Merge pull request #12082 from dkurt:dnn_ie_faster_rcnn

modules/dnn/perf/perf_net.cpp

@@ -258,6 +258,17 @@ PERF_TEST_P_(DNNTestNetwork, FastNeuralStyle_eccv16)
     processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", "", Mat(cv::Size(320, 240), CV_32FC3));
 }
 
+PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
+{
+    if (backend == DNN_BACKEND_HALIDE ||
+        (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
+        (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
+        throw SkipTestException("");
+    processNet("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb",
+               "dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", "",
+               Mat(cv::Size(800, 600), CV_32FC3));
+}
+
 const tuple<DNNBackend, DNNTarget> testCases[] = {
 #ifdef HAVE_HALIDE
     tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),

modules/dnn/src/dnn.cpp

@@ -1408,7 +1408,7 @@ struct Net::Impl
             bool fused = ld.skip;
 
             Ptr<Layer> layer = ld.layerInstance;
-            if (!layer->supportBackend(preferableBackend))
+            if (!fused && !layer->supportBackend(preferableBackend))
             {
                 addInfEngineNetOutputs(ld);
                 net = Ptr<InfEngineBackendNet>();
@@ -2050,10 +2050,10 @@ struct Net::Impl
         TickMeter tm;
         tm.start();
 
-        if (preferableBackend == DNN_BACKEND_OPENCV ||
-            !layer->supportBackend(preferableBackend))
+        if( !ld.skip )
         {
-            if( !ld.skip )
+            std::map<int, Ptr<BackendNode> >::iterator it = ld.backendNodes.find(preferableBackend);
+            if (preferableBackend == DNN_BACKEND_OPENCV || it == ld.backendNodes.end() || it->second.empty())
             {
                 if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
                 {
@@ -2196,24 +2196,25 @@ struct Net::Impl
                 }
             }
             else
-                tm.reset();
-        }
-        else if (!ld.skip)
-        {
-            Ptr<BackendNode> node = ld.backendNodes[preferableBackend];
-            if (preferableBackend == DNN_BACKEND_HALIDE)
-            {
-                forwardHalide(ld.outputBlobsWrappers, node);
-            }
-            else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
             {
-                forwardInfEngine(node);
-            }
-            else
-            {
-                CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
+                Ptr<BackendNode> node = it->second;
+                CV_Assert(!node.empty());
+                if (preferableBackend == DNN_BACKEND_HALIDE)
+                {
+                    forwardHalide(ld.outputBlobsWrappers, node);
+                }
+                else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
+                {
+                    forwardInfEngine(node);
+                }
+                else
+                {
+                    CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
+                }
             }
         }
+        else
+            tm.reset();
 
         tm.stop();
         layersTimings[ld.id] = tm.getTimeTicks();

modules/dnn/src/layers/detection_output_layer.cpp

@@ -196,7 +196,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && !_locPredTransposed && _bboxesNormalized;
+               backendId == DNN_BACKEND_INFERENCE_ENGINE && !_locPredTransposed && _bboxesNormalized && !_clip;
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/scale_layer.cpp

@@ -48,9 +48,8 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-        return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_HALIDE && haveHalide() ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
+               backendId == DNN_BACKEND_INFERENCE_ENGINE && axis == 1;
     }
 
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE

modules/dnn/src/layers/slice_layer.cpp

@@ -111,7 +111,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && sliceRanges.size() == 1;
+               backendId == DNN_BACKEND_INFERENCE_ENGINE && sliceRanges.size() == 1 && sliceRanges[0].size() == 4;
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,

modules/dnn/src/layers/softmax_layer.cpp

@@ -307,15 +307,17 @@ public:
         return Ptr<BackendNode>();
     }
 
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
+    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
 #ifdef HAVE_INF_ENGINE
+        InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
+
         InferenceEngine::LayerParams lp;
         lp.name = name;
         lp.type = "SoftMax";
         lp.precision = InferenceEngine::Precision::FP32;
         std::shared_ptr<InferenceEngine::SoftMaxLayer> ieLayer(new InferenceEngine::SoftMaxLayer(lp));
-        ieLayer->axis = axisRaw;
+        ieLayer->axis = clamp(axisRaw, input->dims.size());
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
 #endif  // HAVE_INF_ENGINE
         return Ptr<BackendNode>();

