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opencv/modules/dnn/test/test_torch_importer.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
namespace opencv_test
{
using namespace std;
using namespace testing;
using namespace cv;
using namespace cv::dnn;
template<typename TStr>
static std::string _tf(TStr filename, bool inTorchDir = true, bool required = true)
{
String path = "dnn/";
if (inTorchDir)
path += "torch/";
path += filename;
return findDataFile(path, required);
}
TEST(Torch_Importer, simple_read)
{
Net net;
ASSERT_NO_THROW(net = readNetFromTorch(_tf("net_simple_net.txt"), false));
ASSERT_FALSE(net.empty());
}
class Test_Torch_layers : public DNNTestLayer
{
public:
void runTorchNet(const String& prefix, String outLayerName = "",
bool check2ndBlob = false, bool isBinary = false, bool evaluate = true,
double l1 = 0.0, double lInf = 0.0)
{
String suffix = (isBinary) ? ".dat" : ".txt";
Mat inp, outRef;
ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
checkBackend(backend, target, &inp, &outRef);
Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary, evaluate);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
if (outLayerName.empty())
outLayerName = net.getLayerNames().back();
net.setInput(inp);
std::vector<Mat> outBlobs;
net.forward(outBlobs, outLayerName);
l1 = l1 ? l1 : default_l1;
lInf = lInf ? lInf : default_lInf;
normAssert(outRef, outBlobs[0], "", l1, lInf);
if (check2ndBlob && backend == DNN_BACKEND_OPENCV)
{
Mat out2 = outBlobs[1];
Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
normAssert(out2, ref2, "", l1, lInf);
}
}
};
TEST_P(Test_Torch_layers, run_convolution)
{
// Output reference values are in range [23.4018, 72.0181]
double l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
l1 = 0.08;
lInf = 0.43;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.08;
lInf = 0.5;
}
runTorchNet("net_conv", "", false, true, true, l1, lInf);
}
TEST_P(Test_Torch_layers, run_pool_max)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_CUDA_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
if (target == DNN_TARGET_CPU_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16);
double l1 = 0.0, lInf = 0.0;
runTorchNet("net_pool_max", "", true, false, true, l1, lInf);
}
TEST_P(Test_Torch_layers, run_pool_ave)
{
runTorchNet("net_pool_ave");
}
TEST_P(Test_Torch_layers, run_reshape_change_batch_size)
{
runTorchNet("net_reshape");
}
TEST_P(Test_Torch_layers, run_reshape)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
runTorchNet("net_reshape_batch");
runTorchNet("net_reshape_channels", "", false, true);
}
TEST_P(Test_Torch_layers, run_reshape_single_sample)
{
// Reference output values in range [14.4586, 18.4492].
double l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
l1 = 0.033;
lInf = 0.05;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.02;
lInf = 0.04;
}
runTorchNet("net_reshape_single_sample", "", false, false, true, l1, lInf);
}
TEST_P(Test_Torch_layers, run_linear)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_CPU_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16);
runTorchNet("net_linear_2d");
}
TEST_P(Test_Torch_layers, run_concat)
{
runTorchNet("net_concat", "l5_torchMerge");
}
TEST_P(Test_Torch_layers, run_depth_concat)
{
double lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
lInf = 0.032;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
lInf = 0.03;
}
runTorchNet("net_depth_concat", "", false, true, true, 0.0, lInf);
}
TEST_P(Test_Torch_layers, run_deconv)
{
runTorchNet("net_deconv");
}
TEST_P(Test_Torch_layers, run_batch_norm)
{
runTorchNet("net_batch_norm", "", false, true);
runTorchNet("net_batch_norm_train", "", false, true, false);
}
TEST_P(Test_Torch_layers, net_prelu)
{
runTorchNet("net_prelu");
}
TEST_P(Test_Torch_layers, net_cadd_table)
{
runTorchNet("net_cadd_table");
}
TEST_P(Test_Torch_layers, net_softmax)
{
runTorchNet("net_softmax");
runTorchNet("net_softmax_spatial");
}
TEST_P(Test_Torch_layers, net_logsoftmax)
{
runTorchNet("net_logsoftmax");
runTorchNet("net_logsoftmax_spatial");
}
TEST_P(Test_Torch_layers, net_lp_pooling_square)
{
runTorchNet("net_lp_pooling_square", "", false, true);
}
TEST_P(Test_Torch_layers, net_lp_pooling_power)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTorchNet("net_lp_pooling_power", "", false, true);
}
TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
double l1 = 0.0, lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16)
{
l1 = 0.046;
lInf = 0.023;
}
else if (target == DNN_TARGET_MYRIAD)
{
l1 = 0.02;
lInf = 0.05;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.0042;
lInf = 0.021;
}
// The OpenCL kernels use the native_ math functions which have
// implementation defined accuracy, so we use relaxed thresholds. See
// https://github.com/opencv/opencv/issues/9821 for more details.
