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daisydendaisyden
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merge release_1.0.6
2 parents 16f8c2b + f77f7e0 commit c7ed327

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‎Makefile‎

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@@ -64,6 +64,11 @@ endif
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#################### MLSL ####################
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ifeq ($(USE_MLSL), 1)
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ifeq ($(CPU_ONLY), 0)
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$(error Multi-node is not supported if CPU_ONLY is disabled. Please set CPU_ONLY=1 if USE_MLSL=1)
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endif
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RETURN_STRING=$(shell ./external/mlsl/prepare_mlsl.sh)
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MLSL_ROOT=$(firstword $(RETURN_STRING))
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MLSL_LDFLAGS=$(lastword $(RETURN_STRING))

‎cmake/Dependencies.cmake‎

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@@ -96,6 +96,10 @@ endif()
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# ---[ MLSL
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if(USE_MLSL)
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if (NOT CPU_ONLY)
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message(FATAL_ERROR "Multi-node is not supported if CPU_ONLY is disabled. Please set CPU_ONLY=1 if USE_MLSL=1.")
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endif()
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#--find mlsl in external/mkl
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set(script_cmd "./external/mlsl/prepare_mlsl.sh" )
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execute_process(COMMAND ${script_cmd}

‎data/Celeb-A/celebA.txt‎

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/Celeb-A_Cropped/000001.jpg 1
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/Celeb-A_Cropped/000002.jpg 1
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/Celeb-A_Cropped/000003.jpg 1
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/Celeb-A_Cropped/000004.jpg 1
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/Celeb-A_Cropped/000005.jpg 1
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/Celeb-A_Cropped/000006.jpg 1
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/Celeb-A_Cropped/000007.jpg 1
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/Celeb-A_Cropped/000008.jpg 1
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/Celeb-A_Cropped/000009.jpg 1
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/Celeb-A_Cropped/000010.jpg 1

‎data/Celeb-A/crop_celebA.py‎

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from PIL import Image
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import os
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import sys
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print ""
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print "Prepare Celeb-A Dataset! (1. Crop the images. 2. Generate a train list file.)"
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print ""
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print "-------------------------------------------------------------------------------"
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current_path = os.getcwd()
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celebA_path = ""
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celebA_cropped_path = ""
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print "The current path containing this python file is: " + current_path
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if len(sys.argv) == 1:
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print "Please give the path of original Celeb-A dataset!"
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exit(0)
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elif len(sys.argv) > 1:
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print "The path of original Celeb-A dataset is: " + str(sys.argv[1])
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celebA_path = sys.argv[1]
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celebA_cropped_path = os.path.dirname(celebA_path) + os.sep + "Cropped" #To avoid crop the generated images again if this parameter is not provided
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if len(sys.argv) > 2:
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print "The path of cropped Celeb-A dataset will be: " + str(sys.argv[2])
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celebA_cropped_path = sys.argv[2]
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else:
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print "The path of cropped Celeb-A dataset will be defult, set as: " + celebA_cropped_path
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if os.path.exists(celebA_cropped_path):
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print "The path of cropped Celeb-A dataset exists."
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else:
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print "The path of cropped Celeb-A dataset doesn't exist! I will create it now!"
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os.makedirs(celebA_cropped_path)
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print "-------------------------------------------------------------------------------"
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training_list_file = os.path.join(celebA_cropped_path, "celebA.txt")
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list_file = open(training_list_file, 'w')
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total_image_num = 0
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x1, y1 = 30, 40
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cropped_box = (x1, y1, x1 + 138, y1 + 138)
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for parent,dirnames,filenames in os.walk(celebA_path):
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for filename in filenames:
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if filename.endswith(".jpg"):
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total_image_num += 1
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#print "parent is:" + parent
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#print "filename is:" + filename
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image_path_and_name = os.path.join(parent,filename)
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print "the full name of the file is: " + image_path_and_name
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input_image = Image.open(image_path_and_name)
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#input_image.show()
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cropped_image = input_image.crop(cropped_box)
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#cropped_image.show()
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scaled_cropped_image = cropped_image.resize((64, 64))
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#scaled_cropped_image.show()
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save_result_image_path_and_name = os.path.join(celebA_cropped_path,filename)
55+
scaled_cropped_image.save(save_result_image_path_and_name, 'jpeg')
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list_file.writelines(save_result_image_path_and_name)
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list_file.writelines(" 1" + "\n") #Must add label to list file
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print "There are " + str(total_image_num) + " images are finished with cropping and scaling operations!"
59+
list_file.close()

