Error calling the interface of createSSDPriorBoxPlugin when prase my caffe model

AI & Data Science Deep Learning (Training & Inference) TensorRT
1

using Jetson xavier and Jetpack 4.1
Hi,
[i]when i calling the interface of createSSDPriorBoxPlugin to parse my network model,the Segmentation problems offen occurs,the error log is:

[url]=========parse caffe model start==========
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.
Warning: Flatten layer ignored. TensorRT implicitly flattens input to FullyConnected layers, but in other circumstances this will result in undefined behavior.
==========createPlugin=========layerName:Inception3/conv/priorbox1
==========createPlugin succ=========
Thread 1 “sample_face_det” received signal SIGSEGV, Segmentation fault.
0x0000007fafe25a30 in nvinfer1::plugin::GridAnchorGeneratorLegacy::getOutputDimensions(int, nvinfer1::Dims const*, int) () from /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.5
(gdb) where
#0 0x0000007fafe25a30 in nvinfer1::plugin::GridAnchorGeneratorLegacy::getOutputDimensions(int, nvinfer1::Dims const*, int) () at /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.5
#1 0x0000007fb057e954 in nvinfer1::Network::addPluginExt(nvinfer1::ITensor* const*, int, nvinfer1::IPluginExt&) ()
at /usr/lib/aarch64-linux-gnu/libnvinfer.so.5
#2 0x0000007fb00b9924 in () at /usr/lib/aarch64-linux-gnu/libnvparsers.so.5
#3 0x0000005555562e74 in caffeToGIEModel(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&, unsigned int, nvcaffeparser1::IPluginFactoryExt*, nvinfer1::IHostMemory*&) ()
#4 0x0000005555567f38 in main ()

part of my code is simple as follows:

nvinfer1::IPlugin* PluginFactory::createPlugin(const char* layerName, const nvinfer1::Weights* weights, int nbWeights) {	assert(PluginFactory::isPluginExt(layerName));	std::cout << "==========createPlugin=========layerName:" << layerName << std::endl;	if(!strcmp(layerName,"Inception3/conv/priorbox1"))	{	assert(Inception3_conv_priorbox1_layer.get() == nullptr);	plugin::PriorBoxParameters params;	float minSize[1] = {32};	float aspectRatios[1] = {1};	params.minSize = minSize;	params.maxSize = nullptr;	params.aspectRatios = aspectRatios;	params.numMinSize = 1;	params.numMaxSize = 0;	params.numAspectRatios = 1;	params.flip = true;	params.clip = true;	params.variance[0] = 0.1;	params.variance[1] = 0.1;	params.variance[2] = 0.2;	params.variance[3] = 0.2;	//params.imgH = 0;	//params.imgW = 0;	//params.stepH = 0;	//params.stepW = 0;	params.offset = 0.5;	Inception3_conv_priorbox1_layer = std::unique_ptr<nvinfer1::plugin::INvPlugin, decltype(pluginDeleter)>(plugin::createSSDPriorBoxPlugin(params),pluginDeleter);	std::cout << "==========createPlugin succ=========" << std::endl;	return Inception3_conv_priorbox1_layer.get();	}

then,how do i deal with these Segmentation fault?

2

Hello,

to help us debug, can you please share a small repro that contains the code and model that demonstrate the seg fault you are seeing?