else if (target == DNN_TARGET_OPENCL)
{
l1 = 0.02;
lInf = 0.02;
}
runTorchNet("net_conv_gemm_lrn", "", false, true, true, l1, lInf);
}
TEST_P(Test_Torch_layers, net_inception_block)
{
runTorchNet("net_inception_block", "", false, true);
}
TEST_P(Test_Torch_layers, net_normalize)
{
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); /* only L1 and L2 norms are supported */
runTorchNet("net_normalize", "", false, true);
}
TEST_P(Test_Torch_layers, net_padding)
{
runTorchNet("net_padding", "", false, true);
runTorchNet("net_spatial_zero_padding", "", false, true);
runTorchNet("net_spatial_reflection_padding", "", false, true);
}
TEST_P(Test_Torch_layers, net_non_spatial)
{
#if defined(INF_ENGINE_RELEASE) && ( \
INF_ENGINE_VER_MAJOR_EQ(2021030000) || \
INF_ENGINE_VER_MAJOR_EQ(2021040000) \
)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
// 2021.3: crash
// 2021.4: [ GENERAL_ERROR ] AssertionFailed: !out.networkInputs.empty()
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
runTorchNet("net_non_spatial", "", false, true);
}
TEST_P(Test_Torch_layers, run_paralel)
{
if (backend != DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU)
throw SkipTestException(""); // TODO: Check this
runTorchNet("net_parallel", "l5_torchMerge");
}
TEST_P(Test_Torch_layers, net_residual)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_OPENCL ||
target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTorchNet("net_residual", "", false, true);
}
class Test_Torch_nets : public DNNTestLayer {};
TEST_P(Test_Torch_nets, OpenFace_accuracy)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
checkBackend();
const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
Net net = readNetFromTorch(model);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat sample = imread(findDataFile("cv/shared/lena.png"));
Mat sampleF32(sample.size(), CV_32FC3);
sample.convertTo(sampleF32, sampleF32.type());
sampleF32 /= 255;
resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
Mat inputBlob = blobFromImage(sampleF32, 1.0, Size(), Scalar(), /*swapRB*/true);
net.setInput(inputBlob);
Mat out = net.forward();
// Reference output values are in range [-0.17212, 0.263492]
// on Myriad problem layer: l4_Pooling - does not use pads_begin
float l1 = 1e-5, lInf = 1e-3;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
l1 = 2e-3;
lInf = 5e-3;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.0004;
lInf = 0.0012;
}
Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
normAssert(out, outRef, "", l1, lInf);
}
static Mat getSegmMask(const Mat& scores)
{
const int rows = scores.size[2];
const int cols = scores.size[3];
const int numClasses = scores.size[1];
Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
Mat maxVal(rows, cols, CV_32FC1, Scalar(0));
for (int ch = 0; ch < numClasses; ch++)
{
for (int row = 0; row < rows; row++)
{
const float *ptrScore = scores.ptr<float>(0, ch, row);
uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
float *ptrMaxVal = maxVal.ptr<float>(row);
for (int col = 0; col < cols; col++)
{
if (ptrScore[col] > ptrMaxVal[col])
{
ptrMaxVal[col] = ptrScore[col];
ptrMaxCl[col] = (uchar)ch;
}
}
}
}
return maxCl;
}
// Computer per-class intersection over union metric.
