From 55f06b76f91ad663ad39a5396923bc7718d288bb Mon Sep 17 00:00:00 2001 From: Alexander Alekhin Date: Sat, 26 Dec 2020 09:40:29 +0000 Subject: [PATCH] dnn: improve debugging of TensorFlow parsing errors --- modules/dnn/src/tensorflow/tf_importer.cpp | 478 ++++++++++++++++++----------- 1 file changed, 307 insertions(+), 171 deletions(-) diff --git a/modules/dnn/src/tensorflow/tf_importer.cpp b/modules/dnn/src/tensorflow/tf_importer.cpp index 493f0ec..2484c84 100644 --- a/modules/dnn/src/tensorflow/tf_importer.cpp +++ b/modules/dnn/src/tensorflow/tf_importer.cpp @@ -11,6 +11,11 @@ Implementation of Tensorflow models parser #include "../precomp.hpp" +#include +#undef CV_LOG_STRIP_LEVEL +#define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG + 1 +#include + #ifdef HAVE_PROTOBUF #include "tf_io.hpp" @@ -93,7 +98,7 @@ void blobShapeFromTensor(const tensorflow::TensorProto &tensor, MatShape& shape) shape[i] = (int)_shape.dim(i).size(); } else - shape.resize(1, 1); // Scalar. + shape.resize(1, 1); // Scalar. // FIXIT: should be empty } else { @@ -258,7 +263,7 @@ const tensorflow::AttrValue& getLayerAttr(const tensorflow::NodeDef &layer, cons return layer.attr().at(name); } -static int getDataLayout(const tensorflow::NodeDef& layer) +static DataLayout getDataLayout(const tensorflow::NodeDef& layer) { if (hasLayerAttr(layer, "data_format")) { @@ -280,10 +285,13 @@ static inline std::string getNodeName(const std::string& tensorName) return tensorName.substr(0, tensorName.rfind(':')); } -static inline int getDataLayout(const std::string& layerName, - const std::map& data_layouts) +static inline +DataLayout getDataLayout( + const std::string& layerName, + const std::map& data_layouts +) { - std::map::const_iterator it = data_layouts.find(getNodeName(layerName)); + std::map::const_iterator it = data_layouts.find(getNodeName(layerName)); return it != data_layouts.end() ? it->second : DATA_LAYOUT_UNKNOWN; } @@ -439,15 +447,20 @@ void ExcludeLayer(tensorflow::GraphDef& net, const int layer_index, const int in net.mutable_node()->DeleteSubrange(layer_index, 1); } -class TFImporter { +class TFImporter +{ public: - TFImporter(const char *model, const char *config = NULL); - TFImporter(const char *dataModel, size_t lenModel, + TFImporter(Net& net, const char *model, const char *config = NULL); + TFImporter(Net& net, const char *dataModel, size_t lenModel, const char *dataConfig = NULL, size_t lenConfig = 0); +protected: + Net& dstNet; + void populateNet(); - void populateNet(Net dstNet); + void parseNode(const tensorflow::NodeDef& layer); + + DataLayout predictOutputDataLayout(const tensorflow::NodeDef& layer); -private: void kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob); void connect(const std::map& layers_name_id_map, Net& network, const Pin& outPin, @@ -467,23 +480,53 @@ private: std::vector netInputsNames; std::vector netInputShapes; + + std::set layers_to_ignore; + std::map data_layouts; + + // find all Const layers for params + std::map value_id; + // A map with constant blobs which are shared between multiple layers. + std::map sharedWeights; + + std::map layer_id; }; -TFImporter::TFImporter(const char *model, const char *config) +TFImporter::TFImporter(Net& net, const char *model, const char *config) + : dstNet(net) { if (model && model[0]) + { + CV_LOG_DEBUG(NULL, "DNN/TF: processing TensorFlow model from file: " << model); ReadTFNetParamsFromBinaryFileOrDie(model, &netBin); + } if (config && config[0]) + { + CV_LOG_DEBUG(NULL, "DNN/TF: processing TensorFlow config from file: " << config); ReadTFNetParamsFromTextFileOrDie(config, &netTxt); + } + + populateNet(); } -TFImporter::TFImporter(const char *dataModel, size_t lenModel, - const char *dataConfig, size_t lenConfig) +TFImporter::TFImporter( + Net& net, + const char *dataModel, size_t lenModel, + const char *dataConfig, size_t lenConfig +) + : dstNet(net) { if (dataModel != NULL && lenModel > 0) + { + CV_LOG_DEBUG(NULL, "DNN/TF: processing TensorFlow model from memory (" << lenModel << " bytes)"); ReadTFNetParamsFromBinaryBufferOrDie(dataModel, lenModel, &netBin); + } if (dataConfig != NULL && lenConfig > 0) + { + CV_LOG_DEBUG(NULL, "DNN/TF: processing TensorFlow config from memory (" << lenConfig << " bytes)"); ReadTFNetParamsFromTextBufferOrDie(dataConfig, lenConfig, &netTxt); + } + populateNet(); } void