-
Notifications
You must be signed in to change notification settings - Fork 3
/
functions.py
468 lines (378 loc) · 15.5 KB
/
functions.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
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
#!/usr/bin/env python
# coding: utf-8
import datetime
import numbers
import os
import shutil
import sys
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from cnn_finetune import make_model
from multiprocessing import cpu_count
from torchvision.transforms.functional import center_crop, hflip, vflip, resize
class IncrementalVariance(object):
def __init__(self, avg=0, count=0, var=0):
self.avg = avg
self.count = count
self.var = var
def update(self, avg, count, var):
delta = self.avg - avg
m_a = self.var * (self.count - 1)
m_b = var * (count - 1)
M2 = m_a + m_b + delta ** 2 * self.count * count / (self.count + count)
self.var = M2 / (self.count + count - 1)
self.avg = (self.avg * self.count + avg * count) / (self.count + count)
self.count = self.count + count
@property
def average(self):
return self.avg
@property
def variance(self):
return self.var
@property
def std(self):
return np.sqrt(self.var)
class Metric(object):
def __init__(self, name):
self.name = name
self.sum = torch.tensor(0.)
self.n = torch.tensor(0.)
def update(self, val):
self.sum += val.item()
self.n += 1
@property
def avg(self):
return self.sum / self.n
def accuracy(output, target):
pred = output.max(1, keepdim=True)[1]
return pred.eq(target.view_as(pred)).cpu().float().mean()
def print_batch(batch, epoch, current_num, total_num, ratio, speed, average_acc, average_loss, logger):
logger.info('Epoch[{}] Batch[{}] [{}/{} ({:.0f}%)]\tspeed: {:.2f} samples/sec\taccuracy: {:.10f}\tloss: {:.10f}'.format(
epoch, batch, current_num, total_num, ratio, speed, average_acc, average_loss))
def report(epoch, phase, loss_name, loss_avg, acc_name, acc_avg, logger, log_writer):
logger.info("Epoch[{}] {}-accuracy: {}".format(epoch, phase, acc_avg))
logger.info("Epoch[{}] {}-loss: {}".format(epoch, phase, loss_avg))
if log_writer:
log_writer.add_scalar(loss_name, loss_avg, epoch)
log_writer.add_scalar(acc_name, acc_avg, epoch)
def report_lr(epoch, lr_name, lr, logger, log_writer):
logger.info("Epoch[{}] learning-rate: {}".format(epoch, lr))
if log_writer:
log_writer.add_scalar(lr_name, lr, epoch)
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
def save_model(args, base_model, optimizer, scheduler, is_best, num_classes, class_names, epoch, acc1, logger):
filepath = '{}-{}-{:04}.model'.format(args.prefix, args.model, epoch+1)
savepath = os.path.join(args.model_dir, filepath)
state = {
'model': base_model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'arch': args.model,
'num_classes': num_classes,
'class_names': class_names,
'args': args,
'epoch': epoch + 1,
'acc1': float(acc1)
}
os.makedirs(args.model_dir, exist_ok=True)
if not (args.save_best_only or args.save_best_and_last):
torch.save(state, savepath)
logger.info("=> Saved checkpoint to \"{}\"".format(savepath))
if is_best:
filepath = '{}-{}-best.model'.format(args.prefix, args.model)
bestpath = os.path.join(args.model_dir, filepath)
if args.save_best_only or args.save_best_and_last:
torch.save(state, bestpath)
else:
shutil.copyfile(savepath, bestpath)
logger.info("=> Saved checkpoint to \"{}\"".format(bestpath))
if (args.epochs - 1 == epoch) and args.save_best_and_last:
torch.save(state, savepath)
logger.info("=> Saved checkpoint to \"{}\"".format(savepath))
def load_checkpoint(args, model_path):
device = torch.device("cuda" if args.cuda else "cpu")
print("=> loading saved checkpoint '{}'".format(model_path))
checkpoint = torch.load(model_path, map_location=device)
return checkpoint
def load_model_from_checkpoint(args, checkpoint, test_num_classes, test_class_names):
device = torch.device("cuda" if args.cuda else "cpu")
model_arch = checkpoint['arch']
num_classes = checkpoint.get('num_classes', 0)
if num_classes == 0:
num_classes = test_num_classes
base_model = make_model(model_arch, num_classes=num_classes, pretrained=False)
base_model.load_state_dict(checkpoint['model'])
class_names = checkpoint.get('class_names', [])
if len(class_names) == 0:
class_names = test_class_names
if args.cuda:
model = nn.DataParallel(base_model)
else:
model = base_model
model.to(device)
return model, num_classes, class_names
def check_args(args):
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.mixup and args.ricap:
warnings.warn('You can only one of the --mixup and --ricap can be activated.')
