forked from HaoranZhao1988/CTKD
/
train.py
576 lines (509 loc) · 24.3 KB
/
train.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
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
"""
PyTorch training code for
"Rocket Launching: A unified and effecient framework for
training well-behaved light net"
This file includes:
* CIFAR ResNet and Wide ResNet training code which exactly reproduces
https://github.com/szagoruyko/wide-residual-networks
* Rocket-Launching light net well-behaved training
* Activation-based attention transfer
* Knowledge distillation implementation
"""
import argparse
import os
import json
import numpy as np
import cv2
import pandas as pd
import torch
import torch.optim
import torch.utils.data
import cvtransforms as T
import torchvision.datasets as datasets
from torch.autograd import Variable
import torch.nn.functional as F
import torchnet as tnt
import math
from torchnet.engine import Engine
import torch.backends.cudnn as cudnn
from utils import conv_params, linear_params, bnparams, bnstats, at_loss, batch_norm, \
distillation, rocket_distillation, cast, data_parallel, flatten_stats, flatten_params
cudnn.benchmark = True
CONST_STEP_FLAG = 0
parser = argparse.ArgumentParser(description='Wide Residual Networks')
# Model options
parser.add_argument('--depth', default=16, type=int)
parser.add_argument('--student_depth', default=0, type=int)
parser.add_argument('--width', default=1, type=float)
parser.add_argument('--dataset', default='CIFAR10', type=str)
parser.add_argument('--data_root', default='./CIFAR10', type=str)
parser.add_argument('--dtype', default='float', type=str)
parser.add_argument('--nthread', default=4, type=int)
parser.add_argument('--teacher_id', default='', type=str)
# Training options
parser.add_argument('--batchSize', default=128, type=int)
parser.add_argument('--lr', default=0.1, type=float)
parser.add_argument('--epochs', default=1, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--weightDecay', default=0.0005, type=float)
parser.add_argument('--epoch_step', default='[60,120,160]', type=str,
help='json list with epochs to drop lr on')
parser.add_argument('--lr_decay_ratio', default=0.2, type=float)
parser.add_argument('--resume', default='', type=str)
parser.add_argument('--optim_method', default='SGD', type=str)
parser.add_argument('--randomcrop_pad', default=4, type=float)
parser.add_argument('--temperature', default=4, type=float)
# wait to remove
parser.add_argument('--sigma_refine_step', default='[120,160,180]', type=str,
help='json list with epochs to refine running_sigma')
parser.add_argument('--running_sigma', default=0, type=float)
# turing option
parser.add_argument('--alpha', default=0, type=float,
help="weight for knowledge distilling")
parser.add_argument('--beta', default=0, type=float,
help="weight for attention transfer")
parser.add_argument('--gamma', default=0, type=float,
help="weight for hint loss")
parser.add_argument('--grad_block', default=True, type=bool,
help="switch for gradient block")
parser.add_argument('--param_share', default=False, type=bool,
help="switch for parameter sharing")
parser.add_argument('--dropout', default=0.0, type=float)
# Device options
parser.add_argument('--cuda', action='store_true')
parser.add_argument('--save', default='./', type=str,
help='save parameters and logs in this folder')
