Merge pull request #9517 from dkurt:tf_mobilenet

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

@@ -359,6 +359,12 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         static Ptr<ReLULayer> create(const LayerParams &params);
     };
 
+    class CV_EXPORTS ReLU6Layer : public ActivationLayer
+    {
+    public:
+        static Ptr<ReLU6Layer> create(const LayerParams &params);
+    };
+
     class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
     {
     public:

modules/dnn/src/init.cpp

@@ -94,6 +94,7 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(LPNormalize,    LPNormalizeLayer);
 
     CV_DNN_REGISTER_LAYER_CLASS(ReLU,           ReLULayer);
+    CV_DNN_REGISTER_LAYER_CLASS(ReLU6,          ReLU6Layer);
     CV_DNN_REGISTER_LAYER_CLASS(ChannelsPReLU,  ChannelsPReLULayer);
     CV_DNN_REGISTER_LAYER_CLASS(Sigmoid,        SigmoidLayer);
     CV_DNN_REGISTER_LAYER_CLASS(TanH,           TanHLayer);

modules/dnn/src/layers/elementwise_layers.cpp

@@ -248,6 +248,62 @@ struct ReLUFunctor
     int64 getFLOPSPerElement() const { return 1; }
 };
 
+struct ReLU6Functor
+{
+    typedef ReLU6Layer Layer;
+    float minValue, maxValue;
+
+    ReLU6Functor(float minValue_ = 0.0f, float maxValue_ = 6.0f)
+        : minValue(minValue_), maxValue(maxValue_)
+    {
+        CV_Assert(minValue <= maxValue);
+    }
+
+    void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
+    {
+        for( int cn = cn0; cn < cn1; cn++, srcptr += planeSize, dstptr += planeSize )
+        {
+            int i = 0;
+#if CV_SIMD128
+            v_float32x4 minV = v_setall_f32(minValue), maxV = v_setall_f32(maxValue);
+            for( ; i <= len - 16; i += 16 )
+            {
+                v_float32x4 x0 = v_load(srcptr + i);
+                v_float32x4 x1 = v_load(srcptr + i + 4);
+                v_float32x4 x2 = v_load(srcptr + i + 8);
+                v_float32x4 x3 = v_load(srcptr + i + 12);
+                x0 = v_min(v_max(minV, x0), maxV);
+                x1 = v_min(v_max(minV, x1), maxV);
+                x2 = v_min(v_max(minV, x2), maxV);
+                x3 = v_min(v_max(minV, x3), maxV);
+                v_store(dstptr + i, x0);
+                v_store(dstptr + i + 4, x1);
+                v_store(dstptr + i + 8, x2);
+                v_store(dstptr + i + 12, x3);
+            }
+#endif
+            for( ; i < len; i++ )
+            {
+                float x = srcptr[i];
+                if (x >= minValue)
+                    dstptr[i] = x <= maxValue ? x : maxValue;
+                else
+                    dstptr[i] = minValue;
+            }
+        }
+    }
+
+#ifdef HAVE_HALIDE
+    void attachHalide(const Halide::Expr& input, Halide::Func& top)
+    {
+        Halide::Var x("x"), y("y"), c("c"), n("n");
+        top(x, y, c, n) = clamp(input, minValue, maxValue);
+    }
+#endif  // HAVE_HALIDE
+
+    int64 getFLOPSPerElement() const { return 2; }
+};
+
 struct TanHFunctor
 {
     typedef TanHLayer Layer;
@@ -517,6 +573,15 @@ Ptr<ReLULayer> ReLULayer::create(const LayerParams& params)
     return l;
 }
 
+Ptr<ReLU6Layer> ReLU6Layer::create(const LayerParams& params)
+{
+    float minValue = params.get<float>("min_value", 0.0f);
+    float maxValue = params.get<float>("max_value", 6.0f);
+    Ptr<ReLU6Layer> l(new ElementWiseLayer<ReLU6Functor>(ReLU6Functor(minValue, maxValue)));
+    l->setParamsFrom(params);
+    return l;
+}
+
 Ptr<TanHLayer> TanHLayer::create(const LayerParams& params)
 {
     Ptr<TanHLayer> l(new ElementWiseLayer<TanHFunctor>());