TFImporter::kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob) @@ -612,84 +655,98 @@ const tensorflow::TensorProto& TFImporter::getConstBlob(const tensorflow::NodeDe static void addConstNodes(tensorflow::GraphDef& net, std::map& const_layers, std::set& layers_to_ignore) { + CV_LOG_DEBUG(NULL, "DNN/TF: addConstNodes(): handling " << net.node_size() << " nodes..."); for (int li = 0; li < net.node_size(); li++) { const tensorflow::NodeDef &layer = net.node(li); String name = layer.name(); String type = layer.op(); - if (type == "Dequantize") + //CV_LOG_DEBUG(NULL, "DNN/TF: layer_id=" << li << " - '" << name << "' @ " << type); + + try { - // Example of Dequantize node: - // name: "conv2d_1/bias" - // op: "Dequantize" - // input: "conv2d_1/bias_quantized_const" (tensor of dtype DT_QUINT8) - // input: "conv2d_1/bias_quantized_min" - // input: "conv2d_1/bias_quantized_max" - // attr { key: "T" value { type: DT_QUINT8 } } (quantized type) - // attr { key: "mode" value { s: "MIN_FIRST" } } (quantization technique) - CV_Assert(layer.input_size() == 3); - for (int i = 0; i < 3; ++i) - CV_Assert(const_layers.find(layer.input(i)) != const_layers.end()); - CV_Assert(hasLayerAttr(layer, "mode") && - getLayerAttr(layer, "mode").s() == "MIN_FIRST"); - - int tensorId = const_layers[layer.input(0)]; - int minId = const_layers[layer.input(1)]; - int maxId = const_layers[layer.input(2)]; - - tensorflow::TensorProto* tensor = net.mutable_node(tensorId) - ->mutable_attr()->at("value") - .mutable_tensor(); - CV_Assert(tensor->dtype() == tensorflow::DT_QUINT8); - - Mat qMin = getTensorContent(net.node(minId).attr().at("value").tensor()); - Mat qMax = getTensorContent(net.node(maxId).attr().at("value").tensor()); - CV_Assert_N(qMin.total() == 1, qMin.type() == CV_32FC1, - qMax.total() == 1, qMax.type() == CV_32FC1); - - Mat content = getTensorContent(*tensor); - - float minVal = qMin.at(0); - float rangeScale = (qMax.at(0) - minVal) / 255; - CV_Assert(rangeScale >= 0); - content.convertTo(content, CV_32FC1, rangeScale, - rangeScale * cvRound(minVal / rangeScale)); - - tensor->set_dtype(tensorflow::DT_FLOAT); - tensor->set_tensor_content(content.data, content.total() * content.elemSize1()); - - net.mutable_node(tensorId)->set_name(name); - CV_Assert(const_layers.insert(std::make_pair(name, tensorId)).second); + if (type == "Dequantize") + { + // Example of Dequantize node: + // name: "conv2d_1/bias" + // op: "Dequantize" + // input: "conv2d_1/bias_quantized_const" (tensor of dtype DT_QUINT8) + // input: "conv2d_1/bias_quantized_min" + // input: "conv2d_1/bias_quantized_max" + // attr { key: "T" value { type: DT_QUINT8 } } (quantized type) + // attr { key: "mode" value { s: "MIN_FIRST" } } (quantization technique) + CV_CheckEQ(layer.input_size(), 3, "Dequantize: 3 inputs is supported only"); + for (int i = 0; i < 3; ++i) + CV_Assert(const_layers.find(layer.input(i)) != const_layers.end()); + CV_Assert(hasLayerAttr(layer, "mode") && + getLayerAttr(layer, "mode").s() == "MIN_FIRST"); + + int tensorId = const_layers[layer.input(0)]; + int minId = const_layers[layer.input(1)]; + int maxId = const_layers[layer.input(2)]; + + tensorflow::TensorProto* tensor = net.mutable_node(tensorId) + ->mutable_attr()->at("value") + .mutable_tensor(); + CV_CheckEQ((int)tensor->dtype(), (int)tensorflow::DT_QUINT8, ""); + + Mat qMin = getTensorContent(net.node(minId).attr().at("value").tensor()); + Mat qMax = getTensorContent(net.node(maxId).attr().at("value").tensor()); + CV_CheckEQ(qMin.total(), (size_t)1, ""); + CV_CheckTypeEQ(qMin.type(), CV_32FC1, ""); + CV_CheckEQ(qMax.total(), (size_t)1, ""); + CV_CheckTypeEQ(qMax.type(), CV_32FC1, ""); + + Mat content = getTensorContent(*tensor); + + float minVal = qMin.at(0); + float rangeScale = (qMax.at(0) - minVal) / 255; + CV_Assert(rangeScale >= 0); + content.convertTo(content, CV_32FC1, rangeScale, + rangeScale * cvRound(minVal / rangeScale)); + + tensor->set_dtype(tensorflow::DT_FLOAT); + tensor->set_tensor_content(content.data, content.total() * content.elemSize1()); + + net.mutable_node(tensorId)->set_name(name); + CV_Assert(const_layers.insert(std::make_pair(name, tensorId)).second); + layers_to_ignore.insert(name); + continue; + } + else if (type != "Const") + continue; // only Const