modules/dnn/test/test_torch_importer.cpp

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  41
  42 #include "test_precomp.hpp"
  43 #include "npy_blob.hpp"
  44 #include <opencv2/dnn/shape_utils.hpp>
  45 #include <opencv2/dnn/layer.details.hpp>  // CV_DNN_REGISTER_LAYER_CLASS
  46
  47 namespace opencv_test
  48 {
  49
  50 using namespace std;
  51 using namespace testing;
  52 using namespace cv;
  53 using namespace cv::dnn;
  54
  55 template<typename TStr>
  56 static std::string _tf(TStr filename, bool inTorchDir = true, bool required = true)
  57 {
  58     String path = "dnn/";
  59     if (inTorchDir)
  60         path += "torch/";
  61     path += filename;
  62     return findDataFile(path, required);
  63 }
  64
  65 TEST(Torch_Importer, simple_read)
  66 {
  67     Net net;
  68     ASSERT_NO_THROW(net = readNetFromTorch(_tf("net_simple_net.txt"), false));
  69     ASSERT_FALSE(net.empty());
  70 }
  71
  72 class Test_Torch_layers : public DNNTestLayer
  73 {
  74 public:
  75     void runTorchNet(const String& prefix, String outLayerName = "",
  76                      bool check2ndBlob = false, bool isBinary = false, bool evaluate = true,
  77                      double l1 = 0.0, double lInf = 0.0)
  78     {
  79         String suffix = (isBinary) ? ".dat" : ".txt";
  80
  81         Mat inp, outRef;
  82         ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
  83         ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
  84
  85         checkBackend(backend, target, &inp, &outRef);
  86
  87         Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary, evaluate);
  88         ASSERT_FALSE(net.empty());
  89
  90         net.setPreferableBackend(backend);
  91         net.setPreferableTarget(target);
  92
  93         if (outLayerName.empty())
  94             outLayerName = net.getLayerNames().back();
  95
  96         net.setInput(inp);
  97         std::vector<Mat> outBlobs;
  98         net.forward(outBlobs, outLayerName);
  99         l1 = l1 ? l1 : default_l1;
 100         lInf = lInf ? lInf : default_lInf;
 101         normAssert(outRef, outBlobs[0], "", l1, lInf);
 102
 103         if (check2ndBlob && backend != DNN_BACKEND_INFERENCE_ENGINE)
 104         {
 105             Mat out2 = outBlobs[1];
 106             Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
 107             normAssert(out2, ref2, "", l1, lInf);
 108         }
 109     }
 110 };
 111
 112 TEST_P(Test_Torch_layers, run_convolution)
 113 {
 114     // Output reference values are in range [23.4018, 72.0181]
 115     double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.08 : default_l1;
 116     double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.42 : default_lInf;
 117     runTorchNet("net_conv", "", false, true, true, l1, lInf);
 118 }
 119
 120 TEST_P(Test_Torch_layers, run_pool_max)
 121 {
 122     if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
 123         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
 124     runTorchNet("net_pool_max", "", true);
 125 }
 126
 127 TEST_P(Test_Torch_layers, run_pool_ave)
 128 {
 129     runTorchNet("net_pool_ave");
 130 }
 131
 132 TEST_P(Test_Torch_layers, run_reshape_change_batch_size)
 133 {
 134     runTorchNet("net_reshape");
 135 }
 136
 137 TEST_P(Test_Torch_layers, run_reshape)
 138 {
 139     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 140         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
 141     runTorchNet("net_reshape_batch");
 142     runTorchNet("net_reshape_channels", "", false, true);
 143 }
 144
 145 TEST_P(Test_Torch_layers, run_reshape_single_sample)
 146 {
 147     // Reference output values in range [14.4586, 18.4492].