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -85,11 +85,38 @@ static Mat getTensorContent(const tensorflow::TensorProto &tensor)
     switch (tensor.dtype())
     {
         case tensorflow::DT_FLOAT:
-            return Mat(1, content.size() / sizeof(float), CV_32FC1, (void*)content.c_str()).clone();
+        {
+            if (!content.empty())
+                return Mat(1, content.size() / sizeof(float), CV_32FC1, (void*)content.c_str()).clone();
+            else
+            {
+                const RepeatedField<float>& field = tensor.float_val();
+                CV_Assert(!field.empty());
+                return Mat(1, field.size(), CV_32FC1, (void*)field.data()).clone();
+            }
+        }
         case tensorflow::DT_DOUBLE:
-            return Mat(1, content.size() / sizeof(double), CV_64FC1, (void*)content.c_str()).clone();
+        {
+            if (!content.empty())
+                return Mat(1, content.size() / sizeof(double), CV_64FC1, (void*)content.c_str()).clone();
+            else
+            {
+                const RepeatedField<double>& field = tensor.double_val();
+                CV_Assert(!field.empty());
+                return Mat(1, field.size(), CV_64FC1, (void*)field.data()).clone();
+            }
+        }
         case tensorflow::DT_INT32:
-            return Mat(1, content.size() / sizeof(int32_t), CV_32SC1, (void*)content.c_str()).clone();
+        {
+            if (!content.empty())
+                return Mat(1, content.size() / sizeof(int32_t), CV_32SC1, (void*)content.c_str()).clone();
+            else
+            {
+                const RepeatedField<int32_t>& field = tensor.int_val();
+                CV_Assert(!field.empty());
+                return Mat(1, field.size(), CV_32SC1, (void*)field.data()).clone();
+            }
+        }
         case tensorflow::DT_HALF:
         {
             Mat halfs;
@@ -573,7 +600,7 @@ void TFImporter::populateNet(Net dstNet)
         if(layers_to_ignore.find(li) != layers_to_ignore.end())
             continue;
 
-        if (type == "Conv2D" || type == "SpaceToBatchND")
+        if (type == "Conv2D" || type == "SpaceToBatchND" || type == "DepthwiseConv2dNative")
         {
             // The first node of dilated convolution subgraph.
             // Extract input node, dilation rate and paddings.
@@ -621,7 +648,28 @@ void TFImporter::populateNet(Net dstNet)
             }
 
             kernelFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
-            const int* kshape = layerParams.blobs[0].size.p;
+            int* kshape = layerParams.blobs[0].size.p;
+            if (type == "DepthwiseConv2dNative")
+            {
+                const int chMultiplier = kshape[0];
+                const int inCh = kshape[1];
+                const int height = kshape[2];
+                const int width = kshape[3];
+
+                Mat copy = layerParams.blobs[0].clone();
+                float* src = (float*)copy.data;
+                float* dst = (float*)layerParams.blobs[0].data;
+                for (int i = 0; i < chMultiplier; ++i)
+                    for (int j = 0; j < inCh; ++j)
+                        for (int s = 0; s < height * width; ++s)
+                            {
+                                int src_i = (i * inCh + j) * height * width + s;
+                                int dst_i = (j * chMultiplier + i) * height* width + s;
+                                dst[dst_i] = src[src_i];
+                            }
+                kshape[0] = inCh * chMultiplier;
+                kshape[1] = 1;
+            }
             layerParams.set("kernel_h", kshape[2]);
             layerParams.set("kernel_w", kshape[3]);
             layerParams.set("num_output", kshape[0]);
@@ -689,6 +737,10 @@ void TFImporter::populateNet(Net dstNet)
             layerParams.blobs.resize(1);
 