‎docker/standalone/cpu-centos/Dockerfile‎

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@@ -4,7 +4,9 @@ MAINTAINER [email protected]
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#ENV http_proxy proxy:port
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#ENV https_proxy proxy:port
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RUN rpm -iUvh http://download.fedoraproject.org/pub/epel/7/x86_64/e/epel-release-7-8.noarch.rpm
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RUN rpm -iUvh http://download.fedoraproject.org/pub/epel/7/x86_64/e/epel-release-7-10.noarch.rpm
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RUN yum upgrade -y
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RUN yum install -y \
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redhat-rpm-config \
@@ -15,6 +17,7 @@ RUN yum install -y \
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cmake \
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git \
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wget \
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ssh \
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atlas-devel \
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boost-devel \
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gflags-devel \

‎docker/standalone/cpu-ubuntu/Dockerfile‎

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FROM ubuntu:14.04
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FROM ubuntu:16.04
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MAINTAINER [email protected]
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#ENV http_proxy proxy:port
@@ -9,6 +9,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
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cmake \
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git \
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wget \
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ssh \
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libboost-all-dev \
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libgflags-dev \
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libgoogle-glog-dev \

‎docker/templates/Dockerfile.template‎

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@@ -8,6 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
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cmake \
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git \
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wget \
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ssh \
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libatlas-base-dev \
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libboost-all-dev \
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libgflags-dev \

‎include/caffe/layers/mkldnn_layers.hpp‎

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@@ -69,7 +69,7 @@ class MKLDNNBatchNormLayer : public MKLDNNLayer<Dtype>, public Layer<Dtype> {
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, bwd_top_diff(), bwd_bottom_diff()
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, BatchNormFwd_pd(), BatchNormBwd_pd()
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, scaleshift_memory(), bwd_scaleshift_diff_memory()
72-
, output_memory(), bwd_bottom_diff_memory(), inplace_buffer_memory()
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, output_memory(), bwd_bottom_diff_memory()
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, input_primitive(), bwd_top_diff_primitive()
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{
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PERFORMANCE_EVENT_ID_RESET(perf_id_fw_);
@@ -95,12 +95,10 @@ class MKLDNNBatchNormLayer : public MKLDNNLayer<Dtype>, public Layer<Dtype> {
9595
void InitBatchNormBwd(const vector<Blob<Dtype>*>& top,
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const vector<bool>& propagate_down,
9797
const vector<Blob<Dtype>*>& bottom);
98-
void InitBatchNormFwdPrimitive(int stats_batch_idx, bool inplace);
99-
void InitBatchNormBwdPrimitive(int stats_batch_idx, bool inplace);
98+
void InitBatchNormFwdPrimitive(int stats_batch_idx);
99+
void InitBatchNormBwdPrimitive(int stats_batch_idx);
100100
template <bool diff> shared_ptr<memory> GetStatsBatchMemory(
101101
shared_ptr<MKLDNNMemoryDescriptor<Dtype, diff> > mkldnn_data, int idx);
102-
template <bool diff> shared_ptr<memory> GetStatsBatchMemoryInplace(
103-
shared_ptr<MKLDNNMemoryDescriptor<Dtype, diff> > mkldnn_data, int idx, shared_ptr<memory > buffer_memory);
104102
void InitStatsBatchVars(int batch_size);
105103
shared_ptr<MKLDNNData<Dtype> > fwd_top_data, fwd_bottom_data;
106104
shared_ptr<MKLDNNDiff<Dtype> > bwd_top_diff, bwd_bottom_diff;
@@ -112,8 +110,8 @@ class MKLDNNBatchNormLayer : public MKLDNNLayer<Dtype>, public Layer<Dtype> {
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113111
shared_ptr<memory> scaleshift_memory, bwd_scaleshift_diff_memory;
114112
shared_ptr<memory> output_memory, bwd_bottom_diff_memory;
115-
shared_ptr<memory> inplace_buffer_memory;
116-
vector<shared_ptr<memory> > input_stats, output_stats, top_diff_stats, bottom_diff_stats, input_inplace_buffer;
113+
114+
vector<shared_ptr<memory> > input_stats, output_stats, top_diff_stats, bottom_diff_stats;
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118116
shared_ptr<primitive> input_primitive, bwd_top_diff_primitive;
119117