parameters are supported + + if (layer.attr().find("value") != layer.attr().end()) + { + CV_Assert(const_layers.insert(std::make_pair(name, li)).second); + } layers_to_ignore.insert(name); - continue; } - else if (type != "Const") - continue; // only Const parameters are supported - - if (layer.attr().find("value") != layer.attr().end()) + catch (const std::exception& e) { - CV_Assert(const_layers.insert(std::make_pair(name, li)).second); + CV_LOG_ERROR(NULL, "DNN/TF: Can't handle node='" << name << "'. Exception: " << e.what()); + throw; } - layers_to_ignore.insert(name); } + CV_LOG_DEBUG(NULL, "DNN/TF: layers_to_ignore.size() = " << layers_to_ignore.size()); } // If all inputs of specific layer have the same data layout we can say that // this layer's output has this data layout too. Returns DATA_LAYOUT_UNKNOWN otherwise. -static int predictOutputDataLayout(const tensorflow::GraphDef& net, - const tensorflow::NodeDef& layer, - const std::map& data_layouts) +DataLayout TFImporter::predictOutputDataLayout(const tensorflow::NodeDef& layer) { - int layout = getDataLayout(layer); + DataLayout layout = getDataLayout(layer); if (layout != DATA_LAYOUT_UNKNOWN) + { + CV_LOG_DEBUG(NULL, "DNN/TF: predictOutputDataLayout(" << layer.name() << " @ " << layer.op() << ") => " << (int)layout << " (from attrs)"); return layout; + } // Determine layout by layer's inputs - std::map::const_iterator it; for (int i = 0, n = layer.input_size(); i < n; ++i) { - it = data_layouts.find(getNodeName(layer.input(i))); + std::map::const_iterator it = data_layouts.find(getNodeName(layer.input(i))); if (it != data_layouts.end()) { if (layout != DATA_LAYOUT_UNKNOWN) @@ -703,71 +760,72 @@ static int predictOutputDataLayout(const tensorflow::GraphDef& net, } if (layout != DATA_LAYOUT_UNKNOWN) + { + CV_LOG_DEBUG(NULL, "DNN/TF: predictOutputDataLayout(" << layer.name() << " @ " << layer.op() << ") => " << (int)layout << " (from inputs)"); return layout; + } // Determine layout by layer's consumers recursively. - it = data_layouts.find(layer.name()); + std::map::const_iterator it = data_layouts.find(layer.name()); CV_Assert(it != data_layouts.end()); return it->second; } -void TFImporter::populateNet(Net dstNet) +void TFImporter::populateNet() { - if (!netTxt.ByteSize()) - removePhaseSwitches(netBin); + CV_Assert(netBin.ByteSize() || netTxt.ByteSize()); - RemoveIdentityOps(netBin); - RemoveIdentityOps(netTxt); + CV_LOG_INFO(NULL, "DNN/TF: parsing model" + << (netBin.has_versions() ? cv::format(" produced by TF v%d (min_consumer=%d)", (int)netBin.versions().producer(), (int)netBin.versions().min_consumer()) : cv::String(" (N/A version info)")) + << ". Number of nodes = " << netBin.node_size() + ); - if (!netTxt.ByteSize()) + if (netTxt.ByteSize()) { - simplifySubgraphs(netBin); - sortByExecutionOrder(netBin); + CV_LOG_INFO(NULL, "DNN/TF: parsing config" + << (netTxt.has_versions() ? cv::format(" produced by TF v%d (min_consumer=%d)", (int)netTxt.versions().producer(), (int)netTxt.versions().min_consumer()) : cv::String(" (N/A version info)")) + << ". Number of nodes = " << netTxt.node_size() + ); + + RemoveIdentityOps(netBin); + CV_LOG_DEBUG(NULL, "DNN/TF: RemoveIdentityOps(model) => " << netBin.node_size() << " nodes"); + RemoveIdentityOps(netTxt); + CV_LOG_DEBUG(NULL, "DNN/TF: RemoveIdentityOps(config) => " << netTxt.node_size() << " nodes"); + + sortByExecutionOrder(netTxt); + CV_LOG_DEBUG(NULL, "DNN/TF: sortByExecutionOrder(config) => " << netTxt.node_size() << " nodes"); } else { - sortByExecutionOrder(netTxt); - } + removePhaseSwitches(netBin); + CV_LOG_DEBUG(NULL, "DNN/TF: removePhaseSwitches(model) => " << netBin.node_size() << " nodes"); - std::set layers_to_ignore; + RemoveIdentityOps(netBin); + CV_LOG_DEBUG(NULL, "DNN/TF: RemoveIdentityOps(model) => " << netBin.node_size() << " nodes"); + + simplifySubgraphs(netBin); + CV_LOG_DEBUG(NULL, "DNN/TF: simplifySubgraphs(model) => " << netBin.node_size() << " nodes"); + sortByExecutionOrder(netBin); + CV_LOG_DEBUG(NULL, "DNN/TF: sortByExecutionOrder(model) => " << netBin.node_size() << " nodes"); + } tensorflow::GraphDef& net = netTxt.ByteSize() != 0 ? netTxt : netBin; int layersSize = net.node_size(); - std::map data_layouts; // Pre-fill data layouts where they are set explicitly. // Assuming that nodes are in