             StrIntVector next_layers = getNextLayers(net, name, "BiasAdd");
+            if (next_layers.empty())
+            {
+                next_layers = getNextLayers(net, name, "Add");
+            }
             if (next_layers.size() == 1) {
                 layerParams.set("bias_term", true);
                 layerParams.blobs.resize(2);
@@ -840,20 +892,20 @@ void TFImporter::populateNet(Net dstNet)
             {
                 // Multiplication by constant.
                 CV_Assert(layer.input_size() == 2);
+                Mat scaleMat = getTensorContent(getConstBlob(layer, value_id));
+                CV_Assert(scaleMat.type() == CV_32FC1);
 
-                float scale;
+                int id;
-                if (!getConstBlob(layer, value_id).float_val().empty())
+                if (scaleMat.total() == 1)  // is a scalar.
-                    scale = getConstBlob(layer, value_id).float_val()[0];
-                else
                 {
-                    Mat scaleMat;
+                    layerParams.set("scale", scaleMat.at<float>(0));
-                    blobFromTensor(getConstBlob(layer, value_id), scaleMat);
+                    id = dstNet.addLayer(name, "Power", layerParams);
-                    CV_Assert(scaleMat.total() == 1 && scaleMat.type() == CV_32FC1);
+                }
-                    scale = scaleMat.at<float>(0, 0);
+                else  // is a vector
+                {
+                    layerParams.blobs.resize(1, scaleMat);
+                    id = dstNet.addLayer(name, "Scale", layerParams);
                 }
-                layerParams.set("scale", scale);
-
-                int id = dstNet.addLayer(name, "Power", layerParams);
                 layer_id[name] = id;
 
                 Pin inp0 = parsePin(layer.input(0));
@@ -1006,12 +1058,13 @@ void TFImporter::populateNet(Net dstNet)
         }
         else if (type == "Abs" || type == "Tanh" || type == "Sigmoid" ||
                  type == "Relu" || type == "Elu" || type == "Softmax" ||
-                 type == "Identity")
+                 type == "Identity" || type == "Relu6")
         {
             std::string dnnType = type;
             if (type == "Abs") dnnType = "AbsVal";
             else if (type == "Tanh") dnnType = "TanH";
             else if (type == "Relu") dnnType = "ReLU";
+            else if (type == "Relu6") dnnType = "ReLU6";
             else if (type == "Elu") dnnType = "ELU";
 
             int id = dstNet.addLayer(name, dnnType, layerParams);

modules/dnn/test/test_tf_importer.cpp

@@ -93,11 +93,12 @@ static void runTensorFlowNet(const std::string& prefix,
     normAssert(target, output, "", l1, lInf);
 }
 
-TEST(Test_TensorFlow, single_conv)
+TEST(Test_TensorFlow, conv)
 {
     runTensorFlowNet("single_conv");
     runTensorFlowNet("atrous_conv2d_valid");
     runTensorFlowNet("atrous_conv2d_same");
+    runTensorFlowNet("depthwise_conv2d");
 }
 
 TEST(Test_TensorFlow, padding)
@@ -116,8 +117,9 @@ TEST(Test_TensorFlow, pad_and_concat)
     runTensorFlowNet("pad_and_concat");
 }
 
-TEST(Test_TensorFlow, fused_batch_norm)
+TEST(Test_TensorFlow, batch_norm)
 {
+    runTensorFlowNet("batch_norm");
     runTensorFlowNet("fused_batch_norm");
 }
 
@@ -133,6 +135,11 @@ TEST(Test_TensorFlow, deconvolution)
     runTensorFlowNet("deconvolution");
 }
 
+TEST(Test_TensorFlow, matmul)
+{
+    runTensorFlowNet("matmul");
+}
+
 TEST(Test_TensorFlow, fp16)
 {
     const float l1 = 1e-3;