This repository has been archived by the owner on Oct 31, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 155
/
main_moco.py
executable file
·438 lines (370 loc) · 17.5 KB
/
main_moco.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
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
from functools import partial
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as torchvision_models
from torch.utils.tensorboard import SummaryWriter
import moco.builder
import moco.loader
import moco.optimizer
import vits
torchvision_model_names = sorted(name for name in torchvision_models.__dict__
if name.islower() and not name.startswith("__")
and callable(torchvision_models.__dict__[name]))
model_names = ['vit_small', 'vit_base', 'vit_conv_small', 'vit_conv_base'] + torchvision_model_names
parser = argparse.ArgumentParser(description='MoCo ImageNet Pre-Training')
parser.add_argument('data', metavar='DIR',
help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet50)')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=100, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=4096, type=int,
metavar='N',
help='mini-batch size (default: 4096), this is the total '
'batch size of all GPUs on all nodes when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.6, type=float,
metavar='LR', help='initial (base) learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-6, type=float,
metavar='W', help='weight decay (default: 1e-6)',
dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--world-size', default=-1, type=int,
help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
help='Use multi-processing distributed training to launch '
'N processes per node, which has N GPUs. This is the '
'fastest way to use PyTorch for either single node or '
'multi node data parallel training')
# moco specific configs:
parser.add_argument('--moco-dim', default=256, type=int,
help='feature dimension (default: 256)')
parser.add_argument('--moco-mlp-dim', default=4096, type=int,
help='hidden dimension in MLPs (default: 4096)')
parser.add_argument('--moco-m', default=0.99, type=float,
help='moco momentum of updating momentum encoder (default: 0.99)')
parser.add_argument('--moco-m-cos', action='store_true',
help='gradually increase moco momentum to 1 with a '
'half-cycle cosine schedule')
parser.add_argument('--moco-t', default=1.0, type=float,
help='softmax temperature (default: 1.0)')
# vit specific configs:
parser.add_argument('--stop-grad-conv1', action='store_true',
help='stop-grad after first conv, or patch embedding')
# other upgrades
parser.add_argument('--optimizer', default='lars', type=str,
choices=['lars', 'adamw'],
help='optimizer used (default: lars)')
parser.add_argument('--warmup-epochs', default=10, type=int, metavar='N',
help='number of warmup epochs')
parser.add_argument('--crop-min', default=0.08, type=float,
help='minimum scale for random cropping (default: 0.08)')
def main():
args = parser.parse_args()
if args.seed is not None:
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.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
ngpus_per_node = torch.cuda.device_count()
if args.multiprocessing_distributed:
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
else:
# Simply call main_worker function
main_worker(args.gpu, ngpus_per_node, args)
def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
# suppress printing if not first GPU on each node
if args.multiprocessing_distributed and (args.gpu != 0 or args.rank != 0):
def print_pass(*args):
pass
builtins.print = print_pass
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
torch.distributed.barrier()
# create model
print("=> creating model '{}'".format(args.arch))
if args.arch.startswith('vit'):
model = moco.builder.MoCo_ViT(
partial(vits.__dict__[args.arch], stop_grad_conv1=args.stop_grad_conv1),
args.moco_dim, args.moco_mlp_dim, args.moco_t)
else:
model = moco.builder.MoCo_ResNet(
partial(torchvision_models.__dict__[args.arch], zero_init_residual=True),
args.moco_dim, args.moco_mlp_dim, args.moco_t)
# infer learning rate before changing batch size
args.lr = args.lr * args.batch_size / 256
if not torch.cuda.is_available():
print('using CPU, this will be slow')
elif args.distributed:
# apply SyncBN
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / args.world_size)
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
# comment out the following line for debugging
raise NotImplementedError("Only DistributedDataParallel is supported.")
else:
# AllGather/rank implementation in this code only supports DistributedDataParallel.
raise NotImplementedError("Only DistributedDataParallel is supported.")