@@ -124,6 +122,7 @@ class MKLDNNBatchNormLayer : public MKLDNNLayer<Dtype>, public Layer<Dtype> {
124122
int stats_batch_size_;
125123
shared_ptr<Blob<Dtype> > scaleshift_blob_;
126124
shared_ptr<Blob<Dtype> > scaleshift_acc_;
125+
Blob<Dtype> inplace_buffer;
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128127
PERFORMANCE_EVENT_ID_DECL(perf_id_fw_);
129128
PERFORMANCE_EVENT_ID_DECL(perf_id_bw_);
@@ -224,7 +223,7 @@ class MKLDNNInnerProductLayer : public MKLDNNLayer<Dtype> , public InnerProductL
224223
, bwdd_top_diff_primitive, bwdd_weights_data_primitive
225224
, bwdw_top_diff_primitive, bwdw_bottom_data_primitive;
226225
int32_t w_, h_;
227-
226+
228227
/* In case of (iter_size > 1) we need additional buffers */
229228
shared_ptr<MKLDNNDiff<Dtype> > bwdw_weights_diff_iter, bwdw_bias_diff_iter;
230229
shared_ptr<memory> bwdw_weights_diff_memory_iter, bwdw_bias_diff_memory_iter;
@@ -322,13 +321,14 @@ class MKLDNNPoolingLayer : public MKLDNNLayer<Dtype>, public Layer<Dtype> {
322321
,const vector<Blob<Dtype>*>& bottom);
323322
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down
324323
,const vector<Blob<Dtype>*>& bottom);
324+
virtual void compute_output_shape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
325325

326326
private:
327327
void InitPoolingFwd(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
328328
void InitPoolingBwd(const vector<Blob<Dtype>*>& bottom
329329
, const vector<bool>& propagate_down
330330
, const vector<Blob<Dtype>*>& top);
331-
331+
332332
shared_ptr<MKLDNNData<Dtype>> fwd_bottom_data, fwd_top_data;
333333
shared_ptr<MKLDNNDiff<Dtype>> bwd_top_diff, bwd_bottom_diff;
334334
shared_ptr<pooling_forward::primitive_desc> poolingFwd_pd;
@@ -408,7 +408,7 @@ class MKLDNNConcatLayer : public MKLDNNLayer<Dtype> , public Layer<Dtype> {
408408
: MKLDNNLayer<Dtype>(), Layer<Dtype>(param),
409409
concatFwd_pd(), fwd_output_memory(),
410410
bwd_reorder_input_memory(), bwd_reorder_output_memory(),
411-
fwd_top_data(), fwd_bottom_data(), split_channels() {
411+
fwd_top_data(), fwd_bottom_data(), split_dims() {
412412
PERFORMANCE_EVENT_ID_RESET(perf_id_fw_);
413413
PERFORMANCE_EVENT_ID_RESET(perf_id_bw_);
414414
}
@@ -440,7 +440,7 @@ class MKLDNNConcatLayer : public MKLDNNLayer<Dtype> , public Layer<Dtype> {
440440
shared_ptr<MKLDNNDiff<Dtype> > bwd_top_diff;
441441
vector<shared_ptr<MKLDNNDiff<Dtype> > > bwd_bottom_diff;
442442
vector<MKLDNNPrimitive<Dtype> > reorders;
443-
vector<int> split_channels;
443+
vector<int> split_dims;
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445445
int32_t num_, width_, height_, channels_, num_concats_;
446446
int concat_dimension;

‎include/caffe/mkldnn_base.hpp‎

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@@ -196,6 +196,8 @@ class MKLDNNLayer {
196196
public:
197197
explicit MKLDNNLayer() {}
198198
virtual ~MKLDNNLayer() {}
199+
protected:
200+
bool reshape;
199201
};
200202

201203
// ===== MKLDNNPrimitive =======================================

‎include/caffe/net.hpp‎

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@@ -326,6 +326,9 @@ class Net {
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/// @brief return whether NetState state meets NetStateRule rule
327327
static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
328328
const string& layer_name);
329+
inline const map<string,int>& blob_names_index() const {
330+
return blob_names_index_;
331+
}
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330333
protected:
331334
// Helpers for Init.

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