Merge pull request #10940 from dkurt:dnn_tf_graph_optim

modules/dnn/src/layers/batch_norm_layer.cpp

@@ -32,7 +32,7 @@ public:
     BatchNormLayerImpl(const LayerParams& params)
     {
         setParamsFrom(params);
-        CV_Assert(blobs.size() >= 3);
+        CV_Assert(blobs.size() >= 2);
 
         hasWeights = params.get<bool>("has_weight", false);
         hasBias = params.get<bool>("has_bias", false);
@@ -46,8 +46,8 @@ public:
                   blobs[0].type() == CV_32F && blobs[1].type() == CV_32F);
 
         float varMeanScale = 1.f;
-        if (!hasWeights && !hasBias) {
-            CV_Assert(blobs[2].type() == CV_32F);
+        if (!hasWeights && !hasBias && blobs.size() > 2) {
+            CV_Assert(blobs.size() == 3, blobs[2].type() == CV_32F);
             varMeanScale = blobs[2].at<float>(0);
             if (varMeanScale != 0)
                 varMeanScale = 1/varMeanScale;

modules/dnn/src/tensorflow/tf_graph_simplifier.hpp

+// 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) 2018, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#ifndef __OPENCV_DNN_TF_SIMPLIFIER_HPP__
+#define __OPENCV_DNN_TF_SIMPLIFIER_HPP__
+
+#include "../precomp.hpp"
+
+#ifdef HAVE_PROTOBUF
+
+#include "tf_io.hpp"
+
+namespace cv { namespace dnn {
+CV__DNN_EXPERIMENTAL_NS_BEGIN
+
+void RemoveIdentityOps(tensorflow::GraphDef& net);
+
+void simplifySubgraphs(tensorflow::GraphDef& net);
+
+Mat getTensorContent(const tensorflow::TensorProto &tensor);
+
+CV__DNN_EXPERIMENTAL_NS_END
+}}  // namespace dnn, namespace cv
+
+#endif  // HAVE_PROTOBUF
+#endif  // __OPENCV_DNN_TF_SIMPLIFIER_HPP__

modules/dnn/test/test_tf_importer.cpp

@@ -150,6 +150,9 @@ TEST_P(Test_TensorFlow_layers, batch_norm)
     runTensorFlowNet("batch_norm_text", targetId, true);
     runTensorFlowNet("mvn_batch_norm", targetId);
     runTensorFlowNet("mvn_batch_norm_1x1", targetId);
+    runTensorFlowNet("unfused_batch_norm", targetId);
+    runTensorFlowNet("fused_batch_norm_no_gamma", targetId);
+    runTensorFlowNet("unfused_batch_norm_no_gamma", targetId);
 }
 
 TEST_P(Test_TensorFlow_layers, pooling)
@@ -159,6 +162,7 @@ TEST_P(Test_TensorFlow_layers, pooling)
     runTensorFlowNet("max_pool_odd_valid", targetId);
     runTensorFlowNet("ave_pool_same", targetId);
     runTensorFlowNet("max_pool_odd_same", targetId);
+    runTensorFlowNet("reduce_mean", targetId);  // an average pooling over all spatial dimensions.
 }
 
 TEST_P(Test_TensorFlow_layers, deconvolution)
@@ -185,6 +189,8 @@ TEST_P(Test_TensorFlow_layers, reshape)
     runTensorFlowNet("shift_reshape_no_reorder", targetId);
     runTensorFlowNet("reshape_reduce", targetId);
     runTensorFlowNet("flatten", targetId, true);
+    runTensorFlowNet("unfused_flatten", targetId);
+    runTensorFlowNet("unfused_flatten_unknown_batch", targetId);
 }
 
 INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, availableDnnTargets());
@@ -332,6 +338,21 @@ TEST(Test_TensorFlow, slice)
     runTensorFlowNet("slice_4d");
 }
 
+TEST(Test_TensorFlow, softmax)
+{
+    runTensorFlowNet("keras_softmax");
+}
+
+TEST(Test_TensorFlow, relu6)
+{
+    runTensorFlowNet("keras_relu6");
+}
+
+TEST(Test_TensorFlow, keras_mobilenet_head)
+{
+    runTensorFlowNet("keras_mobilenet_head");
+}
+
 TEST(Test_TensorFlow, memory_read)
 {
     double l1 = 1e-5;