topological order - for (int i = net.node_size() - 1; i >= 0; --i) + for (int i = layersSize - 1; i >= 0; --i) { const tensorflow::NodeDef& layer = net.node(i); std::string name = layer.name(); - int layout = getDataLayout(layer); - std::map::iterator it = data_layouts.find(name); - if (it != data_layouts.end()) - { - if (layout != DATA_LAYOUT_UNKNOWN) - { - if (it->second == DATA_LAYOUT_UNKNOWN) - it->second = layout; - else if (it->second != layout) - { - it->second = DATA_LAYOUT_UNKNOWN; - layout = DATA_LAYOUT_UNKNOWN; - } - } - else - layout = it->second; - } - else - data_layouts[name] = layout; + CV_LOG_DEBUG(NULL, "DNN/TF: node(" << i << " - '" << name << "') propagating layout..."); - // Specify input layers to have the same data layout. - for (int j = 0; j < layer.input_size(); ++j) + try { - name = getNodeName(layer.input(j)); - it = data_layouts.find(name); + DataLayout layout = getDataLayout(layer); + std::map::iterator it = data_layouts.find(name); if (it != data_layouts.end()) { if (layout != DATA_LAYOUT_UNKNOWN) @@ -775,38 +833,94 @@ void TFImporter::populateNet(Net dstNet) if (it->second == DATA_LAYOUT_UNKNOWN) it->second = layout; else if (it->second != layout) + { it->second = DATA_LAYOUT_UNKNOWN; + layout = DATA_LAYOUT_UNKNOWN; + } } + else + layout = it->second; } else data_layouts[name] = layout; + + // Specify input layers to have the same data layout. + for (int j = 0; j < layer.input_size(); ++j) + { + name = getNodeName(layer.input(j)); + it = data_layouts.find(name); + if (it != data_layouts.end()) + { + if (layout != DATA_LAYOUT_UNKNOWN) + { + if (it->second == DATA_LAYOUT_UNKNOWN) + it->second = layout; + else if (it->second != layout) + it->second = DATA_LAYOUT_UNKNOWN; + } + } + else + data_layouts[name] = layout; + } + } + catch (const std::exception& e) + { + CV_LOG_ERROR(NULL, "DNN/TF: Can't propagate layout for node='" << name << "'. Exception: " << e.what()); + throw; } } - // find all Const layers for params - std::map value_id; - // A map with constant blobs which are shared between multiple layers. - std::map sharedWeights; addConstNodes(netBin, value_id, layers_to_ignore); addConstNodes(netTxt, value_id, layers_to_ignore); - std::map layer_id; for (int li = 0; li < layersSize; li++) { - tensorflow::NodeDef layer = net.node(li); - String name = layer.name(); - String type = layer.op(); + const tensorflow::NodeDef& layer = net.node(li); + + const std::string name = layer.name(); + const std::string type = layer.op(); + const int ninputs = layer.input_size(); + CV_LOG_DEBUG(NULL, "DNN/TF: (" << li << "/" << layersSize << ") Parse layer " << name << " @ " << type << " with " << ninputs << " inputs"); + + parseNode(layer); + } + + for (size_t i = 0; i < netInputsNames.size(); i++) + { + CV_LOG_DEBUG(NULL, "DNN/TF: Model input: " << i << " - '" << netInputsNames[i] << "'"); + CV_Assert(!netInputsNames[i].empty()); + } + dstNet.setInputsNames(netInputsNames); + CV_LOG_DEBUG(NULL, "DNN/TF: ===================== Import completed ====================="); +} + +void TFImporter::parseNode(const tensorflow::NodeDef& layer_) +{ + tensorflow::NodeDef layer = layer_; + + tensorflow::GraphDef& net = netTxt.ByteSize() != 0 ? netTxt : netBin; + + /*const*/ std::string name = layer.name(); + /*const*/ std::string type = layer.op(); + /*const*/ int num_inputs = layer.input_size(); + + try + { LayerParams layerParams; - if(layers_to_ignore.find(name) != layers_to_ignore.end()) - continue; + if (layers_to_ignore.find(name) != layers_to_ignore.end()) + { + CV_LOG_DEBUG(NULL, "DNN/TF: ignored"); + return; + } - int predictedLayout = predictOutputDataLayout(net, layer, data_layouts); + DataLayout predictedLayout = predictOutputDataLayout(layer); data_layouts[name] = predictedLayout; if (type == "Conv2D" || type == "SpaceToBatchND" || type == "DepthwiseConv2dNative" || type == "Pad" || type == "MirrorPad" || type == "Conv3D") { + CV_CheckGT(num_inputs, 0, ""); // The first node of dilated convolution subgraph. // Extract input node, dilation rate and paddings. std::string input = layer.input(0); @@ -824,7 +938,7 @@ void TFImporter::populateNet(Net dstNet) // input: "input" // input: "SpaceToBatchND/block_shape" // input: "SpaceToBatchND/paddings" - CV_Assert(layer.input_size() == 3); + CV_CheckEQ(num_inputs, 