Grayscale colorization model (https://github.com/richzhang/colorization) test.

modules/dnn/src/caffe/caffe_importer.cpp

@@ -293,14 +293,13 @@ public:
         addedBlobs.reserve(layersSize + 1);
 
         //setup input layer names
+        std::vector<String> netInputs(net.input_size());
         {
-            std::vector<String> netInputs(net.input_size());
             for (int inNum = 0; inNum < net.input_size(); inNum++)
             {
                 addedBlobs.push_back(BlobNote(net.input(inNum), 0, inNum));
                 netInputs[inNum] = net.input(inNum);
             }
-            dstNet.setInputsNames(netInputs);
         }
 
         for (int li = 0; li < layersSize; li++)
@@ -317,6 +316,13 @@ public:
             if (repetitions)
                 name += String("_") + toString(repetitions);
 
+            if (type == "Input")
+            {
+                addedBlobs.push_back(BlobNote(name, 0, netInputs.size()));
+                netInputs.push_back(name);
+                continue;
+            }
+
             int id = dstNet.addLayer(name, type, layerParams);
 
             for (int inNum = 0; inNum < layer.bottom_size(); inNum++)
@@ -325,6 +331,7 @@ public:
             for (int outNum = 0; outNum < layer.top_size(); outNum++)
                 addOutput(layer, id, outNum);
         }
+        dstNet.setInputsNames(netInputs);
 
         addedBlobs.clear();
     }

modules/dnn/src/init.cpp

@@ -106,6 +106,7 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(MaxUnpool,      MaxUnpoolLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Dropout,        BlankLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Identity,       BlankLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Silence,        BlankLayer);
 
     CV_DNN_REGISTER_LAYER_CLASS(Crop,           CropLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Eltwise,        EltwiseLayer);

modules/dnn/src/layers/convolution_layer.cpp

@@ -311,15 +311,15 @@ public:
                          Size kernel, Size pad, Size stride, Size dilation,
                          const ActivationLayer* activ, int ngroups, int nstripes )
         {
-            CV_Assert( input.dims == 4 && output.dims == 4 &&
-                       input.size[0] == output.size[0] &&
-                       weights.rows == output.size[1] &&
-                       weights.cols == (input.size[1]/ngroups)*kernel.width*kernel.height &&
-                       input.type() == output.type() &&
-                       input.type() == weights.type() &&
-                       input.type() == CV_32F &&
-                       input.isContinuous() &&
-                       output.isContinuous() &&
+            CV_Assert( input.dims == 4 && output.dims == 4,
+                       input.size[0] == output.size[0],
+                       weights.rows == output.size[1],
+                       weights.cols == (input.size[1]/ngroups)*kernel.width*kernel.height,
+                       input.type() == output.type(),
+                       input.type() == weights.type(),
+                       input.type() == CV_32F,
+                       input.isContinuous(),
+                       output.isContinuous(),
                        biasvec.size() == (size_t)output.size[1]+2);
             ParallelConv p;
 
@@ -1237,7 +1237,6 @@ static void initConvDeconvLayerFromCaffe(Ptr<BaseConvolutionLayer> l, const Laye
                                l->pad.width, l->stride.height, l->stride.width, l->dilation.height,
                                l->dilation.width, l->padMode);
 
-    bool bias = params.get<bool>("bias_term", true);
     l->numOutput = params.get<int>("num_output");
     int ngroups = params.get<int>("group", 1);
 
@@ -1245,7 +1244,6 @@ static void initConvDeconvLayerFromCaffe(Ptr<BaseConvolutionLayer> l, const Laye
     l->adjustPad.width = params.get<int>("adj_w", 0);
 
     CV_Assert(l->numOutput % ngroups == 0);
-    CV_Assert((bias && l->blobs.size() == 2) || (!bias && l->blobs.size() == 1));
     CV_Assert(l->adjustPad.width < l->stride.width &&
               l->adjustPad.height < l->stride.height);
 }

modules/dnn/src/layers/scale_layer.cpp

@@ -33,6 +33,7 @@ public:
                          std::vector<MatShape> &outputs,
                          std::vector<MatShape> &internals) const
     {
+        CV_Assert(blobs.size() == 1 + hasBias);
         Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
         return true;
     }
@@ -48,8 +49,6 @@ public:
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        CV_Assert(blobs.size() == 1 + hasBias);
-
         for (size_t ii = 0; ii < outputs.size(); ii++)
         {
             Mat &inpBlob = *inputs[ii];

modules/dnn/test/test_caffe_importer.cpp

@@ -211,4 +211,27 @@ TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
     normAssert(out, ref, "", l1, lInf);
 }
 
+// https://github.com/richzhang/colorization
+TEST(Reproducibility_Colorization, Accuracy)
+{
+    const float l1 = 1e-5;
+    const float lInf = 3e-3;
+
+    Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
+    Mat ref = blobFromNPY(_tf("colorization_out.npy"));
+    Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));
+
+    const string proto = findDataFile("dnn/colorization_deploy_v2.prototxt", false);
+    const string model = findDataFile("dnn/colorization_release_v2.caffemodel", false);
+    Net net = readNetFromCaffe(proto, model);
+
+    net.getLayer(net.getLayerId("class8_ab"))->blobs.push_back(kernel);
+    net.getLayer(net.getLayerId("conv8_313_rh"))->blobs.push_back(Mat(1, 313, CV_32F, 2.606));
+
+    net.setInput(inp);
+    Mat out = net.forward();
+
+    normAssert(out, ref, "", l1, lInf);
+}
+
 }