sys.exit(1)
if args.cutout and args.random_erasing:
warnings.warn('You can only one of the --cutout and --random-erasing can be activated.')
sys.exit(1)
try:
args.lr_step_epochs = [int(epoch) for epoch in args.lr_step_epochs.split(',')]
except ValueError:
warnings.warn('invalid --lr-step-epochs')
sys.exit(1)
try:
args.random_resized_crop_scale = [float(scale) for scale in args.random_resized_crop_scale.split(',')]
if len(args.random_resized_crop_scale) != 2:
raise ValueError
except ValueError:
warnings.warn('invalid --random-resized-crop-scale')
sys.exit(1)
try:
args.random_resized_crop_ratio = [float(ratio) for ratio in args.random_resized_crop_ratio.split(',')]
if len(args.random_resized_crop_ratio) != 2:
raise ValueError
except ValueError:
warnings.warn('invalid --random-resized-crop-ratio')
sys.exit(1)
if args.prefix == 'auto':
args.prefix = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
if args.workers is None:
args.workers = max(1, int(0.8 * cpu_count()))
elif args.workers == -1:
args.workers = cpu_count()
if args.seed is not None:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
args.rgb_mean = [float(mean) for mean in args.rgb_mean.split(',')]
args.rgb_std = [float(std) for std in args.rgb_std.split(',')]
if args.model == 'pnasnet5large':
scale_size = 352
input_size = 331
elif 'inception' in args.model:
scale_size = 320
input_size = 299
elif 'xception' in args.model:
scale_size = 320
input_size = 299
else:
scale_size = 256
input_size = 224
if args.scale_size:
scale_size = args.scale_size
else:
args.scale_size = scale_size
if args.input_size:
input_size = args.input_size
else:
args.input_size = input_size
if not args.cutout:
args.cutout_holes = None
args.cutout_length = None
if not args.random_erasing:
args.random_erasing_p = None
args.random_erasing_r1 = None
args.random_erasing_r2 = None
args.random_erasing_sh = None
args.random_erasing_sl = None
if not args.mixup:
args.mixup_alpha = None
if not args.ricap:
args.ricap_beta = None
args.ricap_with_line = False
return args
def custom_six_crop(img, size):
"""Crop the given PIL Image into custom six crops.
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
Returns:
tuple: tuple (tl, tr, bl, br, center, full)
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
w, h = img.size
crop_h, crop_w = size
if crop_w > w or crop_h > h:
raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
(h, w)))
tl = img.crop((0, 0, crop_w, crop_h))
tr = img.crop((w - crop_w, 0, w, crop_h))
bl = img.crop((0, h - crop_h, crop_w, h))
br = img.crop((w - crop_w, h - crop_h, w, h))
center = center_crop(img, (crop_h, crop_w))
full = resize(img, (crop_h, crop_w))
return (tl, tr, bl, br, center, full)
def custom_seven_crop(img, size):
"""Crop the given PIL Image into custom seven crops.