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -954,6 +954,13 @@ void TFImporter::populateNet(Net dstNet)
         {
             CV_Assert(layer.input_size() == 2);
 
+            // For the object detection networks, TensorFlow Object Detection API
+            // predicts deltas for bounding boxes in yxYX (ymin, xmin, ymax, xmax)
+            // order. We can manage it at DetectionOutput layer parsing predictions
+            // or shuffle last Faster-RCNN's matmul weights.
+            bool locPredTransposed = hasLayerAttr(layer, "loc_pred_transposed") &&
+                                     getLayerAttr(layer, "loc_pred_transposed").b();
+
             layerParams.set("bias_term", false);
             layerParams.blobs.resize(1);
 
@@ -970,6 +977,17 @@ void TFImporter::populateNet(Net dstNet)
                 blobFromTensor(getConstBlob(net.node(weights_layer_index), value_id), layerParams.blobs[1]);
                 ExcludeLayer(net, weights_layer_index, 0, false);
                 layers_to_ignore.insert(next_layers[0].first);
+
+                if (locPredTransposed)
+                {
+                    const int numWeights = layerParams.blobs[1].total();
+                    float* biasData = reinterpret_cast<float*>(layerParams.blobs[1].data);
+                    CV_Assert(numWeights % 4 == 0);
+                    for (int i = 0; i < numWeights; i += 2)
+                    {
+                        std::swap(biasData[i], biasData[i + 1]);
+                    }
+                }
             }
 
             int kernel_blob_index = -1;
@@ -983,6 +1001,16 @@ void TFImporter::populateNet(Net dstNet)
             }
 
             layerParams.set("num_output", layerParams.blobs[0].size[0]);
+            if (locPredTransposed)
+            {
+                CV_Assert(layerParams.blobs[0].dims == 2);
+                for (int i = 0; i < layerParams.blobs[0].size[0]; i += 2)
+                {
+                    cv::Mat src = layerParams.blobs[0].row(i);
+                    cv::Mat dst = layerParams.blobs[0].row(i + 1);
+                    std::swap_ranges(src.begin<float>(), src.end<float>(), dst.begin<float>());
+                }
+            }
 
             int id = dstNet.addLayer(name, "InnerProduct", layerParams);
             layer_id[name] = id;
@@ -1010,6 +1038,7 @@ void TFImporter::populateNet(Net dstNet)
                 layer_id[permName] = permId;
                 connect(layer_id, dstNet, inpId, permId, 0);
                 inpId = Pin(permName);
+                inpLayout = DATA_LAYOUT_NCHW;
             }
             else if (newShape.total() == 4 && inpLayout == DATA_LAYOUT_NHWC)
             {
@@ -1024,7 +1053,7 @@ void TFImporter::populateNet(Net dstNet)
 
