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