regards,
NVIDIA Enterprise Support

3

Hello,my code is similar to the sample of sampleFasterRCNN https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#fasterrcnn_sample,for the reason of using the API of createSSDPriorBoxPlugin,we only create the class of IPluginFactoryExt as follows:

class PluginFactory : public nvinfer1::IPluginFactory,public nvcaffeparser1::IPluginFactoryExt { public:	virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const nvinfer1::Weights* weights, int nbWeights) override;	nvinfer1::IPlugin* createPlugin(const char* layerName,const void* seriaData,size_t seriaLength)override; // caffe parser plugin implementation	bool isPlugin(const char* name) override { return isPluginExt(name); } bool isPluginExt(const char* name) override ; void destroyPlugin(); void (*pluginDeleter)( nvinfer1::plugin::INvPlugin*) {[]( nvinfer1::plugin::INvPlugin* ptr) {ptr->destroy();}};	//priorbox layer std::unique_ptr< nvinfer1::plugin::INvPlugin, decltype(pluginDeleter)> Inception3_conv_priorbox1_layer{nullptr, pluginDeleter};	std::unique_ptr< nvinfer1::plugin::INvPlugin, decltype(pluginDeleter)> Inception3_conv_priorbox2_layer{nullptr, pluginDeleter};	std::unique_ptr< nvinfer1::plugin::INvPlugin, decltype(pluginDeleter)> Inception3_conv_priorbox3_layer{nullptr, pluginDeleter};	std::unique_ptr< nvinfer1::plugin::INvPlugin, decltype(pluginDeleter)> conv6_priorbox_layer{nullptr, pluginDeleter};	std::unique_ptr< nvinfer1::plugin::INvPlugin, decltype(pluginDeleter)> conv7_priorbox_layer{nullptr, pluginDeleter};	//detection output layer	std::unique_ptr< nvinfer1::plugin::INvPlugin, decltype(pluginDeleter)> mDetection_out{nullptr, pluginDeleter}; }; void caffeToGIEModel(const std::string& deployFile,	// name for caffe prototxt const std::string& modelFile,	// name for model const std::vector<std::string>& outputs, // network outputs unsigned int maxBatchSize,// batch size - NB must be at least as large as the batch we want to run with) nvcaffeparser1::IPluginFactoryExt* pluginFactory, //factory for plugin layers IHostMemory *&gieModelStream) // output buffer for the GIE model {	// create the builder	IBuilder* builder = createInferBuilder(gLogger);	// parse the caffe model to populate the network, then set the outputs	INetworkDefinition* network = builder->createNetwork();	ICaffeParser* parser = createCaffeParser();	parser->setPluginFactoryExt(pluginFactory);	std::cout << "=========parse caffe model start==========" << std::endl;	//if platform support fp16 calculation,change the data tpye	bool useFp16 = builder->platformHasFastFp16();	DataType modelDataType = useFp16?DataType::kHALF:DataType::kFLOAT;	const IBlobNameToTensor* blobNameToTensor = parser->parse(locateFile(deployFile, directories).c_str(),locateFile(modelFile, directories).c_str(), *network, modelDataType);	std::cout << "=========parse caffe model done==========useFp16:" << useFp16 << std::endl;	// specify which tensors are outputs	for (auto& s : outputs)	{	network->markOutput(*blobNameToTensor->find(s.c_str()));	}	// Build the engine	builder->setMaxBatchSize(maxBatchSize);	builder->setMaxWorkspaceSize(2 << 20);	builder->setFp16Mode(useFp16);	ICudaEngine* engine = builder->buildCudaEngine(*network);	assert(engine);	std::cout << "=========build cuda engine done==========" << std::endl;	// we don't need the network any more, and we can destroy the parser	network->destroy();	parser->destroy();	// serialize the engine, then close everything down	gieModelStream = engine->serialize();	engine->destroy();	builder->destroy();	shutdownProtobufLibrary(); }

my caffe model as follows:

