clean code

ppgan/datasets/paired_dataset.py

@@ -23,7 +23,7 @@ class PairedDataset(BaseDataset):
                                    cfg.phase)  # get the image directory
         self.AB_paths = sorted(make_dataset(
             self.dir_AB, cfg.max_dataset_size))  # get image paths
-        # assert(self.cfg.transform.load_size >= self.cfg.transform.crop_size)   # crop_size should be smaller than the size of loaded image
+
         self.input_nc = self.cfg.output_nc if self.cfg.direction == 'BtoA' else self.cfg.input_nc
         self.output_nc = self.cfg.input_nc if self.cfg.direction == 'BtoA' else self.cfg.output_nc
         self.transforms = build_transforms(cfg.transforms)
@@ -53,15 +53,6 @@ class PairedDataset(BaseDataset):
         B = AB[:h, w2:, :]
 
         # apply the same transform to both A and B
-        # transform_params = get_params(self.opt, A.size)
-        # transform_params = get_params(self.cfg.transform, (w2, h))
-
-        # A_transform = get_transform(self.cfg.transform, transform_params, grayscale=(self.input_nc == 1))
-        # B_transform = get_transform(self.cfg.transform, transform_params, grayscale=(self.output_nc == 1))
-
-        # A = A_transform(A)
-        # B = B_transform(B)
-        # A, B = self.transforms((A, B))
         A, B = self.transforms((A, B))
 
         return {'A': A, 'B': B, 'A_paths': AB_path, 'B_paths': AB_path}

ppgan/datasets/transforms/builder.py

@@ -25,11 +25,6 @@ class Compose(object):
     def __call__(self, data):
         for f in self.transforms:
             try:
-                # multi-fileds in a sample
-                # if isinstance(data, Sequence):
-                #     data = f(*data)
-                # # single field in a sample, call transform directly
-                # else:
                 data = f(data)
             except Exception as e:
                 stack_info = traceback.format_exc()
@@ -39,10 +34,6 @@ class Compose(object):
         return data
 
 
-def build_transform(cfg):
-    pass
-
-
 def build_transforms(cfg):
     transforms = []
 

ppgan/datasets/transforms/transforms.py

 import sys
-import types
 import random
 import numbers
-import warnings
-import traceback
 import collections
 import numpy as np
 
 from paddle.utils import try_import
 import paddle.vision.transforms.functional as F
-import paddle.vision.transforms.transforms as T
 
 from .builder import TRANSFORMS
 
@@ -31,7 +27,6 @@ class Transform():
         """
         if args:
             for k, v in args.items():
-                # print(k, v)
                 if k != "self" and not k.startswith("_"):
                     setattr(self, k, v)
 
@@ -39,7 +34,6 @@ class Transform():
         raise NotImplementedError
 
     def __call__(self, inputs):
-        # print('debug:', type(inputs), type(inputs[0]))
         if isinstance(inputs, tuple):
             inputs = list(inputs)
         if self.keys is not None:
@@ -177,10 +171,6 @@ class RandomHorizontalFlip(Transform):
         return img
 
 
-# import paddle
-# paddle.vision.transforms.RandomHorizontalFlip
-
-
 @TRANSFORMS.register()
 class PairedRandomHorizontalFlip(RandomHorizontalFlip):
     def __init__(self, prob=0.5, keys=None):
@@ -271,11 +261,6 @@ class Permute(Transform):
         return img
 
 
-# import paddle
-# paddle.vision.transforms.Normalize
-# TRANSFORMS.register(T.Normalize)
-
-
 class Crop():
     def __init__(self, pos, size):
         self.pos = pos

ppgan/datasets/unpaired_dataset.py

@@ -35,8 +35,7 @@ class UnpairedDataset(BaseDataset):
         btoA = self.cfg.direction == 'BtoA'
         input_nc = self.cfg.output_nc if btoA else self.cfg.input_nc  # get the number of channels of input image
         output_nc = self.cfg.input_nc if btoA else self.cfg.output_nc  # get the number of channels of output image
-        # self.transform_A = get_transform(self.cfg.transform, grayscale=(input_nc == 1))
-        # self.transform_B = get_transform(self.cfg.transform, grayscale=(output_nc == 1))
+
         self.transform_A = build_transforms(self.cfg.transforms)
         self.transform_B = build_transforms(self.cfg.transforms)