             // one input only
             connect(layer_id, dstNet, inpId, id, 0);
-            data_layouts[name] = newShape.total() == 2 ? DATA_LAYOUT_PLANAR : DATA_LAYOUT_UNKNOWN;
+            data_layouts[name] = newShape.total() == 2 ? DATA_LAYOUT_PLANAR : inpLayout;
         }
         else if (type == "Flatten" || type == "Squeeze")
         {
@@ -1696,41 +1725,6 @@ void TFImporter::populateNet(Net dstNet)
             connect(layer_id, dstNet, parsePin(layer.input(1)), id, 1);
             data_layouts[name] = DATA_LAYOUT_UNKNOWN;
         }
-        else if (type == "DetectionOutput")
-        {
-            // op: "DetectionOutput"
-            // input_0: "locations"
-            // input_1: "classifications"
-            // input_2: "prior_boxes"
-            if (hasLayerAttr(layer, "num_classes"))
-                layerParams.set("num_classes", getLayerAttr(layer, "num_classes").i());
-            if (hasLayerAttr(layer, "share_location"))
-                layerParams.set("share_location", getLayerAttr(layer, "share_location").b());
-            if (hasLayerAttr(layer, "background_label_id"))
-                layerParams.set("background_label_id", getLayerAttr(layer, "background_label_id").i());
-            if (hasLayerAttr(layer, "nms_threshold"))
-                layerParams.set("nms_threshold", getLayerAttr(layer, "nms_threshold").f());
-            if (hasLayerAttr(layer, "top_k"))
-                layerParams.set("top_k", getLayerAttr(layer, "top_k").i());
-            if (hasLayerAttr(layer, "code_type"))
-                layerParams.set("code_type", getLayerAttr(layer, "code_type").s());
-            if (hasLayerAttr(layer, "keep_top_k"))
-                layerParams.set("keep_top_k", getLayerAttr(layer, "keep_top_k").i());
-            if (hasLayerAttr(layer, "confidence_threshold"))
-                layerParams.set("confidence_threshold", getLayerAttr(layer, "confidence_threshold").f());
-            if (hasLayerAttr(layer, "loc_pred_transposed"))
-                layerParams.set("loc_pred_transposed", getLayerAttr(layer, "loc_pred_transposed").b());
-            if (hasLayerAttr(layer, "clip"))
-                layerParams.set("clip", getLayerAttr(layer, "clip").b());
-            if (hasLayerAttr(layer, "variance_encoded_in_target"))
-                layerParams.set("variance_encoded_in_target", getLayerAttr(layer, "variance_encoded_in_target").b());
-
-            int id = dstNet.addLayer(name, "DetectionOutput", layerParams);
-            layer_id[name] = id;
-            for (int i = 0; i < 3; ++i)
-                connect(layer_id, dstNet, parsePin(layer.input(i)), id, i);
-            data_layouts[name] = DATA_LAYOUT_UNKNOWN;
-        }
         else if (type == "Softmax")
         {
             if (hasLayerAttr(layer, "axis"))

modules/dnn/test/test_tf_importer.cpp

@@ -323,7 +323,7 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
 TEST_P(Test_TensorFlow_nets, Inception_v2_Faster_RCNN)
 {
     checkBackend();
-    if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
+    if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
         (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
         throw SkipTestException("");
 

samples/dnn/tf_text_graph_common.py

+import tensorflow as tf
+from tensorflow.core.framework.node_def_pb2 import NodeDef
+from google.protobuf import text_format
+
+def tensorMsg(values):
+    if all([isinstance(v, float) for v in values]):
+        dtype = 'DT_FLOAT'
+        field = 'float_val'
+    elif all([isinstance(v, int) for v in values]):
+        dtype = 'DT_INT32'
+        field = 'int_val'
+    else:
+        raise Exception('Wrong values types')
+
+    msg = 'tensor { dtype: ' + dtype + ' tensor_shape { dim { size: %d } }' % len(values)
+    for value in values:
+        msg += '%s: %s ' % (field, str(value))
+    return msg + '}'
+
+def addConstNode(name, values, graph_def):
+    node = NodeDef()
+    node.name = name
+    node.op = 'Const'
+    text_format.Merge(tensorMsg(values), node.attr["value"])
+    graph_def.node.extend([node])

samples/dnn/tf_text_graph_faster_rcnn.py

@@ -6,6 +6,8 @@ from tensorflow.core.framework.node_def_pb2 import NodeDef
 from tensorflow.tools.graph_transforms import TransformGraph
 from google.protobuf import text_format
 
+from tf_text_graph_common import tensorMsg, addConstNode
+
 parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
                                              'SSD model from TensorFlow Object Detection API. '
                                              'Then pass it with .pb file to cv::dnn::readNetFromTensorflow function.')
@@ -93,21 +95,6 @@ while True:
     if node.op == 'CropAndResize':
         break
 
-def tensorMsg(values):
-    if all([isinstance(v, float) for v in values]):
-        dtype = 'DT_FLOAT'
-        field = 'float_val'
-    elif all([isinstance(v, int) for v in values]):
-        dtype = 'DT_INT32'
-        field = 'int_val'
-    else:
-        raise Exception('Wrong values types')
-
-    msg = 'tensor { dtype: ' + dtype + ' tensor_shape { dim { size: %d } }' % len(values)
-    for value in values:
-        msg += '%s: %s ' % (field, str(value))
-    return msg + '}'
-
 def addSlice(inp, out, begins, sizes):
     beginsNode = NodeDef()
     beginsNode.name = out + '/begins'
@@ -151,17 +138,25 @@ def addSoftMax(inp, out):
     softmax.input.append(inp)
     graph_def.node.extend([softmax])
 