name: "FaceBoxes" input: "data" input_shape { dim: 1 dim: 3 dim: 1024 dim: 1024 } #conv1 layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 24 pad: 0 kernel_size: 7	stride: 4 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv1/bn" type: "BatchNorm" bottom: "conv1" top: "conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv1/neg" type: "Power" bottom: "conv1" top: "conv1/neg" power_param { power: 1 scale: -1.0 shift: 0 } } layer { name: "conv1/concat" type: "Concat" bottom: "conv1" bottom: "conv1/neg" top: "conv1_CR" } layer { name: "conv1/scale" type: "Scale" bottom: "conv1_CR" top: "conv1_CR" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 2.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv1/relu" type: "ReLU" bottom: "conv1_CR" top: "conv1_CR" } layer { name: "pool1" type: "Pooling" bottom: "conv1_CR" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } #conv2 layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 64 pad: 0 kernel_size: 5	stride: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv2/bn" type: "BatchNorm" bottom: "conv2" top: "conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv2/neg" type: "Power" bottom: "conv2" top: "conv2/neg" power_param { power: 1 scale: -1.0 shift: 0 } } layer { name: "conv2/concat" type: "Concat" bottom: "conv2" bottom: "conv2/neg" top: "conv2_CR" } layer { name: "conv2/scale" type: "Scale" bottom: "conv2_CR" top: "conv2_CR" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 2.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv2/relu" type: "ReLU" bottom: "conv2_CR" top: "conv2_CR" } layer { name: "pool2" type: "Pooling" bottom: "conv2_CR" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } #Inception1 layer { name: "conv3/incep0/conv" type: "Convolution" bottom: "pool2" top: "conv3/incep0/conv" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv3/incep0/bn" type: "BatchNorm" bottom: "conv3/incep0/conv" top: "conv3/incep0/conv" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv3/incep0/bn_scale" type: "Scale" bottom: "conv3/incep0/conv" top: "conv3/incep0/conv" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv3/incep0/relu" type: "ReLU" bottom: "conv3/incep0/conv" top: "conv3/incep0/conv" } layer { name: "conv3/incep1/conv1" type: "Convolution" bottom: "pool2" top: "conv3/incep1/conv1" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 24 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv3/incep1/bn1" type: "BatchNorm" bottom: "conv3/incep1/conv1" top: "conv3/incep1/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv3/incep1/bn_scale1" type: "Scale" bottom: "conv3/incep1/conv1" top: "conv3/incep1/conv1" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv3/incep1/relu1" type: "ReLU" bottom: "conv3/incep1/conv1" top: "conv3/incep1/conv1" } layer { name: "conv3/incep1/conv2" type: "Convolution" bottom: "conv3/incep1/conv1" top: "conv3/incep1/conv2" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32	weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 1 } } layer { name: "conv3/incep1/bn2" type: "BatchNorm" bottom: "conv3/incep1/conv2" top: "conv3/incep1/conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv3/incep1/bn_scale2" type: "Scale" bottom: "conv3/incep1/conv2" top: "conv3/incep1/conv2" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv3/incep1/relu2" type: "ReLU" bottom: "conv3/incep1/conv2" top: "conv3/incep1/conv2" } layer { name: "conv3/incep2/conv1" type: "Convolution" bottom: "pool2" top: "conv3/incep2/conv1" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 24 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv3/incep2/bn1" type: "BatchNorm" bottom: "conv3/incep2/conv1" top: "conv3/incep2/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv3/incep2/bn_scale1" type: "Scale" bottom: "conv3/incep2/conv1" top: "conv3/incep2/conv1" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv3/incep2/relu1" type: "ReLU" bottom: "conv3/incep2/conv1" top: "conv3/incep2/conv1" } layer { name: "conv3/incep2/conv2" type: "Convolution" bottom: "conv3/incep2/conv1" top: "conv3/incep2/conv2" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 1 } } layer { name: "conv3/incep2/bn2" type: "BatchNorm" bottom: "conv3/incep2/conv2" top: "conv3/incep2/conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv3/incep2/bn_scale2" type: "Scale" bottom: "conv3/incep2/conv2" top: "conv3/incep2/conv2" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv3/incep2/relu2" type: "ReLU" bottom: "conv3/incep2/conv2" top: "conv3/incep2/conv2" } layer { name: "conv3/incep2/conv3" type: "Convolution" bottom: "conv3/incep2/conv2" top: "conv3/incep2/conv3" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 1 } } layer { name: "conv3/incep2/bn3" type: "BatchNorm" bottom: "conv3/incep2/conv3" top: "conv3/incep2/conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv3/incep2/bn_scale3" type: "Scale" bottom: "conv3/incep2/conv3" top: "conv3/incep2/conv3" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv3/incep2/relu3" type: "ReLU" bottom: "conv3/incep2/conv3" top: "conv3/incep2/conv3" } layer { name: "conv3/incep3/pool" type: "Pooling" bottom: "pool2" top: "conv3/incep3/pool" pooling_param { pool: MAX kernel_size: 3 stride: 1 pad: 1 } } layer { name: "conv3/incep3/conv" type: "Convolution" bottom: "conv3/incep3/pool" top: "conv3/incep3/conv" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv3/incep3/bn" type: "BatchNorm" bottom: "conv3/incep3/conv" top: "conv3/incep3/conv" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv3/incep3/bn_scale" type: "Scale" bottom: "conv3/incep3/conv" top: "conv3/incep3/conv" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv3/incep3/relu" type: "ReLU" bottom: "conv3/incep3/conv" top: "conv3/incep3/conv" } layer { name: "conv3/incep" type: "Concat" bottom: "conv3/incep0/conv" bottom: "conv3/incep1/conv2" bottom: "conv3/incep2/conv3" bottom: "conv3/incep3/conv" top: "conv3/incep" } #Inception2 layer { name: "conv4/incep0/conv" type: "Convolution" bottom: "conv3/incep" top: "conv4/incep0/conv" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv4/incep0/bn" type: "BatchNorm" bottom: "conv4/incep0/conv" top: "conv4/incep0/conv" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv4/incep0/bn_scale" type: "Scale" bottom: "conv4/incep0/conv" top: "conv4/incep0/conv" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv4/incep0/relu" type: "ReLU" bottom: "conv4/incep0/conv" top: "conv4/incep0/conv" } layer { name: "conv4/incep1/conv1" type: "Convolution" bottom: "conv3/incep" top: "conv4/incep1/conv1" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 24 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv4/incep1/bn1" type: "BatchNorm" bottom: "conv4/incep1/conv1" top: "conv4/incep1/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv4/incep1/bn_scale1" type: "Scale" bottom: "conv4/incep1/conv1" top: "conv4/incep1/conv1" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv4/incep1/relu1" type: "ReLU" bottom: "conv4/incep1/conv1" top: "conv4/incep1/conv1" } layer { name: "conv4/incep1/conv2" type: "Convolution" bottom: "conv4/incep1/conv1" top: "conv4/incep1/conv2" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 1 } } layer { name: "conv4/incep1/bn2" type: "BatchNorm" bottom: "conv4/incep1/conv2" top: "conv4/incep1/conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv4/incep1/bn_scale2" type: "Scale" bottom: "conv4/incep1/conv2" top: "conv4/incep1/conv2" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv4/incep1/relu2" type: "ReLU" bottom: "conv4/incep1/conv2" top: "conv4/incep1/conv2" } layer { name: "conv4/incep2/conv1" type: "Convolution" bottom: "conv3/incep" top: "conv4/incep2/conv1" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 24 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv4/incep2/bn1" type: "BatchNorm" bottom: "conv4/incep2/conv1" top: "conv4/incep2/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv4/incep2/bn_scale1" type: "Scale" bottom: "conv4/incep2/conv1" top: "conv4/incep2/conv1" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv4/incep2/relu1" type: "ReLU" bottom: "conv4/incep2/conv1" top: "conv4/incep2/conv1" } layer { name: "conv4/incep2/conv2" type: "Convolution" bottom: "conv4/incep2/conv1" top: "conv4/incep2/conv2" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 1 } } layer { name: "conv4/incep2/bn2" type: "BatchNorm" bottom: "conv4/incep2/conv2" top: "conv4/incep2/conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv4/incep2/bn_scale2" type: "Scale" bottom: "conv4/incep2/conv2" top: "conv4/incep2/conv2" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv4/incep2/relu2" type: "ReLU" bottom: "conv4/incep2/conv2" top: "conv4/incep2/conv2" } layer { name: "conv4/incep2/conv3" type: "Convolution" bottom: "conv4/incep2/conv2" top: "conv4/incep2/conv3" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 1 } } layer { name: "conv4/incep2/bn3" type: "BatchNorm" bottom: "conv4/incep2/conv3" top: "conv4/incep2/conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv4/incep2/bn_scale3" type: "Scale" bottom: "conv4/incep2/conv3" top: "conv4/incep2/conv3" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv4/incep2/relu3" type: "ReLU" bottom: "conv4/incep2/conv3" top: "conv4/incep2/conv3" } layer { name: "conv4/incep3/pool" type: "Pooling" bottom: "conv3/incep" top: "conv4/incep3/pool" pooling_param { pool: MAX kernel_size: 3 stride: 1 pad: 1 } } layer { name: "conv4/incep3/conv" type: "Convolution" bottom: "conv4/incep3/pool" top: "conv4/incep3/conv" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv4/incep3/bn" type: "BatchNorm" bottom: "conv4/incep3/conv" top: "conv4/incep3/conv" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv4/incep3/bn_scale" type: "Scale" bottom: "conv4/incep3/conv" top: "conv4/incep3/conv" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv4/incep3/relu" type: "ReLU" bottom: "conv4/incep3/conv" top: "conv4/incep3/conv" } layer { name: "conv4/incep" type: "Concat" bottom: "conv4/incep0/conv" bottom: "conv4/incep1/conv2" bottom: "conv4/incep2/conv3" bottom: "conv4/incep3/conv" top: "conv4/incep" } layer { name: "conv5/incep0/conv" type: "Convolution" bottom: "conv4/incep" top: "conv5/incep0/conv" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv5/incep0/bn" type: "BatchNorm" bottom: "conv5/incep0/conv" top: "conv5/incep0/conv" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv5/incep0/bn_scale" type: "Scale" bottom: "conv5/incep0/conv" top: "conv5/incep0/conv" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv5/incep0/relu" type: "ReLU" bottom: "conv5/incep0/conv" top: "conv5/incep0/conv" } layer { name: "conv5/incep1/conv1" type: "Convolution" bottom: "conv4/incep" top: "conv5/incep1/conv1" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 24 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv5/incep1/bn1" type: "BatchNorm" bottom: "conv5/incep1/conv1" top: "conv5/incep1/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv5/incep1/bn_scale1" type: "Scale" bottom: "conv5/incep1/conv1" top: "conv5/incep1/conv1" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv5/incep1/relu1" type: "ReLU" bottom: "conv5/incep1/conv1" top: "conv5/incep1/conv1" } layer { name: "conv5/incep1/conv2" type: "Convolution" bottom: "conv5/incep1/conv1" top: "conv5/incep1/conv2" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 1 } } layer { name: "conv5/incep1/bn2" type: "BatchNorm" bottom: "conv5/incep1/conv2" top: "conv5/incep1/conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv5/incep1/bn_scale2" type: "Scale" bottom: "conv5/incep1/conv2" top: "conv5/incep1/conv2" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv5/incep1/relu2" type: "ReLU" bottom: "conv5/incep1/conv2" top: "conv5/incep1/conv2" } layer { name: "conv5/incep2/conv1" type: "Convolution" bottom: "conv4/incep" top: "conv5/incep2/conv1" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 24 