parser.add_argument('--ngpu', default=1, type=int,
help='number of GPUs to use for training')
parser.add_argument('--gpu_id', default='0', type=str,
help='id(s) for CUDA_VISIBLE_DEVICES')
opt = parser.parse_args()
if not os.path.exists("./logs"):
os.mkdir("./logs")
def create_dataset(opt, mode):
convert = tnt.transform.compose([
lambda x: x.astype(np.float32),
T.Normalize([125.3, 123.0, 113.9], [63.0, 62.1, 66.7]),
lambda x: x.transpose(2, 0, 1).astype(np.float32),
torch.from_numpy,
])
train_transform = tnt.transform.compose([
T.RandomHorizontalFlip(),
T.Pad(opt.randomcrop_pad, cv2.BORDER_REFLECT),
T.RandomCrop(32),
convert,
])
ds = getattr(datasets, opt.dataset)(
opt.data_root, train=mode, download=True)
smode = 'train' if mode else 'test'
ds = tnt.dataset.TensorDataset([
getattr(ds, smode + '_data'),
getattr(ds, smode + '_labels')])
return ds.transform({0: train_transform if mode else convert})
def resnet(depth, width, num_classes, stu_depth=0):
assert (depth - 4) % 6 == 0, 'depth should be 6n+4'
n = (depth - 4) // 6
if stu_depth != 0:
assert (stu_depth - 4) % 6 == 0, 'student depth should be 6n+4'
n_s = (stu_depth - 4) // 6
else:
n_s = 0
widths = torch.Tensor([16, 32, 64]).mul(width).int()
def gen_block_params(ni, no):
return {'conv0': conv_params(ni, no, 3),
'conv1': conv_params(no, no, 3),
'bn0': bnparams(ni),
'bn1': bnparams(no),
'bns0': bnparams(ni),
'bns1': bnparams(no),
'convdim': conv_params(ni, no, 1) if ni != no else None,
}
def gen_group_params(ni, no, count):
return {'block%d' % i: gen_block_params(ni if i == 0 else no, no)
for i in range(count)}
def gen_group_stats(ni, no, count):
return {'block%d' % i: {'bn0': bnstats(ni if i == 0 else no), 'bn1': bnstats(no), 'bns0': bnstats(ni if i == 0 else no), 'bns1': bnstats(no)}
for i in range(count)}
if stu_depth != 0 and not opt.param_share:
params = {'conv0': conv_params(3, 16, 3),
'group0': gen_group_params(16, widths[0], n),
'group1': gen_group_params(widths[0], widths[1], n),
'group2': gen_group_params(widths[1], widths[2], n),
'groups0': gen_group_params(16, widths[0], n_s),
'groups1': gen_group_params(widths[0], widths[1], n_s),
'groups2': gen_group_params(widths[1], widths[2], n_s),
'bn': bnparams(widths[2]),
'bns': bnparams(widths[2]),
'fc': linear_params(widths[2], num_classes),
'fcs': linear_params(widths[2], num_classes),
}
stats = {'group0': gen_group_stats(16, widths[0], n),
'group1': gen_group_stats(widths[0], widths[1], n),
'group2': gen_group_stats(widths[1], widths[2], n),
'groups0': gen_group_stats(16, widths[0], n_s),
'groups1': gen_group_stats(widths[0], widths[1], n_s),
'groups2': gen_group_stats(widths[1], widths[2], n_s),
'bn': bnstats(widths[2]),
'bns': bnstats(widths[2]),
}
else:
params = {'conv0': conv_params(3, 16, 3),
'group0': gen_group_params(16, widths[0], n),
'group1': gen_group_params(widths[0], widths[1], n),
'group2': gen_group_params(widths[1], widths[2], n),
'bn': bnparams(widths[2]),
'bns': bnparams(widths[2]),
'fc': linear_params(widths[2], num_classes),
'fcs': linear_params(widths[2], num_classes),
}
stats = {'group0': gen_group_stats(16, widths[0], n),
'group1': gen_group_stats(widths[0], widths[1], n),
'group2': gen_group_stats(widths[1], widths[2], n),
'bn': bnstats(widths[2]),
'bns': bnstats(widths[2]),
}
flat_params = flatten_params(params)
flat_stats = flatten_stats(stats)