+def addFlatten(inp, out):
+    flatten = NodeDef()
+    flatten.name = out
+    flatten.op = 'Flatten'
+    flatten.input.append(inp)
+    graph_def.node.extend([flatten])
+
 addReshape('FirstStageBoxPredictor/ClassPredictor/BiasAdd',
            'FirstStageBoxPredictor/ClassPredictor/reshape_1', [0, -1, 2])
 
 addSoftMax('FirstStageBoxPredictor/ClassPredictor/reshape_1',
            'FirstStageBoxPredictor/ClassPredictor/softmax')  # Compare with Reshape_4
 
-flatten = NodeDef()
-flatten.name = 'FirstStageBoxPredictor/BoxEncodingPredictor/flatten'  # Compare with FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd
-flatten.op = 'Flatten'
-flatten.input.append('FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd')
-graph_def.node.extend([flatten])
+addFlatten('FirstStageBoxPredictor/ClassPredictor/softmax',
+           'FirstStageBoxPredictor/ClassPredictor/softmax/flatten')
+
+# Compare with FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd
+addFlatten('FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd',
+           'FirstStageBoxPredictor/BoxEncodingPredictor/flatten')
 
 proposals = NodeDef()
 proposals.name = 'proposals'  # Compare with ClipToWindow/Gather/Gather (NOTE: normalized)
@@ -194,7 +189,7 @@ detectionOut.name = 'detection_out'
 detectionOut.op = 'DetectionOutput'
 
 detectionOut.input.append('FirstStageBoxPredictor/BoxEncodingPredictor/flatten')
-detectionOut.input.append('FirstStageBoxPredictor/ClassPredictor/softmax')
+detectionOut.input.append('FirstStageBoxPredictor/ClassPredictor/softmax/flatten')
 detectionOut.input.append('proposals')
 
 text_format.Merge('i: 2', detectionOut.attr['num_classes'])
@@ -204,11 +199,21 @@ text_format.Merge('f: 0.7', detectionOut.attr['nms_threshold'])
 text_format.Merge('i: 6000', detectionOut.attr['top_k'])
 text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type'])
 text_format.Merge('i: 100', detectionOut.attr['keep_top_k'])
-text_format.Merge('b: true', detectionOut.attr['clip'])
-text_format.Merge('b: true', detectionOut.attr['loc_pred_transposed'])
+text_format.Merge('b: false', detectionOut.attr['clip'])
 
 graph_def.node.extend([detectionOut])
 
+addConstNode('clip_by_value/lower', [0.0], graph_def)
+addConstNode('clip_by_value/upper', [1.0], graph_def)
+
+clipByValueNode = NodeDef()
+clipByValueNode.name = 'detection_out/clip_by_value'
+clipByValueNode.op = 'ClipByValue'
+clipByValueNode.input.append('detection_out')
+clipByValueNode.input.append('clip_by_value/lower')
+clipByValueNode.input.append('clip_by_value/upper')
+graph_def.node.extend([clipByValueNode])
+
 # Save as text.
 for node in reversed(topNodes):
     graph_def.node.extend([node])
@@ -225,17 +230,13 @@ addReshape('SecondStageBoxPredictor/Reshape_1/slice',
 # Replace Flatten subgraph onto a single node.
 for i in reversed(range(len(graph_def.node))):
     if graph_def.node[i].op == 'CropAndResize':
-        graph_def.node[i].input.insert(1, 'detection_out')
+        graph_def.node[i].input.insert(1, 'detection_out/clip_by_value')
 
     if graph_def.node[i].name == 'SecondStageBoxPredictor/Reshape':
-        shapeNode = NodeDef()
-        shapeNode.name = 'SecondStageBoxPredictor/Reshape/shape2'
-        shapeNode.op = 'Const'
-        text_format.Merge(tensorMsg([1, -1, 4]), shapeNode.attr["value"])
-        graph_def.node.extend([shapeNode])
+        addConstNode('SecondStageBoxPredictor/Reshape/shape2', [1, -1, 4], graph_def)
 