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv5/incep2/bn1" type: "BatchNorm" bottom: "conv5/incep2/conv1" top: "conv5/incep2/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv5/incep2/bn_scale1" type: "Scale" bottom: "conv5/incep2/conv1" top: "conv5/incep2/conv1" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv5/incep2/relu1" type: "ReLU" bottom: "conv5/incep2/conv1" top: "conv5/incep2/conv1" } layer { name: "conv5/incep2/conv2" type: "Convolution" bottom: "conv5/incep2/conv1" top: "conv5/incep2/conv2" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 1 } } layer { name: "conv5/incep2/bn2" type: "BatchNorm" bottom: "conv5/incep2/conv2" top: "conv5/incep2/conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv5/incep2/bn_scale2" type: "Scale" bottom: "conv5/incep2/conv2" top: "conv5/incep2/conv2" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv5/incep2/relu2" type: "ReLU" bottom: "conv5/incep2/conv2" top: "conv5/incep2/conv2" } layer { name: "conv5/incep2/conv3" type: "Convolution" bottom: "conv5/incep2/conv2" top: "conv5/incep2/conv3" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 1 } } layer { name: "conv5/incep2/bn3" type: "BatchNorm" bottom: "conv5/incep2/conv3" top: "conv5/incep2/conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv5/incep2/bn_scale3" type: "Scale" bottom: "conv5/incep2/conv3" top: "conv5/incep2/conv3" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv5/incep2/relu3" type: "ReLU" bottom: "conv5/incep2/conv3" top: "conv5/incep2/conv3" } layer { name: "conv5/incep3/pool" type: "Pooling" bottom: "conv4/incep" top: "conv5/incep3/pool" pooling_param { pool: MAX kernel_size: 3 stride: 1 pad: 1 } } layer { name: "conv5/incep3/conv" type: "Convolution" bottom: "conv5/incep3/pool" top: "conv5/incep3/conv" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv5/incep3/bn" type: "BatchNorm" bottom: "conv5/incep3/conv" top: "conv5/incep3/conv" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv5/incep3/bn_scale" type: "Scale" bottom: "conv5/incep3/conv" top: "conv5/incep3/conv" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv5/incep3/relu" type: "ReLU" bottom: "conv5/incep3/conv" top: "conv5/incep3/conv" } layer { name: "conv5/incep" type: "Concat" bottom: "conv5/incep0/conv" bottom: "conv5/incep1/conv2" bottom: "conv5/incep2/conv3" bottom: "conv5/incep3/conv" top: "conv5/incep" } layer { name: "Inception3/conv/loc1" type: "Convolution" bottom: "conv5/incep" top: "Inception3/conv/loc1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 4 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "Inception3/conv/loc1/perm" type: "Permute" bottom: "Inception3/conv/loc1" top: "Inception3/conv/loc1/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "Inception3/conv/loc1/flat" type: "Flatten" bottom: "Inception3/conv/loc1/perm" top: "Inception3/conv/loc1/flat" flatten_param { axis: 1 } } layer { name: "Inception3/conv/conf1" type: "Convolution" bottom: "conv5/incep" top: "Inception3/conv/conf1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 2 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "Inception3/conv/conf1/perm" type: "Permute" bottom: "Inception3/conv/conf1" top: "Inception3/conv/conf1/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "Inception3/conv/conf1/flat" type: "Flatten" bottom: "Inception3/conv/conf1/perm" top: "Inception3/conv/conf1/flat" flatten_param { axis: 1 } } layer { name: "Inception3/conv/priorbox1" type: "PriorBox" bottom: "conv5/incep" bottom: "data" top: "Inception3/conv/priorbox1" prior_box_param { min_size: 32 aspect_ratio: 1 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "Inception3/conv/loc2" type: "Convolution" bottom: "conv5/incep" top: "Inception3/conv/loc2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 4 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "Inception3/conv/loc2/perm" type: "Permute" bottom: "Inception3/conv/loc2" top: "Inception3/conv/loc2/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "Inception3/conv/loc2/flat" type: "Flatten" bottom: "Inception3/conv/loc2/perm" top: "Inception3/conv/loc2/flat" flatten_param { axis: 1 } } layer { name: "Inception3/conv/conf2" type: "Convolution" bottom: "conv5/incep" top: "Inception3/conv/conf2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 2 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "Inception3/conv/conf2/perm" type: "Permute" bottom: "Inception3/conv/conf2" top: "Inception3/conv/conf2/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "Inception3/conv/conf2/flat" type: "Flatten" bottom: "Inception3/conv/conf2/perm" top: "Inception3/conv/conf2/flat" flatten_param { axis: 1 } } layer { name: "Inception3/conv/priorbox2" type: "PriorBox" bottom: "conv5/incep" bottom: "data" top: "Inception3/conv/priorbox2" prior_box_param { min_size: 64 aspect_ratio: 1 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "Inception3/conv/loc3" type: "Convolution" bottom: "conv5/incep" top: "Inception3/conv/loc3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 4 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "Inception3/conv/loc3/perm" type: "Permute" bottom: "Inception3/conv/loc3" top: "Inception3/conv/loc3/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "Inception3/conv/loc3/flat" type: "Flatten" bottom: "Inception3/conv/loc3/perm" top: "Inception3/conv/loc3/flat" flatten_param { axis: 1 } } layer { name: "Inception3/conv/conf3" type: "Convolution" bottom: "conv5/incep" top: "Inception3/conv/conf3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 2 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "Inception3/conv/conf3/perm" type: "Permute" bottom: "Inception3/conv/conf3" top: "Inception3/conv/conf3/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "Inception3/conv/conf3/flat" type: "Flatten" bottom: "Inception3/conv/conf3/perm" top: "Inception3/conv/conf3/flat" flatten_param { axis: 1 } } layer { name: "Inception3/conv/priorbox3" type: "PriorBox" bottom: "conv5/incep" bottom: "data" top: "Inception3/conv/priorbox3" prior_box_param { min_size: 128 aspect_ratio: 1 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "conv6_1" type: "Convolution" bottom: "conv5/incep" top: "conv6_1" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 128 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv6/bn1" type: "BatchNorm" bottom: "conv6_1" top: "conv6_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv6/bn_scale1" type: "Scale" bottom: "conv6_1" top: "conv6_1" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv6/relu1" type: "ReLU" bottom: "conv6_1" top: "conv6_1" } layer { name: "conv6_2" type: "Convolution" bottom: "conv6_1" top: "conv6_2" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 256 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 2 } } layer { name: "conv6/bn2" type: "BatchNorm" bottom: "conv6_2" top: "conv6_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv6/bn_scale2" type: "Scale" bottom: "conv6_2" top: "conv6_2" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv6/relu2" type: "ReLU" bottom: "conv6_2" top: "conv6_2" } layer { name: "conv6/loc" type: "Convolution" bottom: "conv6_2" top: "conv6/loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 4 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv6/loc/perm" type: "Permute" bottom: "conv6/loc" top: "conv6/loc/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv6/loc/perm/flat" type: "Flatten" bottom: "conv6/loc/perm" top: "conv6/loc/perm/flat" flatten_param { axis: 1 } } layer { name: "conv6/conf" type: "Convolution" bottom: "conv6_2" top: "conv6/conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 2 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv6/conf/perm" type: "Permute" bottom: "conv6/conf" top: "conv6/conf/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv6/conf/perm/flat" type: "Flatten" bottom: "conv6/conf/perm" top: "conv6/conf/perm/flat" flatten_param { axis: 1 } } layer { name: "conv6/priorbox" type: "PriorBox" bottom: "conv6_2" bottom: "data" top: "conv6/priorbox" prior_box_param { min_size: 256 aspect_ratio: 1 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "conv7_1" type: "Convolution" bottom: "conv6_2" top: "conv7_1" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 128 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 0 kernel_size: 1 stride: 1 } } layer { name: "conv7/bn1" type: "BatchNorm" bottom: "conv7_1" top: "conv7_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv7/bn_scale1" type: "Scale" bottom: "conv7_1" top: "conv7_1" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv7/relu1" type: "ReLU" bottom: "conv7_1" top: "conv7_1" } layer { name: "conv7_2" type: "Convolution" bottom: "conv7_1" top: "conv7_2" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 256 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad: 1 kernel_size: 3 stride: 2 } } layer { name: "conv7/bn2" type: "BatchNorm" bottom: "conv7_2" top: "conv7_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: false } } layer { name: "conv7/bn_scale2" type: "Scale" bottom: "conv7_2" top: "conv7_2" param { lr_mult: 1.0 decay_mult: 0 } param { lr_mult: 1.0 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "conv7/relu2" type: "ReLU" bottom: "conv7_2" top: "conv7_2" } layer { name: "conv7/loc" type: "Convolution" bottom: "conv7_2" top: "conv7/loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 4 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv7/loc/perm" type: "Permute" bottom: "conv7/loc" top: "conv7/loc/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv7/loc/perm/flat" type: "Flatten" bottom: "conv7/loc/perm" top: "conv7/loc/perm/flat" flatten_param { axis: 1 } } layer { name: "conv7/conf" type: "Convolution" bottom: "conv7_2" top: "conv7/conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 2 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv7/conf/perm" type: "Permute" bottom: "conv7/conf" top: "conv7/conf/perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv7/conf/perm/flat" type: "Flatten" bottom: "conv7/conf/perm" top: "conv7/conf/perm/flat" flatten_param { axis: 1 } } layer { name: "conv7/priorbox" type: "PriorBox" bottom: "conv7_2" bottom: "data" top: "conv7/priorbox" prior_box_param { min_size: 512 aspect_ratio: 1 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "mbox_loc" type: "Concat" bottom: "Inception3/conv/loc1/flat" bottom: "Inception3/conv/loc2/flat" bottom: "Inception3/conv/loc3/flat" bottom: "conv6/loc/perm/flat" bottom: "conv7/loc/perm/flat" top: "mbox_loc" concat_param { axis: 1 } } layer { name: "mbox_conf" type: "Concat" bottom: "Inception3/conv/conf1/flat" bottom: "Inception3/conv/conf2/flat" bottom: "Inception3/conv/conf3/flat" bottom: "conv6/conf/perm/flat" bottom: "conv7/conf/perm/flat" top: "mbox_conf" concat_param { axis: 1 } } layer { name: "mbox_priorbox" type: "Concat" bottom: "Inception3/conv/priorbox1" bottom: "Inception3/conv/priorbox2" bottom: "Inception3/conv/priorbox3" bottom: "conv6/priorbox" bottom: "conv7/priorbox" top: "mbox_priorbox" concat_param { axis: 2 } } layer { name: "detection_out" type: "DetectionOutput" bottom: "mbox_loc" bottom: "mbox_conf" bottom: "mbox_priorbox" top: "detection_out" include { phase: TEST } detection_output_param { num_classes: 2 share_location: true background_label_id: 0 nms_param { nms_threshold: 0.45 top_k: 100 } code_type: CENTER_SIZE keep_top_k: 100 confidence_threshold: 0.5 } }
4

TRT doesn’t support flatten layer explicitly, but you can use reshape layer instead (like below).
And TRT 5.0 published caffe based SSD sample, you can get a complete reference there.

layer { name: "mbox_conf_flatten" type: "Reshape" bottom: "mbox_conf_softmax" top: "mbox_conf_flatten" reshape_param { shape { dim: 0 dim: -1 dim: 1 dim: 1 } } }
5

Hello,
There is a new problem arose in the building trt engine,which link is in this website[url]https://devtalk.nvidia.com/default/topic/1045372/cuda-error-in-nchwtonchw/[/url], please have a look, thank you !