diff --git a/CMakeLists.txt b/CMakeLists.txt index c66218e07..127ac1dbe 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -10,8 +10,8 @@ endif() project(Caffe C CXX) # ---[ Caffe version -set(CAFFE_TARGET_VERSION "1.1.4" CACHE STRING "Caffe logical version") -set(CAFFE_TARGET_SOVERSION "1.1.4" CACHE STRING "Caffe soname version") +set(CAFFE_TARGET_VERSION "1.1.5" CACHE STRING "Caffe logical version") +set(CAFFE_TARGET_SOVERSION "1.1.5" CACHE STRING "Caffe soname version") add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION}) # ---[ Using cmake scripts and modules diff --git a/README.md b/README.md index c543f330d..4a7d915e1 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Intel® Distribution of Caffe* -This fork Caffe is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (HSW, BDW, Xeon Phi) +This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors. ## Building Build procedure is the same as on bvlc-caffe-master branch, see section "Caffe". Both Make and CMake can be used. diff --git a/cmake/MKLDNN.cmake b/cmake/MKLDNN.cmake index e43f86612..733d4992b 100644 --- a/cmake/MKLDNN.cmake +++ b/cmake/MKLDNN.cmake @@ -40,7 +40,7 @@ function(Download_MKLDNN) ) set(MKLDNN_INCLUDE_DIR ${MKLDNN_INSTALL_DIR}/include CACHE PATH "Include files for MKLDNN") - set(MKLDNN_LIB_DIR ${MKLDNN_INSTALL_DIR}/lib) + set(MKLDNN_LIB_DIR ${MKLDNN_INSTALL_DIR}/lib64) add_library(mkldnn SHARED IMPORTED ${MKLDNN_INSTALL_DIR}) set_property(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB_DIR}/libmkldnn.so) add_dependencies(mkldnn MKLDNN_Build) diff --git a/docker/README.md b/docker/README.md index dc47a7620..8a01484aa 100644 --- a/docker/README.md +++ b/docker/README.md @@ -17,7 +17,7 @@ docker run -ti caffe:cpu caffe --version ``` which should show a message like: ``` -caffe version 1.1.4 +caffe version 1.1.5 ``` One can also build and run the Caffe tests in the image using: diff --git a/docs/release_notes.md b/docs/release_notes.md index f1bea1980..20407895c 100644 --- a/docs/release_notes.md +++ b/docs/release_notes.md @@ -56,7 +56,7 @@ All modification made by Intel Corporation: © 2016 Intel Corporation ## Introduction -This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors (Haswell, Broadwell, Xenon Phi) +This fork is dedicated to improving Caffe performance when running on CPU, in particular Intel® Xeon processors. ## Installation diff --git a/include/caffe/layers/axpy_layer.hpp b/include/caffe/layers/axpy_layer.hpp new file mode 100644 index 000000000..e74629167 --- /dev/null +++ b/include/caffe/layers/axpy_layer.hpp @@ -0,0 +1,62 @@ +/* + * * Axpy Layer + * * + * * Created on: May 1, 2017 + * * Author: hujie + * */ + +#ifndef CAFFE_AXPY_LAYER_HPP_ +#define CAFFE_AXPY_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" + +namespace caffe { + +/** + * * @brief For reduce memory and time both on training and testing, we combine + * * channel-wise scale operation and element-wise addition operation + * * into a single layer called "axpy". + * * + * */ +template +class AxpyLayer: public Layer { + public: + explicit AxpyLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Axpy"; } + virtual inline int ExactNumBottomBlobs() const { return 3; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: +/** + * * @param Formulation: + * * F = a * X + Y + * * Shape info: + * * a: N x C --> bottom[0] + * * X: N x C x H x W --> bottom[1] + * * Y: N x C x H x W --> bottom[2] + * * F: N x C x H x W --> top[0] + * */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Blob spatial_sum_multiplier_; +}; + +} // namespace caffe + +#endif // CAFFE_AXPY_LAYER_HPP_ diff --git a/include/caffe/layers/mkldnn_layers.hpp b/include/caffe/layers/mkldnn_layers.hpp index dae314208..286d6c254 100644 --- a/include/caffe/layers/mkldnn_layers.hpp +++ b/include/caffe/layers/mkldnn_layers.hpp @@ -426,8 +426,8 @@ class MKLDNNReLULayer : public MKLDNNLayer , public NeuronLayer { shared_ptr > fwd_top_data, fwd_bottom_data, bwd_bottom_data; shared_ptr > bwd_top_diff, bwd_bottom_diff; - shared_ptr reluFwd_pd; - shared_ptr reluBwd_pd; + shared_ptr reluFwd_pd; + shared_ptr reluBwd_pd; MKLDNNPrimitive reluFwd, reluBwd; shared_ptr fwd_top_data_memory, bwd_bottom_diff_memory; shared_ptr fwd_bottom_data_primitive, bwd_top_diff_primitive, bwd_bottom_data_primitive; @@ -525,6 +525,8 @@ class MKLDNNSplitLayer : public MKLDNNLayer , public Layer { vector bwd_top_diffs_primitives_at_; vector > > bwd_top_diffs_; + bool first; + PERFORMANCE_EVENT_ID_DECL(perf_id_bw_); }; diff --git a/include/caffe/mkldnn_memory.hpp b/include/caffe/mkldnn_memory.hpp index 77b95f4a7..0204f017d 100644 --- a/include/caffe/mkldnn_memory.hpp +++ b/include/caffe/mkldnn_memory.hpp @@ -47,6 +47,8 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include "caffe/util/math_functions.hpp" #include "mkldnn.hpp" #include "mkldnn_base.hpp" +#include "caffe/syncedmem.hpp" +#include "caffe/net.hpp" using namespace mkldnn; @@ -135,7 +137,19 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr new memory(*_prv_memory_pd, (void*)_mlsl_memory.get())); } else { #endif - _prv_memory = shared_ptr(new memory(*_prv_memory_pd)); + // BufSize is the switch of whether enabling circle buffer mechanism to + // boost up mkldnn primitive execution on inference path. + if (CircleBuf::Instance()->GetBufSize()) { + if (!_is_weight) { + // find out a free buf in the circleBuf queue + _m_memory = CircleBuf::Instance()->GetFreeBuf(); + } else { + bool cuda; + CaffeMallocHost(&_m_memory, _prv_memory_pd->get_size(), &cuda); + } + _prv_memory = shared_ptr(new memory(*_prv_memory_pd, _m_memory)); + } else + _prv_memory = shared_ptr(new memory(*_prv_memory_pd)); #ifdef USE_MLSL } #endif @@ -195,6 +209,8 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr #ifdef USE_MLSL shared_ptr _mlsl_memory; #endif + void* _m_memory; + bool _is_weight; }; template diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index aaab91650..4a0a3cdff 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -54,6 +54,60 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. namespace caffe { +class CircleBuf { + private: + /* The first element of pair is the pointer to the buffer. The second one is the reference count of this buffer */ + vector> circleBuf; + /* All circle buffers in the queue use same size. It's calculated by getting largest blob size at Net Init phase + * and multiplying 4 as mkldnn winograd conv algorithm may need 4x space of original blob size. */ + size_t BufSize; + + public: + /* Static access method. */ + static CircleBuf* Instance() { + static CircleBuf* instance = new CircleBuf(); + return instance; + } + + /* Get free buffer from the queue, using refcnt to keep track of the state of each buffer. + * if not exist free buffer, then allocate a new one. */ + inline void* GetFreeBuf() { + // BufSize being 0 means the circle buf feature is disabled. + if (BufSize == 0) return NULL; + + for (auto & p : circleBuf) { + if (p.second == 0) {p.second = 1; return p.first;} + } + + // not found free buf or queue is empty, allocate a new buf and insert to queue. + void* buf = NULL; + bool cuda; + CaffeMallocHost(&buf, BufSize * 4, &cuda); + circleBuf.push_back(make_pair(buf, 1)); + return buf; + } + /* Increase the reference count of specified buffer */ + inline void IncRefCnt(const void* buf, size_t refcnt) { + for (auto & p : circleBuf) { + if (p.first == buf) {p.second += refcnt;} + } + } + /* Decrease the reference count of specified buffer */ + inline void DecRefCnt(const void* buf) { + for (auto & p : circleBuf) { + if (p.first == buf) {if (p.second > 0) p.second -= 1;} + } + } + + inline void SetBufSize(size_t size) {BufSize = size;} + inline size_t GetBufSize() {return BufSize;} + inline size_t GetQueueSize() {return circleBuf.size();} + + private: + /* Private constructor to prevent instancing. */ + CircleBuf() { BufSize = 0; circleBuf.clear(); } +}; + /** * @brief Connects Layer%s together into a directed acyclic graph (DAG) * specified by a NetParameter. @@ -69,6 +123,9 @@ class Net { const Net* root_net = NULL, std::string engine = ""); virtual ~Net() {} + /// @brief Buffer Queue for reducing cache missing and saving memory footprint of MKLDNN layer. + static vector circleBuf; + /// @brief Initialize a network with a NetParameter. void Init(const NetParameter& param); @@ -523,6 +580,7 @@ class Net { vector has_params_decay_; /// The bytes of memory used by this net size_t memory_used_; + size_t max_blob_count; /// Whether to compute and display debug info for the net. bool debug_info_; /// The root net that actually holds the shared layers in data parallelism @@ -530,6 +588,7 @@ class Net { DISABLE_COPY_AND_ASSIGN(Net); }; +template vector Net::circleBuf; } // namespace caffe diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp index 5b6d869ed..0ea0f13f7 100644 --- a/include/caffe/syncedmem.hpp +++ b/include/caffe/syncedmem.hpp @@ -75,7 +75,9 @@ inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) { #ifdef USE_MKL *ptr = mkl_malloc(size ? size : 1, 64); #else - *ptr = malloc(size); + //*ptr = malloc(size); + int rc = ::posix_memalign(ptr, 64, size); + assert(rc == 0); #endif #ifdef USE_MLSL @@ -103,7 +105,8 @@ inline void CaffeFreeHost(void* ptr, bool use_cuda) { #ifdef USE_MKL mkl_free(ptr); #else - free(ptr); + //free(ptr); + ::free(ptr); #endif #ifdef USE_MLSL diff --git a/models/intel_optimized_models/int8/resnet50_int8.prototxt b/models/intel_optimized_models/int8/resnet50_int8.prototxt index b95dccb11..1dc693d4c 100644 --- a/models/intel_optimized_models/int8/resnet50_int8.prototxt +++ b/models/intel_optimized_models/int8/resnet50_int8.prototxt @@ -9,7 +9,7 @@ layer { value: 0.01 } shape { - dim: 64 + dim: 1 dim: 3 dim: 224 dim: 224 diff --git a/models/intel_optimized_models/resnet50_v1/resnet50_fp32_acc.prototxt b/models/intel_optimized_models/resnet50_v1/resnet50_fp32_acc.prototxt new file mode 100755 index 000000000..f27b6cd1e --- /dev/null +++ b/models/intel_optimized_models/resnet50_v1/resnet50_fp32_acc.prototxt @@ -0,0 +1,3282 @@ +name: "ResNet-50" +layer { + name: "data" + type: "Data" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mirror: false + crop_size: 224 + scale: 0.0078125 + mean_value: 104 + mean_value: 117 + mean_value: 123 + random_resize_param { + min_size: 256 + max_size: 256 + resize_param { + interp_mode: CUBIC + } + } + } + data_param { + source: "/lustre/dataset/lmdb/imagenet/lmdb_raw_compressed/ilsvrc12_val_lmdb" + batch_size: 50 + backend: LMDB + } +} + +layer { + bottom: "data" + top: "conv1" + name: "conv1" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 7 + pad: 3 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "conv1" + top: "conv1" + name: "bn_conv1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "conv1" + top: "conv1" + name: "scale_conv1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "conv1" + top: "conv1" + name: "conv1_relu" + type: "ReLU" + relu_param { + } +} + +layer { + bottom: "conv1" + top: "pool1" + name: "pool1" + type: "Pooling" + pooling_param { + kernel_size: 3 + stride: 2 + pool: MAX + } +} + +layer { + bottom: "pool1" + top: "res2a_branch1" + name: "res2a_branch1" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch1" + top: "res2a_branch1" + name: "bn2a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch1" + top: "res2a_branch1" + name: "scale2a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "pool1" + top: "res2a_branch2a" + name: "res2a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 64 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2a" + name: "bn2a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2a" + name: "scale2a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2a" + name: "res2a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2b" + name: "res2a_branch2b" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2b" + name: "bn2a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2b" + name: "scale2a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2b" + name: "res2a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2c" + name: "res2a_branch2c" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch2c" + top: "res2a_branch2c" + name: "bn2a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch2c" + top: "res2a_branch2c" + name: "scale2a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a_branch1" + bottom: "res2a_branch2c" + top: "res2a" + name: "res2a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res2a" + top: "res2a" + name: "res2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2a" + top: "res2b_branch2a" + name: "res2b_branch2a" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2a" + name: "bn2b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2a" + name: "scale2b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2a" + name: "res2b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2b" + name: "res2b_branch2b" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2b" + name: "bn2b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2b" + name: "scale2b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2b" + name: "res2b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2c" + name: "res2b_branch2c" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2b_branch2c" + top: "res2b_branch2c" + name: "bn2b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2b_branch2c" + top: "res2b_branch2c" + name: "scale2b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a" + bottom: "res2b_branch2c" + top: "res2b" + name: "res2b" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res2b" + top: "res2b" + name: "res2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2b" + top: "res2c_branch2a" + name: "res2c_branch2a" + type: "Convolution" + convolution_param { + + num_output: 64 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2a" + name: "bn2c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2a" + name: "scale2c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2a" + name: "res2c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2b" + name: "res2c_branch2b" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2b" + name: "bn2c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2b" + name: "scale2c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2b" + name: "res2c_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2c" + name: "res2c_branch2c" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2c_branch2c" + top: "res2c_branch2c" + name: "bn2c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res2c_branch2c" + top: "res2c_branch2c" + name: "scale2c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2b" + bottom: "res2c_branch2c" + top: "res2c" + name: "res2c" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res2c" + top: "res2c" + name: "res2c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2c" + top: "res3a_branch1" + name: "res3a_branch1" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch1" + top: "res3a_branch1" + name: "bn3a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch1" + top: "res3a_branch1" + name: "scale3a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2c" + top: "res3a_branch2a" + name: "res3a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2a" + name: "bn3a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2a" + name: "scale3a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2a" + name: "res3a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2b" + name: "res3a_branch2b" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2b" + name: "bn3a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2b" + name: "scale3a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2b" + name: "res3a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2c" + name: "res3a_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch2c" + top: "res3a_branch2c" + name: "bn3a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch2c" + top: "res3a_branch2c" + name: "scale3a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a_branch1" + bottom: "res3a_branch2c" + top: "res3a" + name: "res3a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3a" + top: "res3a" + name: "res3a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3a" + top: "res3b_branch2a" + name: "res3b_branch2a" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2a" + name: "bn3b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2a" + name: "scale3b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2a" + name: "res3b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2b" + name: "res3b_branch2b" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2b" + name: "bn3b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2b" + name: "scale3b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2b" + name: "res3b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2c" + name: "res3b_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3b_branch2c" + top: "res3b_branch2c" + name: "bn3b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3b_branch2c" + top: "res3b_branch2c" + name: "scale3b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a" + bottom: "res3b_branch2c" + top: "res3b" + name: "res3b" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3b" + top: "res3b" + name: "res3b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3b" + top: "res3c_branch2a" + name: "res3c_branch2a" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2a" + name: "bn3c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2a" + name: "scale3c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2a" + name: "res3c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2b" + name: "res3c_branch2b" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2b" + name: "bn3c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2b" + name: "scale3c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2b" + name: "res3c_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2c" + name: "res3c_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3c_branch2c" + top: "res3c_branch2c" + name: "bn3c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3c_branch2c" + top: "res3c_branch2c" + name: "scale3c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3b" + bottom: "res3c_branch2c" + top: "res3c" + name: "res3c" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3c" + top: "res3c" + name: "res3c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3c" + top: "res3d_branch2a" + name: "res3d_branch2a" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2a" + name: "bn3d_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2a" + name: "scale3d_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2a" + name: "res3d_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2b" + name: "res3d_branch2b" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2b" + name: "bn3d_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2b" + name: "scale3d_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2b" + name: "res3d_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2c" + name: "res3d_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3d_branch2c" + top: "res3d_branch2c" + name: "bn3d_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res3d_branch2c" + top: "res3d_branch2c" + name: "scale3d_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3c" + bottom: "res3d_branch2c" + top: "res3d" + name: "res3d" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3d" + top: "res3d" + name: "res3d_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3d" + top: "res4a_branch1" + name: "res4a_branch1" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch1" + top: "res4a_branch1" + name: "bn4a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch1" + top: "res4a_branch1" + name: "scale4a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3d" + top: "res4a_branch2a" + name: "res4a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2a" + name: "bn4a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2a" + name: "scale4a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2a" + name: "res4a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2b" + name: "res4a_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2b" + name: "bn4a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2b" + name: "scale4a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2b" + name: "res4a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2c" + name: "res4a_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch2c" + top: "res4a_branch2c" + name: "bn4a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch2c" + top: "res4a_branch2c" + name: "scale4a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a_branch1" + bottom: "res4a_branch2c" + top: "res4a" + name: "res4a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4a" + top: "res4a" + name: "res4a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4a" + top: "res4b_branch2a" + name: "res4b_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2a" + name: "bn4b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2a" + name: "scale4b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2a" + name: "res4b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2b" + name: "res4b_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2b" + name: "bn4b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2b" + name: "scale4b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2b" + name: "res4b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2c" + name: "res4b_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4b_branch2c" + top: "res4b_branch2c" + name: "bn4b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4b_branch2c" + top: "res4b_branch2c" + name: "scale4b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a" + bottom: "res4b_branch2c" + top: "res4b" + name: "res4b" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4b" + top: "res4b" + name: "res4b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4b" + top: "res4c_branch2a" + name: "res4c_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2a" + name: "bn4c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2a" + name: "scale4c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2a" + name: "res4c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2b" + name: "res4c_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2b" + name: "bn4c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2b" + name: "scale4c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2b" + name: "res4c_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2c" + name: "res4c_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4c_branch2c" + top: "res4c_branch2c" + name: "bn4c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4c_branch2c" + top: "res4c_branch2c" + name: "scale4c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4b" + bottom: "res4c_branch2c" + top: "res4c" + name: "res4c" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4c" + top: "res4c" + name: "res4c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4c" + top: "res4d_branch2a" + name: "res4d_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2a" + name: "bn4d_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2a" + name: "scale4d_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2a" + name: "res4d_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2b" + name: "res4d_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2b" + name: "bn4d_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2b" + name: "scale4d_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2b" + name: "res4d_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2c" + name: "res4d_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4d_branch2c" + top: "res4d_branch2c" + name: "bn4d_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4d_branch2c" + top: "res4d_branch2c" + name: "scale4d_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4c" + bottom: "res4d_branch2c" + top: "res4d" + name: "res4d" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4d" + top: "res4d" + name: "res4d_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4d" + top: "res4e_branch2a" + name: "res4e_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2a" + name: "bn4e_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2a" + name: "scale4e_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2a" + name: "res4e_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2b" + name: "res4e_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2b" + name: "bn4e_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2b" + name: "scale4e_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2b" + name: "res4e_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2c" + name: "res4e_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4e_branch2c" + top: "res4e_branch2c" + name: "bn4e_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4e_branch2c" + top: "res4e_branch2c" + name: "scale4e_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4d" + bottom: "res4e_branch2c" + top: "res4e" + name: "res4e" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4e" + top: "res4e" + name: "res4e_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4e" + top: "res4f_branch2a" + name: "res4f_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2a" + name: "bn4f_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2a" + name: "scale4f_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2a" + name: "res4f_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2b" + name: "res4f_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2b" + name: "bn4f_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2b" + name: "scale4f_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2b" + name: "res4f_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2c" + name: "res4f_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4f_branch2c" + top: "res4f_branch2c" + name: "bn4f_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res4f_branch2c" + top: "res4f_branch2c" + name: "scale4f_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4e" + bottom: "res4f_branch2c" + top: "res4f" + name: "res4f" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4f" + top: "res4f" + name: "res4f_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4f" + top: "res5a_branch1" + name: "res5a_branch1" + type: "Convolution" + convolution_param { + + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch1" + top: "res5a_branch1" + name: "bn5a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch1" + top: "res5a_branch1" + name: "scale5a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4f" + top: "res5a_branch2a" + name: "res5a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2a" + name: "bn5a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2a" + name: "scale5a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2a" + name: "res5a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2b" + name: "res5a_branch2b" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2b" + name: "bn5a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2b" + name: "scale5a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2b" + name: "res5a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2c" + name: "res5a_branch2c" + type: "Convolution" + convolution_param { + + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch2c" + top: "res5a_branch2c" + name: "bn5a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch2c" + top: "res5a_branch2c" + name: "scale5a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a_branch1" + bottom: "res5a_branch2c" + top: "res5a" + name: "res5a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res5a" + top: "res5a" + name: "res5a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5a" + top: "res5b_branch2a" + name: "res5b_branch2a" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2a" + name: "bn5b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2a" + name: "scale5b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2a" + name: "res5b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2b" + name: "res5b_branch2b" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2b" + name: "bn5b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2b" + name: "scale5b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2b" + name: "res5b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2c" + name: "res5b_branch2c" + type: "Convolution" + convolution_param { + + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5b_branch2c" + top: "res5b_branch2c" + name: "bn5b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5b_branch2c" + top: "res5b_branch2c" + name: "scale5b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a" + bottom: "res5b_branch2c" + top: "res5b" + name: "res5b" + type: "Eltwise" + eltwise_param { + } +} + +layer { + bottom: "res5b" + top: "res5b" + name: "res5b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5b" + top: "res5c_branch2a" + name: "res5c_branch2a" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2a" + name: "bn5c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2a" + name: "scale5c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2a" + name: "res5c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2b" + name: "res5c_branch2b" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2b" + name: "bn5c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2b" + name: "scale5c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2b" + name: "res5c_branch2b_relu" + type: "ReLU" + relu_param { + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2c" + name: "res5c_branch2c" + type: "Convolution" + convolution_param { + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5c_branch2c" + top: "res5c_branch2c" + name: "bn5c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res5c_branch2c" + top: "res5c_branch2c" + name: "scale5c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5b" + bottom: "res5c_branch2c" + top: "res5c" + name: "res5c" + type: "Eltwise" + eltwise_param { + } +} + +layer { + bottom: "res5c" + top: "res5c" + name: "res5c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5c" + top: "pool5" + name: "pool5" + type: "Pooling" + pooling_param { + kernel_size: 7 + stride: 1 + pool: AVE + } +} + +layer { + bottom: "pool5" + top: "fc1000" + name: "fc1000" + type: "InnerProduct" + inner_product_param { + num_output: 1000 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + name: "loss3/top-1" + type: "Accuracy" + bottom: "fc1000" + bottom: "label" + top: "loss3/top-1" +} +layer { + name: "loss3/top-5" + type: "Accuracy" + bottom: "fc1000" + bottom: "label" + top: "loss3/top-5" + accuracy_param { + top_k: 5 + } +} diff --git a/models/intel_optimized_models/resnet50_v1/resnet50_fp32_perf.prototxt b/models/intel_optimized_models/resnet50_v1/resnet50_fp32_perf.prototxt new file mode 100755 index 000000000..b6bff5540 --- /dev/null +++ b/models/intel_optimized_models/resnet50_v1/resnet50_fp32_perf.prototxt @@ -0,0 +1,3253 @@ +name: "ResNet-50" +layer { + name: "data" + type: "DummyData" + top: "data" + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape { + dim:1 + dim: 3 + dim: 224 + dim: 224 + } + } +} + +layer { + bottom: "data" + top: "conv1" + name: "conv1" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 7 + pad: 3 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "conv1" + top: "conv1" + name: "bn_conv1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "conv1" + top: "conv1" + name: "scale_conv1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "conv1" + top: "conv1" + name: "conv1_relu" + type: "ReLU" + relu_param { + } +} + +layer { + bottom: "conv1" + top: "pool1" + name: "pool1" + type: "Pooling" + pooling_param { + kernel_size: 3 + stride: 2 + pool: MAX + } +} + +layer { + bottom: "pool1" + top: "res2a_branch1" + name: "res2a_branch1" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch1" + top: "res2a_branch1" + name: "bn2a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch1" + top: "res2a_branch1" + name: "scale2a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "pool1" + top: "res2a_branch2a" + name: "res2a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 64 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2a" + name: "bn2a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2a" + name: "scale2a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2a" + name: "res2a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2b" + name: "res2a_branch2b" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2b" + name: "bn2a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2b" + name: "scale2a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2b" + name: "res2a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2c" + name: "res2a_branch2c" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch2c" + top: "res2a_branch2c" + name: "bn2a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch2c" + top: "res2a_branch2c" + name: "scale2a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a_branch1" + bottom: "res2a_branch2c" + top: "res2a" + name: "res2a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res2a" + top: "res2a" + name: "res2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2a" + top: "res2b_branch2a" + name: "res2b_branch2a" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2a" + name: "bn2b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2a" + name: "scale2b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2a" + name: "res2b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2b" + name: "res2b_branch2b" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2b" + name: "bn2b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2b" + name: "scale2b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2b" + name: "res2b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2c" + name: "res2b_branch2c" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2b_branch2c" + top: "res2b_branch2c" + name: "bn2b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2b_branch2c" + top: "res2b_branch2c" + name: "scale2b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a" + bottom: "res2b_branch2c" + top: "res2b" + name: "res2b" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res2b" + top: "res2b" + name: "res2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2b" + top: "res2c_branch2a" + name: "res2c_branch2a" + type: "Convolution" + convolution_param { + + num_output: 64 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2a" + name: "bn2c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2a" + name: "scale2c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2a" + name: "res2c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2b" + name: "res2c_branch2b" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2b" + name: "bn2c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2b" + name: "scale2c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2b" + name: "res2c_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2c" + name: "res2c_branch2c" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2c_branch2c" + top: "res2c_branch2c" + name: "bn2c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res2c_branch2c" + top: "res2c_branch2c" + name: "scale2c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2b" + bottom: "res2c_branch2c" + top: "res2c" + name: "res2c" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res2c" + top: "res2c" + name: "res2c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2c" + top: "res3a_branch1" + name: "res3a_branch1" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch1" + top: "res3a_branch1" + name: "bn3a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch1" + top: "res3a_branch1" + name: "scale3a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2c" + top: "res3a_branch2a" + name: "res3a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2a" + name: "bn3a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2a" + name: "scale3a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2a" + name: "res3a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2b" + name: "res3a_branch2b" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2b" + name: "bn3a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2b" + name: "scale3a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2b" + name: "res3a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2c" + name: "res3a_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch2c" + top: "res3a_branch2c" + name: "bn3a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch2c" + top: "res3a_branch2c" + name: "scale3a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a_branch1" + bottom: "res3a_branch2c" + top: "res3a" + name: "res3a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3a" + top: "res3a" + name: "res3a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3a" + top: "res3b_branch2a" + name: "res3b_branch2a" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2a" + name: "bn3b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2a" + name: "scale3b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2a" + name: "res3b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2b" + name: "res3b_branch2b" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2b" + name: "bn3b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2b" + name: "scale3b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2b" + name: "res3b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2c" + name: "res3b_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3b_branch2c" + top: "res3b_branch2c" + name: "bn3b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3b_branch2c" + top: "res3b_branch2c" + name: "scale3b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a" + bottom: "res3b_branch2c" + top: "res3b" + name: "res3b" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3b" + top: "res3b" + name: "res3b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3b" + top: "res3c_branch2a" + name: "res3c_branch2a" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2a" + name: "bn3c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2a" + name: "scale3c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2a" + name: "res3c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2b" + name: "res3c_branch2b" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2b" + name: "bn3c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2b" + name: "scale3c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2b" + name: "res3c_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2c" + name: "res3c_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3c_branch2c" + top: "res3c_branch2c" + name: "bn3c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3c_branch2c" + top: "res3c_branch2c" + name: "scale3c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3b" + bottom: "res3c_branch2c" + top: "res3c" + name: "res3c" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3c" + top: "res3c" + name: "res3c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3c" + top: "res3d_branch2a" + name: "res3d_branch2a" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2a" + name: "bn3d_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2a" + name: "scale3d_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2a" + name: "res3d_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2b" + name: "res3d_branch2b" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2b" + name: "bn3d_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2b" + name: "scale3d_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2b" + name: "res3d_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2c" + name: "res3d_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3d_branch2c" + top: "res3d_branch2c" + name: "bn3d_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res3d_branch2c" + top: "res3d_branch2c" + name: "scale3d_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3c" + bottom: "res3d_branch2c" + top: "res3d" + name: "res3d" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3d" + top: "res3d" + name: "res3d_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3d" + top: "res4a_branch1" + name: "res4a_branch1" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch1" + top: "res4a_branch1" + name: "bn4a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch1" + top: "res4a_branch1" + name: "scale4a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3d" + top: "res4a_branch2a" + name: "res4a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2a" + name: "bn4a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2a" + name: "scale4a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2a" + name: "res4a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2b" + name: "res4a_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2b" + name: "bn4a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2b" + name: "scale4a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2b" + name: "res4a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2c" + name: "res4a_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch2c" + top: "res4a_branch2c" + name: "bn4a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch2c" + top: "res4a_branch2c" + name: "scale4a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a_branch1" + bottom: "res4a_branch2c" + top: "res4a" + name: "res4a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4a" + top: "res4a" + name: "res4a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4a" + top: "res4b_branch2a" + name: "res4b_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2a" + name: "bn4b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2a" + name: "scale4b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2a" + name: "res4b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2b" + name: "res4b_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2b" + name: "bn4b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2b" + name: "scale4b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2b" + name: "res4b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2c" + name: "res4b_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4b_branch2c" + top: "res4b_branch2c" + name: "bn4b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4b_branch2c" + top: "res4b_branch2c" + name: "scale4b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a" + bottom: "res4b_branch2c" + top: "res4b" + name: "res4b" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4b" + top: "res4b" + name: "res4b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4b" + top: "res4c_branch2a" + name: "res4c_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2a" + name: "bn4c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2a" + name: "scale4c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2a" + name: "res4c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2b" + name: "res4c_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2b" + name: "bn4c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2b" + name: "scale4c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2b" + name: "res4c_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2c" + name: "res4c_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4c_branch2c" + top: "res4c_branch2c" + name: "bn4c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4c_branch2c" + top: "res4c_branch2c" + name: "scale4c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4b" + bottom: "res4c_branch2c" + top: "res4c" + name: "res4c" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4c" + top: "res4c" + name: "res4c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4c" + top: "res4d_branch2a" + name: "res4d_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2a" + name: "bn4d_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2a" + name: "scale4d_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2a" + name: "res4d_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2b" + name: "res4d_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2b" + name: "bn4d_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2b" + name: "scale4d_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2b" + name: "res4d_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2c" + name: "res4d_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4d_branch2c" + top: "res4d_branch2c" + name: "bn4d_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4d_branch2c" + top: "res4d_branch2c" + name: "scale4d_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4c" + bottom: "res4d_branch2c" + top: "res4d" + name: "res4d" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4d" + top: "res4d" + name: "res4d_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4d" + top: "res4e_branch2a" + name: "res4e_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2a" + name: "bn4e_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2a" + name: "scale4e_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2a" + name: "res4e_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2b" + name: "res4e_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2b" + name: "bn4e_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2b" + name: "scale4e_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2b" + name: "res4e_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2c" + name: "res4e_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4e_branch2c" + top: "res4e_branch2c" + name: "bn4e_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4e_branch2c" + top: "res4e_branch2c" + name: "scale4e_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4d" + bottom: "res4e_branch2c" + top: "res4e" + name: "res4e" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4e" + top: "res4e" + name: "res4e_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4e" + top: "res4f_branch2a" + name: "res4f_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2a" + name: "bn4f_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2a" + name: "scale4f_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2a" + name: "res4f_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2b" + name: "res4f_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2b" + name: "bn4f_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2b" + name: "scale4f_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2b" + name: "res4f_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2c" + name: "res4f_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4f_branch2c" + top: "res4f_branch2c" + name: "bn4f_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res4f_branch2c" + top: "res4f_branch2c" + name: "scale4f_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4e" + bottom: "res4f_branch2c" + top: "res4f" + name: "res4f" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4f" + top: "res4f" + name: "res4f_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4f" + top: "res5a_branch1" + name: "res5a_branch1" + type: "Convolution" + convolution_param { + + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch1" + top: "res5a_branch1" + name: "bn5a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch1" + top: "res5a_branch1" + name: "scale5a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4f" + top: "res5a_branch2a" + name: "res5a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2a" + name: "bn5a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2a" + name: "scale5a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2a" + name: "res5a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2b" + name: "res5a_branch2b" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2b" + name: "bn5a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2b" + name: "scale5a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2b" + name: "res5a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2c" + name: "res5a_branch2c" + type: "Convolution" + convolution_param { + + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch2c" + top: "res5a_branch2c" + name: "bn5a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch2c" + top: "res5a_branch2c" + name: "scale5a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a_branch1" + bottom: "res5a_branch2c" + top: "res5a" + name: "res5a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res5a" + top: "res5a" + name: "res5a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5a" + top: "res5b_branch2a" + name: "res5b_branch2a" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2a" + name: "bn5b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2a" + name: "scale5b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2a" + name: "res5b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2b" + name: "res5b_branch2b" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2b" + name: "bn5b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2b" + name: "scale5b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2b" + name: "res5b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2c" + name: "res5b_branch2c" + type: "Convolution" + convolution_param { + + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5b_branch2c" + top: "res5b_branch2c" + name: "bn5b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5b_branch2c" + top: "res5b_branch2c" + name: "scale5b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a" + bottom: "res5b_branch2c" + top: "res5b" + name: "res5b" + type: "Eltwise" + eltwise_param { + } +} + +layer { + bottom: "res5b" + top: "res5b" + name: "res5b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5b" + top: "res5c_branch2a" + name: "res5c_branch2a" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2a" + name: "bn5c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2a" + name: "scale5c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2a" + name: "res5c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2b" + name: "res5c_branch2b" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2b" + name: "bn5c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2b" + name: "scale5c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2b" + name: "res5c_branch2b_relu" + type: "ReLU" + relu_param { + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2c" + name: "res5c_branch2c" + type: "Convolution" + convolution_param { + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5c_branch2c" + top: "res5c_branch2c" + name: "bn5c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res5c_branch2c" + top: "res5c_branch2c" + name: "scale5c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5b" + bottom: "res5c_branch2c" + top: "res5c" + name: "res5c" + type: "Eltwise" + eltwise_param { + } +} + +layer { + bottom: "res5c" + top: "res5c" + name: "res5c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5c" + top: "pool5" + name: "pool5" + type: "Pooling" + pooling_param { + kernel_size: 7 + stride: 1 + pool: AVE + } +} + +layer { + bottom: "pool5" + top: "fc1000" + name: "fc1000" + type: "InnerProduct" + inner_product_param { + num_output: 1000 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} + diff --git a/models/intel_optimized_models/resnet50_v1/resnet50_int8_acc.prototxt b/models/intel_optimized_models/resnet50_v1/resnet50_int8_acc.prototxt new file mode 100644 index 000000000..aab140a98 --- /dev/null +++ b/models/intel_optimized_models/resnet50_v1/resnet50_int8_acc.prototxt @@ -0,0 +1,31529 @@ +name: "ResNet-50" +layer { + name: "data" + type: "Data" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + scale: 0.0078125 + mirror: false + crop_size: 224 + mean_value: 104.0 + mean_value: 117.0 + mean_value: 123.0 + random_resize_param { + min_size: 256 + max_size: 256 + resize_param { + interp_mode: CUBIC + } + } + } + data_param { + source: "examples/imagenet/ilsvrc12_val_lmdb" + batch_size: 50 + backend: LMDB + } +} +layer { + name: "conv1" + type: "Convolution" + bottom: "data" + top: "conv1" + convolution_param { + num_output: 64 + pad: 3 + kernel_size: 7 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 107.655629139 + scale_out: 36.3122416624 + scale_params: 1629.96413222 + scale_params: 250.628354807 + scale_params: 309.605453236 + scale_params: 159.631326843 + scale_params: 822.373506278 + scale_params: 613.281454451 + scale_params: 201.263328755 + scale_params: 448.854206982 + scale_params: 332.76920822 + scale_params: 1569.89514441 + scale_params: 812.354535047 + scale_params: 245.707504129 + scale_params: 1143.53598512 + scale_params: 334.262174522 + scale_params: 799.239823082 + scale_params: 385.446604763 + scale_params: 4434.43711721 + scale_params: 2567.22559952 + scale_params: 495.852263606 + scale_params: 582.763291794 + scale_params: 3469.66554599 + scale_params: 621.870162851 + scale_params: 353.605339164 + scale_params: 1711.31216494 + 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+ top: "res2b" + eltwise_param { + } +} +layer { + name: "res2b_relu" + type: "ReLU" + bottom: "res2b" + top: "res2b" + relu_param { + } +} +layer { + name: "res2c_branch2a" + type: "Convolution" + bottom: "res2b" + top: "res2c_branch2a" + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 67.7806526679 + scale_out: 83.6919147579 + scale_params: 901.576540634 + scale_params: 807.837762617 + scale_params: 477.69777406 + scale_params: 462.204466344 + scale_params: 897.94325679 + scale_params: 988.301179322 + scale_params: 434.209728148 + scale_params: 719.438413612 + scale_params: 859.299959992 + scale_params: 910.517058627 + scale_params: 997.502007279 + scale_params: 1147.65778066 + scale_params: 909.764884323 + scale_params: 665.480993231 + scale_params: 608.635758078 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bottom: "data" + top: "conv1" + convolution_param { + num_output: 64 + pad: 3 + kernel_size: 7 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 107.655629139 + scale_out: 36.3122416624 + scale_params: 1629.96413222 + scale_params: 250.628354807 + scale_params: 309.605453236 + scale_params: 159.631326843 + scale_params: 822.373506278 + scale_params: 613.281454451 + scale_params: 201.263328755 + scale_params: 448.854206982 + scale_params: 332.76920822 + scale_params: 1569.89514441 + scale_params: 812.354535047 + scale_params: 245.707504129 + scale_params: 1143.53598512 + scale_params: 334.262174522 + scale_params: 799.239823082 + scale_params: 385.446604763 + scale_params: 4434.43711721 + scale_params: 2567.22559952 + scale_params: 495.852263606 + scale_params: 582.763291794 + scale_params: 3469.66554599 + scale_params: 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decay_mult: 0.0 + } + param { + decay_mult: 0.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "res2c_branch2a_relu" + type: "ReLU" + bottom: "res2c_branch2a" + top: "res2c_branch2a" + relu_param { + } +} +layer { + name: "res2c_branch2b" + type: "Convolution" + bottom: "res2c_branch2a" + top: "res2c_branch2b" + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 83.6919147579 + scale_out: 113.967005061 + scale_params: 395.282894767 + scale_params: 770.333215744 + scale_params: 771.438097366 + scale_params: 679.782306331 + scale_params: 780.291405202 + scale_params: 742.270705729 + scale_params: 952.525499043 + scale_params: 644.553294408 + scale_params: 843.288312643 + scale_params: 627.871317857 + scale_params: 714.892765401 + scale_params: 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"$j" -gt "14" ];then + continue + fi + + sed -i "1,/dim/s/dim.*/dim:$bs/" $prototxt + + rm -rf logs/bs${bs}_inst$j + mkdir logs/bs${bs}_inst$j + + NUM_INSTANCE=$j + INTERVAL=$(($NUM_CORE / $NUM_INSTANCE)) + export KMP_HW_SUBSET=1t + export KMP_AFFINITY=granularity=fine,compact,1,0 + export OMP_NUM_THREADS=$INTERVAL + + sudo sh -c 'echo 3 > /proc/sys/vm/drop_caches' + + for ((i = 0; i < $NUM_CORE; i += $INTERVAL)) + do + end=$(($i + $INTERVAL - 1)) + NUMA_NUM=$(($i/28)) + echo "KMP_HW_SUBSET=1T KMP_AFFINITY=granularity=fine,compact,1,0 OMP_NUM_THREADS=$INTERVAL numactl -C $i-$end -m $NUMA_NUM ./build/tools/caffe time --forward_only --phase TEST -model $prototxt -iterations $iterations >logs/bs${bs}_inst${j}/numa$i 2>&1 &" >>temp.sh + + done + + echo "wait" >>temp.sh + bash ./temp.sh + + rm -f logs/bs${bs}_inst${j}/latency + + for ((i = 0; i < $NUM_CORE; i += $INTERVAL)) + do + grep "###" logs/bs${bs}_inst${j}/numa$i |awk -F' ' '{ print $5 }'|awk -F'#' '{ print $4 }' | awk -F':' -v iter=$iterations -v s=$skip 'BEGIN {sum=0} {if($1>=s && $1<(iter-s)) sum+=$2;} END{ print (sum /(iter-s-s))'} >> logs/bs${bs}_inst${j}/latency + done + + latency=$(cat logs/bs${bs}_inst${j}/latency | awk 'BEGIN {sum=0} {sum+=$1} END{print (sum / NR)}') + + fps=`echo "1000 * $bs * $j / $latency" | bc ` + echo "bs: $bs instance: $j latency: $latency fps: $fps" + + done +done + + diff --git a/run_accuracy.sh b/run_accuracy.sh new file mode 100755 index 000000000..80cd1b919 --- /dev/null +++ b/run_accuracy.sh @@ -0,0 +1,13 @@ +#Prepare data: +#Create raw lmdb of imagenet following https://github.com/intel/caffe/wiki/How-to-create-ImageNet-LMDB, please set "RESIZE=false" in create_imagenet.sh to create raw lmdb +#ln -s ilsvrc12_val_lmdb "examples/imagenet/ilsvrc12_val_lmdb" + +#expected result: +# loss3/top-1 = 0.75522 +# loss3/top-5 = 0.926621 + +prototxt="./models/intel_optimized_models/resnet50_v1/resnet50_int8_acc.prototxt" +sed -i "1,/dim/s/dim.