def block(x, params, stats, base, mode, stride, flag, drop_switch=True):
if flag == 's':
o1 = F.relu(batch_norm(x, params, stats, base + '.bns0', mode))
y = F.conv2d(o1, params[base + '.conv0'], stride=stride, padding=1)
o2 = F.relu(batch_norm(y, params, stats, base + '.bns1', mode))
z = F.conv2d(o2, params[base + '.conv1'], stride=1, padding=1)
if base + '.convdim' in params:
return z + F.conv2d(o1, params[base + '.convdim'], stride=stride)
else:
return z + x
o1 = F.relu(batch_norm(x, params, stats, base + '.bn0', mode))
y = F.conv2d(o1, params[base + '.conv0'], stride=stride, padding=1)
o2 = F.relu(batch_norm(y, params, stats, base + '.bn1', mode))
if opt.dropout > 0 and drop_switch:
o2 = F.dropout(o2, p=opt.dropout, training=mode)
z = F.conv2d(o2, params[base + '.conv1'], stride=1, padding=1)
if base + '.convdim' in params:
return z + F.conv2d(o1, params[base + '.convdim'], stride=stride)
else:
return z + x
def group(o, params, stats, base, mode, stride):
for i in range(n):
o = block(o, params, stats, '%s.block%d' % (base, i),
mode, stride if i == 0 else 1, 't', False)
return o
def group_student(o, params, stats, base, mode, stride, n_layer):
for i in range(n_layer):
o = block(o, params, stats, '%s.block%d' %
(base, i), mode, stride if i == 0 else 1, 's', False)
return o
def f(input, params, stats, mode, prefix=''):
x = F.conv2d(input, params[prefix + 'conv0'], padding=1)
g0 = group(x, params, stats, prefix + 'group0', mode, 1)
g1 = group(g0, params, stats, prefix + 'group1', mode, 2)
g2 = group(g1, params, stats, prefix + 'group2', mode, 2)
o = F.relu(batch_norm(g2, params, stats, prefix + 'bn', mode))
o = F.avg_pool2d(o, 8, 1, 0)
o = o.view(o.size(0), -1)
o = F.linear(o, params[prefix + 'fc.weight'], params[prefix + 'fc.bias'])
if stu_depth != 0:
if opt.param_share:
# with parameter sharing
gs0 = group_student(x, params, stats, prefix + 'group0', mode, 1, n_s)
gs1 = group_student(gs0, params, stats, prefix + 'group1', mode, 2, n_s)
gs2 = group_student(gs1, params, stats, prefix + 'group2', mode, 2, n_s)
else:
gs0 = group_student(x, params, stats, prefix + 'groups0', mode, 1, n_s)
gs1 = group_student(gs0, params, stats, prefix + 'groups1', mode, 2, n_s)
gs2 = group_student(gs1, params, stats, prefix + 'groups2', mode, 2, n_s)
os = F.relu(batch_norm(gs2, params, stats, prefix + 'bns', mode))
os = F.avg_pool2d(os, 8, 1, 0)
os = os.view(os.size(0), -1)
os = F.linear(os, params[prefix + 'fcs.weight'], params[prefix + 'fcs.bias'])
return os, o, [g0, g1, g2, gs0, gs1, gs2]
else:
return o, [g0, g1, g2]
return f, flat_params, flat_stats
def main():
global CONST_STEP_FLAG
CONST_STEP_FLAG = 0
# opt = parser.parse_args()
# option note
# assert not(opt.beta and opt.gamma), "Can't support attention-transfer and rocket-launching together"
print 'parsed options:', vars(opt)
epoch_step = json.loads(opt.epoch_step)
sigma_refine_step = json.loads(opt.sigma_refine_step)
num_classes = 10 if opt.dataset == 'CIFAR10' else 100
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
# to prevent opencv from initializing CUDA in workers
torch.randn(8).cuda()
os.environ['CUDA_VISIBLE_DEVICES'] = ''
torch.cuda.manual_seed(1)
def create_iterator(mode):
ds = create_dataset(opt, mode)
return ds.parallel(batch_size=opt.batchSize, shuffle=mode,
num_workers=opt.nthread, pin_memory=True)
