update comments

models/base_model.py

@@ -22,7 +22,7 @@ def __init__(self, opt):
             opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
 
         When creating your custom class, you need to implement your own initialization.
-        In this fucntion, you should first call  `BaseModel.__init__(self, opt)`
+        In this fucntion, you should first call <BaseModel.__init__(self, opt)>
         Then, you need to define four lists:
             -- self.loss_names (str list):          specify the training losses that you want to plot and save.
             -- self.model_names (str list):         specify the images that you want to display and save.

models/pix2pix_model.py

@@ -24,7 +24,7 @@ def modify_commandline_options(parser, is_train=True):
         Returns:
             the modified parser.
 
-        For pix2pix, we do not use image buffer (pool_size=0),
+        For pix2pix, we do not use image buffer
         The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1
         By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets.
         """

train.py

@@ -1,7 +1,7 @@
 """General-purpose training script for image-to-image translation.
 
 This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
-different datasets (with option '--dataset_mode': e.g., aligned, unaligned, `single, colorization).
+different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
 You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').
 
 It first creates model, dataset, and visualizer given the option.