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c524f669c7
It fixes tensorflow ocl testcase of MobileNetSSD and Inception_v2_SSD Signed-off-by: Li Peng <peng.li@intel.com>
390 lines
12 KiB
C++
390 lines
12 KiB
C++
// 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 opencv_test
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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, int targetId = DNN_TARGET_CPU, 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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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(targetId);
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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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OCL_TEST(Test_TensorFlow, eltwise_add_mul)
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{
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runTensorFlowNet("eltwise_add_mul", DNN_TARGET_OPENCL);
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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", DNN_TARGET_CPU, true);
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runTensorFlowNet("mvn_batch_norm");
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runTensorFlowNet("mvn_batch_norm_1x1");
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}
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OCL_TEST(Test_TensorFlow, batch_norm)
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{
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runTensorFlowNet("batch_norm", DNN_TARGET_OPENCL);
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runTensorFlowNet("fused_batch_norm", DNN_TARGET_OPENCL);
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runTensorFlowNet("batch_norm_text", DNN_TARGET_OPENCL, 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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runTensorFlowNet("ave_pool_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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runTensorFlowNet("deconvolution_same");
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runTensorFlowNet("deconvolution_stride_2_same");
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runTensorFlowNet("deconvolution_adj_pad_valid");
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runTensorFlowNet("deconvolution_adj_pad_same");
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}
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OCL_TEST(Test_TensorFlow, deconvolution)
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{
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runTensorFlowNet("deconvolution", DNN_TARGET_OPENCL);
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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", DNN_TARGET_CPU, 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", DNN_TARGET_CPU, false, l1, lInf);
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runTensorFlowNet("fp16_deconvolution", DNN_TARGET_CPU, false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_odd_same", DNN_TARGET_CPU, false, l1, lInf);
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runTensorFlowNet("fp16_padding_valid", DNN_TARGET_CPU, false, l1, lInf);
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runTensorFlowNet("fp16_eltwise_add_mul", DNN_TARGET_CPU, false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_odd_valid", DNN_TARGET_CPU, false, l1, lInf);
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runTensorFlowNet("fp16_pad_and_concat", DNN_TARGET_CPU, false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_even", DNN_TARGET_CPU, false, l1, lInf);
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runTensorFlowNet("fp16_padding_same", DNN_TARGET_CPU, 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, 3e-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, Inception_v2_SSD)
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{
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std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false);
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std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
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Net net = readNetFromTensorflow(model, proto);
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Mat img = imread(findDataFile("dnn/street.png", false));
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Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
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net.setInput(blob);
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// Output has shape 1x1xNx7 where N - number of detections.
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// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
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Mat out = net.forward();
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out = out.reshape(1, out.total() / 7);
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Mat detections;
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for (int i = 0; i < out.rows; ++i)
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{
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if (out.at<float>(i, 2) > 0.5)
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detections.push_back(out.row(i).colRange(1, 7));
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}
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Mat ref = (Mat_<float>(5, 6) << 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
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3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
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3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
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10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
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10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
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normAssert(detections, ref);
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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, 3e-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, Inception_v2_SSD)
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{
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std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false);
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std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
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Net net = readNetFromTensorflow(model, proto);
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Mat img = imread(findDataFile("dnn/street.png", false));
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Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
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net.setPreferableBackend(DNN_BACKEND_DEFAULT);
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net.setPreferableTarget(DNN_TARGET_OPENCL);
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net.setInput(blob);
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// Output has shape 1x1xNx7 where N - number of detections.
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// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
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Mat out = net.forward();
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out = out.reshape(1, out.total() / 7);
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Mat detections;
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for (int i = 0; i < out.rows; ++i)
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{
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if (out.at<float>(i, 2) > 0.5)
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detections.push_back(out.row(i).colRange(1, 7));
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}
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Mat ref = (Mat_<float>(5, 6) << 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
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3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
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3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
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10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
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10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
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normAssert(detections, ref);
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}
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TEST(Test_TensorFlow, lstm)
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{
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runTensorFlowNet("lstm", DNN_TARGET_CPU, 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, slice)
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{
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runTensorFlowNet("slice_4d");
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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", DNN_TARGET_CPU, true, l1, lInf, true);
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runTensorFlowNet("batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
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runTensorFlowNet("fused_batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
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runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true, l1, lInf, true);
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}
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}
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