*/dim:$bs/" $prototxt + +./build/tools/caffe test -model $prototxt -weights ./default_resnet_50_16_nodes_iter_56300.caffemodel --iterations 1000 --engine MKLDNN + diff --git a/scripts/calibrator.py b/scripts/calibrator.py index 2b9c94353..1c3b4068c 100644 --- a/scripts/calibrator.py +++ b/scripts/calibrator.py @@ -47,7 +47,8 @@ import sampling import numpy as np -int8_layers = ["Convolution", "ReLU", "Split", "Concat", "Pooling", "Eltwise"] +int8_layers = ["Convolution", "ReLU", "Split", "Concat", "Pooling", "Eltwise", "InnerProduct"] +quantize_layers = ["Convolution", "InnerProduct"] def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) @@ -120,7 +121,7 @@ def analyze_conv_output_with_relu(compiled_net): convs_output_with_relu = [] for _, layer in enumerate(compiled_net.layer): if layer.type == 'Convolution': - if layer.convolution_param.relu: + if layer.convolution_param.relu and isclose(layer.convolution_param.negative_slope, 0.0): convs_output_with_relu.append(layer.name) return convs_output_with_relu @@ -147,7 +148,7 @@ def is_convolution_input_u8(l, net, end, interesting_layers, convs_output_with_r break for input_layer in input_layers: - if input_layer[2] == "Convolution" and (input_layer[1] not in convs_output_with_relu): + if input_layer[2] in quantize_layers and (input_layer[1] not in convs_output_with_relu): return False processed_layers = input_layers # sync inputLayers change @@ -175,7 +176,7 @@ def analyze_conv_output_with_relu_from_net(convs_output_with_relu, compiled_net, if len(compiled_relu_layers) != 0: relu_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'ReLU'] for (l, index) in relu_layers: - conv_inputs = get_input_convolutions(l, net, index, ["Convolution"]) + conv_inputs = get_input_convolutions(l, net, index, ["Convolution"]) # FIXME new_convs_output_with_relu.append(conv_inputs[0][1]) return new_convs_output_with_relu @@ -208,8 +209,8 @@ def transform_convolutions(model_path, compiled_model_path, top_blobs_map, botto # extended_convs_output_with_relu = analyze_conv_output_with_relu_from_net(convs_output_with_relu, compiled_net, net) new_net = copy.deepcopy(net) - convolution_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'Convolution'] - compiled_convolution_layers = [(value, index) for index, value in enumerate(compiled_net.layer) if value.type == 'Convolution'] + convolution_layers = [(value, index) for index, value in enumerate(net.layer) if value.type in quantize_layers] + compiled_convolution_layers = [(value, index) for index, value in enumerate(compiled_net.layer) if value.type in quantize_layers] u8_max = 255 s8_max = 127 @@ -292,7 +293,7 @@ def transform_convolutions(model_path, compiled_model_path, top_blobs_map, botto non_int8_layers = [(value, index) for index, value in enumerate(net.layer) if value.type not in int8_layers] last_conv_indexes = find_last_conv_layers(top_blobs_map, bottom_blobs_map, convolution_layers, non_int8_layers) - + # TODO: support last convolution without consumer for index in last_conv_indexes: new_net.layer[index].quantization_param.bw_layer_out = 32 @@ -331,7 +332,7 @@ def generate_sample_impl(input_model, quantized_model, inputs_max, outputs_max, txtf.Merge(s, net) new_net = copy.deepcopy(net) - convolution_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'Convolution'] + convolution_layers = [(value, index) for index, value in enumerate(net.layer) if value.type in quantize_layers] first_conv = False if enable_1st_conv else True for (l, index) in convolution_layers: if first_conv: @@ -343,7 +344,6 @@ def generate_sample_impl(input_model, quantized_model, inputs_max, outputs_max, new_net.layer[index].quantization_param.scale_in[:] = inputs_max[l.name] new_net.layer[index].quantization_param.scale_out[:] = outputs_max[l.name] new_net.layer[index].quantization_param.scale_params[:] = params_max[l.name] - #new_net.layer[index].quantization_param.min_in[:] = inputs_min[l.name] with open(quantized_model, 'w') as f: f.write(str(new_net)) diff --git a/scripts/conv_param.csv b/scripts/conv_param.csv new file mode 100644 index 000000000..843a0a587 --- /dev/null +++ b/scripts/conv_param.csv @@ -0,0 +1,36 @@ +Layer IFM OFM IH IW OH OW KH KW S Multiplier post-conv MACs %MACs pad_h pad_w +conv1_1 3 64 300 300 300 300 3 3 1 154829568 0.52% 1 1 +conv1_2 64 64 300 300 300 300 3 3 1 3303030784 11.02% 1 1 +conv2_1 64 128 150 150 150 150 3 3 1 1644167168 5.49% 1 1 +conv2_2 128 128 150 150 150 150 3 3 1 3288334336 10.97% 1 1 +conv3_1 128 256 75 75 75 75 3 3 1 1629519872 5.44% 1 1 +conv3_2 256 256 75 75 75 75 3 3 1 3259039744 10.88% 1 1 +conv3_3 256 256 75 75 75 75 3 3 1 3259039744 10.88% 1 1 +conv4_1 256 512 38 38 38 38 3 3 1 1644167168 5.49% 1 1 +conv4_2 512 512 38 38 38 38 3 3 1 3288334336 10.97% 1 1 +conv4_3 512 512 38 38 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b/scripts/fullyconnected2_weight.txt new file mode 100755 index 000000000..2a4e0c138 --- /dev/null +++ b/scripts/fullyconnected2_weight.txt @@ -0,0 +1,2 @@ +0.05475 0.04138 0.01032 0.04404 -0.00828 0.05638 0.00594 0.02925 0.00156 0.04180 0.14186 -0.00675 -0.00535 0.02814 -0.00675 -0.00425 0.00638 0.00748 -0.02885 0.00966 0.06460 0.02220 0.00242 -0.00630 -0.04709 0.07324 -0.00049 0.08063 -0.00550 0.34419 0.11215 0.01746 -0.00040 0.00121 0.01598 0.08494 0.00459 0.01049 -0.00731 0.03127 0.00486 0.06990 0.00063 0.02583 0.07850 -0.01284 0.00192 0.04871 -0.00957 -0.00578 0.00155 -0.01310 0.04457 -0.00886 0.00675 0.06613 0.01257 0.00345 -0.00662 0.07155 -0.01610 0.00334 0.01016 0.03726 -0.01639 -0.00059 0.06772 0.00034 -0.00375 0.00376 0.00265 0.06986 0.06801 -0.00090 -0.00260 0.13769 0.00718 -0.00732 0.00158 0.00042 -0.00477 -0.00241 0.06637 -0.03738 0.07309 -0.00386 0.02818 -0.00557 -0.00716 0.01844 -0.00269 0.15258 -0.00738 -0.01102 0.00486 -0.00256 0.00059 0.08059 0.07076 0.03567 +-0.05857 -0.04227 -0.02106 -0.05449 0.00624 -0.06212 0.00664 -0.03500 0.01170 -0.05479 -0.14921 -0.00158 -0.00732 -0.02731 -0.00564 0.00706 0.00205 0.00091 0.01503 0.00318 -0.06232 -0.02818 -0.00590 -0.00043 0.03822 -0.06244 -0.01345 -0.09005 -0.00817 -0.33807 -0.09574 -0.01528 0.00001 0.00975 -0.02421 -0.07780 -0.00316 -0.01251 -0.00497 -0.03225 -0.00156 -0.07102 -0.00032 -0.01176 -0.08131 -0.00242 -0.00351 -0.05073 0.00902 -0.00383 -0.00504 -0.00301 -0.05006 -0.00440 -0.02233 -0.07276 -0.00396 0.01514 -0.00065 -0.06951 0.01994 -0.00622 0.00606 -0.03784 0.02202 -0.00954 -0.07022 0.00964 0.00542 0.00191 0.00003 -0.06577 -0.06617 -0.00287 0.00510 -0.12928 -0.00364 -0.00414 0.00200 -0.00440 0.00480 -0.01200 -0.06600 0.03217 -0.06861 0.00284 -0.02132 0.00463 0.01788 -0.01700 -0.01470 -0.14685 0.00858 -0.00594 -0.01149 -0.00973 -0.00171 -0.07285 -0.07734 -0.02677 diff --git a/scripts/gen_header.py b/scripts/gen_header.py new file mode 100644 index 000000000..f88704359 --- /dev/null +++ b/scripts/gen_header.py @@ -0,0 +1,59 @@ +import os +import sys + +def readFile(filePath): + file = open(filePath, 'r') + lines = file.readlines() + file.close() + return lines + +def writeFile(filePath, lines): + file = open(filePath, 'w') + for line in lines: + file.write(line) + file.close() + +def gen_header(input_file): + lines = readFile(input_file) + outputs = [] + for index in range(1, len(lines)): + line = lines[index].strip() + output = "" + items = line.split("\t") + if len(items) == 1: continue + #['conv1_1', '3', '64', '300', '300', '300', '300', '3', '3', '1', '', '', '154829568', '0.52%', '1', '1'] + conv_name = items[0] + ic = items[1] + oc = items[2] + ih = items[3] + iw = items[4] + kh = items[7] + kw = items[8] + ph = items[14] + pw = items[15] + s = items[9] + output += "# if defined(SSD_VGG16_" + conv_name.upper() + "_" + kh + "X" + kw + ")\n" + output += " # define NUM_IFMs " + ic + "\n" + output += " # define NUM_OFMs " + oc + "\n" + output += " # define KERNEL_H " + kh + "\n" + output += " # define KERNEL_W " + kw + "\n" + output += " # define IFM_H_NOPAD " + ih + "\n" + output += " # define IFM_W_NOPAD " + iw + "\n" + output += " # define PAD_H " + ph + "\n" + output += " # define PAD_W " + pw + "\n" + output += " # define STRIDE_H " + s + "\n" + output += " # define STRIDE_W " + s + "\n" + # FIXME: enhance to detect whether RELU and RESIDUAL is after convolution + output += " # define RELU 1\n" + output += " # define RESIDUAL 0\n" + output += "#endif\n\n" + outputs.append(output) + + writeFile("header.h", outputs) + +if __name__ == '__main__': + if len(sys.argv) != 2: + print "Usgae: python gen_header.py input_file" + sys.exit(0) + + gen_header(sys.argv[1]) diff --git a/scripts/optimze_input_u8.diff b/scripts/optimze_input_u8.diff new file mode 100644 index 000000000..9f2b090f3 --- /dev/null +++ b/scripts/optimze_input_u8.diff @@ -0,0 +1,117 @@ +diff --git a/scripts/calibrator.py b/scripts/calibrator.py +index c79b1fd..9be289e 100644 +--- a/scripts/calibrator.py ++++ b/scripts/calibrator.py +@@ -109,6 +109,34 @@ def get_input_convolutions(l, net, end, interesting_layers): + + return all_input_layers + ++def is_convolution_input_u8(l, net, end, interesting_layers, convs_output_with_relu): ++ all_input_layers = [] ++ input_layers = get_input_layers(l, net, end) ++ while True: ++ if len(input_layers) == 0: ++ break ++ ++ for input_layer in input_layers: ++ if input_layer[2] == "Convolution" and (input_layer[1] not in convs_output_with_relu): ++ return False ++ ++ processed_layers = input_layers # sync inputLayers change ++ for lp in processed_layers: ++ if lp[2] not in int8_layers: ++ input_layers.remove(lp) ++ continue ++ if lp[2] in interesting_layers: ++ input_layers.remove(lp) ++ if lp not in all_input_layers: ++ all_input_layers.append(lp) ++ continue ++ ++ new_input_layers = get_input_layers(net.layer[lp[0]], net, lp[0]) ++ input_layers.remove(lp) ++ input_layers.extend(new_input_layers) ++ ++ return True ++ + def analyze_conv_output_with_relu(compiled_net): + convs_output_with_relu = [] + for _, layer in enumerate(compiled_net.layer): +@@ -120,6 +148,7 @@ def analyze_conv_output_with_relu(compiled_net): + + def analyze_conv_input_u8(conv_inputs, convs_output_with_relu): + for conv_input in conv_inputs: ++ print conv_input + if conv_input[1] not in convs_output_with_relu: + return False + return True +@@ -152,19 +181,9 @@ def transform_convolutions(model_path, compiled_model_path, use_unsigned_range, + u8_max = 255 + s8_max = 127 + for (l, index) in convolution_layers: +- for si in range(0, len(new_net.layer[index].quantization_param.scale_out)): +- if l.name in convs_output_with_relu: # u8 +- new_net.layer[index].quantization_param.scale_out[si] = u8_max / new_net.layer[index].quantization_param.scale_out[si] +- else: # s8 +- if use_unsigned_range: +- new_net.layer[index].quantization_param.scale_out[si] = u8_max / new_net.layer[index].quantization_param.scale_out[si] +- else: +- new_net.layer[index].quantization_param.scale_out[si] = s8_max / new_net.layer[index].quantization_param.scale_out[si] +- + index_in_compiled_net = find_index_by_name(l.name, compiled_convolution_layers) + assert(index_in_compiled_net >= 0) +- conv_inputs = get_input_convolutions(l, compiled_net, index_in_compiled_net, ["Convolution"]) +- conv_input_u8 = analyze_conv_input_u8(conv_inputs, convs_output_with_relu) ++ conv_input_u8 = is_convolution_input_u8(l, compiled_net, index_in_compiled_net, ["Convolution"], convs_output_with_relu) + for si in range(0, len(new_net.layer[index].quantization_param.scale_in)): + if conv_input_u8: # u8 + new_net.layer[index].quantization_param.scale_in[si] = u8_max / new_net.layer[index].quantization_param.scale_in[si] +@@ -185,48 +204,6 @@ def transform_convolutions(model_path, compiled_model_path, use_unsigned_range, + for si in range(0, len(new_net.layer[index_bottom_layer].quantization_param.scale_out)): + new_net.layer[index_bottom_layer].quantization_param.scale_out[si] = new_net.layer[index].quantization_param.scale_in[si] + +- concat_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'Concat'] +- if len(concat_layers) > 0: +- compiled_concat_layers = [(value, index) for index, value in enumerate(compiled_net.layer) if value.type == 'Concat'] +- concat_layers.reverse() +- if unify_concat_scales: +- for (l, index) in concat_layers: +- index_in_compiled_net = find_index_by_name(l.name, compiled_concat_layers) +- assert(index_in_compiled_net >= 0) +- conv_inputs = get_input_convolutions(l, compiled_net, index_in_compiled_net, ["Convolution"]) +- min_concat_scale = sys.float_info.max +- concat_input_indexes = [] +- for conv_input in conv_inputs: +- index_in_net = find_index_by_name(conv_input[1], convolution_layers) +- assert(index_in_net >= 0) +- concat_input_indexes.append(index_in_net) +- if new_net.layer[index_in_net].quantization_param.scale_out[0] < min_concat_scale: +- min_concat_scale = new_net.layer[index_in_net].quantization_param.scale_out[0] +- +- for concat_input_index in concat_input_indexes: +- new_net.layer[concat_input_index].quantization_param.scale_out[0] = min_concat_scale +- else: +- if concat_use_fp32: +- for (l, index) in concat_layers: +- index_in_compiled_net = find_index_by_name(l.name, compiled_concat_layers) +- assert(index_in_compiled_net >= 0) +- conv_inputs = get_input_convolutions(l, compiled_net, index_in_compiled_net, ["Convolution"]) +- for conv_input in conv_inputs: +- index_in_net = find_index_by_name(conv_input[1], convolution_layers) +- assert(index_in_net >= 0) +- new_net.layer[index_in_net].quantization_param.bw_layer_out = 32 +- new_net.layer[index_in_net].quantization_param.scale_out[:] = [1.0] +- +- # Force FP32 output for convolution acting as FC +- fc_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'InnerProduct'] +- if len(fc_layers) == 0 and convolution_layers[-1][0].convolution_param.num_output == 1000: +- new_net.layer[convolution_layers[-1][1]].quantization_param.bw_layer_out = 32 +- new_net.layer[convolution_layers[-1][1]].quantization_param.scale_out[:] = [1.0] +- +- with open(model_path, 'w') as f: +- f.write(str(new_net)) +- +- + def generate_sample_bak(sample_path, input_model, weights, + quantized_model, detection, scaling_mode, iterations=1, error_margin=1): + cmd = '{0} quantize -model {1} -weights {2} -model_quantized {3} -iterations {4} -error_margin {5} ' \ diff --git a/scripts/output.csv b/scripts/output.csv new file mode 100644 index 000000000..34d12fa64 --- /dev/null +++ b/scripts/output.csv @@ -0,0 +1,525 @@ +# if defined(SSD_VGG16_CONV1_1_3X3) + # define NUM_IFMs 3 + # define NUM_OFMs 64 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 300 + # define IFM_W_NOPAD 300 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV1_2_3X3) + # define NUM_IFMs 64 + # define NUM_OFMs 64 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 300 + # define IFM_W_NOPAD 300 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV2_1_3X3) + # define NUM_IFMs 64 + # define NUM_OFMs 128 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 150 + # define IFM_W_NOPAD 150 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV2_2_3X3) + # define NUM_IFMs 128 + # define NUM_OFMs 128 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 150 + # define IFM_W_NOPAD 150 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV3_1_3X3) + # define NUM_IFMs 128 + # define NUM_OFMs 256 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 75 + # define IFM_W_NOPAD 75 