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fixed indentation rules, followed by pep8 #50

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fixed indentation rules, followed by pep8 #50

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b5bfbb3
update indentation
dev-sungman Aug 5, 2020
68584d7
update indent
Aug 5, 2020
4a521a7
fixed some errors (after editing indentation), add some line in .giti…
Aug 5, 2020
9b47044
remove .vscode folder
Aug 5, 2020
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2 changes: 2 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@ debug*
videos/
build/
dist/
datasets/portrait/
logs/
torch.egg-info/
*/**/__pycache__
torch/version.py
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138 changes: 69 additions & 69 deletions src/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,85 +5,85 @@
import random

class dataset_single(data.Dataset):
def __init__(self, opts, setname, input_dim):
self.dataroot = opts.dataroot
images = os.listdir(os.path.join(self.dataroot, opts.phase + setname))
self.img = [os.path.join(self.dataroot, opts.phase + setname, x) for x in images]
self.size = len(self.img)
self.input_dim = input_dim
def __init__(self, opts, setname, input_dim):
self.dataroot = opts.dataroot
images = os.listdir(os.path.join(self.dataroot, opts.phase + setname))
self.img = [os.path.join(self.dataroot, opts.phase + setname, x) for x in images]
self.size = len(self.img)
self.input_dim = input_dim

# setup image transformation
transforms = [Resize((opts.resize_size, opts.resize_size), Image.BICUBIC)]
transforms.append(CenterCrop(opts.crop_size))
transforms.append(ToTensor())
transforms.append(Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]))
self.transforms = Compose(transforms)
print('%s: %d images'%(setname, self.size))
return
# setup image transformation
transforms = [Resize((opts.resize_size, opts.resize_size), Image.BICUBIC)]
transforms.append(CenterCrop(opts.crop_size))
transforms.append(ToTensor())
transforms.append(Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]))
self.transforms = Compose(transforms)
print('%s: %d images'%(setname, self.size))
return

def __getitem__(self, index):
data = self.load_img(self.img[index], self.input_dim)
return data
def __getitem__(self, index):
data = self.load_img(self.img[index], self.input_dim)
return data

def load_img(self, img_name, input_dim):
img = Image.open(img_name).convert('RGB')
img = self.transforms(img)
if input_dim == 1:
img = img[0, ...] * 0.299 + img[1, ...] * 0.587 + img[2, ...] * 0.114
img = img.unsqueeze(0)
return img
def load_img(self, img_name, input_dim):
img = Image.open(img_name).convert('RGB')
img = self.transforms(img)
if input_dim == 1:
img = img[0, ...] * 0.299 + img[1, ...] * 0.587 + img[2, ...] * 0.114
img = img.unsqueeze(0)
return img

def __len__(self):
return self.size
def __len__(self):
return self.size

class dataset_unpair(data.Dataset):
def __init__(self, opts):
self.dataroot = opts.dataroot
def __init__(self, opts):
self.dataroot = opts.dataroot

# A
images_A = os.listdir(os.path.join(self.dataroot, opts.phase + 'A'))
self.A = [os.path.join(self.dataroot, opts.phase + 'A', x) for x in images_A]
# A
images_A = os.listdir(os.path.join(self.dataroot, opts.phase + 'A'))
self.A = [os.path.join(self.dataroot, opts.phase + 'A', x) for x in images_A]

# B
images_B = os.listdir(os.path.join(self.dataroot, opts.phase + 'B'))
self.B = [os.path.join(self.dataroot, opts.phase + 'B', x) for x in images_B]
# B
images_B = os.listdir(os.path.join(self.dataroot, opts.phase + 'B'))
self.B = [os.path.join(self.dataroot, opts.phase + 'B', x) for x in images_B]

self.A_size = len(self.A)
self.B_size = len(self.B)
self.dataset_size = max(self.A_size, self.B_size)
self.input_dim_A = opts.input_dim_a
self.input_dim_B = opts.input_dim_b
self.A_size = len(self.A)
self.B_size = len(self.B)
self.dataset_size = max(self.A_size, self.B_size)
self.input_dim_A = opts.input_dim_a
self.input_dim_B = opts.input_dim_b

# setup image transformation
transforms = [Resize((opts.resize_size, opts.resize_size), Image.BICUBIC)]
if opts.phase == 'train':
transforms.append(RandomCrop(opts.crop_size))
else:
transforms.append(CenterCrop(opts.crop_size))
if not opts.no_flip:
transforms.append(RandomHorizontalFlip())
transforms.append(ToTensor())
transforms.append(Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]))
self.transforms = Compose(transforms)
print('A: %d, B: %d images'%(self.A_size, self.B_size))
return
# setup image transformation
transforms = [Resize((opts.resize_size, opts.resize_size), Image.BICUBIC)]
if opts.phase == 'train':
transforms.append(RandomCrop(opts.crop_size))
else:
transforms.append(CenterCrop(opts.crop_size))
if not opts.no_flip:
transforms.append(RandomHorizontalFlip())
transforms.append(ToTensor())
transforms.append(Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]))
self.transforms = Compose(transforms)
print('A: %d, B: %d images'%(self.A_size, self.B_size))
return

def __getitem__(self, index):
if self.dataset_size == self.A_size:
data_A = self.load_img(self.A[index], self.input_dim_A)
data_B = self.load_img(self.B[random.randint(0, self.B_size - 1)], self.input_dim_B)
else:
data_A = self.load_img(self.A[random.randint(0, self.A_size - 1)], self.input_dim_A)
data_B = self.load_img(self.B[index], self.input_dim_B)
return data_A, data_B
def __getitem__(self, index):
if self.dataset_size == self.A_size:
data_A = self.load_img(self.A[index], self.input_dim_A)
data_B = self.load_img(self.B[random.randint(0, self.B_size - 1)], self.input_dim_B)
else:
data_A = self.load_img(self.A[random.randint(0, self.A_size - 1)], self.input_dim_A)
data_B = self.load_img(self.B[index], self.input_dim_B)
return data_A, data_B

def load_img(self, img_name, input_dim):
img = Image.open(img_name).convert('RGB')
img = self.transforms(img)
if input_dim == 1:
img = img[0, ...] * 0.299 + img[1, ...] * 0.587 + img[2, ...] * 0.114
img = img.unsqueeze(0)
return img
def load_img(self, img_name, input_dim):
img = Image.open(img_name).convert('RGB')
img = self.transforms(img)
if input_dim == 1:
img = img[0, ...] * 0.299 + img[1, ...] * 0.587 + img[2, ...] * 0.114
img = img.unsqueeze(0)
return img

def __len__(self):
return self.dataset_size
def __len__(self):
return self.dataset_size
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