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