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV3_2_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 256 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 75 + # define IFM_W_NOPAD 75 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV3_3_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 256 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 75 + # define IFM_W_NOPAD 75 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV4_1_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 512 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 38 + # define IFM_W_NOPAD 38 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV4_2_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 512 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 38 + # define IFM_W_NOPAD 38 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV4_3_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 512 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 38 + # define IFM_W_NOPAD 38 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV5_1_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 512 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 19 + # define IFM_W_NOPAD 19 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV5_2_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 512 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 19 + # define IFM_W_NOPAD 19 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV5_3_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 512 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 19 + # define IFM_W_NOPAD 19 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_FC6_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 1024 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 19 + # define IFM_W_NOPAD 19 + # define PAD_H 6 + # define PAD_W 6 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_FC7_1X1) + # define NUM_IFMs 1024 + # define NUM_OFMs 1024 + # define KERNEL_H 1 + # define KERNEL_W 1 + # define IFM_H_NOPAD 19 + # define IFM_W_NOPAD 19 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV6_1_1X1) + # define NUM_IFMs 1024 + # define NUM_OFMs 256 + # define KERNEL_H 1 + # define KERNEL_W 1 + # define IFM_H_NOPAD 19 + # define IFM_W_NOPAD 19 + # define PAD_H 0 + # define PAD_W 0 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV6_2_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 512 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 19 + # define IFM_W_NOPAD 19 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 2 + # define STRIDE_W 2 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV7_1_1X1) + # define NUM_IFMs 512 + # define NUM_OFMs 128 + # define KERNEL_H 1 + # define KERNEL_W 1 + # define IFM_H_NOPAD 10 + # define IFM_W_NOPAD 10 + # define PAD_H 0 + # define PAD_W 0 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV7_2_3X3) + # define NUM_IFMs 128 + # define NUM_OFMs 256 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 10 + # define IFM_W_NOPAD 10 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 2 + # define STRIDE_W 2 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV8_1_1X1) + # define NUM_IFMs 256 + # define NUM_OFMs 128 + # define KERNEL_H 1 + # define KERNEL_W 1 + # define IFM_H_NOPAD 5 + # define IFM_W_NOPAD 5 + # define PAD_H 0 + # define PAD_W 0 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV8_2_3X3) + # define NUM_IFMs 128 + # define NUM_OFMs 256 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 5 + # define IFM_W_NOPAD 5 + # define PAD_H 0 + # define PAD_W 0 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV9_1_1X1) + # define NUM_IFMs 256 + # define NUM_OFMs 128 + # define KERNEL_H 1 + # define KERNEL_W 1 + # define IFM_H_NOPAD 3 + # define IFM_W_NOPAD 3 + # define PAD_H 0 + # define PAD_W 0 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV9_2_3X3) + # define NUM_IFMs 128 + # define NUM_OFMs 256 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 3 + # define IFM_W_NOPAD 3 + # define PAD_H 0 + # define PAD_W 0 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV4_3_NORM_MBOX_LOC_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 16 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 38 + # define IFM_W_NOPAD 38 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV4_3_NORM_MBOX_CONF_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 84 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 38 + # define IFM_W_NOPAD 38 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_FC7_MBOX_LOC_3X3) + # define NUM_IFMs 1024 + # define NUM_OFMs 24 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 19 + # define IFM_W_NOPAD 19 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_FC7_MBOX_CONF_3X3) + # define NUM_IFMs 1024 + # define NUM_OFMs 126 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 19 + # define IFM_W_NOPAD 19 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV6_2_MBOX_LOC_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 24 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 10 + # define IFM_W_NOPAD 10 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV6_2_MBOX_CONF_3X3) + # define NUM_IFMs 512 + # define NUM_OFMs 126 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 10 + # define IFM_W_NOPAD 10 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV7_2_MBOX_LOC_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 24 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 5 + # define IFM_W_NOPAD 5 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV7_2_MBOX_CONF_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 126 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 5 + # define IFM_W_NOPAD 5 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV8_2_MBOX_LOC_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 16 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 3 + # define IFM_W_NOPAD 3 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV8_2_MBOX_CONF_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 84 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 3 + # define IFM_W_NOPAD 3 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV9_2_MBOX_LOC_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 16 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 1 + # define IFM_W_NOPAD 1 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + +# if defined(SSD_VGG16_CONV9_2_MBOX_CONF_3X3) + # define NUM_IFMs 256 + # define NUM_OFMs 84 + # define KERNEL_H 3 + # define KERNEL_W 3 + # define IFM_H_NOPAD 1 + # define IFM_W_NOPAD 1 + # define PAD_H 1 + # define PAD_W 1 + # define STRIDE_H 1 + # define STRIDE_W 1 + # define RELU 1 + # define RESIDUAL 0 +#endif + diff --git a/scripts/result.txt b/scripts/result.txt new file mode 100644 index 000000000..c68ff2e8b --- /dev/null +++ b/scripts/result.txt @@ -0,0 +1,75 @@ + +../tr_logs/tr_fp32_ssd_vgg16_throughput_bs252.log +Layer Count msec %% +Normalize 1 135.009 0.0222822695775 +DetectionEvaluate 1 5.32736 0.00087924265535 +Convolution 35 5146.50372 0.849393620206 +Flatten 13 0.03591 5.92668859503e-06 +Reshape 1 0.00337 5.56194390567e-07 +DetectionOutput 1 553.417 0.0913375166303 +Softmax 1 8.2555 0.00136251121404 +PriorBox 6 0.18545 3.06071957658e-05 +Pooling 5 164.35504 0.0271256235339 +DummyData 2 0.00285 4.70372110717e-07 +Permute 12 39.43443 0.00650837055228 +Concat 3 6.46113 0.00106636328271 +Split 8 0.04194 6.9218969556e-06 +Total 6059.0327 + +../tr_logs/tr_int8_ssd_mobilenetv1_throughput_bs252.log +Layer Count msec %% +DetectionEvaluate 1 6.30355 0.0120020708097 +Convolution 47 348.43858 0.663433225718 +Reshape 1 0.00326 6.20709772104e-06 +DetectionOutput 1 158.455 0.301701125579 +Softmax 1 1.92074 0.00365712296832 +PriorBox 6 0.05314 0.000101179500888 +Split 7 0.02648 5.04183888507e-05 +Flatten 13 0.02424 4.61533891896e-05 +Permute 12 8.53202 0.0162451171466 +Concat 3 1.44396 0.00274932540653 +DummyData 2 0.00423 8.05399489571e-06 +Total 525.2052 + +/home/hshen14/ssd_int8.log +Layer Count msec %% +Normalize 1 164.555 0.0450713082986 +Convolution 35 3389.0968 0.928267307142 +Flatten 13 0.03861 1.05752071551e-05 +Reshape 1 0.00385 1.05450783598e-06 +Softmax 1 7.68872 0.00210592610095 +PriorBox 6 0.12256 3.35689559423e-05 +Pooling 5 39.45381 0.010806325144 +DummyData 2 0.00322 8.81952008275e-07 +Permute 12 43.33475 0.0118693073884 +Concat 3 6.65517 0.00182283867916 +Split 8 0.03982 1.09066239036e-05 +Total 3650.99231 + +/home/hshen14/ssd_int8.log +Layer Count msec %% +Normalize 1 163.488 0.0452009410172 +Convolution 35 3399.74266 0.939956250295 +Flatten 1 0.00546 1.50957341183e-06 +Reshape 1 4.19603 0.00116011269657 +Softmax 1 7.72603 0.00213608231997 +PriorBox 6 0.1521 4.20524021868e-05 +Pooling 5 39.98437 0.011054824513 +DummyData 2 0.00291 8.04552862351e-07 +Concat 3 1.57865 0.000436463015859 +Split 8 0.03964 1.09596135614e-05 +Total 3616.91585 + +/home/hshen14/ssd_int8.log +Layer Count msec %% +Normalize 1 163.535 0.0454561267921 +Convolution 35 3381.03186 0.939790337948 +Flatten 1 0.00476 1.32308780096e-06 +Reshape 1 4.15656 0.00115535584663 +Softmax 1 7.44192 0.00206855327053 +PriorBox 6 0.13238 3.67962947671e-05 +Pooling 5 39.74109 0.0110464183563 +DummyData 2 0.00304 8.44997251035e-07 +Concat 3 1.56386 0.000434689934541 +Split 8 0.03437 9.55347220989e-06 +Total 3597.64484 diff --git a/scripts/sampling.py b/scripts/sampling.py index f42f45e68..b88213720 100644 --- a/scripts/sampling.py +++ b/scripts/sampling.py @@ -11,12 +11,13 @@ calibration_algos = ["DIRECT", "KL", "MAXP"] scaling_modes = ["SINGLE", "MULTIPLE"] +sampling_layers = ["Convolution", "InnerProduct"] def get_blob_map(test_net, enable_first_conv = False): layers = list(test_net._layer_names) conv_layers = [] for idx in range(0, len(layers)): - if test_net.layers[idx].type == "Convolution": + if test_net.layers[idx].type in sampling_layers : conv_layers.append(layers[idx]) if not enable_first_conv: @@ -330,7 +331,7 @@ def calibrate_activations(blobs, conv_top_blob_layer_map, conv_bottom_blob_layer conv_blobs_max = {} conv_blobs_min = {} for k, v in conv_blobs.items(): - print "Calibrate activation for", k + print "Calibrate activation for", k, v[0].shape if is_wino_conv: if k in winograd_bottoms: v_max = calibrate_max((np_abs(v), calibration_algo, scaling_mode, maxp, 1, True)) @@ -360,7 +361,7 @@ def calibrate_activations(blobs, conv_top_blob_layer_map, conv_bottom_blob_layer def calibrate_parameters(params, winograd_convolutions, calibration_algo="DIRECT", scaling_mode="SINGLE", is_wino_conv=False, maxp=0.99995): params_max = {} for k, v in params.items(): - print "Calibrate parameters for", k + print "Calibrate parameters for", k, v[0].shape if is_wino_conv: if k in winograd_convolutions: v_max = calibrate_max((v, calibration_algo, scaling_mode, maxp, 0, True)) @@ -416,7 +417,7 @@ def wino_transform_param(blob): #GgG^t sys.exit(0) (blobs, params, top_blobs_map, bottom_blobs_map, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, winograd_convolutions) = sample(sys.argv[1], sys.argv[2], False, 2, True) (inputs_max, outputs_max, inputs_min) = calibrate_activations(blobs, conv_top_blob_layer_map, conv_bottom_blob_layer_map,winograd_bottoms, "DIRECT", "SINGLE") -# params_max = calibrate_parameters(params, winograd_convolutions, "DIRECT", "MULTIPLE", True) + params_max = calibrate_parameters(params, winograd_convolutions, "DIRECT", "MULTIPLE", True) # for k, v in inputs_max.items(): # print "***", k, v[0], outputs_max[k][0] # for k, v in params_max.items(): diff --git a/src/caffe/layers/axpy_layer.cpp b/src/caffe/layers/axpy_layer.cpp new file mode 100644 index 000000000..b8f6ceb36 --- /dev/null +++ b/src/caffe/layers/axpy_layer.cpp @@ -0,0 +1,94 @@ +/* + * * Axpy Layer + * * + * * Created on: May 1, 2017 + * * Author: hujie + * */ + +#include "caffe/layers/axpy_layer.hpp" + +namespace caffe { + +template +void AxpyLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + CHECK_EQ(bottom[0]->shape(0), bottom[1]->shape(0)); + CHECK_EQ(bottom[0]->shape(1), bottom[1]->shape(1)); + if (bottom[0]->num_axes() == 4) { + CHECK_EQ(bottom[0]->shape(2), 1); + CHECK_EQ(bottom[0]->shape(3), 1); + } + CHECK(bottom[1]->shape() == bottom[2]->shape()); + top[0]->ReshapeLike(*bottom[1]); + int spatial_dim = bottom[1]->count(2); + if (spatial_sum_multiplier_.count() < spatial_dim) { + spatial_sum_multiplier_.Reshape(vector(1, spatial_dim)); + caffe_set(spatial_dim, Dtype(1), + spatial_sum_multiplier_.mutable_cpu_data()); + } +} + +template +void AxpyLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + int channel_dim = bottom[1]->channels(); + int spatial_dim = bottom[1]->count(2); + const Dtype* scale_data = bottom[0]->cpu_data(); + const Dtype* x_data = bottom[1]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + caffe_copy(bottom[2]->count(), bottom[2]->cpu_data(), top_data); + for (int n = 0; n < bottom[1]->num(); ++n) { + for (int c = 0; c < channel_dim; ++c) { + int scale_offset = n * channel_dim + c; + caffe_axpy(spatial_dim, scale_data[scale_offset], + x_data + scale_offset * spatial_dim, + top_data + scale_offset * spatial_dim); + } + } +} + +template +void AxpyLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + const int count = top[0]->count(); + const Dtype* top_diff = top[0]->cpu_diff(); + if (propagate_down[0]) { + int spatial_dim = bottom[1]->count(2); + const Dtype* x_data = bottom[1]->cpu_data(); + Dtype* x_diff = bottom[1]->mutable_cpu_diff(); + Dtype* scale_diff = bottom[0]->mutable_cpu_diff(); + caffe_mul(count, top_diff, x_data, x_diff); + caffe_set(bottom[0]->count(), Dtype(0), scale_diff); + caffe_cpu_gemv(CblasNoTrans, bottom[0]->count(), spatial_dim, Dtype(1), + x_diff, spatial_sum_multiplier_.cpu_data(), Dtype(1), scale_diff); + if (!propagate_down[1]) { + caffe_set(bottom[1]->count(), Dtype(0), x_diff); + } + } + if (propagate_down[0]) { + int channel_dim = bottom[1]->channels(); + int spatial_dim = bottom[1]->count(2); + const Dtype* scale_data = bottom[0]->cpu_data(); + Dtype* x_diff = bottom[1]->mutable_cpu_diff(); + for (int n = 0; n < bottom[1]->num(); ++n) { + for (int c = 0; c < channel_dim; ++c) { + int scale_offset = n * channel_dim + c; + caffe_cpu_scale(spatial_dim, scale_data[scale_offset], + top_diff + scale_offset * spatial_dim, + x_diff + scale_offset * spatial_dim); + } + } + } + if (propagate_down[2]) { + caffe_copy(count, top_diff, bottom[2]->mutable_cpu_diff()); + } +} + +#ifdef CPU_ONLY +STUB_GPU(AxpyLayer); +#endif + +INSTANTIATE_CLASS(AxpyLayer); +REGISTER_LAYER_CLASS(Axpy); + +} // namespace diff --git a/src/caffe/layers/mkldnn_concat_layer.cpp b/src/caffe/layers/mkldnn_concat_layer.cpp index 6143f57ae..53fab39af 100644 --- a/src/caffe/layers/mkldnn_concat_layer.cpp +++ b/src/caffe/layers/mkldnn_concat_layer.cpp @@ -173,7 +173,6 @@ void MKLDNNConcatLayer::InitConcatFwd(const vector*>& bottom, } } std::vector src_mfmts; - std::vector bottom_scales(num_concats_, 1.); std::vector >> mem_descr; std::vector prv_dt_tmp(num_concats_, memory::data_type::f32); @@ -213,7 +212,6 @@ void MKLDNNConcatLayer::InitConcatFwd(const vector*>& bottom, mem_descr[i]->prv_memory_pd()->desc().data.format); prv_dt = static_cast(mem_descr[i]->prv_memory_pd()->desc().data.data_type); scale = mem_descr[i]->get_scale(0); - bottom_scales[i] = scale; if(scale != 1.) scale = scale_min; } prv_src_mpd.reset(new memory::primitive_desc( @@ -252,15 +250,13 @@ void MKLDNNConcatLayer::InitConcatFwd(const vector*>& bottom, fwd_output_memory = fwd_top_data->create_output_memory(); memory::format base_mfmt = mfmt_out; - float base_scale = 1.; this->in_place_ = true; for(auto i = 0; i < num_concats_; i++){ if(i == 0) { base_mfmt = src_mfmts[i]; - base_scale = bottom_scales[i]; } - else if((concat_dimension != 0 && bottom[i]->shape()[concat_dimension - 1] != 1) || base_mfmt != src_mfmts[i] || fabs(base_scale-bottom_scales[i]) > FLT_MIN || different_input_dt || bottom[i]->prv_data() == NULL) { + else if((concat_dimension != 0 && bottom[i]->shape()[concat_dimension - 1] != 1) || base_mfmt != src_mfmts[i] || scale_top[0] != 1.