static void normAssertSegmentation(const Mat& ref, const Mat& test)
{
CV_Assert_N(ref.dims == 4, test.dims == 4);
const int numClasses = ref.size[1];
CV_Assert(numClasses == test.size[1]);
Mat refMask = getSegmMask(ref);
Mat testMask = getSegmMask(test);
EXPECT_EQ(countNonZero(refMask != testMask), 0);
}
TEST_P(Test_Torch_nets, ENet_accuracy)
{
applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
checkBackend();
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
if (target == DNN_TARGET_CPU_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#else
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
throw SkipTestException("");
}
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
throw SkipTestException("");
}
Net net;
{
const string model = findDataFile("dnn/Enet-model-best.net", false);
net = readNetFromTorch(model, true);
ASSERT_TRUE(!net.empty());
}
net.enableWinograd(false);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat sample = imread(_tf("street.png", false));
Mat inputBlob = blobFromImage(sample, 1./255, Size(), Scalar(), /*swapRB*/true);
net.setInput(inputBlob, "");
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
// Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
// thresholds for ENet must be changed. Accuracy of results was checked on
// Cityscapes dataset and difference in mIOU with Torch is 10E-4%
normAssert(ref, out, "", 0.0005, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
normAssertSegmentation(ref, out);
const int N = 3;
for (int i = 0; i < N; i++)
{
net.setInput(inputBlob, "");
Mat out = net.forward();
normAssert(ref, out, "", 0.0005, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
normAssertSegmentation(ref, out);
}
}
// Check accuracy of style transfer models from https://github.com/jcjohnson/fast-neural-style
// th fast_neural_style.lua \
// -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
// -output_image lena.png \
// -median_filter 0 \
// -image_size 0 \
// -model models/eccv16/starry_night.t7
// th fast_neural_style.lua \
// -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
// -output_image lena.png \
// -median_filter 0 \
// -image_size 0 \
// -model models/instance_norm/feathers.t7
TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{
#if defined INF_ENGINE_RELEASE
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
checkBackend();
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_RELEASE <= 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#endif
std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
"dnn/fast_neural_style_instance_norm_feathers.t7"};
std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
for (int i = 0; i < 2; ++i)
{
const string model = findDataFile(models[i], false);
Net net = readNetFromTorch(model);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(findDataFile("dnn/googlenet_1.png"));
Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
net.setInput(inputBlob);
Mat out = net.forward();
// Deprocessing.
getPlane(out, 0, 0) += 103.939;
getPlane(out, 0, 1) += 116.779;
getPlane(out, 0, 2) += 123.68;
out = cv::min(cv::max(0, out), 255);
Mat ref = imread(findDataFile(targets[i]));
Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
double normL1 = cvtest::norm(refBlob, out, cv::NORM_L1) / refBlob.total();
if (target == DNN_TARGET_MYRIAD)
EXPECT_LE(normL1, 4.0f);
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
EXPECT_LE(normL1, 1.0f);
else
EXPECT_LE(normL1, 0.6f);
}
else if(target == DNN_TARGET_CUDA_FP16)
{
normAssert(out, refBlob, "", 0.6, 26);
}
else if (target == DNN_TARGET_CPU_FP16)
{
normAssert(out, refBlob, "", 0.62, 25);
}
else
normAssert(out, refBlob, "", 0.5, 1.11);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_nets, dnnBackendsAndTargets());
// Test a custom layer
// https://github.com/torch/nn/blob/master/doc/convolution.md#nn.SpatialUpSamplingNearest
class SpatialUpSamplingNearestLayer CV_FINAL : public Layer
{
public:
SpatialUpSamplingNearestLayer(const LayerParams &params) : Layer(params)
{
scale = params.get<int>("scale_factor");
}
static Ptr<Layer> create(LayerParams& params)
{
return Ptr<Layer>(new SpatialUpSamplingNearestLayer(params));
}
virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
const int requiredOutputs,
std::vector<std::vector<int> > &outputs,
std::vector<std::vector<int> > &internals) const CV_OVERRIDE
{
std::vector<int> outShape(4);
outShape[0] = inputs[0][0]; // batch size
outShape[1] = inputs[0][1]; // number of channels
outShape[2] = scale * inputs[0][2];
outShape[3] = scale * inputs[0][3];
outputs.assign(1, outShape);
return false;
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
Mat& inp = inputs[0];
Mat& out = outputs[0];
const int outHeight = out.size[2];
const int outWidth = out.size[3];
for (size_t n = 0; n < inp.size[0]; ++n)
{
for (size_t ch = 0; ch < inp.size[1]; ++ch)
{
resize(getPlane(inp, n, ch), getPlane(out, n, ch),
Size(outWidth, outHeight), 0, 0, INTER_NEAREST);
}
}
}
private:
int scale;
};
TEST_P(Test_Torch_layers, upsampling_nearest)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // TODO
#endif
// Test a custom layer.
CV_DNN_REGISTER_LAYER_CLASS(SpatialUpSamplingNearest, SpatialUpSamplingNearestLayer);
try
{
runTorchNet("net_spatial_upsampling_nearest", "", false, true);
}
catch (...)
{
LayerFactory::unregisterLayer("SpatialUpSamplingNearest");
throw;
}
LayerFactory::unregisterLayer("SpatialUpSamplingNearest");
// Test an implemented layer.
runTorchNet("net_spatial_upsampling_nearest", "", false, true);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_layers, dnnBackendsAndTargets());
}
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