         graph_def.node[i].input.pop()
-        graph_def.node[i].input.append(shapeNode.name)
+        graph_def.node[i].input.append('SecondStageBoxPredictor/Reshape/shape2')
 
     if graph_def.node[i].name in ['SecondStageBoxPredictor/Flatten/flatten/Shape',
                                   'SecondStageBoxPredictor/Flatten/flatten/strided_slice',
@@ -246,12 +247,15 @@ for node in graph_def.node:
     if node.name == 'SecondStageBoxPredictor/Flatten/flatten/Reshape':
         node.op = 'Flatten'
         node.input.pop()
-        break
+
+    if node.name in ['FirstStageBoxPredictor/BoxEncodingPredictor/Conv2D',
+                     'SecondStageBoxPredictor/BoxEncodingPredictor/MatMul']:
+        text_format.Merge('b: true', node.attr["loc_pred_transposed"])
 
 ################################################################################
 ### Postprocessing
 ################################################################################
-addSlice('detection_out', 'detection_out/slice', [0, 0, 0, 3], [-1, -1, -1, 4])
+addSlice('detection_out/clip_by_value', 'detection_out/slice', [0, 0, 0, 3], [-1, -1, -1, 4])
 
 variance = NodeDef()
 variance.name = 'proposals/variance'
@@ -268,12 +272,13 @@ text_format.Merge('i: 2', varianceEncoder.attr["axis"])
 graph_def.node.extend([varianceEncoder])
 
 addReshape('detection_out/slice', 'detection_out/slice/reshape', [1, 1, -1])
+addFlatten('variance_encoded', 'variance_encoded/flatten')
 
 detectionOut = NodeDef()
 detectionOut.name = 'detection_out_final'
 detectionOut.op = 'DetectionOutput'
 
-detectionOut.input.append('variance_encoded')
+detectionOut.input.append('variance_encoded/flatten')
 detectionOut.input.append('SecondStageBoxPredictor/Reshape_1/Reshape')
 detectionOut.input.append('detection_out/slice/reshape')
 
@@ -283,7 +288,6 @@ text_format.Merge('i: %d' % (args.num_classes + 1), detectionOut.attr['backgroun
 text_format.Merge('f: 0.6', detectionOut.attr['nms_threshold'])
 text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type'])
 text_format.Merge('i: 100', detectionOut.attr['keep_top_k'])
-text_format.Merge('b: true', detectionOut.attr['loc_pred_transposed'])
 text_format.Merge('b: true', detectionOut.attr['clip'])
 text_format.Merge('b: true', detectionOut.attr['variance_encoded_in_target'])
 graph_def.node.extend([detectionOut])

samples/dnn/tf_text_graph_ssd.py

@@ -15,6 +15,7 @@ from math import sqrt
 from tensorflow.core.framework.node_def_pb2 import NodeDef
 from tensorflow.tools.graph_transforms import TransformGraph
 from google.protobuf import text_format
+from tf_text_graph_common import tensorMsg, addConstNode
 
 parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
                                              'SSD model from TensorFlow Object Detection API. '
@@ -160,28 +161,6 @@ graph_def.node[1].input.append(weights)
 # Create SSD postprocessing head ###############################################
 
 # Concatenate predictions of classes, predictions of bounding boxes and proposals.
-def tensorMsg(values):
-    if all([isinstance(v, float) for v in values]):
-        dtype = 'DT_FLOAT'
-        field = 'float_val'
-    elif all([isinstance(v, int) for v in values]):
-        dtype = 'DT_INT32'
-        field = 'int_val'
-    else:
-        raise Exception('Wrong values types')
-
-    msg = 'tensor { dtype: ' + dtype + ' tensor_shape { dim { size: %d } }' % len(values)
-    for value in values:
-        msg += '%s: %s ' % (field, str(value))
-    return msg + '}'
-
-def addConstNode(name, values):
-    node = NodeDef()
-    node.name = name
-    node.op = 'Const'
-    text_format.Merge(tensorMsg(values), node.attr["value"])
-    graph_def.node.extend([node])
-
 def addConcatNode(name, inputs, axisNodeName):
     concat = NodeDef()
     concat.name = name