 148     runTorchNet("net_reshape_single_sample", "", false, false, true,
 149                 (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.033 : default_l1,
 150                 (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.05 : default_lInf);
 151 }
 152
 153 TEST_P(Test_Torch_layers, run_linear)
 154 {
 155     if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
 156         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
 157     runTorchNet("net_linear_2d");
 158 }
 159
 160 TEST_P(Test_Torch_layers, run_concat)
 161 {
 162     runTorchNet("net_concat", "l5_torchMerge");
 163 }
 164
 165 TEST_P(Test_Torch_layers, run_depth_concat)
 166 {
 167     runTorchNet("net_depth_concat", "", false, true, true, 0.0,
 168                 target == DNN_TARGET_OPENCL_FP16 ? 0.021 : 0.0);
 169 }
 170
 171 TEST_P(Test_Torch_layers, run_deconv)
 172 {
 173     runTorchNet("net_deconv");
 174 }
 175
 176 TEST_P(Test_Torch_layers, run_batch_norm)
 177 {
 178     runTorchNet("net_batch_norm", "", false, true);
 179     runTorchNet("net_batch_norm_train", "", false, true, false);
 180 }
 181
 182 TEST_P(Test_Torch_layers, net_prelu)
 183 {
 184     runTorchNet("net_prelu");
 185 }
 186
 187 TEST_P(Test_Torch_layers, net_cadd_table)
 188 {
 189     runTorchNet("net_cadd_table");
 190 }
 191
 192 TEST_P(Test_Torch_layers, net_softmax)
 193 {
 194     runTorchNet("net_softmax");
 195     runTorchNet("net_softmax_spatial");
 196 }
 197
 198 TEST_P(Test_Torch_layers, net_logsoftmax)
 199 {
 200     runTorchNet("net_logsoftmax");
 201     runTorchNet("net_logsoftmax_spatial");
 202 }
 203
 204 TEST_P(Test_Torch_layers, net_lp_pooling)
 205 {
 206     runTorchNet("net_lp_pooling_square", "", false, true);
 207     runTorchNet("net_lp_pooling_power", "", false, true);
 208 }
 209
 210 TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
 211 {
 212     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 213         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
 214     runTorchNet("net_conv_gemm_lrn", "", false, true, true,
 215                 target == DNN_TARGET_OPENCL_FP16 ? 0.046 : 0.0,
 216                 target == DNN_TARGET_OPENCL_FP16 ? 0.023 : 0.0);
 217 }
 218
 219 TEST_P(Test_Torch_layers, net_inception_block)
 220 {
 221     runTorchNet("net_inception_block", "", false, true);
 222 }
 223
 224 TEST_P(Test_Torch_layers, net_normalize)
 225 {
 226     runTorchNet("net_normalize", "", false, true);
 227 }
 228
 229 TEST_P(Test_Torch_layers, net_padding)
 230 {
 231     runTorchNet("net_padding", "", false, true);
 232     runTorchNet("net_spatial_zero_padding", "", false, true);
 233     runTorchNet("net_spatial_reflection_padding", "", false, true);
 234 }
 235
 236 TEST_P(Test_Torch_layers, net_non_spatial)
 237 {
 238     if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
 239         (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
 240         applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
 241     runTorchNet("net_non_spatial", "", false, true);
 242 }
 243
 244 TEST_P(Test_Torch_layers, run_paralel)
 245 {
 246     if (backend != DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU)
 247         throw SkipTestException("");  // TODO: Check this
 248     runTorchNet("net_parallel", "l5_torchMerge");
 249 }
 250
 251 TEST_P(Test_Torch_layers, net_residual)
 252 {
 253 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
 254     if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL ||
 255                                                     target == DNN_TARGET_OPENCL_FP16))
 256         applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
 257 #endif
 258     runTorchNet("net_residual", "", false, true);
 259 }
 260
 261 class Test_Torch_nets : public DNNTestLayer {};
 262
 263 TEST_P(Test_Torch_nets, OpenFace_accuracy)
 264 {
 265 #if defined(INF_ENGINE_RELEASE)
 266     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
 267         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
 268 #endif
 269     checkBackend();
 270
 271     const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
 272     Net net = readNetFromTorch(model);
 273
 274     net.setPreferableBackend(backend);
 275     net.setPreferableTarget(target);
 276
 277     Mat sample = imread(findDataFile("cv/shared/lena.png"));
 278     Mat sampleF32(sample.size(), CV_32FC3);
 279     sample.convertTo(sampleF32, sampleF32.type());
 280     sampleF32 /= 255;
 281     resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
 282
 283     Mat inputBlob = blobFromImage(sampleF32, 1.0, Size(), Scalar(), /*swapRB*/true);
 284
 285     net.setInput(inputBlob);
 286     Mat out = net.forward();
 287
 288     // Reference output values are in range [-0.17212, 0.263492]
 289     // on Myriad problem layer: l4_Pooling - does not use pads_begin
 290     float l1 = (target == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-5;
 291     float lInf = (target == DNN_TARGET_OPENCL_FP16) ? 1.5e-3 : 1e-3;
