update train—faster method

README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 The repository is a 3DUNet implemented with pytorch, referring to this [project](https://github.com/panxiaobai/lits_pytorch). I have redesigned the code structure and used the model to perform liver and tumor segmentation on the lits2017 dataset.  
-requirement:  
+#### requirement:  
 ```angular2
 pytorch >= 1.1.0
 torchvision
@@ -12,16 +12,16 @@ Scipy
 ```
 ## 
 
-### Quickly Start
-#### 1) LITS2017 dataset preprocessing: 
+## Quickly Start
+### 1) LITS2017 dataset preprocessing: 
 1. Download dataset from google drive: [Liver Tumor Segmentation Challenge.](https://drive.google.com/drive/folders/0B0vscETPGI1-Q1h1WFdEM2FHSUE)  
 Or from my share: https://pan.baidu.com/s/1WgP2Ttxn_CV-yRT4UyqHWw 
 Extraction code：hfl8   
 2. Then you need decompress the data set and put the volume data and segmentation labels into different local folders, such as `./dataset/data` and `./dataset/label`
 3. Finally, you need to change the root path of the volume data and segmentation labels in `preprocess/preprocess_LiTS.py`, such as:
 ```
     row_dataset_path = './dataset/'  # path of origin dataset
-    fixed_dataset_path = './fixed/'  # path of fixed/preprocessed dataset
+    fixed_dataset_path = './fixed/'  # path of fixed(preprocessed) dataset
 ```   
 4 run `python preprocess/preprocess_LiTS.py`   
 If nothing goes wrong, you can see the following files in the dir `./fixed`
@@ -41,7 +41,9 @@ If nothing goes wrong, you can see the following files in the dir `./fixed`
         segmentation-10.nii
         ...
 ```  
-#### 2) Training 3DUNet
+### 2) Training 3DUNet
 1. Firstly, you should change the some parameters in `config.py`,especially, please set `--dataset_path` to `./fixed`  
 All parameters are commented in the file `config.py`  
 2. Secondely,run `python train.py`  
+---
+In addition, during the training process you will find that loading train data is time-consuming, you can use `train_faster.py` to train model. `train_faster.py` calls `./dataset/dataset2_lits.py`, which will crop multiple training samples from an input sample to form a batch for quick training

train_faster.py

@@ -97,7 +97,7 @@ def train(model, train_loader, optimizer, epoch, logger):
     model = UNet(1, [32, 48, 64, 96, 128], 3, net_mode='3d',conv_block=RecombinationBlock).to(device)
     optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
     init_util.print_network(model)
-    # model = nn.DataParallel(model, device_ids=[0])  # multi-GPU
+    # model = nn.DataParallel(model, device_ids=[0])   # multi-GPU
 
     logger = logger.Logger('./output/{}'.format(args.save))
     for epoch in range(1, args.epochs + 1):