This repository has been archived by the owner on Jul 3, 2024. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 56
/
PyTorchDatasets.py
316 lines (241 loc) · 10.5 KB
/
PyTorchDatasets.py
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
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
from PIL import Image
import os
import pickle
import torch
import random
import torchvision
from torchvision.datasets.vision import VisionDataset
from torchvision import datasets, transforms, utils
import numpy as np
from matplotlib import pyplot as plt
class CocoAnimals(VisionDataset):
def __init__(self, root=None, batch_size = 80, classes = None, transform=None, return_all=False , test_mode = False, imsize=128):
self.num_classes = len(classes)
if root==None:
root = os.path.join(os.environ["SSD"],"animals")
self.root = root
self.return_all = return_all
self.names = classes
self.nclasses = len(classes)
print(self.names)
self.mask_trans =transforms.Compose(
[ transforms.Resize(imsize),
transforms.CenterCrop(imsize),
transforms.ToTensor(),
])
self.fixed_transform = transforms.Compose(
[ transforms.Resize(imsize),
transforms.CenterCrop(imsize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
self.image_files = [os.listdir(os.path.join(self.root, n )) for n in self.names]
self.lenghts = [len(f) for f in self.image_files]
print("images per class ", self.lenghts)
self.transform = transform
self.totensor = transforms.ToTensor()
self.batch_size = batch_size
self.k = int(batch_size/self.num_classes)
self.length = sum([len(folder) for folder in self.image_files])
all_files = [ [os.path.join(self.root, n , f ) for f in files] for n, files in zip(self.names,self.image_files)]
self.all_files = []
self.all_labels = []
for i, f in enumerate(all_files):
self.all_files += f
self.all_labels += [i]*len(f)
with open( os.path.join(root, "merged_bbdict_v2.p"), "rb") as h:
self.bbox = pickle.load(h)
with open(os.path.join(root,"coco_ann_dict.p"),"rb") as h:
self.mask_coco = pickle.load(h)
self.fixed_files = []
self.fixed_impaths = []
self.fixed_labels = []
for _ in range(batch_size):
id = np.random.randint(self.nclasses)
file = random.choice(self.image_files[id])
image_path = os.path.join(self.root, self.names[id], file)
self.fixed_files.append(file)
self.fixed_impaths.append(image_path)
self.fixed_labels.append(id)
self.fixed_labels = torch.tensor(self.fixed_labels).long().cuda()
def __len__(self):
# Here, we need to return the number of samples in this dataset.
return self.length
def fixed_batch(self, return_labels = False):
if return_labels == True:
images = torch.stack([self.random_batch(0,0,file=fi,image_path=im)[0].cuda() for fi,im in zip(self.fixed_files,self.fixed_impaths) ])
labels = self.fixed_labels
return images, labels
else:
return torch.stack([self.random_batch(0,0,file=fi,image_path=im)[0].cuda() for fi,im in zip(self.fixed_files,self.fixed_impaths) ])
def single_batch(self):
w = [self.__getitem__(np.random.randint(self.length)) for _ in range(self.batch_size) ]
x = torch.stack([e[0].cuda() for e in w])
y = torch.stack([e[1].long().cuda() for e in w])
return x, y
def random_batch(self,index, id=0, file=None, image_path=None):