train_loader = create_iterator(True)
test_loader = create_iterator(False)
# deal with student first
f_s, params_s, stats_s = resnet(opt.depth, opt.width, num_classes, opt.student_depth)
# load pre-trained teacher params
# with open(os.path.join('/root/data/outputs/haoran/rocket/resnet_40_1_booster', opt.teacher_id, 'log.txt'),
# 'r') as ff:
# line = ff.readline()
# r = line.find('json_stats')
# info = json.loads(line[r + 12:])
# f_t = resnet(info['depth'], info['width'], num_classes)[0]
# model_data = torch.load(
# os.path.join('/root/data/outputs/haoran/rocket/resnet_40_1_booster', opt.teacher_id, 'model.pt7'))
# params_t = model_data['params']
# stats_t = model_data['stats']
#
# params_t = flatten_params(params_t)
# for k, v in params_s.iteritems():
# if 'block0' in k or 'block1'in k:
# #if 'conv0' in k:
# ks = k
# params_s[ks] = params_t[ks.replace('groups','group')]
# deal with teacher
if opt.teacher_id != '':
with open(os.path.join('/root/data/outputs/haoran/rocket', opt.teacher_id, 'log.txt'), 'r') as ff:
line = ff.readline()
r = line.find('json_stats')
info = json.loads(line[r + 12:])
f_t = resnet(info['depth'], info['width'], num_classes)[0]
model_data = torch.load(os.path.join(
'/root/data/outputs/haoran/rocket', opt.teacher_id, 'model.pt7'))
params_t = model_data['params']
stats_t = model_data['stats']
# merge teacher and student params and stats
params = {'student.' + k: v for k, v in params_s.iteritems()}
for k, v in params_t.iteritems():
params['teacher.' + k] = Variable(v)
stats = {'student.' + k: v for k, v in stats_s.iteritems()}
stats.update({'teacher.' + k: v for k, v in stats_t.iteritems()})
def f(inputs, params, stats, mode):
if opt.gamma:
y_s, y_t_auto, g_s = f_s(inputs, params, stats, mode, 'student.')
y_t, g_t = f_t(inputs, params, stats, False, 'teacher.')
return y_s, y_t_auto, y_t, [at_loss(x, y) for x, y in zip(g_s, g_t)]
else:
y_s, g_s = f_s(inputs, params, stats, mode, 'student.')
y_t, g_t = f_t(inputs, params, stats, False, 'teacher.')
return y_s, y_t, [at_loss(x, y) for x, y in zip(g_s, g_t)]
else:
f, params, stats = f_s, params_s, stats_s
# print(params)
optimizable = [v for v in params.itervalues() if v.requires_grad]
def create_optimizer(opt, lr):
print 'creating optimizer with lr = ', lr
if opt.optim_method == 'SGD':
return torch.optim.SGD(optimizable, lr, 0.9, weight_decay=opt.weightDecay)
elif opt.optim_method == 'Adam':
return torch.optim.Adam(optimizable, lr)
optimizer = create_optimizer(opt, opt.lr)
epoch = 0
if opt.resume != '':
state_dict = torch.load(opt.resume)
epoch = state_dict['epoch']
params_tensors, stats = state_dict['params'], state_dict['stats']
for k, v in params.iteritems():
v.data.copy_(params_tensors[k])
optimizer.load_state_dict(state_dict['optimizer'])
print '\nParameters:'
print pd.DataFrame([(key, v.size(), torch.typename(v.data)) for key, v in params.items()])
print '\nAdditional buffers:'
print pd.DataFrame([(key, v.size(), torch.typename(v)) for key, v in stats.items()])
n_parameters = sum(p.numel() for p in params_s.values())
print '\nTotal number of parameters:', n_parameters
if opt.gamma:
meter_loss_s = tnt.meter.AverageValueMeter()
meter_loss_t = tnt.meter.AverageValueMeter()
meter_loss_c = tnt.meter.AverageValueMeter()
meter_loss_d = tnt.meter.AverageValueMeter()
classacc_s = tnt.meter.ClassErrorMeter(accuracy=True)
