diff --git a/modules/dnn/src/tensorflow/tf_importer.cpp b/modules/dnn/src/tensorflow/tf_importer.cpp index 6c5faa4caa..6ce260d6ad 100644 --- a/modules/dnn/src/tensorflow/tf_importer.cpp +++ b/modules/dnn/src/tensorflow/tf_importer.cpp @@ -63,10 +63,15 @@ void blobShapeFromTensor(const tensorflow::TensorProto &tensor, MatShape& shape) { const tensorflow::TensorShapeProto &_shape = tensor.tensor_shape(); int i, n = _shape.dim_size(); - shape.resize(n); + if (n) + { + shape.resize(n); - for (i = 0; i < n; i++) - shape[i] = (int)_shape.dim(i).size(); + for (i = 0; i < n; i++) + shape[i] = (int)_shape.dim(i).size(); + } + else + shape.resize(1, 1); // Scalar. } else { @@ -74,6 +79,43 @@ void blobShapeFromTensor(const tensorflow::TensorProto &tensor, MatShape& shape) } } +static Mat getTensorContent(const tensorflow::TensorProto &tensor) +{ + std::string content = tensor.tensor_content(); + switch (tensor.dtype()) + { + case tensorflow::DT_FLOAT: + return Mat(1, content.size() / sizeof(float), CV_32FC1, (void*)content.c_str()).clone(); + case tensorflow::DT_DOUBLE: + return Mat(1, content.size() / sizeof(double), CV_64FC1, (void*)content.c_str()).clone(); + case tensorflow::DT_HALF: + { + Mat halfs; + if (!content.empty()) + { + static const int kHalfSize = 2; + halfs = Mat(1, content.size() / kHalfSize, CV_16UC1, (void*)content.c_str()); + } + else + { + const RepeatedField& field = tensor.half_val(); + CV_Assert(!field.empty()); + Mat ints(1, field.size(), CV_32SC1, (void*)field.data()); + ints.convertTo(halfs, CV_16UC1); + } + // Reinterpret as a signed shorts just for a convertFp16 call. + Mat halfsSigned(halfs.size(), CV_16SC1, halfs.data); + Mat floats(halfs.size(), CV_32FC1); + convertFp16(halfsSigned, floats); + return floats; + } + default: + CV_Error(Error::StsError, "Tensor's data type is not supported"); + break; + } + return Mat(); +} + template void parseTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob) { @@ -90,11 +132,12 @@ void parseTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob) dstBlob.create(shape, CV_32F); - int size = tensor.tensor_content().size() / sizeof(T); + Mat tensorContent = getTensorContent(tensor); + int size = tensorContent.total(); CV_Assert(size == (int)dstBlob.total()); float *dstData = dstBlob.ptr(); - const T *data = reinterpret_cast(tensor.tensor_content().c_str()); + const T *data = reinterpret_cast(tensorContent.data); if (dims == 4) { @@ -125,6 +168,7 @@ void blobFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob) { switch (tensor.dtype()) { case tensorflow::DT_FLOAT: + case tensorflow::DT_HALF: parseTensor(tensor, dstBlob); break; case tensorflow::DT_DOUBLE: @@ -406,7 +450,8 @@ void TFImporter::kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &ds int dims = (int)shape.size(); // TODO: other blob types - CV_Assert(tensor.dtype() == tensorflow::DT_FLOAT); + CV_Assert(tensor.dtype() == tensorflow::DT_FLOAT || + tensor.dtype() == tensorflow::DT_HALF); CV_Assert(dims == 4); // REORDER kernel HWIO to OIHW @@ -416,11 +461,12 @@ void TFImporter::kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &ds dstBlob.create(shape, CV_32F); - int size = tensor.tensor_content().size() / sizeof(float); + Mat tensorContent = getTensorContent(tensor); + int size = tensorContent.total(); CV_Assert(size == (int)dstBlob.total()); float *dstData = dstBlob.ptr(); - const float *data = reinterpret_cast(tensor.tensor_content().c_str()); + const float *data = reinterpret_cast(tensorContent.data); int out_c = shape[0], input_c = shape[1], height = shape[2], width = shape[3]; int total = out_c*input_c*height*width; @@ -753,7 +799,16 @@ void TFImporter::populateNet(Net dstNet) // Multiplication by constant. CV_Assert(layer.input_size() == 2); - float scale = getConstBlob(layer, value_id).float_val()[0]; + float scale; + if (!getConstBlob(layer, value_id).float_val().empty()) + scale = getConstBlob(layer, value_id).float_val()[0]; + else + { + Mat scaleMat; + blobFromTensor(getConstBlob(layer, value_id), scaleMat); + CV_Assert(scaleMat.total() == 1 && scaleMat.type() == CV_32FC1); + scale = scaleMat.at(0, 0); + } layerParams.set("scale", scale); int id = dstNet.addLayer(name, "Power", layerParams); diff --git a/modules/dnn/test/test_tf_importer.cpp b/modules/dnn/test/test_tf_importer.cpp index 9df211cfbc..4d2c64aa43 100644 --- a/modules/dnn/test/test_tf_importer.cpp +++ b/modules/dnn/test/test_tf_importer.cpp @@ -76,7 +76,8 @@ static std::string path(const std::string& file) return findDataFile("dnn/tensorflow/" + file, false); } -static void runTensorFlowNet(const std::string& prefix) +static void runTensorFlowNet(const std::string& prefix, + double l1 = 1e-5, double lInf = 1e-4) { std::string netPath = path(prefix + "_net.pb"); std::string inpPath = path(prefix + "_in.npy"); @@ -89,7 +90,7 @@ static void runTensorFlowNet(const std::string& prefix) net.setInput(input); cv::Mat output = net.forward(); - normAssert(target, output); + normAssert(target, output, "", l1, lInf); } TEST(Test_TensorFlow, single_conv) @@ -130,4 +131,19 @@ TEST(Test_TensorFlow, deconvolution) runTensorFlowNet("deconvolution"); } +TEST(Test_TensorFlow, fp16) +{ + const float l1 = 1e-3; + const float lInf = 1e-2; + runTensorFlowNet("fp16_single_conv", l1, lInf); + runTensorFlowNet("fp16_deconvolution", l1, lInf); + runTensorFlowNet("fp16_max_pool_odd_same", l1, lInf); + runTensorFlowNet("fp16_padding_valid", l1, lInf); + runTensorFlowNet("fp16_eltwise_add_mul", l1, lInf); + runTensorFlowNet("fp16_max_pool_odd_valid", l1, lInf); + runTensorFlowNet("fp16_pad_and_concat", l1, lInf); + runTensorFlowNet("fp16_max_pool_even", l1, lInf); + runTensorFlowNet("fp16_padding_same", l1, lInf); +} + }