109 lines
4.7 KiB
Python
109 lines
4.7 KiB
Python
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import numpy as np
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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 SingleImageDataset(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, 'trainA') # create a path '/path/to/data/trainA'
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self.dir_B = os.path.join(opt.dataroot, 'trainB') # create a path '/path/to/data/trainB'
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if os.path.exists(self.dir_A) and os.path.exists(self.dir_B):
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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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assert len(self.A_paths) == 1 and len(self.B_paths) == 1,\
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"SingleImageDataset class should be used with one image in each domain"
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A_img = Image.open(self.A_paths[0]).convert('RGB')
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B_img = Image.open(self.B_paths[0]).convert('RGB')
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print("Image sizes %s and %s" % (str(A_img.size), str(B_img.size)))
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self.A_img = A_img
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self.B_img = B_img
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# In single-image translation, we augment the data loader by applying
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# random scaling. Still, we design the data loader such that the
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# amount of scaling is the same within a minibatch. To do this,
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# we precompute the random scaling values, and repeat them by |batch_size|.
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A_zoom = 1 / self.opt.random_scale_max
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zoom_levels_A = np.random.uniform(A_zoom, 1.0, size=(len(self) // opt.batch_size + 1, 1, 2))
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self.zoom_levels_A = np.reshape(np.tile(zoom_levels_A, (1, opt.batch_size, 1)), [-1, 2])
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B_zoom = 1 / self.opt.random_scale_max
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zoom_levels_B = np.random.uniform(B_zoom, 1.0, size=(len(self) // opt.batch_size + 1, 1, 2))
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self.zoom_levels_B = np.reshape(np.tile(zoom_levels_B, (1, opt.batch_size, 1)), [-1, 2])
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# While the crop locations are randomized, the negative samples should
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# not come from the same location. To do this, we precompute the
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# crop locations with no repetition.
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self.patch_indices_A = list(range(len(self)))
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random.shuffle(self.patch_indices_A)
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self.patch_indices_B = list(range(len(self)))
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random.shuffle(self.patch_indices_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[0]
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B_path = self.B_paths[0]
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A_img = self.A_img
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B_img = self.B_img
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# apply image transformation
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if self.opt.phase == "train":
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param = {'scale_factor': self.zoom_levels_A[index],
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'patch_index': self.patch_indices_A[index],
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'flip': random.random() > 0.5}
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transform_A = get_transform(self.opt, params=param, method=Image.BILINEAR)
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A = transform_A(A_img)
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param = {'scale_factor': self.zoom_levels_B[index],
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'patch_index': self.patch_indices_B[index],
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'flip': random.random() > 0.5}
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transform_B = get_transform(self.opt, params=param, method=Image.BILINEAR)
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B = transform_B(B_img)
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else:
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transform = get_transform(self.opt, method=Image.BILINEAR)
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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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""" Let's pretend the single image contains 100,000 crops for convenience.
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"""
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return 100000
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