1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
|
import random
import numpy as np
import torch.utils.data
import torchvision.transforms as transforms
from data.base_data_loader import BaseDataLoader
from data.image_folder import ImageFolder
from pdb import set_trace as st
# pip install future --upgrade
from builtins import object
class PairedData(object):
def __init__(self, data_loader, fineSize, max_dataset_size, flip):
self.data_loader = data_loader
self.fineSize = fineSize
self.max_dataset_size = max_dataset_size
self.flip = flip
# st()
def __iter__(self):
self.data_loader_iter = iter(self.data_loader)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
AB, AB_paths = next(self.data_loader_iter)
w_total = AB.size(3)
w = int(w_total / 2)
h = AB.size(2)
w_offset = random.randint(0, max(0, w - self.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.fineSize - 1))
A = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset:w_offset + self.fineSize]
B = AB[:, :, h_offset:h_offset + self.fineSize,
w + w_offset:w + w_offset + self.fineSize]
if self.flip and random.random() < 0.5:
idx = [i for i in range(A.size(3) - 1, -1, -1)]
idx = torch.LongTensor(idx)
A = A.index_select(3, idx)
B = B.index_select(3, idx)
return {'A': A, 'A_paths': AB_paths, 'B': B, 'B_paths': AB_paths}
class AlignedDataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
self.fineSize = opt.fineSize
transformations = [
# TODO: Scale
transforms.Scale(opt.loadSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
transform = transforms.Compose(transformations)
# Dataset A
dataset = ImageFolder(root=opt.dataroot + '/' + opt.phase,
transform=transform, return_paths=True)
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=self.opt.batchSize,
shuffle=not self.opt.serial_batches,
num_workers=int(self.opt.nThreads))
self.dataset = dataset
flip = opt.isTrain and not opt.no_flip
self.paired_data = PairedData(data_loader, opt.fineSize,
opt.max_dataset_size, flip)
def name(self):
return 'AlignedDataLoader'
def load_data(self):
return self.paired_data
def __len__(self):
return min(len(self.dataset), self.opt.max_dataset_size)
|