3, ""); DictValue dilation = parseDims(getConstBlob(layer, value_id, 1)); CV_Assert(dilation.size() == 2); @@ -839,10 +953,14 @@ void TFImporter::populateNet(Net dstNet) layerParams.set("pad_w", paddings.at(2)); CV_Assert(next_layers.size() == 1); - layer = net.node(next_layers[0].second); layers_to_ignore.insert(next_layers[0].first); + + // FIXIT don't override, rewrite this code + layer = net.node(next_layers[0].second); name = layer.name(); type = layer.op(); + num_inputs = layer.input_size(); + CV_LOG_DEBUG(NULL, "DNN/TF: switched to layer " << name << " @ " << type << ") with " << num_inputs << " inputs"); } else if (type == "Pad" || type == "MirrorPad") { @@ -876,7 +994,7 @@ void TFImporter::populateNet(Net dstNet) layer_id[name] = id; connect(layer_id, dstNet, parsePin(input), id, 0); - continue; + return; } else { @@ -886,10 +1004,14 @@ void TFImporter::populateNet(Net dstNet) layerParams.set("pad_h", paddings.at(4)); layerParams.set("pad_w", paddings.at(6)); - layer = net.node(next_layers[0].second); layers_to_ignore.insert(next_layers[0].first); + + // FIXIT don't override, rewrite this code + layer = net.node(next_layers[0].second); name = layer.name(); type = layer.op(); + num_inputs = layer.input_size(); + CV_LOG_DEBUG(NULL, "DNN/TF: switched to layer " << name << " @ " << type << ") with " << num_inputs << " inputs"); } } @@ -1011,13 +1133,14 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "BiasAdd" || type == "Add" || type == "AddV2" || type == "Sub" || type=="AddN") { + CV_CheckGT(num_inputs, 0, ""); bool haveConst = false; - for(int ii = 0; !haveConst && ii < layer.input_size(); ++ii) + for(int ii = 0; !haveConst && ii < num_inputs; ++ii) { Pin input = parsePin(layer.input(ii)); haveConst = value_id.find(input.name) != value_id.end(); } - CV_Assert(!haveConst || layer.input_size() == 2); + CV_Assert(!haveConst || num_inputs == 2); if (haveConst) { @@ -1054,7 +1177,7 @@ void TFImporter::populateNet(Net dstNet) int id = dstNet.addLayer(name, "Eltwise", layerParams); layer_id[name] = id; - for (int ii = 0; ii < layer.input_size(); ii++) + for (int ii = 0; ii < num_inputs; ii++) { Pin inp = parsePin(layer.input(ii)); if (layer_id.find(inp.name) == layer_id.end()) @@ -1065,7 +1188,7 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "MatMul") { - CV_Assert(layer.input_size() == 2); + CV_CheckEQ(num_inputs, 2, ""); // For the object detection networks, TensorFlow Object Detection API // predicts deltas for bounding boxes in yxYX (ymin, xmin, ymax, xmax) @@ -1077,7 +1200,7 @@ void TFImporter::populateNet(Net dstNet) layerParams.set("bias_term", false); layerParams.blobs.resize(1); - StrIntVector next_layers = getNextLayers(net, name, "BiasAdd"); + StrIntVector next_layers = getNextLayers(net, name, "BiasAdd"); // FIXIT Use layers fusion instead if (next_layers.empty()) { next_layers = getNextLayers(net, name, "Add"); @@ -1135,8 +1258,9 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "Reshape") { + CV_CheckGT(num_inputs, 0, ""); Pin inpId = parsePin(layer.input(0)); - int inpLayout = getDataLayout(layer.input(0), data_layouts); + DataLayout inpLayout = getDataLayout(layer.input(0), data_layouts); // There are two possible implementations: reshape an input using // predefined sizes or use a second input blob as a source of new shape. if (value_id.find(layer.input(1)) != value_id.end()) @@ -1185,6 +1309,7 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "Flatten" || type == "Squeeze") { + CV_CheckGT(num_inputs, 0, ""); Pin inpId = parsePin(layer.input(0)); int inpLayout = getDataLayout(layer.input(0), data_layouts); if (type == "Squeeze") @@ -1231,6 +1356,7 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "Transpose") { + CV_CheckGT(num_inputs, 0, ""); Mat perm = getTensorContent(getConstBlob(layer, value_id, 1)); CV_Assert(perm.type() == CV_32SC1); int* permData = (int*)perm.data; @@ -1304,6 +1430,7 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "LRN") { + CV_CheckGT(num_inputs, 0, ""); if(hasLayerAttr(layer, "alpha")) { layerParams.set("alpha", getLayerAttr(layer, "alpha").f()); } @@ -1322,11 +1449,12 @@ void TFImporter::populateNet(Net dstNet) int id = dstNet.addLayer(name, "LRN", layerParams); layer_id[name] = id; - connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size()); + connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs); } else if (type == "Concat" || type == "ConcatV2") { - int axisId = (type == "Concat" ? 