classacc_t = tnt.meter.ClassErrorMeter(accuracy=True)
classacc_s_top1 = tnt.meter.ClassErrorMeter(topk=[1])
else:
classacc = tnt.meter.ClassErrorMeter(accuracy=True)
meter_loss = tnt.meter.AverageValueMeter()
timer_train = tnt.meter.TimeMeter('s')
timer_test = tnt.meter.TimeMeter('s')
meters_at = [tnt.meter.AverageValueMeter() for i in range(3)]
if not os.path.exists(opt.save):
os.mkdir(opt.save)
def h(sample):
inputs = Variable(cast(sample[0], opt.dtype))
targets = Variable(cast(sample[1], 'long'))
if opt.teacher_id != '':
if opt.gamma:
ys, y_t_auto, y_t, loss_groups = data_parallel(f, inputs, params, stats, sample[2], np.arange(opt.ngpu))[:4]
loss_groups = [v.sum() for v in loss_groups]
[m.add(v.data[0]) for m, v in zip(meters_at, loss_groups)]
loss_l2 = torch.nn.MSELoss()
T = 4
loss_student = F.cross_entropy(ys, targets)
loss_teacher = F.cross_entropy(y_t_auto, targets)
loss_course = opt.gamma * ((y_t_auto - ys) * (y_t_auto - ys)).sum() / opt.batchSize
y_tech_temp = torch.autograd.Variable(y_t_auto.data, requires_grad=False)
log_kd = rocket_distillation(ys, y_t, targets, opt.temperature, opt.alpha)
return opt.beta * sum(loss_groups) + F.cross_entropy(y_t_auto, targets) + F.cross_entropy(ys, targets) + opt.gamma * ((y_tech_temp - ys) * (
y_tech_temp - ys)).sum() / opt.batchSize, (ys, y_t_auto, loss_student, loss_teacher, loss_course, log_kd)
else:
y_s, y_t, loss_groups = data_parallel(f, inputs, params, stats, sample[2], np.arange(opt.ngpu))
loss_groups = [v.sum() for v in loss_groups]
[m.add(v.data[0]) for m, v in zip(meters_at, loss_groups)]
return distillation(y_s, y_t, targets, opt.temperature, opt.alpha) \
+ opt.beta * sum(loss_groups), y_s
else:
if opt.gamma:
ys, y = data_parallel(f, inputs, params, stats, sample[
2], np.arange(opt.ngpu))[:2]
loss_l2 = torch.nn.MSELoss()
T = 4
loss_student = F.cross_entropy(ys, targets)
loss_teacher = F.cross_entropy(y, targets)
loss_course = opt.gamma * \
((y - ys) * (y - ys)).sum() / opt.batchSize
if opt.grad_block:
#with gradient block
#with gradient block
y_course = torch.autograd.Variable(
y.data, requires_grad=False)
else:
y_course = y
return F.cross_entropy(y, targets) + F.cross_entropy(ys, targets) + opt.gamma * ((y_course - ys) * (y_course - ys)).sum() / opt.batchSize, (ys, y, loss_student, loss_teacher, loss_course)
else:
y = data_parallel(f, inputs, params, stats, sample[
2], np.arange(opt.ngpu))[0]
return F.cross_entropy(y, targets), y
def log(t, state):
torch.save(dict(params={k: v.data for k, v in params.iteritems()},
stats=stats,
optimizer=state['optimizer'].state_dict(),
epoch=t['epoch']),
open(os.path.join(opt.save, 'model.pt7'), 'w'))
z = vars(opt).copy()
z.update(t)
logname = os.path.join(opt.save, 'log.txt')
with open(logname, 'a') as f:
f.write('json_stats: ' + json.dumps(z) + '\n')
print z
def on_sample(state):
state['sample'].append(state['train'])
if opt.gamma:
def on_forward(state):
classacc_s.add(state['output'][0].data, torch.LongTensor(state['sample'][1]))
classacc_t.add(state['output'][1].data, torch.LongTensor(state['sample'][1]))
meter_loss.add(state['loss'].data[0])
meter_loss_s.add(state['output'][2].data[0])
meter_loss_t.add(state['output'][3].data[0])
meter_loss_c.add(state['output'][4].data[0])
classacc_s_top1.add(state['output'][0].data, torch.LongTensor(state['sample'][1]))
