Test_TensorFlow, lstm) |
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{ |
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runTensorFlowNet("lstm", true); |
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} |
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TEST(Test_TensorFlow, split) |
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{ |
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runTensorFlowNet("split_equals"); |
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} |
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TEST(Test_TensorFlow, resize_nearest_neighbor) |
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{ |
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runTensorFlowNet("resize_nearest_neighbor"); |
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} |
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TEST(Test_TensorFlow, memory_read) |
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{ |
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double l1 = 1e-5; |
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double lInf = 1e-4; |
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runTensorFlowNet("lstm", true, l1, lInf, true); |
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runTensorFlowNet("batch_norm", false, l1, lInf, true); |
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runTensorFlowNet("fused_batch_norm", false, l1, lInf, true); |
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runTensorFlowNet("batch_norm_text", true, l1, lInf, true); |
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} |
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}
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