為什么HDF5輸出Caffe層寫入看似不正確的尺寸數據？

Question

我有興趣將給定Caffe圖層的輸出寫入文件。 我想為多個圖像執行此操作，因此我對HDF5輸出層代碼進行了一些修改，以便為包含每個圖像的功能的每個圖像創建文件。 這是修改后的SaveBlobs函數：

template <typename Dtype> void HDF5OutputLayer<Dtype>::SaveBlobs() {                               
  LOG(INFO) << "Saving HDF5 file " << file_name_ << "ds: " << ds_iter_;  
  CHECK_EQ(data_blob_.num(), label_blob_.num()) <<                       
      "data blob and label blob must have the same batch size";          

  // Open hdf5 file to write this blob                                   
  file_name_ = this->layer_param_.hdf5_output_param().file_name();          
  ostringstream appender;                                                
  appender << "_" << ds_iter_ << ".h5";                                  
  file_name_.append(appender.str());                                     
  file_id_ = H5Fcreate(file_name_.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,   
                       H5P_DEFAULT);                                     
  CHECK_GE(file_id_, 0) << "Failed to open HDF5 file" << file_name_;        

  // Write the data and label                                            
  hdf5_save_nd_dataset(file_id_, HDF5_DATA_DATASET_NAME, data_blob_);       
  hdf5_save_nd_dataset(file_id_, HDF5_DATA_LABEL_NAME, label_blob_);        
  LOG(INFO) << "Successfully saved " << data_blob_.num() << " rows";        
  LOG(INFO) << "SAVEBLOB - Data size is: " << data_blob_.shape_string(); 
  LOG(INFO) << "SAVEBLOB - Label size is: " << label_blob_.shape_string();

  // Close the file                                                      
  herr_t status = H5Fclose(file_id_);                                    
  CHECK_GE(status, 0) << "Failed to close HDF5 file " << file_name_;        

  // Update iterator for next image                                      
  ds_iter_++;                                                            
}   

代碼幾乎可以很好地工作，因為我能夠為每個實際上包含數據的圖像成功創建文件。 不幸的是，似乎寫入了錯誤的數據，因為日志和結果輸出文件中顯示的維度都不正確。 這是我指定輸出層的位置（在網絡prototext中）：

layer {                                                                  
  name: "conv5_3"                                                        
  type: "Convolution"                                                    
  bottom: "conv5_2"                                                      
  top: "conv5_3"                                                         
  param {                                                                
    lr_mult: 1                                                           
    decay_mult: 1                                                        
  }                                                                      
  param {                                                                
    lr_mult: 2                                                           
    decay_mult: 0                                                        
  }                                                                      
  convolution_param {                                                    
    num_output: 512                                                      
    pad: 1                                                               
    kernel_size: 3                                                       
  }                                                                      
}                                                                        
layer {                                                                  
  name: "relu5_3"                                                        
  type: "ReLU"                                                           
  bottom: "conv5_3"                                                      
  top: "conv5_3"                                                         
}                                                                        

#===== Data Logging =======                                              

layer {                                                                  
   type: "HDF5Output"                                                    
   name: "hdf5output"                                                    
   bottom: "conv5_3"   #                                                 
   bottom: "conv5_3"   #                                                 
   hdf5_output_param {                                                   
     # File name is only a base                                          
     file_name: "./test_features/image"                                  
   }                                                                     
 }   