 292     Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
 293     normAssert(out, outRef, "", l1, lInf);
 294 }
 295
 296 static Mat getSegmMask(const Mat& scores)
 297 {
 298     const int rows = scores.size[2];
 299     const int cols = scores.size[3];
 300     const int numClasses = scores.size[1];
 301
 302     Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
 303     Mat maxVal(rows, cols, CV_32FC1, Scalar(0));
 304     for (int ch = 0; ch < numClasses; ch++)
 305     {
 306         for (int row = 0; row < rows; row++)
 307         {
 308             const float *ptrScore = scores.ptr<float>(0, ch, row);
 309             uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
 310             float *ptrMaxVal = maxVal.ptr<float>(row);
 311             for (int col = 0; col < cols; col++)
 312             {
 313                 if (ptrScore[col] > ptrMaxVal[col])
 314                 {
 315                     ptrMaxVal[col] = ptrScore[col];
 316                     ptrMaxCl[col] = (uchar)ch;
 317                 }
 318             }
 319         }
 320     }
 321     return maxCl;
 322 }
 323
 324 // Computer per-class intersection over union metric.
 325 static void normAssertSegmentation(const Mat& ref, const Mat& test)
 326 {
 327     CV_Assert_N(ref.dims == 4, test.dims == 4);
 328     const int numClasses = ref.size[1];
 329     CV_Assert(numClasses == test.size[1]);
 330
 331     Mat refMask = getSegmMask(ref);
 332     Mat testMask = getSegmMask(test);
 333     EXPECT_EQ(countNonZero(refMask != testMask), 0);
 334 }
 335
 336 TEST_P(Test_Torch_nets, ENet_accuracy)
 337 {
 338     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
 339     checkBackend();
 340     if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
 341         (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
 342         applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
 343
 344     Net net;
 345     {
 346         const string model = findDataFile("dnn/Enet-model-best.net", false);
 347         net = readNetFromTorch(model, true);
 348         ASSERT_TRUE(!net.empty());
 349     }
 350
 351     net.setPreferableBackend(backend);
 352     net.setPreferableTarget(target);
 353
 354     Mat sample = imread(_tf("street.png", false));
 355     Mat inputBlob = blobFromImage(sample, 1./255, Size(), Scalar(), /*swapRB*/true);
 356
 357     net.setInput(inputBlob, "");
 358     Mat out = net.forward();
 359     Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
 360     // Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
 361     // thresholds for ENet must be changed. Accuracy of results was checked on
 362     // Cityscapes dataset and difference in mIOU with Torch is 10E-4%
 363     normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
 364     normAssertSegmentation(ref, out);
 365
 366     const int N = 3;
 367     for (int i = 0; i < N; i++)
 368     {
 369         net.setInput(inputBlob, "");
 370         Mat out = net.forward();
 371         normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
 372         normAssertSegmentation(ref, out);
 373     }
 374 }
 375
 376 // Check accuracy of style transfer models from https://github.com/jcjohnson/fast-neural-style
 377 // th fast_neural_style.lua \
 378 //   -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
 379 //   -output_image lena.png \
 380 //   -median_filter 0 \
 381 //   -image_size 0 \
 382 //   -model models/eccv16/starry_night.t7
 383 // th fast_neural_style.lua \
 384 //   -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
 385 //   -output_image lena.png \
 386 //   -median_filter 0 \
 387 //   -image_size 0 \
 388 //   -model models/instance_norm/feathers.t7
 389 TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
 390 {
 391 #if defined INF_ENGINE_RELEASE
 392     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
 393             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
 394         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
 395 #endif
 396
 397     checkBackend();
 398
 399 #if defined(INF_ENGINE_RELEASE)
 400 #if INF_ENGINE_RELEASE <= 2018050000
 401     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
 402         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
 403 #endif
 404 #endif
 405
 406     std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
 407                             "dnn/fast_neural_style_instance_norm_feathers.t7"};
 408     std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
 409
 410     for (int i = 0; i < 2; ++i)
 411     {
 412         const string model = findDataFile(models[i], false);
 413         Net net = readNetFromTorch(model);
 414
 415         net.setPreferableBackend(backend);
 416         net.setPreferableTarget(target);
 417
 418         Mat img = imread(findDataFile("dnn/googlenet_1.png"));
 419         Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
 420
 421         net.setInput(inputBlob);
 422         Mat out = net.forward();
 423
 424         // Deprocessing.