# this function adds some data augmentation by cropping and resizing the
# images with the help of the bounding boxes. In particular we make sure
# that the animal is still in the frame if we crop randomly and resize.
if image_path==None:
id = np.random.randint(self.nclasses)
file = random.choice(self.image_files[id])
image_path = os.path.join(self.root, self.names[id], file)
#image_name = self.random.choice(self.image_files[random.choice(self.names)]["image_files"])
fixed = False
usebbx = True
else:
fixed = True
usebbx = False
img = Image.open( image_path ).convert('RGB')
w,h = img.size
im_id = file.strip(".jpg")
if usebbx:
if im_id.isdigit():
origin = "coco"
bbox = {"bbox":self.mask_coco[file]["bbox"], "label": self.mask_coco[file]["category_id"]}
else:
origin = "oi"
bbox = self.bbox[file.strip(".jpg")]
if len(bbox["bbox"])>0:
usebbx = True
else:
usebbx = False
if usebbx:
if isinstance(bbox["bbox"][0], list):
idx = random.choice(np.arange(len(bbox["bbox"])))
bbox = bbox["bbox"][idx] # choose a random bbox from the list
else:
bbox = bbox["bbox"]
if usebbx:
if origin == "coco":
a = bbox[0]
b = bbox[1]
c = bbox[2]
d = bbox[3]
else:
a = float(bbox[0])*w
b = float(bbox[1])*w
c = float(bbox[2])*h
d = float(bbox[3])*h
a, b, c, d = a, c, b-a, d-c
eps = 0.0001
longer_side = max(h,d)
r_max = min(float(longer_side)/(d+eps), float(longer_side)/(c+eps))
r_min = 1.5
if r_max > r_min and w > 200 and h > 200 and c*d > 30*30:
r = 1 + np.random.rand()*(r_max-1)
d_new = r*d
c_new = r*c
longer_side = min ( max(c_new ,d_new ) , h, w)
d_new = max(longer_side, 150)
c_new = max(longer_side, 150)
a_new = max(0, a - 0.5*(c_new - c) )
b_new = max(0, b - 0.5*(d_new - d) )
if c_new+a_new > w:
a_new = max(0,a_new - ((c_new+a_new)-w))
if d_new+b_new>h:
b_new = max(0,b_new - ((d_new+b_new)-h))
c_new = c_new + a_new
d_new = d_new + b_new
img = img.crop((a_new,b_new,c_new,d_new))
idx = image_path
if not fixed:
img = self.transform(img)
elif fixed:
img = self.fixed_transform(img)
label = torch.LongTensor([id])
bbox = self.bbox[file.strip(".jpg")]
out = (img, label , idx)
return out
def exact_batch(self,index):
image_path = self.all_files[index]
img = Image.open( image_path ).convert('RGB')
img = self.transform(img)
id = self.all_labels[index]
label = torch.LongTensor([id])
return img, label , image_path
def __getitem__(self,index):
if self.return_all:
return self.exact_batch(index)
else:
return self.random_batch(index)
class FFHQ(VisionDataset):
def __init__(self, root, transform, batch_size = 60, test_mode = False, return_all = False, imsize=256):
self.root = root
self.transform = transform
self.return_all = return_all
print("root:",self.root)
all_folders = os.listdir(self.root)
self.length = sum([len(os.listdir(os.path.join(self.root,folder))) for folder in all_folders]) # = 70000
self.fixed_transform = transforms.Compose(
[ transforms.Resize(imsize),
transforms.CenterCrop(imsize),
#transforms.RandomHorizontalFlip(),
#transforms.ColorJitter(brightness=0.01, contrast=0.01, saturation=0.01, hue=0.01),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
self.fixed_indices = []
for _ in range(batch_size):
id = np.random.randint(self.length)
self.fixed_indices.append(id)
def __len__(self):
return self.length
def fixed_batch(self, random = False):
if random == False:
return torch.stack([self.random_batch(idx, True)[0].cuda() for idx in self.fixed_indices])
else:
return torch.stack([self.random_batch(np.random.randint(self.length), True)[0].cuda() for _ in range(len(self.fixed_indices))])
def random_batch(self,index, fixed=False):
folder = str(int(np.floor(index/1000)*1000)).zfill(5)
file = str(index).zfill(5) + ".png"
image_path = os.path.join(self.root, folder , file )
img = Image.open( image_path).convert('RGB')
if fixed:
img = self.fixed_transform(img)
else:
img = self.transform(img)
return img, torch.zeros(1).long(), image_path
def __getitem__(self,index):
if self.return_all:
return self.exact_batch(index)
else:
return self.random_batch(index)
class Celeba(VisionDataset):
def __init__(self, root, transform, batch_size = 60, test_mode = False, return_all = False, imsize=128):
self.root = root
self.transform = transform
self.return_all = return_all
all_files = os.listdir(self.root)
self.length = len(all_files)
self.fixed_transform = transforms.Compose(
[ transforms.Resize(imsize),
transforms.CenterCrop(imsize),
#transforms.RandomHorizontalFlip(),
#transforms.ColorJitter(brightness=0.01, contrast=0.01, saturation=0.01, hue=0.01),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
self.fixed_indices = []
for _ in range(batch_size):
id = np.random.randint(self.length)
self.fixed_indices.append(id)
def __len__(self):
return self.length
def fixed_batch(self):
return torch.stack([self.random_batch(idx, True)[0].cuda() for idx in self.fixed_indices])
def random_batch(self,index, fixed=False):
file = str(index+1).zfill(6) + '.png'
image_path = os.path.join(self.root, file )
img = Image.open( image_path).convert('RGB')
if fixed:
img = self.fixed_transform(img)
else:
img = self.transform(img)
return img, torch.zeros(1).long(), image_path
def __getitem__(self,index):
if self.return_all:
return self.exact_batch(index)
else:
return self.random_batch(index)