我相信保存錯誤數據的原因是因為當我觀察網絡​​設置時，top of conv5_3的尺寸如下所示：

I0206 23:07:44.815330  7630 layer_factory.hpp:77] Creating layer conv5_3_relu5_3_0_split
I0206 23:07:44.815343  7630 net.cpp:106] Creating Layer conv5_3_relu5_3_0_split
I0206 23:07:44.815348  7630 net.cpp:454] conv5_3_relu5_3_0_split <- conv5_3
I0206 23:07:44.815356  7630 net.cpp:411] conv5_3_relu5_3_0_split -> conv5_3_relu5_3_0_split_0
I0206 23:07:44.815366  7630 net.cpp:411] conv5_3_relu5_3_0_split -> conv5_3_relu5_3_0_split_1
I0206 23:07:44.815372  7630 net.cpp:411] conv5_3_relu5_3_0_split -> conv5_3_relu5_3_0_split_2
I0206 23:07:44.815382  7630 net.cpp:411] conv5_3_relu5_3_0_split -> conv5_3_relu5_3_0_split_3
I0206 23:07:44.815459  7630 net.cpp:150] Setting up conv5_3_relu5_3_0_split
I0206 23:07:44.815467  7630 net.cpp:157] Top shape: 1 512 14 14 (100352)
I0206 23:07:44.815474  7630 net.cpp:157] Top shape: 1 512 14 14 (100352)
I0206 23:07:44.815479  7630 net.cpp:157] Top shape: 1 512 14 14 (100352)
I0206 23:07:44.815484  7630 net.cpp:157] Top shape: 1 512 14 14 (100352)
I0206 23:07:44.815495  7630 net.cpp:165] Memory required for data: 116006912
I0206 23:07:44.815500  7630 layer_factory.hpp:77] Creating layer hdf5output
I0206 23:07:44.815511  7630 net.cpp:106] Creating Layer hdf5output
I0206 23:07:44.815515  7630 net.cpp:454] hdf5output <- conv5_3_relu5_3_0_split_0
I0206 23:07:44.815521  7630 net.cpp:454] hdf5output <- conv5_3_relu5_3_0_split_1
I0206 23:07:44.815527  7630 net.cpp:150] Setting up hdf5output
I0206 23:07:44.815531  7630 net.cpp:165] Memory required for data: 116006912

太棒了，我希望得到尺寸為1 512 14 14的數據。不幸的是，當我對模型進行推理時，我在日志中看到錯誤的尺寸顯示出來：

I0206 23:07:46.108660  7630 hdf5_output_layer.cpp:31] Saving HDF5 file ds: 0
I0206 23:07:46.115536  7630 hdf5_output_layer.cpp:48] Successfully saved 1 rows
I0206 23:07:46.115557  7630 hdf5_output_layer.cpp:49] SAVEBLOB - Data size is: 1 512 54 38 (1050624)
I0206 23:07:46.115566  7630 hdf5_output_layer.cpp:50] SAVEBLOB - Label size is: 1 512 54 38 (1050624)
I0206 23:07:46.316557  7630 hdf5_output_layer.cpp:31] Saving HDF5 file ./test_features/image_0.h5ds: 1
I0206 23:07:46.322437  7630 hdf5_output_layer.cpp:48] Successfully saved 1 rows
I0206 23:07:46.322456  7630 hdf5_output_layer.cpp:49] SAVEBLOB - Data size is: 1 512 56 38 (1089536)
I0206 23:07:46.322463  7630 hdf5_output_layer.cpp:50] SAVEBLOB - Label size is: 1 512 56 38 (1089536)
I0206 23:07:46.457828  7630 hdf5_output_layer.cpp:31] Saving HDF5 file ./test_features/image_1.h5ds: 2
I0206 23:07:46.463618  7630 hdf5_output_layer.cpp:48] Successfully saved 1 rows
I0206 23:07:46.463636  7630 hdf5_output_layer.cpp:49] SAVEBLOB - Data size is: 1 512 38 50 (972800)
I0206 23:07:46.463644  7630 hdf5_output_layer.cpp:50] SAVEBLOB - Label size is: 1 512 38 50 (972800)
I0206 23:07:46.594746  7630 hdf5_output_layer.cpp:31] Saving HDF5 file ./test_features/image_2.h5ds: 3

這表明不僅輸出的尺寸不正確，而且它們在迭代（圖像）之間也有所不同！ 日志中顯示的維度與寫入h5文件的數據的維度相匹配，因此日志准確描述了代碼的行為。 我的問題是為什么會出現這種情況？ 似乎我已經正確地設置了一切，但必須有一些我不知道的東西......

Answer 1

正如@hbaderts幫助我發現的那樣，事實證明HDF5層是正確的並輸出正確尺寸的數據。 我對維度的困惑是由於網絡原型文本的測試版本中看似靜態的輸入維度定義。 事實證明，因為我通過使用pycaffe函數net.forward（** forward_kwargs）加載數據，卷積層正在自我縮放以滿足輸入圖像的不同輸入維度。 這解釋了大於預期的特征尺寸以及它們在圖像之間變化的事實。