Batch normalization in training phase from Torch

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -46,9 +46,9 @@
 #include <opencv2/core.hpp>
 
 #if !defined CV_DOXYGEN && !defined CV_DNN_DONT_ADD_EXPERIMENTAL_NS
-#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v10 {
+#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v11 {
 #define CV__DNN_EXPERIMENTAL_NS_END }
-namespace cv { namespace dnn { namespace experimental_dnn_34_v10 { } using namespace experimental_dnn_34_v10; }}
+namespace cv { namespace dnn { namespace experimental_dnn_34_v11 { } using namespace experimental_dnn_34_v11; }}
 #else
 #define CV__DNN_EXPERIMENTAL_NS_BEGIN
 #define CV__DNN_EXPERIMENTAL_NS_END
@@ -754,6 +754,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      *  @brief Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
      *  @param model    path to the file, dumped from Torch by using torch.save() function.
      *  @param isBinary specifies whether the network was serialized in ascii mode or binary.
+     *  @param evaluate specifies testing phase of network. If true, it's similar to evaluate() method in Torch.
      *  @returns Net object.
      *
      *  @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language,
@@ -775,7 +776,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      *
      * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
      */
-     CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
+     CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true, bool evaluate = true);
 
      /**
       * @brief Read deep learning network represented in one of the supported formats.

modules/dnn/src/torch/torch_importer.cpp

@@ -129,13 +129,15 @@ struct TorchImporter
     Module *rootModule;
     Module *curModule;
     int moduleCounter;
+    bool testPhase;
 
-    TorchImporter(String filename, bool isBinary)
+    TorchImporter(String filename, bool isBinary, bool evaluate)
     {
         CV_TRACE_FUNCTION();
 
         rootModule = curModule = NULL;
         moduleCounter = 0;
+        testPhase = evaluate;
 
         file = cv::Ptr<THFile>(THDiskFile_new(filename, "r", 0), THFile_free);
         CV_Assert(file && THFile_isOpened(file));
@@ -680,7 +682,8 @@ struct TorchImporter
                     layerParams.blobs.push_back(tensorParams["bias"].second);
                 }
 
-                if (nnName == "InstanceNormalization")
+                bool trainPhase = scalarParams.get<bool>("train", false);
+                if (nnName == "InstanceNormalization" || (trainPhase && !testPhase))
                 {
                     cv::Ptr<Module> mvnModule(new Module(nnName));
                     mvnModule->apiType = "MVN";
@@ -1243,18 +1246,18 @@ struct TorchImporter
 
 Mat readTorchBlob(const String &filename, bool isBinary)
 {
-    TorchImporter importer(filename, isBinary);
+    TorchImporter importer(filename, isBinary, true);
     importer.readObject();
     CV_Assert(importer.tensors.size() == 1);
 
     return importer.tensors.begin()->second;
 }
 
-Net readNetFromTorch(const String &model, bool isBinary)
+Net readNetFromTorch(const String &model, bool isBinary, bool evaluate)
 {
     CV_TRACE_FUNCTION();
 
-    TorchImporter importer(model, isBinary);
+    TorchImporter importer(model, isBinary, evaluate);
     Net net;
     importer.populateNet(net);
     return net;

modules/dnn/test/test_torch_importer.cpp

@@ -73,7 +73,7 @@ class Test_Torch_layers : public DNNTestLayer
 {
 public:
     void runTorchNet(const String& prefix, String outLayerName = "",
-                     bool check2ndBlob = false, bool isBinary = false,
+                     bool check2ndBlob = false, bool isBinary = false, bool evaluate = true,
                      double l1 = 0.0, double lInf = 0.0)
     {
         String suffix = (isBinary) ? ".dat" : ".txt";
@@ -84,7 +84,7 @@ public:
 
         checkBackend(backend, target, &inp, &outRef);
 
-        Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
+        Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary, evaluate);
         ASSERT_FALSE(net.empty());
 
         net.setPreferableBackend(backend);
@@ -114,7 +114,7 @@ TEST_P(Test_Torch_layers, run_convolution)
     // Output reference values are in range [23.4018, 72.0181]
     double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.08 : default_l1;
     double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.42 : default_lInf;
-    runTorchNet("net_conv", "", false, true, l1, lInf);
+    runTorchNet("net_conv", "", false, true, true, l1, lInf);
 }
 
 TEST_P(Test_Torch_layers, run_pool_max)
@@ -147,7 +147,7 @@ TEST_P(Test_Torch_layers, run_reshape)
 TEST_P(Test_Torch_layers, run_reshape_single_sample)
 {
     // Reference output values in range [14.4586, 18.4492].
-    runTorchNet("net_reshape_single_sample", "", false, false,
+    runTorchNet("net_reshape_single_sample", "", false, false, true,
                 (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.0073 : default_l1,
                 (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.025 : default_lInf);
 }
@@ -166,7 +166,7 @@ TEST_P(Test_Torch_layers, run_concat)
 
 TEST_P(Test_Torch_layers, run_depth_concat)
 {
-    runTorchNet("net_depth_concat", "", false, true, 0.0,
+    runTorchNet("net_depth_concat", "", false, true, true, 0.0,
                 target == DNN_TARGET_OPENCL_FP16 ? 0.021 : 0.0);
 }
 
@@ -182,6 +182,7 @@ TEST_P(Test_Torch_layers, run_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)
@@ -216,7 +217,7 @@ TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
         throw SkipTestException("");
-    runTorchNet("net_conv_gemm_lrn", "", false, true,
+    runTorchNet("net_conv_gemm_lrn", "", false, true, true,
                 target == DNN_TARGET_OPENCL_FP16 ? 0.046 : 0.0,
                 target == DNN_TARGET_OPENCL_FP16 ? 0.023 : 0.0);
 }