Add shell and Python syntax highlightings to README.md

README.md

@@ -17,7 +17,7 @@ This code was written with PyTorch<0.4, but most people must be using PyTorch>=0
 * [tensorboardX](https://github.com/lanpa/tensorboard-pytorch) (TensorBoard for PyTorch)
 
 ### 2. Train DnCNN-S (DnCNN with known noise level)
-```
+```shell
 python train.py \
   --preprocess True \
   --num_of_layers 17 \
@@ -31,7 +31,7 @@ python train.py \
 * *noiseL* is used for training and *val_noiseL* is used for validation. They should be set to the same value for unbiased validation. You can set whatever noise level you need.
 
 ### 3. Train DnCNN-B (DnCNN with blind noise level)
-```
+```shell
 python train.py \
   --preprocess True \
   --num_of_layers 20 \
@@ -44,7 +44,7 @@ python train.py \
 * *noiseL* is ingnored when training DnCNN-B. You can set *val_noiseL* to whatever you need.
 
 ### 4. Test
-```
+```shell
 python test.py \
   --num_of_layers 17 \
   --logdir logs/DnCNN-S-15 \
@@ -77,7 +77,7 @@ python test.py \
 ## Tricks useful for boosting performance
 * Parameter initialization:  
 Use *kaiming_normal* initialization for *Conv*; Pay attention to the initialization of *BatchNorm*
-```
+```python
 def weights_init_kaiming(m):
     classname = m.__class__.__name__
     if classname.find('Conv') != -1:
@@ -90,11 +90,11 @@ def weights_init_kaiming(m):
 ```
 * The definition of loss function  
 Set *size_average* to be False when defining the loss function. When *size_average=True*, the **pixel-wise average** will be computed, but what we need is **sample-wise average**.
-```
+```python
 criterion = nn.MSELoss(size_average=False)
 ```
 The computation of loss will be like:
-```
+```python
 loss = criterion(out_train, noise) / (imgn_train.size()[0]*2)
 ```
 where we divide the sum over one batch of samples by *2N*, with *N* being # samples.