changed code formatting

README.rst

@@ -20,113 +20,112 @@ Features
 
 - **Trainers** (currently Vanilla, VanillaGAN, WGAN-GP, BEGAN, FisherGAN)
 
-.. code:: python3
-
-    trainer = dlt.train.VanillaGANTrainer(generator, discriminator, g_optim, d_optim)
-    for batch, (prediction, losses) in trainer(data_loader):
-        # Training happens in the iterator and relevant results are returned for each step
-
+```python
+trainer = dlt.train.VanillaGANTrainer(generator, discriminator, g_optim, d_optim)
+for batch, (prediction, losses) in trainer(data_loader):
+    # Training happens in the iterator and relevant results are returned for each step
+```
 - Built in configurable **parser** with arguments.
 
-.. code:: python3
-
-    opt = dlt.config.parse() # Has built in options (can add extra)
-    print('Some Settings: ', opt.experiment_name, opt.batch_size, opt.lr)
+```python
+opt = dlt.config.parse() # Has built in options (can add extra)
+print('Some Settings: ', opt.experiment_name, opt.batch_size, opt.lr)
+```
 
 - **Configuration files** support and parser compatible functions.
 
 .. code:: bash
-
-    $ python main.py @settings.cfg
-    Some Settings:  config_test 32 0.0001
+```bash
+$ python main.py @settings.cfg
+Some Settings:  config_test 32 0.0001
+```
 
 - **HDR imaging** support (.hdr, .exr, and .pfm formats)
 
-.. code:: python3
-
-    img = dlt.hdr.imread('test.pfm')
-    dlt.hdr.imwrite('test.exr', img)
+```python
+img = dlt.hdr.imread('test.pfm')
+dlt.hdr.imwrite('test.exr', img)
+```
 
 - **Checkpointing** of (torch serializable) objects; Network state dicts supported.
 
-.. code:: python3
-
-    data_chkp = Checkpointer('data')
-    data_chkp.save(np.array([1,2,3]))
-    a = data_chkp.load()
+```python
+data_chkp = Checkpointer('data')
+data_chkp.save(np.array([1,2,3]))
+a = data_chkp.load()
+```
 
 - **Image operations** and easy conversions between multiple library views (torch, cv, plt)
 
-.. code:: python3
-
-    img = cv2.imread('image.jpg') # Height x Width x Channels - BGR
-    dlt.viz.imshow(img, view='cv')  # Height x Width x Channels - RGB
-    tensor_with_torch_view = cv2torch(img) # Channels x Height x Width - RGB
+```python
+img = cv2.imread('image.jpg') # Height x Width x Channels - BGR
+dlt.viz.imshow(img, view='cv')  # Height x Width x Channels - RGB
+tensor_with_torch_view = cv2torch(img) # Channels x Height x Width - RGB
+```
 
 - Easy **visualization** (and make_grid supporting Arrays, Tensors, Variables and lists)
 
-.. code:: python3
-
-    for batch, (prediction, loss) in trainer(loader):
-        grid = dlt.util.make_grid([ batch[0], batch[1], prediction], size(3, opt.batch_size))
-        dlt.viz.imshow(grid, pause=0.01, title='Training Progress')
+```python
+for batch, (prediction, loss) in trainer(loader):
+    grid = dlt.util.make_grid([ batch[0], batch[1], prediction], size(3, opt.batch_size))
+    dlt.viz.imshow(grid, pause=0.01, title='Training Progress')
+```
 
 - Model parameter and layer input/outputs/gradients visualization.
 
-.. code:: python3
-
-    net = nn.Sequential(nn.Linear(10, 10))
-    dlt.viz.modules.forward_hook(net, [nn.Linear], tag='layer_outputs', histogram=False)
-    net(Variable(torch.Tensor(3,10)))
+```python
+net = nn.Sequential(nn.Linear(10, 10))
+dlt.viz.modules.forward_hook(net, [nn.Linear], tag='layer_outputs', histogram=False)
+net(Variable(torch.Tensor(3,10)))
+```
 
 - CSV **Logger**.
 
-.. code:: python3
-
-    log = dlt.util.Logger('losses', ['train_loss', 'val_loss'])
-    log({'train_loss': 10, 'val_loss':20})
+```python
+log = dlt.util.Logger('losses', ['train_loss', 'val_loss'])
+log({'train_loss': 10, 'val_loss':20})
+```
 
 - Command line tool for easy **plotting** of CSV files (with live updating).
 
-.. code:: bash
-
-    $ dlt-plot --file losses.csv train_loss val_loss --refresh 5 --loglog True --tail 100
+```bash
+$ dlt-plot --file losses.csv train_loss val_loss --refresh 5 --loglog True --tail 100
+```
 
 - A minimal **Progress bar** (with global on/off switch).
 
-.. code:: python3
-
-    from dlt.util import barit
-    barit.silent = False # Default is False
-    for batch in barit(loader, start='Loading'):
-        pass
-
+```python
+from dlt.util import barit
+barit.silent = False # Default is False
+for batch in barit(loader, start='Loading'):
+    pass
+```
 
 Installation
 ---------------------
 
 Make sure you have PyTorch_ installed. OpenCV is also required:
 
-.. code:: bash
-    
-    conda install -c menpo opencv
+```bash    
+conda install -c menpo opencv
+```
 
 conda install (recommended):
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. code:: bash
 
-    conda install -c demetris pydlt
+```bash
+conda install -c demetris pydlt
+```
 
 From source:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. code:: bash
-
-    git clone https://github.com/dmarnerides/pydlt.git
-    cd pydlt
-    python setup.py install
-
+```bash
+git clone https://github.com/dmarnerides/pydlt.git
+cd pydlt
+python setup.py install
+```
 
 About
 --------