diff --git a/modules/dnn/src/layers/flatten_layer.cpp b/modules/dnn/src/layers/flatten_layer.cpp index aa264a749b..72f67529fa 100644 --- a/modules/dnn/src/layers/flatten_layer.cpp +++ b/modules/dnn/src/layers/flatten_layer.cpp @@ -105,6 +105,16 @@ public: return true; } + void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE + { + std::vector inputs; + inputs_arr.getMatVector(inputs); + + int numAxes = inputs[0].dims; + _startAxis = clamp(_startAxis, numAxes); + _endAxis = clamp(_endAxis, numAxes); + } + #ifdef HAVE_OPENCL bool forward_ocl(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) { diff --git a/modules/dnn/src/tensorflow/tf_graph_simplifier.cpp b/modules/dnn/src/tensorflow/tf_graph_simplifier.cpp index 37e57505da..7f1001888a 100644 --- a/modules/dnn/src/tensorflow/tf_graph_simplifier.cpp +++ b/modules/dnn/src/tensorflow/tf_graph_simplifier.cpp @@ -646,6 +646,30 @@ public: } }; +class SoftMaxSlimV2Subgraph : public Subgraph +{ +public: + SoftMaxSlimV2Subgraph() + { + int input = addNodeToMatch(""); + int shape = addNodeToMatch("Shape", input); + int shape_2 = addNodeToMatch("Shape", input); + int rank = addNodeToMatch("Const"); + int y = addNodeToMatch("Const"); + int sub = addNodeToMatch("Sub", rank, y); + int begin = addNodeToMatch("Pack", sub); + int size = addNodeToMatch("Const"); + int slice = addNodeToMatch("Slice", shape, begin, size); + int values = addNodeToMatch("Const"); + int axis = addNodeToMatch("Const"); + int concat = addNodeToMatch("ConcatV2", values, slice, axis); + int reshape = addNodeToMatch("Reshape", input, concat); + int softmax = addNodeToMatch("Softmax", reshape); + addNodeToMatch("Reshape", softmax, shape_2); + setFusedNode("Softmax", input); + } +}; + void simplifySubgraphs(tensorflow::GraphDef& net) { std::vector > subgraphs; @@ -663,6 +687,7 @@ void simplifySubgraphs(tensorflow::GraphDef& net) subgraphs.push_back(Ptr(new UpsamplingKerasSubgraph())); subgraphs.push_back(Ptr(new ReshapeAsShapeSubgraph())); subgraphs.push_back(Ptr(new SoftMaxSlimSubgraph())); + subgraphs.push_back(Ptr(new SoftMaxSlimV2Subgraph())); int numNodes = net.node_size(); std::vector matchedNodesIds; diff --git a/modules/dnn/src/tensorflow/tf_importer.cpp b/modules/dnn/src/tensorflow/tf_importer.cpp index 547948f6f6..ef0b196f44 100644 --- a/modules/dnn/src/tensorflow/tf_importer.cpp +++ b/modules/dnn/src/tensorflow/tf_importer.cpp @@ -1125,18 +1125,25 @@ void TFImporter::populateNet(Net dstNet) { CV_Assert(hasLayerAttr(layer, "squeeze_dims")); const tensorflow::AttrValue& dims = getLayerAttr(layer, "squeeze_dims"); - if (inpLayout == DATA_LAYOUT_NHWC) + std::vector dimsVector(dims.list().i_size()); + for (int i = 0; i < dimsVector.size(); ++i) + dimsVector[i] = dims.list().i(i); + + // Flatten layer can squeeze dimensions range into one. + std::sort(dimsVector.begin(), dimsVector.end()); + for (int i = 1; i < dimsVector.size(); ++i) { - if (dims.list().i_size() != 2 || dims.list().i(0) != 1 || dims.list().i(1) != 2) + if (dimsVector[i] != dimsVector[i - 1] + 1) CV_Error(Error::StsNotImplemented, "Unsupported squeeze configuration"); } - else if (inpLayout == DATA_LAYOUT_NCHW) + int start = dimsVector.front() - 1, end = dimsVector.back(); + if (start == -1 && end == 0) // squeeze 0th dimension { - if (dims.list().i_size() != 2 || dims.list().i(0) != 2 || dims.list().i(1) != 3) - CV_Error(Error::StsNotImplemented, "Unsupported squeeze configuration"); + start = 0; + end = 1; } - else - CV_Error(Error::StsNotImplemented, "Unsupported squeeze configuration"); + layerParams.set("axis", start); + layerParams.set("end_axis", end); } if (inpLayout == DATA_LAYOUT_NHWC) { diff --git a/modules/dnn/test/test_tf_importer.cpp b/modules/dnn/test/test_tf_importer.cpp index 8abe02064c..8b750bbb44 100644 --- a/modules/dnn/test/test_tf_importer.cpp +++ b/modules/dnn/test/test_tf_importer.cpp @@ -637,6 +637,17 @@ TEST_P(Test_TensorFlow_layers, softmax) runTensorFlowNet("slim_softmax"); } +TEST_P(Test_TensorFlow_layers, slim_softmax_v2) +{ +#if defined(INF_ENGINE_RELEASE) + if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD && + getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2 + ) + throw SkipTestException("Test is disabled for Myriad2"); +#endif + runTensorFlowNet("slim_softmax_v2"); +} + TEST_P(Test_TensorFlow_layers, relu6) { runTensorFlowNet("keras_relu6"); @@ -654,6 +665,44 @@ TEST_P(Test_TensorFlow_layers, resize_bilinear) runTensorFlowNet("resize_bilinear_factor"); } +TEST_P(Test_TensorFlow_layers, squeeze) +{ +#if defined(INF_ENGINE_RELEASE) + if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD + && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2 + ) + throw SkipTestException("Test is disabled for Myriad2"); +#endif + int inpShapes[][4] = {{1, 3, 4, 2}, {1, 3, 1, 2}, {1, 3, 4, 1}, {1, 3, 4, 1}}; // TensorFlow's shape (NHWC) + int outShapes[][3] = {{3, 4, 2}, {1, 3, 2}, {1, 3, 4}, {1, 3, 4}}; + int squeeze_dims[] = {0, 2, 3, -1}; + for (int i = 0; i < 4; ++i) + { + SCOPED_TRACE(format("i=%d", i)); + std::string pbtxt = + "node { name: \"input\" op: \"Placeholder\"" + "attr { key: \"data_format\" value { s: \"NHWC\" } } }" + "node { name: \"squeeze\" op: \"Squeeze\" input: \"input\"" + "attr { key: \"squeeze_dims\" value { list { i:" + format("%d", squeeze_dims[i]) + "}}}}"; + Net net = readNetFromTensorflow(0, 0, pbtxt.c_str(), pbtxt.size()); + net.setPreferableBackend(backend); + net.setPreferableTarget(target); + Mat tfInp(4, &inpShapes[i][0], CV_32F); + randu(tfInp, -1, 1); + + // NHWC to NCHW + CV_Assert(inpShapes[i][0] == 1); + std::swap(inpShapes[i][2], inpShapes[i][3]); + std::swap(inpShapes[i][1], inpShapes[i][2]); + Mat cvInp = tfInp.reshape(1, tfInp.total() / inpShapes[i][1]).t(); + cvInp = cvInp.reshape(1, 4, &inpShapes[i][0]); + + net.setInput(cvInp); + Mat out = net.forward(); + normAssert(tfInp.reshape(1, 3, &outShapes[i][0]), out, "", default_l1, default_lInf); + } +} + INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, dnnBackendsAndTargets()); TEST(Test_TensorFlow, two_inputs)