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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') |
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self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') |
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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)) |
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self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size)) |
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self.A_size = len(self.A_paths) |
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self.B_size = len(self.B_paths) |
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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] |
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if self.opt.serial_batches: |
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index_B = index % self.B_size |
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else: |
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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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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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