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