add get weight task

Makefile

@@ -15,7 +15,7 @@ include mshadow/make/mshadow.mk
 # all tge possible warning tread
 WARNFLAGS= -Wall
 CFLAGS = -DMSHADOW_FORCE_STREAM $(WARNFLAGS)
-CFLAGS += -g -O3 -I./mshadow/  -fPIC $(MSHADOW_CFLAGS)
+CFLAGS += -g -O3 -I./mshadow/ -fPIC $(MSHADOW_CFLAGS)
 LDFLAGS = -pthread $(MSHADOW_LDFLAGS)
 NVCCFLAGS = --use_fast_math -g -O3 -ccbin $(CXX) $(MSHADOW_NVCCFLAGS)
 

src/cxxnet_main.cpp

@@ -34,6 +34,9 @@ class CXXNetLearnTask {
     reset_net_type = -1;
     extract_node_name = "";
     output_format = 1;
+    weight_name = "wmat";
+    extract_layer_name = "";
+    weight_filename = "";
 #if MSHADOW_USE_CUDA
     this->SetParam("dev", "gpu");
 #else
@@ -77,6 +80,7 @@ class CXXNetLearnTask {
     if (task == "train" || task == "finetune") this->TaskTrain();
     if (task == "pred")   this->TaskPredict();
     if (task == "extract") this->TaskExtractFeature();
+    if (task == "get_weight") this->TaskGetWeight();
     return 0;
   }
 
@@ -97,6 +101,8 @@ class CXXNetLearnTask {
     if (!strcmp(name, "dev"))               device = val;
     if (!strcmp(name, "test_io"))           test_io = atoi(val);
     if (!strcmp(name, "extract_node_name"))         extract_node_name = val;
+    if (!strcmp(name, "extract_layer_name"))         extract_layer_name = val;
+    if (!strcmp(name, "weight_filename"))         weight_filename = val;
     if (!strcmp(name, "output_format")) {
       if  (!strcmp(val, "txt")) output_format = 1;
       else output_format = 0;
@@ -239,8 +245,7 @@ class CXXNetLearnTask {
           itr_evals.push_back(cxxnet::CreateIterator(itcfg));
           eval_names.push_back(evname);
         }
-        if (flag == 3 && (task == "pred" || task == "pred_raw" ||
-                          task == "extract")) {
+        if (flag == 3 && (task == "pred" || task == "extract")) {
           utils::Assert(itr_pred == NULL, "can only have one data:test");
           itr_pred = cxxnet::CreateIterator(itcfg);
         }
@@ -281,6 +286,33 @@ class CXXNetLearnTask {
     fclose(fo);
     printf("finished prediction, write into %s\n", name_pred.c_str());
   }
+  inline void TaskGetWeight(void) {
+    FILE *fo = utils::FopenCheck(weight_filename.c_str(), "wb");
+    mshadow::TensorContainer<mshadow::cpu, 2> weight;
+    std::vector<index_t> shape;
+    net_trainer->GetWeight(&weight, &shape, extract_layer_name.c_str(), weight_name.c_str());
+    for (index_t i = 0; i < weight[i].size(0); ++i) {
+      mshadow::Tensor<mshadow::cpu, 2> d = weight[i].FlatTo2D();
+      for (index_t j = 0; j < d.size(0); ++j) {
+        if (output_format) {
+          for (index_t k = 0; k < d.size(1); ++k) {
+            fprintf(fo, "%g ", d[j].dptr_[k]);
+          }
+          fprintf(fo, "\n");
+        } else {
+          fwrite(d[j].dptr_, sizeof(float), d.size(1), fo);
+        }  
+      }
+    }
+    fclose(fo);
+    std::string name_meta = weight_filename + ".meta";
+    FILE *fm = utils::FopenCheck(name_meta.c_str(), "w");
+    for (index_t i = 0; i < shape.size(); ++i) {
+      fprintf(fm, "%u ", shape[i]);
+    }
+    fclose(fm);
+    printf("finished getting weight, write into %s\n", weight_filename.c_str());
+  }
   inline void TaskExtractFeature() {
     long nrow = 0;
     mshadow::Shape<3> dshape;
@@ -468,6 +500,12 @@ class CXXNetLearnTask {
   std::string extract_node_name;
   /*! \brief output format of network */
   int output_format;
+  /*! \brief the layer name for weight extraction */
+  std::string extract_layer_name;
+  /*! \brief the output filename of the extracted weight */
+  std::string weight_filename;
+  /*! \brief wmat of bias */
+  std::string weight_name;
  };
 }  // namespace cxxnet
 

src/io/image_augmenter-inl.hpp

@@ -92,7 +92,6 @@ class ImageAugmenter {
     // new width and height
     float new_width = std::max(min_img_size_, std::min(max_img_size_, scale * src.cols));
     float new_height = std::max(min_img_size_, std::min(max_img_size_, scale * src.rows));
-    //printf("%f %f %f %f %f %f %f %f %f\n", s, a, b, scale, ratio, hs, ws, new_width, new_height);
     cv::Mat M(2, 3, CV_32F);
     M.at<float>(0, 0) = hs * a - s * b * ws;
     M.at<float>(1, 0) = -b * ws;

src/nnet/neural_net-inl.hpp

@@ -563,10 +563,11 @@ class NeuralNetThread {
           oparam_weight->Resize(mshadow::Shape2(0, 0));
         } else {
           oparam_weight->Resize(vs.data[0].shape_);
-          mshadow::Copy(*oparam_weight, vs.data[0]);
+          mshadow::Copy(*oparam_weight, vs.data[0], stream);
           *oparam_shape = vs.shapes[0];
           utils::Assert(vs.fields[0] == iparam_tag,
                         "GetWeight:shape mismatch");
+          stream->Wait();
         }
         return;
       }