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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
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