def on_start_epoch(state):
classacc_s.reset()
classacc_t.reset()
classacc_s_top1.reset()
meter_loss.reset()
meter_loss_s.reset()
meter_loss_t.reset()
meter_loss_c.reset()
timer_train.reset()
[meter.reset() for meter in meters_at]
# state['iterator'] = tqdm(train_loader)
epoch = state['epoch'] + 1
if epoch in sigma_refine_step:
opt.running_sigma += opt.gamma
if epoch in epoch_step:
lr = state['optimizer'].param_groups[0]['lr']
state['optimizer'] = create_optimizer(
opt, lr * opt.lr_decay_ratio)
def on_end_epoch(state):
train_loss = meter_loss.value()
train_loss_s = meter_loss_s.value()
train_loss_t = meter_loss_t.value()
train_loss_c = meter_loss_c.value()
train_acc_s = classacc_s.value()
train_acc_t = classacc_t.value()
train_time = timer_train.value()
meter_loss.reset()
meter_loss_s.reset()
meter_loss_t.reset()
meter_loss_c.reset()
classacc_s.reset()
classacc_t.reset()
classacc_s_top1.reset()
timer_test.reset()
engine.test(h, test_loader)
test_acc_s = classacc_s.value()[0]
test_acc_t = classacc_t.value()[0]
test_acc_s_top1 = classacc_s_top1.value()[0]
print log({
"train_loss": train_loss[0],
"train_acc_student": train_acc_s[0],
"train_acc_teacher": train_acc_t[0],
"train_loss_student": train_loss_s[0],
"train_loss_teacher": train_loss_t[0],
"test_loss": meter_loss.value()[0],
"test_loss_student": meter_loss_s.value()[0],
"test_loss_teacher": meter_loss_t.value()[0],
"test_loss_course": meter_loss_c.value()[0],
"test_acc_student": test_acc_s,
"test_acc_teacher": test_acc_t,
"test_acc_student_top1": test_acc_s_top1,
"epoch": state['epoch'],
"num_classes": num_classes,
"n_parameters": n_parameters,
"train_time": train_time,
"test_time": timer_test.value(),
"at_losses": [m.value() for m in meters_at],
}, state)
print '==> id: %s (%d/%d), test_acc: \33[91m%.2f\033[0m' % \
(opt.save, state['epoch'], opt.epochs, test_acc_s)
else:
def on_forward(state):
classacc.add(state['output'].data,
torch.LongTensor(state['sample'][1]))
meter_loss.add(state['loss'].data[0])
def on_start_epoch(state):
classacc.reset()
meter_loss.reset()
timer_train.reset()
[meter.reset() for meter in meters_at]
# state['iterator'] = tqdm(train_loader)
epoch = state['epoch'] + 1
if epoch in epoch_step:
lr = state['optimizer'].param_groups[0]['lr']
state['optimizer'] = create_optimizer(
opt, lr * opt.lr_decay_ratio)
def on_end_epoch(state):
train_loss = meter_loss.value()
train_acc = classacc.value()
train_time = timer_train.value()
meter_loss.reset()
classacc.reset()
timer_test.reset()
engine.test(h, test_loader)
test_acc = classacc.value()[0]
print log({
"train_loss": train_loss[0],
"train_acc": train_acc[0],
"test_loss": meter_loss.value()[0],
"test_acc": test_acc,
"epoch": state['epoch'],
"num_classes": num_classes,
"n_parameters": n_parameters,
"train_time": train_time,
"test_time": timer_test.value(),
"at_losses": [m.value() for m in meters_at],
}, state)
print '==> id: %s (%d/%d), test_acc: \33[91m%.2f\033[0m' % \
(opt.save, state['epoch'], opt.epochs, test_acc)
def on_start(state):
state['epoch'] = epoch
engine = Engine()
engine.hooks['on_sample'] = on_sample
engine.hooks['on_forward'] = on_forward
engine.hooks['on_start_epoch'] = on_start_epoch
engine.hooks['on_end_epoch'] = on_end_epoch
engine.hooks['on_start'] = on_start
engine.train(h, train_loader, opt.epochs, optimizer)
if __name__ == '__main__':
main()