0 : layer.input_size() - 1); + CV_CheckGT(num_inputs, 0, ""); + int axisId = (type == "Concat" ? 0 : num_inputs - 1); int axis = getConstBlob(layer, value_id, axisId).int_val().Get(0); if (getDataLayout(name, data_layouts) == DATA_LAYOUT_NHWC) @@ -1337,7 +1465,7 @@ void TFImporter::populateNet(Net dstNet) // input(0) or input(n-1) is concat_dim int from = (type == "Concat" ? 1 : 0); - int to = (type == "Concat" ? layer.input_size() : layer.input_size() - 1); + int to = (type == "Concat" ? num_inputs : num_inputs - 1); for (int ii = from; ii < to; ii++) { @@ -1370,6 +1498,7 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "MaxPool" || type == "MaxPool3D") { + CV_CheckGT(num_inputs, 0, ""); layerParams.set("pool", "max"); setKSize(layerParams, layer); @@ -1381,10 +1510,11 @@ void TFImporter::populateNet(Net dstNet) int id = dstNet.addLayer(name, "Pooling", layerParams); layer_id[name] = id; - connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size()); + connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs); } else if (type == "AvgPool" || type == "AvgPool3D") { + CV_CheckGT(num_inputs, 0, ""); layerParams.set("pool", "ave"); layerParams.set("ave_pool_padded_area", false); setKSize(layerParams, layer); @@ -1394,11 +1524,11 @@ void TFImporter::populateNet(Net dstNet) int id = dstNet.addLayer(name, "Pooling", layerParams); layer_id[name] = id; - connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size()); + connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs); } else if (type == "MaxPoolGrad") { - CV_Assert(layer.input_size() == 3); + CV_CheckEQ(num_inputs, 3, ""); layerParams.set("pool_k_h", 0); layerParams.set("pool_k_w", 0); @@ -1457,7 +1587,7 @@ void TFImporter::populateNet(Net dstNet) // TODO: slicing input may be Const op // TODO: slicing kernels for convolutions - in current implementation it is impossible // TODO: add parsing num of slices parameter - CV_Assert(layer.input_size() == 2); + CV_CheckEQ(num_inputs, 2, ""); // num_split // 1st blob is dims tensor int axis = getConstBlob(layer, value_id, 0).int_val().Get(0); @@ -1480,7 +1610,7 @@ void TFImporter::populateNet(Net dstNet) // input: "input_node" // input: "Slice/begin" // input: "Slice/size" - CV_Assert(layer.input_size() == 3); + CV_CheckEQ(num_inputs, 3, ""); Mat begins = getTensorContent(getConstBlob(layer, value_id, 1)); Mat sizes = getTensorContent(getConstBlob(layer, value_id, 2)); CV_Assert_N(!begins.empty(), !sizes.empty()); @@ -1505,7 +1635,7 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "StridedSlice") { - CV_Assert(layer.input_size() == 4); + CV_CheckEQ(num_inputs, 4, ""); Mat begins = getTensorContent(getConstBlob(layer, value_id, 1)); Mat ends = getTensorContent(getConstBlob(layer, value_id, 2)); Mat strides = getTensorContent(getConstBlob(layer, value_id, 3)); @@ -1544,8 +1674,9 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "Mul" || type == "RealDiv") { + CV_CheckGT(num_inputs, 0, ""); int constId = -1; - for(int ii = 0; ii < layer.input_size(); ++ii) + for(int ii = 0; ii < num_inputs; ++ii) { Pin input = parsePin(layer.input(ii)); if (value_id.find(input.name) != value_id.end()) @@ -1554,12 +1685,12 @@ void TFImporter::populateNet(Net dstNet) break; } } - CV_Assert((constId != -1) || (layer.input_size() == 2)); + CV_Assert((constId != -1) || (num_inputs == 2)); if (constId != -1) { // Multiplication by constant. - CV_Assert(layer.input_size() == 2); + CV_CheckEQ(num_inputs, 2, ""); Mat scaleMat = getTensorContent(getConstBlob(layer, value_id)); CV_Assert(scaleMat.type() == CV_32FC1); if (type == "RealDiv") @@ -1643,7 +1774,7 @@ void TFImporter::populateNet(Net dstNet) // Check if all the inputs have the same shape. bool equalInpShapes = true; MatShape outShape0; - for (int ii = 0; ii < layer.input_size() && !netInputShapes.empty(); ii++) + for (int ii = 0; ii < num_inputs && !netInputShapes.empty(); ii++) { Pin pin = parsePin(layer.input(ii)); int inpId = layer_id.find(pin.name)->second; @@ -1681,7 +1812,7 @@ void TFImporter::populateNet(Net dstNet) layer_id[name] = id; - for (int ii = 0; ii < layer.input_size(); ii++) + for (int ii = 0; ii < num_inputs; ii++) { Pin inp = parsePin(layer.input(ii)); if (layer_id.find(inp.name) == layer_id.end()) @@ -1698,9 +1829,7 @@ void TFImporter::populateNet(Net dstNet) // input: "BatchNorm/beta" // input: "BatchNorm/moving_mean" // input: "BatchNorm/moving_variance" - if (layer.input_size() != 5) - CV_Error(Error::StsNotImplemented, - "Expected gamma, beta, mean and std"); + CV_CheckEQ(num_inputs, 5, "Expected gamma, beta, mean and std"); Pin inpId = parsePin(layer.input(0)); bool isTraining = hasLayerAttr(layer, "is_training") && getLayerAttr(layer, "is_training").b(); @@ -1768,9 +1897,7 @@ void TFImporter::populateNet(Net dstNet) // input: "conv2d_transpose/output_shape" // input: "weights" // input: "input" - if (layer.input_size() != 3) - CV_Error(Error::StsNotImplemented, - "Expected output shape, weights and input nodes"); + CV_CheckEQ(num_inputs, 3, "Expected output shape, weights and input nodes"); layerParams.set("bias_term", false); layerParams.blobs.resize(1); @@ -1845,8 +1972,7 @@ void TFImporter::populateNet(Net dstNet) // input: "lstm_block_wrapper/w_f_diag" // input: "lstm_block_wrapper/w_o_diag" // input: "lstm_block_wrapper/bias" - if (layer.input_size() != 9) - CV_Error(Error::StsNotImplemented, "Unexpected number of input nodes"); + CV_CheckEQ(num_inputs, 9, "Unexpected number of input nodes"); if (hasLayerAttr(layer, "forget_bias")) layerParams.set("forget_bias", getLayerAttr(layer, "forget_bias").f()); @@ -1912,6 +2038,7 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "ResizeNearestNeighbor" || type == "ResizeBilinear" || type == "FusedResizeAndPadConv2D") { + CV_CheckGT(num_inputs, 0, ""); std::string convWeights = ""; if (type == "FusedResizeAndPadConv2D") { @@ -1919,30 +2046,32 @@ void TFImporter::populateNet(Net dstNet) // input: "decoder/ResizeBilinear/size" // input: "decoder/decoder_conv0/Conv2D_dummy_paddings" // input: "decoder/decoder_conv0/weights" - CV_CheckEQ(layer.input_size(), 4, "Number of input for FusedResizeAndPadConv2D"); + CV_CheckEQ(num_inputs, 4, "Number of input for FusedResizeAndPadConv2D"); Mat paddings = getTensorContent(getConstBlob(layer, value_id, 2)); CV_CheckEQ(countNonZero(paddings), 0, "Unsupported mode"); convWeights = layer.input(3); - layer.mutable_input()->DeleteSubrange(2, 2); + layer.mutable_input()->DeleteSubrange(2, 2); // FIXIT do NOT modify input model + num_inputs = layer.input_size(); name = name + "/resize"; if (hasLayerAttr(layer, "resize_align_corners")) { + // FIXIT do NOT modify input model layer.mutable_attr()->insert( ::google::protobuf::MapPair("align_corners", getLayerAttr(layer, "resize_align_corners"))); } } - if (layer.input_size() == 2) + if (num_inputs == 2) { Mat outSize = getTensorContent(getConstBlob(layer, value_id, 1)); CV_CheckTypeEQ(outSize.type(), CV_32SC1, ""); CV_CheckEQ(outSize.total(), (size_t)2, ""); layerParams.set("height", outSize.at(0, 0)); layerParams.set("width", outSize.at(0, 1)); } - else if (layer.input_size() == 3) + else if (num_inputs == 3) { Mat factorHeight = getTensorContent(getConstBlob(layer, value_id, 1)); Mat factorWidth = getTensorContent(getConstBlob(layer, value_id, 2)); @@ -1952,7 +2081,7 @@ void TFImporter::populateNet(Net dstNet) layerParams.set("zoom_factor_y", factorHeight.at(0)); } else - CV_Assert(layer.input_size() == 2 || layer.input_size() == 3); + CV_Check(num_inputs, num_inputs == 2 || num_inputs == 3, ""); if (type == "ResizeNearestNeighbor") layerParams.set("interpolation", "nearest"); @@ -1973,12 +2102,12 @@ void TFImporter::populateNet(Net dstNet) // Step back to add convolution if (type == "FusedResizeAndPadConv2D") { - tensorflow::NodeDef* conv = net.mutable_node(li); - conv->clear_input(); - conv->add_input(name); - conv->add_input(convWeights); - conv->set_op("Conv2D"); - li -= 1; + tensorflow::NodeDef conv = layer_; + conv.clear_input(); + conv.add_input(name); + conv.add_input(convWeights); + conv.set_op("Conv2D"); + parseNode(conv); } } else if (type == "L2Normalize") @@ -1986,7 +2115,7 @@ void TFImporter::populateNet(Net dstNet) // op: "L2Normalize" // input: "input" // input: "reduction_indices" (axis) - CV_Assert(layer.input_size() == 2); + CV_CheckEQ(num_inputs, 2, ""); Mat reductionIndices = getTensorContent(getConstBlob(layer, value_id, 1)); CV_Assert(reductionIndices.type() == CV_32SC1); @@ -2011,6 +2140,7 