Spaces:
Runtime error
Runtime error
import os.path | |
from data.base_dataset import BaseDataset, get_transform | |
from data.image_folder import make_dataset | |
from PIL import Image | |
import random | |
import util.util as util | |
class UnalignedDataset(BaseDataset): | |
""" | |
This dataset class can load unaligned/unpaired datasets. | |
It requires two directories to host training images from domain A '/path/to/data/trainA' | |
and from domain B '/path/to/data/trainB' respectively. | |
You can train the model with the dataset flag '--dataroot /path/to/data'. | |
Similarly, you need to prepare two directories: | |
'/path/to/data/testA' and '/path/to/data/testB' during test time. | |
""" | |
def __init__(self, opt): | |
"""Initialize this dataset class. | |
Parameters: | |
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions | |
""" | |
BaseDataset.__init__(self, opt) | |
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') # create a path '/path/to/data/trainA' | |
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainB' | |
if opt.phase == "test" and not os.path.exists(self.dir_A) \ | |
and os.path.exists(os.path.join(opt.dataroot, "valA")): | |
self.dir_A = os.path.join(opt.dataroot, "valA") | |
self.dir_B = os.path.join(opt.dataroot, "valB") | |
self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA' | |
self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size)) # load images from '/path/to/data/trainB' | |
self.A_size = len(self.A_paths) # get the size of dataset A | |
self.B_size = len(self.B_paths) # get the size of dataset B | |
def __getitem__(self, index): | |
"""Return a data point and its metadata information. | |
Parameters: | |
index (int) -- a random integer for data indexing | |
Returns a dictionary that contains A, B, A_paths and B_paths | |
A (tensor) -- an image in the input domain | |
B (tensor) -- its corresponding image in the target domain | |
A_paths (str) -- image paths | |
B_paths (str) -- image paths | |
""" | |
A_path = self.A_paths[index % self.A_size] # make sure index is within then range | |
if self.opt.serial_batches: # make sure index is within then range | |
index_B = index % self.B_size | |
else: # randomize the index for domain B to avoid fixed pairs. | |
index_B = random.randint(0, self.B_size - 1) | |
B_path = self.B_paths[index_B] | |
A_img = Image.open(A_path).convert('RGB') | |
B_img = Image.open(B_path).convert('RGB') | |
# Apply image transformation | |
# For FastCUT mode, if in finetuning phase (learning rate is decaying), | |
# do not perform resize-crop data augmentation of CycleGAN. | |
# print('current_epoch', self.current_epoch) | |
is_finetuning = self.opt.isTrain and self.current_epoch > self.opt.n_epochs | |
modified_opt = util.copyconf(self.opt, load_size=self.opt.crop_size if is_finetuning else self.opt.load_size) | |
transform = get_transform(modified_opt) | |
A = transform(A_img) | |
B = transform(B_img) | |
return {'A': A, 'B': B, 'A_paths': A_path, 'B_paths': B_path} | |
def __len__(self): | |
"""Return the total number of images in the dataset. | |
As we have two datasets with potentially different number of images, | |
we take a maximum of | |
""" | |
return max(self.A_size, self.B_size) | |