 425         getPlane(out, 0, 0) += 103.939;
 426         getPlane(out, 0, 1) += 116.779;
 427         getPlane(out, 0, 2) += 123.68;
 428         out = cv::min(cv::max(0, out), 255);
 429
 430         Mat ref = imread(findDataFile(targets[i]));
 431         Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
 432
 433         if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
 434         {
 435             double normL1 = cvtest::norm(refBlob, out, cv::NORM_L1) / refBlob.total();
 436             if (target == DNN_TARGET_MYRIAD)
 437                 EXPECT_LE(normL1, 4.0f);
 438             else
 439                 EXPECT_LE(normL1, 0.6f);
 440         }
 441         else
 442             normAssert(out, refBlob, "", 0.5, 1.1);
 443     }
 444 }
 445
 446 INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_nets, dnnBackendsAndTargets());
 447
 448 // Test a custom layer
 449 // https://github.com/torch/nn/blob/master/doc/convolution.md#nn.SpatialUpSamplingNearest
 450 class SpatialUpSamplingNearestLayer CV_FINAL : public Layer
 451 {
 452 public:
 453     SpatialUpSamplingNearestLayer(const LayerParams &params) : Layer(params)
 454     {
 455         scale = params.get<int>("scale_factor");
 456     }
 457
 458     static Ptr<Layer> create(LayerParams& params)
 459     {
 460         return Ptr<Layer>(new SpatialUpSamplingNearestLayer(params));
 461     }
 462
 463     virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
 464                                  const int requiredOutputs,
 465                                  std::vector<std::vector<int> > &outputs,
 466                                  std::vector<std::vector<int> > &internals) const CV_OVERRIDE
 467     {
 468         std::vector<int> outShape(4);
 469         outShape[0] = inputs[0][0];  // batch size
 470         outShape[1] = inputs[0][1];  // number of channels
 471         outShape[2] = scale * inputs[0][2];
 472         outShape[3] = scale * inputs[0][3];
 473         outputs.assign(1, outShape);
 474         return false;
 475     }
 476
 477     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays) CV_OVERRIDE
 478     {
 479         CV_TRACE_FUNCTION();
 480         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 481
 482         std::vector<Mat> inputs, outputs;
 483         inputs_arr.getMatVector(inputs);
 484         outputs_arr.getMatVector(outputs);
 485
 486         Mat& inp = inputs[0];
 487         Mat& out = outputs[0];
 488         const int outHeight = out.size[2];
 489         const int outWidth = out.size[3];
 490         for (size_t n = 0; n < inp.size[0]; ++n)
 491         {
 492             for (size_t ch = 0; ch < inp.size[1]; ++ch)
 493             {
 494                 resize(getPlane(inp, n, ch), getPlane(out, n, ch),
 495                        Size(outWidth, outHeight), 0, 0, INTER_NEAREST);
 496             }
 497         }
 498     }
 499
 500 private:
 501     int scale;
 502 };
 503
 504 TEST_P(Test_Torch_layers, upsampling_nearest)
 505 {
 506     // Test a custom layer.
 507     CV_DNN_REGISTER_LAYER_CLASS(SpatialUpSamplingNearest, SpatialUpSamplingNearestLayer);
 508     try
 509     {
 510         runTorchNet("net_spatial_upsampling_nearest", "", false, true);
 511     }
 512     catch (...)
 513     {
 514         LayerFactory::unregisterLayer("SpatialUpSamplingNearest");
 515         throw;
 516     }
 517     LayerFactory::unregisterLayer("SpatialUpSamplingNearest");
 518
 519     // Test an implemented layer.
 520     runTorchNet("net_spatial_upsampling_nearest", "", false, true);
 521 }
 522
 523 INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_layers, dnnBackendsAndTargets());
 524
 525 }