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "PriorBox") { + CV_CheckEQ(num_inputs, 2, ""); if (hasLayerAttr(layer, "min_size")) layerParams.set("min_size", getLayerAttr(layer, "min_size").i()); if (hasLayerAttr(layer, "max_size")) @@ -2043,12 +2173,13 @@ void TFImporter::populateNet(Net dstNet) } else if (type == "Softmax") { + CV_CheckGT(num_inputs, 0, ""); if (hasLayerAttr(layer, "axis")) layerParams.set("axis", getLayerAttr(layer, "axis").i()); int id = dstNet.addLayer(name, "Softmax", layerParams); layer_id[name] = id; - connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size()); + connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs); } else if (type == "CropAndResize") { @@ -2056,7 +2187,7 @@ void TFImporter::populateNet(Net dstNet) // input: "input" // input: "boxes" // input: "sizes" - CV_Assert(layer.input_size() == 3); + CV_CheckEQ(num_inputs, 3, ""); Mat cropSize = getTensorContent(getConstBlob(layer, value_id, 2)); CV_CheckTypeEQ(cropSize.type(), CV_32SC1, ""); CV_CheckEQ(cropSize.total(), (size_t)2, ""); @@ -2084,6 +2215,7 @@ void TFImporter::populateNet(Net dstNet) // determine out shape: NxCxHxW --Slice--> 1xCxHxW // out_shape = 1xCxHxW if keepDims else (1xCxHxW --Flatten--> CxHxW) // global pool: NxCxHxW --Flatten--> Nx(C*H*W) --Reshape--> 1x1xNx(C*H*W) --Pooling--> 1x1x1x(C*H*W) --Reshape--> out_shape + CV_CheckGT(num_inputs, 0, ""); Mat indices = getTensorContent(getConstBlob(layer, value_id, 1)); CV_Assert(indices.type() == CV_32SC1); @@ -2218,6 +2350,7 @@ void TFImporter::populateNet(Net dstNet) // Example: given a list with "N" tensors of shape (C, H, W): // if axis == 0 then the output tensor will have the shape (N, C, H, W), // if axis == 1 then the output tensor will have the shape (C, N, H, W). + CV_CheckGT(num_inputs, 0, ""); CV_Assert(hasLayerAttr(layer, "axis")); int dim = (int)getLayerAttr(layer, "axis").i(); if (dim != 0) @@ -2225,7 +2358,7 @@ void TFImporter::populateNet(Net dstNet) CV_Assert(hasLayerAttr(layer, "N")); int num = (int)getLayerAttr(layer, "N").i(); - CV_Assert(layer.input_size() == num); + CV_CheckEQ(num_inputs, num, ""); std::string base_name = name + "/reshape_"; std::vector reshape_ids; for (int i = 0; i < num; i++) { @@ -2256,7 +2389,7 @@ void TFImporter::populateNet(Net dstNet) // input: "input" // input: "mix" // input: "max" - CV_Assert(layer.input_size() == 3); + CV_CheckEQ(num_inputs, 3, ""); Mat minValue = getTensorContent(getConstBlob(layer, value_id, 1)); Mat maxValue = getTensorContent(getConstBlob(layer, value_id, 2)); @@ -2275,6 +2408,7 @@ void TFImporter::populateNet(Net dstNet) type == "Relu" || type == "Elu" || type == "Identity" || type == "Relu6") { + CV_CheckGT(num_inputs, 0, ""); std::string dnnType = type; if (type == "Abs") dnnType = "AbsVal"; else if (type == "Tanh") dnnType = "TanH"; @@ -2284,7 +2418,7 @@ void TFImporter::populateNet(Net dstNet) int id = dstNet.addLayer(name, dnnType, layerParams); layer_id[name] = id; - connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size()); + connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs); } else { @@ -2308,7 +2442,7 @@ void TFImporter::populateNet(Net dstNet) // All the Const input nodes are added to layer's blobs. std::vector inputsNames; - for (int i = 0; i < layer.input_size(); ++i) + for (int i = 0; i < num_inputs; ++i) { // Check if input is a Const node. if (value_id.find(layer.input(i)) != value_id.end()) @@ -2328,7 +2462,11 @@ void TFImporter::populateNet(Net dstNet) } } } - dstNet.setInputsNames(netInputsNames); + catch (const std::exception& e) + { + CV_LOG_ERROR(NULL, "DNN/TF: Can't parse layer for node='" << name << "'. Exception: " << e.what()); + throw; + } } } // namespace @@ -2337,18 +2475,16 @@ void TFImporter::populateNet(Net dstNet) Net readNetFromTensorflow(const String &model, const String &config) { - TFImporter importer(model.c_str(), config.c_str()); Net net; - importer.populateNet(net); + TFImporter importer(net, model.c_str(), config.c_str()); return net; } Net readNetFromTensorflow(const char* bufferModel, size_t lenModel, const char* bufferConfig, size_t lenConfig) { - TFImporter importer(bufferModel, lenModel, bufferConfig, lenConfig); Net net; - importer.populateNet(net); + TFImporter importer(net, bufferModel, lenModel, bufferConfig, lenConfig); return net; } -- 2.7.4