|| different_input_dt || bottom[i]->prv_data() == NULL) { this->in_place_ = false; break; } @@ -270,13 +266,8 @@ void MKLDNNConcatLayer::InitConcatFwd(const vector*>& bottom, size_t offset = 0; for(auto i = 0; i < num_concats_; i++){ if(bottom[i]->prv_data()){ - if (scale_top[0] != 1.){ - memcpy(static_cast(fwd_output_memory->get_data_handle()) + offset, static_cast(mem_descr[i]->get_prv_memory()->get_data_handle()), sizeof(char) * bottom[i]->count()); - mem_descr[i]->get_prv_memory()->set_data_handle(static_cast(fwd_output_memory->get_data_handle())+offset); - }else{ caffe_copy(bottom[i]->count(), static_cast(mem_descr[i]->get_prv_memory()->get_data_handle()), static_cast(fwd_output_memory->get_data_handle()) + offset); mem_descr[i]->get_prv_memory()->set_data_handle(static_cast(fwd_output_memory->get_data_handle()) + offset); - } } else{ caffe_copy(bottom[i]->count(), bottom[i]->cpu_data(), static_cast(fwd_output_memory->get_data_handle()) + offset); bottom[i]->set_cpu_data(static_cast(fwd_output_memory->get_data_handle()) + offset); @@ -391,8 +382,11 @@ void MKLDNNConcatLayer::Forward_cpu(const vector*>& bottom, #ifdef DEBUG LOG(INFO) << "MKLDNNConcatLayer::Forward_cpu: " << this->layer_param_.name(); #endif - if ((NULL == concatFwd_pd) || (true == this->reshape)) + bool _mkldnn_primitive = false; + if ((NULL == concatFwd_pd) || (true == this->reshape)) { InitConcatFwd(bottom, top); + _mkldnn_primitive = true; + } for (auto i = 0; i < num_concats_; i++) { // making reorders if needed. @@ -406,6 +400,12 @@ void MKLDNNConcatLayer::Forward_cpu(const vector*>& bottom, PERFORMANCE_MEASUREMENT_BEGIN(); concatFwd.submit(); PERFORMANCE_MEASUREMENT_END_ID(perf_id_fw_); + + if(_mkldnn_primitive) { + for (auto i = 0; i < num_concats_; i++) { + CircleBuf::Instance()->DecRefCnt(bottom[i]->prv_data()); + } + } } } diff --git a/src/caffe/layers/mkldnn_convolution_layer.cpp b/src/caffe/layers/mkldnn_convolution_layer.cpp index 23873c197..81d7b86b4 100644 --- a/src/caffe/layers/mkldnn_convolution_layer.cpp +++ b/src/caffe/layers/mkldnn_convolution_layer.cpp @@ -140,6 +140,10 @@ void MKLDNNConvolutionLayer::init_properties(const vector*>& { conv_algorithm = algorithm::convolution_winograd; } + //else if(_conv_algorithm == "auto") + //{ + // conv_algorithm = algorithm::convolution_auto; + //} else { LOG(ERROR) << "Unsupported convolution algorithm."; @@ -205,7 +209,7 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* Dtype negative_slope = 0; if(relu) { - negative_slope = this->layer_param_.relu_param().negative_slope(); + negative_slope = this->layer_param_.convolution_param().negative_slope(); } int32_t g = std::max(this->group_, 1); @@ -269,7 +273,7 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* memory::data_type top_dt = memory::data_type::f32; if (this->need_quantize_) { if (this->bw_layer_out_ == 8) { - if (relu) { + if (relu && negative_slope == 0) { top_dt = memory::data_type::u8; } else { @@ -284,7 +288,8 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* if (this->layer_param_.convolution_param().fusion_type() == ConvolutionParameter::SUM_FUSION && bottom.size() > 1) { - is_sum = true; + if(relu) + is_sum = true; memory::data_type bottom_1_dt = memory::data_type::f32; if (const_cast(bottom[1]->prv_data()) != NULL){ @@ -478,8 +483,7 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* // --- init primitive and prv_memory descriptors ---------------------- if (this->need_quantize_){ - std::vector scale_bottom; - scale_bottom.push_back(this->scale_in_[0]); + std::vector scale_bottom(1, this->scale_in_[0]); fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_memory_pd, prv_fwd_bottom_data_memory_pd, bottom[0], this, scale_bottom)); } else { fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_memory_pd, prv_fwd_bottom_data_memory_pd, bottom[0], this)); @@ -488,8 +492,7 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* fwd_bottom_data_primitive = fwd_bottom_data->create_input(false); if (this->need_quantize_){ - std::vector scale_top; - scale_top.push_back(this->scale_out_[0]); + std::vector scale_top(1, this->scale_out_[0]); fwd_top_data.reset(new MKLDNNData(usr_top_data_memory_pd, prv_fwd_top_data_memory_pd, top[0], this, scale_top, 0, is_sum)); } else{ fwd_top_data.reset(new MKLDNNData(usr_top_data_memory_pd, prv_fwd_top_data_memory_pd, top[0], this)); @@ -515,7 +518,7 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* } fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, scale_weight, reorder_mask, false, false, true)); } else{ - fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, {1.}, 0, is_sum, is_wino)); + fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, {1.}, 0, is_sum, is_wino, true)); } fwd_weights_data->name = "fwd_weights_data @ " + this->layer_param_.name(); fwd_weights_data_primitive = fwd_weights_data->create_input(true); @@ -541,9 +544,9 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* else scale_bias[i] = this->scale_in_[0] * this->scale_params_[i]; } - fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, scale_bias, reorder_mask)); + fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, scale_bias, reorder_mask, false, false, true)); } else{ - fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this)); + fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, {1.}, 0, false, false, true)); } fwd_bias_data->name = "fwd_bias_data @ " + this->layer_param_.name(); fwd_bias_data_primitive = fwd_bias_data->create_input(true); @@ -580,19 +583,33 @@ void MKLDNNConvolutionLayer::Forward_cpu(const vector*>& bott { VLOG(1) << "MKLDNNConvolutionLayer::Forward_cpu: " << this->layer_param_.name(); - if( convFwd_pd == NULL || this->reshape) + bool _mkldnn_primitive = false; + if( convFwd_pd == NULL || this->reshape){ InitConvolutionFwd(bottom, top); + if(this->phase_ == TEST){ + fwd_weights_data->sync_before_read(); + if (this->bias_term_) + fwd_bias_data->sync_before_read(); + _mkldnn_primitive = true; + } + } fwd_bottom_data->sync_before_read(); - fwd_weights_data->sync_before_read(); - if (this->bias_term_) - fwd_bias_data->sync_before_read(); + if(this->phase_ != TEST){ + fwd_weights_data->sync_before_read(); + if (this->bias_term_) + fwd_bias_data->sync_before_read(); + } // update top that head at prv fwd_top_data->sync_before_write(); PERFORMANCE_EVENT_ID_INIT(perf_id_fw_, PERFORMANCE_MKLDNN_NAME("FW")); PERFORMANCE_MEASUREMENT_BEGIN(); convFwd.submit(); + + if(_mkldnn_primitive) { + CircleBuf::Instance()->DecRefCnt(bottom[0]->prv_data()); + } PERFORMANCE_MEASUREMENT_END_ID(perf_id_fw_); } diff --git a/src/caffe/layers/mkldnn_eltwise_layer.cpp b/src/caffe/layers/mkldnn_eltwise_layer.cpp index b67ce4052..4bd96beb6 100644 --- a/src/caffe/layers/mkldnn_eltwise_layer.cpp +++ b/src/caffe/layers/mkldnn_eltwise_layer.cpp @@ -242,8 +242,11 @@ void MKLDNNEltwiseLayer::Forward_cpu(const vector*>& bottom, { VLOG(1) << "MKLDNNEltwiseLayer::Forward_cpu: " << this->layer_param_.name(); - if(eltwiseFwd_pd == NULL || this->reshape) + bool _mkldnn_primitive = false; + if(eltwiseFwd_pd == NULL || this->reshape) { InitEltwiseFwd(bottom, top); + _mkldnn_primitive = true; + } for (auto i = 0; i < num_bottoms_; i++) { // making reorders if needed. @@ -255,6 +258,11 @@ void MKLDNNEltwiseLayer::Forward_cpu(const vector*>& bottom, PERFORMANCE_EVENT_ID_INIT(perf_id_fw_, PERFORMANCE_MKLDNN_NAME("FW")); PERFORMANCE_MEASUREMENT_BEGIN(); eltwiseFwd.submit(); + if(_mkldnn_primitive) { + for (auto i = 0; i < num_bottoms_; i++) { + CircleBuf::Instance()->DecRefCnt(bottom[i]->prv_data()); + } + } PERFORMANCE_MEASUREMENT_END_ID(perf_id_fw_); } diff --git a/src/caffe/layers/mkldnn_inner_product_layer.cpp b/src/caffe/layers/mkldnn_inner_product_layer.cpp index 8d839ee32..5f9aff809 100644 --- a/src/caffe/layers/mkldnn_inner_product_layer.cpp +++ b/src/caffe/layers/mkldnn_inner_product_layer.cpp @@ -107,6 +107,7 @@ void MKLDNNInnerProductLayer::LayerSetUp(const vector*>& bott , const vector*>& top) { VLOG(1) << "MKLDNNInnerProductLayer::LayerSetUp: " << this->layer_param_.name(); + if (this->layer_param_.has_quantization_param() && this->phase_ == TEST) this->need_quantize_ = true; InnerProductLayer::LayerSetUp(bottom, top); // support for (iter_size > 1) requires additional buffer for weights diff and bias diff @@ -177,10 +178,36 @@ void MKLDNNInnerProductLayer::InitInnerProductFwd(const vectorneed_quantize_) { + bool bottom_data_is_prv = (const_cast(bottom[0]->prv_data()) != NULL); + bool top_data_is_prv = (const_cast(top[0]->prv_data()) != NULL); + if (bottom_data_is_prv || top_data_is_prv) { + shared_ptr > mem_descr + = get_mkldnn_prv_descriptor(bottom[0]); + src_dt = static_cast(mem_descr->prv_memory_pd()->desc().data.data_type); + } + } + + memory::data_type top_dt = memory::data_type::f32; + if (this->need_quantize_) { + if(this->bw_layer_in_ == 8 && src_dt == mkldnn::memory::data_type::s8) + src_dt = memory::data_type::u8; //mkldnn don't surpport s8 input by current version. + if (this->bw_layer_out_ == 8) { + top_dt = memory::data_type::s8; //TODO: enable s8 / u8 output after mkldnn implementation + } + else { + top_dt = memory::data_type::f32; + } + } + + memory::data_type weights_dt = this->need_quantize_ ? memory::data_type::s8 : memory::data_type::f32; + memory::data_type bias_dt = this->need_quantize_ ? memory::data_type::s32 : memory::data_type::f32; + + memory::desc init_bottom_md({bottom_tz}, src_dt, mfmt); + memory::desc init_top_md({top_tz}, top_dt, mfmt); + memory::desc init_weights_md({weights_tz}, weights_dt, mfmt); + memory::desc init_bias_md({bias_tz}, bias_dt, mfmt); // Initialize inner_product primitive descriptor shared_ptr ipFwd_desc; @@ -193,6 +220,31 @@ void MKLDNNInnerProductLayer::InitInnerProductFwd(const vectorneed_quantize_) { + int mask = 0; + int count = 1; //single channel + if(this->scale_params_.size() > 1){ + int oc_dim_id = 1; + mask = 1 << oc_dim_id; + count = oc; //multi channel + } + std::vector scales(count); + float scale; + #ifdef _OPENMP + #pragma omp parallel for if (count > 1) + #endif + for(int i=0; iscale_params_[i] == 0.0) + scale = this->scale_out_[0]; + else + scale = this->scale_out_[0] / (this->scale_in_[0] * this->scale_params_[i]); + scales[i] = scale; + } + attr.set_output_scales(mask, scales); + attr.set_int_output_round_mode(round_nearest); + } + // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") @@ -202,8 +254,13 @@ void MKLDNNInnerProductLayer::InitInnerProductFwd(const vectorneed_quantize_){ + ipFwd_pd.reset(new inner_product_forward::primitive_desc(*ipFwd_desc, attr, + ep.getMKLDNNSubEngine(subEngineIndex))); + } else{ + ipFwd_pd.reset(new inner_product_forward::primitive_desc(*ipFwd_desc, + ep.getMKLDNNSubEngine(subEngineIndex))); + } } catch(...) { continue; @@ -234,16 +291,41 @@ void MKLDNNInnerProductLayer::InitInnerProductFwd(const vector(usr_bottom_data_memory_pd, prv_fwd_bottom_data_memory_pd, bottom[0], this)); + std::vector scale_bottom(1); + scale_bottom[0] = 1.0f; + if(this->need_quantize_) scale_bottom = this->scale_in_; + fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_memory_pd, prv_fwd_bottom_data_memory_pd, bottom[0], this, scale_bottom)); fwd_bottom_data ->name = "fwd_bottom_data @ " + this->layer_param_.name(); fwd_bottom_data_primitive = fwd_bottom_data->create_input(false); + std::vector scale_top(1); + scale_top[0] = 1.0f; + if(this->need_quantize_) scale_top = this->scale_out_; fwd_top_data.reset(new MKLDNNData(usr_top_data_memory_pd, prv_fwd_top_data_memory_pd, top[0], this)); fwd_top_data ->name = "fwd_top_data @ " + this->layer_param_.name(); fwd_top_data_memory = fwd_top_data->create_output_memory(); if (fwd_weights_data == NULL) { - fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this)); + std::vector scale_weight(1); + int reorder_mask = 0; + if (this->need_quantize_){ + int count = 1; //single channel + if(this->scale_params_.size() > 1){ + count = oc; //multi channel + reorder_mask = 1<<0; + } + scale_weight.resize(count); +#ifdef _OPENMP +#pragma omp parallel for if (count > 1) +#endif + for(int i=0; iscale_params_[i]; + } + } else{ + scale_weight[0] = 1.0f; + } + + fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, scale_weight, reorder_mask, false, false, true)); fwd_weights_data->name = "fwd_weights_data @ " + this->layer_param_.name(); fwd_weights_data_primitive = fwd_weights_data->create_input(true); } @@ -251,7 +333,28 @@ void MKLDNNInnerProductLayer::InitInnerProductFwd(const vectorbias_term_) { if (fwd_bias_data == NULL) { shared_ptr prv_fwd_bias_data_memory_pd(new MemPD(ipFwd_pd->bias_primitive_desc())); - fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this)); + std::vector scale_bias(1); + int reorder_mask = 0; + if (this->need_quantize_){ + int count = 1; //single channel + if(this->scale_params_.size() > 1){ + count = oc; //multi channel + reorder_mask = 1<<0; + } + scale_bias.resize(count); + #ifdef _OPENMP + #pragma omp parallel for if (count > 1) + #endif + for(int i=0; iscale_params_[i] == 0.0) + scale_bias[i] = 1.0; + else + scale_bias[i] = this->scale_in_[0] * this->scale_params_[i]; + } + } else{ + scale_bias[0] = 1.0f; + } + fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, scale_bias, reorder_mask, false, false, true)); fwd_bias_data ->name = "fwd_bias_data @ " + this->layer_param_.name(); fwd_bias_data_primitive = fwd_bias_data->create_input(true); } @@ -294,19 +397,32 @@ void MKLDNNInnerProductLayer::Forward_cpu(const vector*>& bot LOG(INFO) << "MKLDNNInnerProductLayer::Forward_cpu: " << this->layer_param_.name(); #endif - if( ipFwd_pd == NULL || this->reshape) + bool _mkldnn_primitive = false; + if( ipFwd_pd == NULL || this->reshape){ InitInnerProductFwd(bottom, top); + if(this->phase_ == TEST){ + fwd_weights_data->sync_before_read(); + if (this->bias_term_) + fwd_bias_data->sync_before_read(); + _mkldnn_primitive = true; + } + } // making reorders if needed. fwd_bottom_data->sync_before_read(); - fwd_weights_data->sync_before_read(); - if (this->bias_term_) - fwd_bias_data->sync_before_read(); // update top that head at prv + if(this->phase_ != TEST){ + fwd_weights_data->sync_before_read(); + if (this->bias_term_) + fwd_bias_data->sync_before_read(); + } fwd_top_data->sync_before_write(); PERFORMANCE_EVENT_ID_INIT(perf_id_fw_, PERFORMANCE_MKLDNN_NAME("FW")); PERFORMANCE_MEASUREMENT_BEGIN(); ipFwd.submit(); + if(_mkldnn_primitive) { + CircleBuf::Instance()->DecRefCnt(bottom[0]->prv_data()); + } PERFORMANCE_MEASUREMENT_END_ID(perf_id_fw_); } diff --git a/src/caffe/layers/mkldnn_lrn_layer.cpp b/src/caffe/layers/mkldnn_lrn_layer.cpp index 0c834d209..4431fbb38 100644 --- a/src/caffe/layers/mkldnn_lrn_layer.cpp +++ b/src/caffe/layers/mkldnn_lrn_layer.cpp @@ -216,8 +216,11 @@ void MKLDNNLRNLayer::Forward_cpu(const vector*>& bottom ,const vector*>& top) { VLOG(1) << "MKLDNNLRNLayer::Forward_cpu: " << this->layer_param_.name(); - if( lrnFwd_pd == NULL || this->reshape) + bool _mkldnn_primitive = false; + if( lrnFwd_pd == NULL || this->reshape) { InitLRNFwd(bottom, top); + _mkldnn_primitive = true; + } // making reorders if needed. fwd_bottom_data->sync_before_read(); @@ -227,6 +230,10 @@ void MKLDNNLRNLayer::Forward_cpu(const vector*>& bottom PERFORMANCE_EVENT_ID_INIT(perf_id_fw_, PERFORMANCE_MKLDNN_NAME("FW")); PERFORMANCE_MEASUREMENT_BEGIN(); lrnFwd.submit(); + + if(_mkldnn_primitive) { + CircleBuf::Instance()->DecRefCnt(bottom[0]->prv_data()); + } PERFORMANCE_MEASUREMENT_END_ID(perf_id_fw_); } diff --git a/src/caffe/layers/mkldnn_pooling_layer.cpp b/src/caffe/layers/mkldnn_pooling_layer.cpp index 17b0ff797..f408fe06a 100644 --- a/src/caffe/layers/mkldnn_pooling_layer.cpp +++ b/src/caffe/layers/mkldnn_pooling_layer.cpp @@ -252,17 +252,16 @@ void MKLDNNPoolingLayer::InitPoolingFwd(const vector*>& botto shared_ptr usr_bottom_data_mpd(new MemPD({{bottom_tz}, mpcsn, mfmt_nchw}, cpu_engine)); shared_ptr usr_top_data_mpd(new MemPD({{top_tz}, mpcsn, mfmt_nchw}, cpu_engine)); - std::vector scale; + std::vector scale(1, 1.); if (bottom_data_is_prv || top_data_is_prv) { shared_ptr > mem_descr = get_mkldnn_prv_descriptor(bottom[0]); cmfmt = static_cast(mem_descr->prv_memory_pd()->desc().data.format); mpcsn = static_cast(mem_descr->prv_memory_pd()->desc().data.data_type); - scale.push_back(mem_descr->get_scale(0)); - } else{ - scale.push_back(1.); + scale[0] = mem_descr->get_scale(0); + mpcsn = (mpcsn==memory::data_type::s8) && mem_descr->get_sum() ? memory::data_type::u8 : mpcsn; } - + shared_ptr init_fwd_bottom_md(new memory::desc({bottom_tz}, mpcsn, cmfmt)); shared_ptr init_fwd_top_md(new memory::desc({top_tz}, mpcsn, cmfmt)); @@ -345,9 +344,11 @@ void MKLDNNPoolingLayer::Forward_cpu(const vector*>& bottom #ifdef DEBUG LOG(INFO) << "MKLDNNPoolingLayer::Forward_cpu: " << this->layer_param_.name(); #endif - - if (NULL == poolingFwd_pd || this->reshape) + bool _mkldnn_primitive = false; + if (NULL == poolingFwd_pd || this->reshape) { InitPoolingFwd(bottom, top); + _mkldnn_primitive = true; + } // making reorders if needed. fwd_bottom_data->sync_before_read(); // update top that head at prv @@ -356,6 +357,9 @@ void MKLDNNPoolingLayer::Forward_cpu(const vector*>& bottom PERFORMANCE_EVENT_ID_INIT(perf_id_fw_, PERFORMANCE_MKLDNN_NAME("FW")); PERFORMANCE_MEASUREMENT_BEGIN(); poolingFwd.submit(); + if (_mkldnn_primitive) { + CircleBuf::Instance()->DecRefCnt(bottom[0]->prv_data()); + } PERFORMANCE_MEASUREMENT_END_ID(perf_id_fw_); } diff --git a/src/caffe/layers/mkldnn_relu_layer.cpp b/src/caffe/layers/mkldnn_relu_layer.cpp index 78e5125c6..362df62bd 100644 --- a/src/caffe/layers/mkldnn_relu_layer.cpp +++ b/src/caffe/layers/mkldnn_relu_layer.cpp @@ -119,7 +119,7 @@ void MKLDNNReLULayer::InitReLUFwd(const vector*>& bottom, con top_data_mpd.reset(new memory::primitive_desc({dim, top_dt, src_mfmt}, cpu_engine)); // ---- Initialize relu primitive descriptor ------------- - //relu_forward::desc reluFwd_desc(propagation, *bottom_data_md, negative_slope); + //eltwise_forward::desc reluFwd_desc(propagation, *bottom_data_md, negative_slope); // MKLDNN is deprecating standalone relu primitive in MKL-DNN. // Now MKLDNN has eltwise primitive with eltwise_relu algorithm inside. eltwise_forward::desc eltwise_reluFwd_desc(propagation, eltwise_relu, *bottom_data_md, negative_slope); @@ -133,7 +133,7 @@ void MKLDNNReLULayer::InitReLUFwd(const vector*>& bottom, con reluFwd_pd = NULL; for(; subEngineIndex < ep.getNumberOfSubEngines(); subEngineIndex++) { try { - reluFwd_pd.reset(new relu_forward::primitive_desc(eltwise_reluFwd_desc, + reluFwd_pd.reset(new eltwise_forward::primitive_desc(eltwise_reluFwd_desc, ep.getMKLDNNSubEngine(subEngineIndex))); } catch(...) { @@ -153,7 +153,7 @@ void MKLDNNReLULayer::InitReLUFwd(const vector*>& bottom, con fwd_top_data_memory = fwd_top_data->create_output_memory(inplace); - reluFwd.reset(new relu_forward(*reluFwd_pd, *fwd_bottom_data_primitive, *fwd_top_data_memory)); + reluFwd.reset(new eltwise_forward(*reluFwd_pd, *fwd_bottom_data_primitive, *fwd_top_data_memory)); //fwd_bottom_data->set_mkldnn_primitive(reluFwd); //Wrong passed primitive! (TODO: Checking!) MKLDNNPrimitive fwd_bottom_data_primitive_transfer(fwd_bottom_data_primitive); fwd_bottom_data->set_mkldnn_primitive(fwd_bottom_data_primitive_transfer); @@ -174,8 +174,11 @@ void MKLDNNReLULayer::Forward_cpu(const vector*>& bottom #endif bool inplace = (bottom[0] == top[0]); - if( reluFwd_pd == NULL || this->reshape) + bool _mkldnn_primitive = false; + if( reluFwd_pd == NULL || this->reshape) { InitReLUFwd(bottom, top); + _mkldnn_primitive = true; + } if(this->layer_param_.relu_param().fuse()) { top[0]->ShareData(*bottom[0]); @@ -190,6 +193,9 @@ void MKLDNNReLULayer::Forward_cpu(const vector*>& bottom PERFORMANCE_EVENT_ID_INIT(perf_id_fw_, PERFORMANCE_MKLDNN_NAME("FW")); PERFORMANCE_MEASUREMENT_BEGIN(); reluFwd.submit(); + if(_mkldnn_primitive) { + CircleBuf::Instance()->DecRefCnt(bottom[0]->prv_data()); + } PERFORMANCE_MEASUREMENT_END_ID(perf_id_fw_); } @@ -295,7 +301,7 @@ void MKLDNNReLULayer::InitReLUBwd(const vector*>& top bottom_diff_md = top_diff_md; // ---- Initialize relu primitive descriptor ------------- - //relu_backward::desc reluBwd_desc(*top_diff_md, *top_data_md, negative_slope); + //eltwise_backward::desc reluBwd_desc(*top_diff_md, *top_data_md, negative_slope); // MKLDNN is deprecating standalone relu primitive in MKL-DNN. // Now MKLDNN has eltwise primitive with eltwise_relu algorithm inside. eltwise_backward::desc eltwise_reluBwd_desc(eltwise_relu, *top_diff_md, *top_data_md, negative_slope); @@ -309,7 +315,7 @@ void MKLDNNReLULayer::InitReLUBwd(const vector*>& top reluBwd_pd = NULL; for(; subEngineIndex < ep.getNumberOfSubEngines(); subEngineIndex++) { try { - reluBwd_pd.reset(new relu_backward::primitive_desc(eltwise_reluBwd_desc, + reluBwd_pd.reset(new eltwise_backward::primitive_desc(eltwise_reluBwd_desc, ep.getMKLDNNSubEngine(subEngineIndex), *reluFwd_pd)); } catch(...) { @@ -332,7 +338,7 @@ void MKLDNNReLULayer::InitReLUBwd(const vector*>& top bwd_bottom_diff->name = "bwd_bottom_diff_data @ " + this->layer_param_.name(); bwd_bottom_diff_memory = bwd_bottom_diff->create_output_memory(inplace); - reluBwd.reset(new relu_backward(*reluBwd_pd, *bwd_bottom_data_primitive, *bwd_top_diff_primitive, *bwd_bottom_diff_memory)); + reluBwd.reset(new eltwise_backward(*reluBwd_pd, *bwd_bottom_data_primitive, *bwd_top_diff_primitive, *bwd_bottom_diff_memory)); //bwd_top_diff->set_mkldnn_primitive(reluBwd); //Wrong passed primitive! (TODO: Checking!) MKLDNNPrimitive bwd_top_diff_primitive_transfer(bwd_top_diff_primitive); bwd_top_diff->set_mkldnn_primitive(bwd_top_diff_primitive_transfer); diff --git a/src/caffe/layers/mkldnn_split_layer.cpp b/src/caffe/layers/mkldnn_split_layer.cpp index 1abea6169..7d3615229 100644 --- a/src/caffe/layers/mkldnn_split_layer.cpp +++ b/src/caffe/layers/mkldnn_split_layer.cpp @@ -206,6 +206,7 @@ template void MKLDNNSplitLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { // TODO: consider doing something + this->first = true; } template @@ -214,6 +215,10 @@ void MKLDNNSplitLayer::Forward_cpu(const vector*>& bottom, for (int i = 0; i < top.size(); ++i) { top[i]->ShareData(*bottom[0]); } + if ((this->reshape || this->first) && bottom[0]->prv_data()) { + CircleBuf::Instance()->IncRefCnt(bottom[0]->prv_data(), top.size() - 1); + } + this->first = false; } template diff --git a/src/caffe/mkldnn_memory.cpp b/src/caffe/mkldnn_memory.cpp index 398d4094c..8a12f9f9e 100644 --- a/src/caffe/mkldnn_memory.cpp +++ b/src/caffe/mkldnn_memory.cpp @@ -55,7 +55,7 @@ MKLDNNMemoryDescriptorBase::MKLDNNMemoryDescriptorBase(shared_ptr::convert_from_extprv(shared_ptr_reorder_extprv2prv.submit(); + + void* prv_mem = this->get_prv_memory()->get_data_handle(); + if(prv_mem) { + CircleBuf::Instance()->DecRefCnt(prv_mem); + } PERFORMANCE_MEASUREMENT_END_STATIC("mkldnn_conversion"); } diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 3cb902c5f..9676930ad 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -64,7 +64,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include "caffe/util/remove_batch_norm.hpp" #include "caffe/util/apply_bn_stats_batch_size.hpp" - +#include "caffe/syncedmem.hpp" PERFORMANCE_CREATE_MONITOR(); namespace caffe { @@ -222,6 +222,7 @@ void Net::Init(const NetParameter& in_param) { param_id_vecs_.resize(param.layer_size()); top_id_vecs_.resize(param.layer_size()); bottom_need_backward_.resize(param.layer_size()); + max_blob_count = 0; for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) { // For non-root solvers, whether this layer is shared from root_net_. bool share_from_root = !Caffe::root_solver() @@ -341,9 +342,12 @@ void Net::Init(const NetParameter& in_param) { << " with loss weight " << layer->loss(top_id); } memory_used_ += top_vecs_[layer_id][top_id]->count(); + if (max_blob_count < top_vecs_[layer_id][top_id]->count()) max_blob_count = top_vecs_[layer_id][top_id]->count(); } LOG_IF(INFO, Caffe::root_solver()) << "Memory required for data: " << memory_used_ * sizeof(Dtype); + LOG_IF(INFO, Caffe::root_solver()) + << "Biggest Memory of single blob: " << max_blob_count * sizeof(Dtype); const int param_size = layer_param.param_size(); const int num_param_blobs = layers_[layer_id]->blobs().size(); CHECK_LE(param_size, num_param_blobs) @@ -369,6 +373,10 @@ void Net::Init(const NetParameter& in_param) { } } + // only enable circle buffer mechanism at TEST phase and "CAFFE_INFERENCE_MEM_OPT" env variable is set. + if ((phase_ == TEST) && getenv("CAFFE_INFERENCE_MEM_OPT")) + CircleBuf::Instance()->SetBufSize(max_blob_count * sizeof(Dtype)); + // Go through the net backwards to determine which blobs contribute to the // loss. We can skip backward computation for blobs that don't contribute // to the loss. @@ -673,7 +681,6 @@ template void Net::CompilationRuleConvReluFusion(const NetParameter& param, NetParameter* param_compiled) { std::set layers_to_drop; - bool use_negative_slope = false; for (int i = 0; i < param.layer_size(); ++i) { LayerParameter* layer_param = (const_cast(param)).mutable_layer(i); @@ -718,10 +725,9 @@ void Net::CompilationRuleConvReluFusion(const NetParameter& param, float negative_slope1 = consumer_layer_param.relu_param().negative_slope(); - if (negative_slope1 != 0) { - use_negative_slope = true; - } - if(param.state().phase() == TEST && !negative_slope1) { + layer_param->mutable_convolution_param()->set_relu(true); + layer_param->mutable_convolution_param()->set_negative_slope(negative_slope1); + if(param.state().phase() == TEST) { const string& scale_top_blob_name = consumer_layer_param.top(0); // Mark Consumer layer (its name) as the one marked for dropping layers_to_drop.insert(consumer_layer_param.name()); @@ -732,11 +738,7 @@ void Net::CompilationRuleConvReluFusion(const NetParameter& param, scale_top_blob_name.size(), scale_top_blob_name); } - if(!use_negative_slope) { - layer_param->mutable_convolution_param()->set_relu(true); - layer_param->mutable_convolution_param()->set_negative_slope(0); - } - if(param.state().phase() == TRAIN && !use_negative_slope) { + if(param.state().phase() == TRAIN) { if(i+1 < param.layer_size()) { LayerParameter* relu_layer_param = (const_cast(param)).mutable_layer(i+1); @@ -746,7 +748,7 @@ void Net::CompilationRuleConvReluFusion(const NetParameter& param, } } - if(param.state().phase() == TEST && !use_negative_slope) { + if(param.state().phase() == TEST) { if (layers_to_drop.find(layer_param->name()) != layers_to_drop.end()) { LOG_IF(INFO, Caffe::root_solver()) << "Dropped layer: " << layer_param->name() << std::endl; diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 9ec4285c0..5857ac86f 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -599,6 +599,7 @@ int time() { std::vector forward_time_per_layer(layers.size(), 0.0); std::vector backward_time_per_layer(layers.size(), 0.0); double forward_time = 0.0; + std::vector forward_time_iteration(FLAGS_iterations, 0.0); double backward_time = 0.0; for (int j = 0; j < FLAGS_iterations; ++j) { Timer iter_timer; @@ -609,6 +610,7 @@ int time() { layers[i]->Forward(bottom_vecs[i], top_vecs[i]); forward_time_per_layer[i] += timer.MicroSeconds(); } + forward_time_iteration[j] = forward_timer.MicroSeconds(); forward_time += forward_timer.MicroSeconds(); if (!FLAGS_forward_only) { backward_timer.Start(); @@ -639,6 +641,8 @@ int time() { } } total_timer.Stop(); + for (int j = 0; j < FLAGS_iterations; ++j) + LOG(INFO) << "###" << j << ":" << forward_time_iteration[j] / 1000 << " ms."; LOG(INFO) << "Average Forward pass: " << forward_time / 1000 / FLAGS_iterations << " ms."; if (!FLAGS_forward_only) {