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
Returns:
tuple: tuple (tl, tr, bl, br, center, semi_full, full)
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
w, h = img.size
crop_h, crop_w = size
if crop_w > w or crop_h > h:
raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
(h, w)))
shift_w = int(round(w - crop_w) / 4.)
shift_h = int(round(h - crop_h) / 4.)
tl = img.crop((0, 0, crop_w, crop_h))
tr = img.crop((w - crop_w, 0, w, crop_h))
bl = img.crop((0, h - crop_h, crop_w, h))
br = img.crop((w - crop_w, h - crop_h, w, h))
center = center_crop(img, (crop_h, crop_w))
semi_full = resize(img.crop((shift_w, shift_h, w - shift_w, h - shift_h)), (crop_h, crop_w))
full = resize(img, (crop_h, crop_w))
return (tl, tr, bl, br, center, semi_full, full)
def custom_ten_crop(img, size):
"""Crop the given PIL Image into custom ten crops.
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
Returns:
tuple: tuple (tl, tr, bl, br, center, tl2, tr2, bl2, br2, full)
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
w, h = img.size
crop_h, crop_w = size
if crop_w > w or crop_h > h:
raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
(h, w)))
shift_w = int(round(w - crop_w) / 4.)
shift_h = int(round(h - crop_h) / 4.)
tl = img.crop((0, 0, crop_w, crop_h))
tr = img.crop((w - crop_w, 0, w, crop_h))
bl = img.crop((0, h - crop_h, crop_w, h))
br = img.crop((w - crop_w, h - crop_h, w, h))
center = center_crop(img, (crop_h, crop_w))
tl2 = img.crop((shift_w, shift_h, crop_w + shift_w, crop_h + shift_h)) # + +
tr2 = img.crop((w - crop_w - shift_w, shift_h, w - shift_w, crop_h + shift_h)) # - +
bl2 = img.crop((shift_w, h - crop_h - shift_h, crop_w + shift_w, h - shift_h)) # + -
br2 = img.crop((w - crop_w - shift_w, h - crop_h - shift_h, w - shift_w, h - shift_h)) # - -
full = resize(img, (crop_h, crop_w))
return (tl, tr, bl, br, center, tl2, tr2, bl2, br2, full)
def custom_twenty_crop(img, size, vertical_flip=False):
r"""Crop the given PIL Image into custom twenty crops.
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
vertical_flip (bool): Use vertical flipping instead of horizontal
Returns:
tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
Corresponding top left, top right, bottom left, bottom right and center crop
and same for the flipped image.
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
first_ten = custom_ten_crop(img, size)
if vertical_flip:
img = vflip(img)
else:
img = hflip(img)
second_ten = custom_ten_crop(img, size)
return first_ten + second_ten
def rand_bbox(size, lam):
W = size[2]
H = size[3]
cut_rat = np.sqrt(1. - lam)
cut_w = np.int(W * cut_rat)
cut_h = np.int(H * cut_rat)
# uniform
cx = np.random.randint(W)
cy = np.random.randint(H)
bbx1 = np.clip(cx - cut_w // 2, 0, W)
bby1 = np.clip(cy - cut_h // 2, 0, H)
bbx2 = np.clip(cx + cut_w // 2, 0, W)
bby2 = np.clip(cy + cut_h // 2, 0, H)
return bbx1, bby1, bbx2, bby2
class CustomSixCrop(object):
def __init__(self, size):
self.size = size
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
self.size = size
def __call__(self, img):
return custom_six_crop(img, self.size)
def __repr__(self):
return self.__class__.__name__ + '(size={0})'.format(self.size)
class CustomSevenCrop(object):
def __init__(self, size):
self.size = size
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
self.size = size
def __call__(self, img):
return custom_seven_crop(img, self.size)
def __repr__(self):
return self.__class__.__name__ + '(size={0})'.format(self.size)
class CustomTenCrop(object):
def __init__(self, size):
self.size = size
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
self.size = size
def __call__(self, img):
return custom_ten_crop(img, self.size)
def __repr__(self):
return self.__class__.__name__ + '(size={0})'.format(self.size)
class CustomTwentyCrop(object):
def __init__(self, size):
self.size = size
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
self.size = size
def __call__(self, img):
return custom_twenty_crop(img, self.size)
def __repr__(self):
return self.__class__.__name__ + '(size={0})'.format(self.size)