print(model) # print model after SyncBatchNorm
if args.optimizer == 'lars':
optimizer = moco.optimizer.LARS(model.parameters(), args.lr,
weight_decay=args.weight_decay,
momentum=args.momentum)
elif args.optimizer == 'adamw':
optimizer = torch.optim.AdamW(model.parameters(), args.lr,
weight_decay=args.weight_decay)
scaler = torch.cuda.amp.GradScaler()
summary_writer = SummaryWriter() if args.rank == 0 else None
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
else:
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scaler.load_state_dict(checkpoint['scaler'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
traindir = os.path.join(args.data, 'train')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# follow BYOL's augmentation recipe: https://arxiv.org/abs/2006.07733
augmentation1 = [
transforms.RandomResizedCrop(224, scale=(args.crop_min, 1.)),
transforms.RandomApply([
transforms.ColorJitter(0.4, 0.4, 0.2, 0.1) # not strengthened
], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.RandomApply([moco.loader.GaussianBlur([.1, 2.])], p=1.0),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
]
augmentation2 = [
transforms.RandomResizedCrop(224, scale=(args.crop_min, 1.)),
transforms.RandomApply([
transforms.ColorJitter(0.4, 0.4, 0.2, 0.1) # not strengthened
], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.RandomApply([moco.loader.GaussianBlur([.1, 2.])], p=0.1),
transforms.RandomApply([moco.loader.Solarize()], p=0.2),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
]
train_dataset = datasets.ImageFolder(
traindir,
moco.loader.TwoCropsTransform(transforms.Compose(augmentation1),
transforms.Compose(augmentation2)))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler, drop_last=True)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train(train_loader, model, optimizer, scaler, summary_writer, epoch, args)
if not args.multiprocessing_distributed or (args.multiprocessing_distributed
and args.rank == 0): # only the first GPU saves checkpoint
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer' : optimizer.state_dict(),
'scaler': scaler.state_dict(),
}, is_best=False, filename='checkpoint_%04d.pth.tar' % epoch)
if args.rank == 0:
summary_writer.close()
def train(train_loader, model, optimizer, scaler, summary_writer, epoch, args):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
learning_rates = AverageMeter('LR', ':.4e')
losses = AverageMeter('Loss', ':.4e')
progress = ProgressMeter(
len(train_loader),
[batch_time, data_time, learning_rates, losses],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
iters_per_epoch = len(train_loader)
moco_m = args.moco_m
for i, (images, _) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
# adjust learning rate and momentum coefficient per iteration
lr = adjust_learning_rate(optimizer, epoch + i / iters_per_epoch, args)
learning_rates.update(lr)
if args.moco_m_cos:
moco_m = adjust_moco_momentum(epoch + i / iters_per_epoch, args)
if args.gpu is not None:
images[0] = images[0].cuda(args.gpu, non_blocking=True)
images[1] = images[1].cuda(args.gpu, non_blocking=True)
# compute output
with torch.cuda.amp.autocast(True):
loss = model(images[0], images[1], moco_m)
losses.update(loss.item(), images[0].size(0))
if args.rank == 0:
summary_writer.add_scalar("loss", loss.item(), epoch * iters_per_epoch + i)
# compute gradient and do SGD step
optimizer.zero_grad()
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
class ProgressMeter(object):
def __init__(self, num_batches, meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(meter) for meter in self.meters]
print('\t'.join(entries))
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = '{:' + str(num_digits) + 'd}'
return '[' + fmt + '/' + fmt.format(num_batches) + ']'
def adjust_learning_rate(optimizer, epoch, args):
"""Decays the learning rate with half-cycle cosine after warmup"""
if epoch < args.warmup_epochs:
lr = args.lr * epoch / args.warmup_epochs
else:
lr = args.lr * 0.5 * (1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
def adjust_moco_momentum(epoch, args):
"""Adjust moco momentum based on current epoch"""
m = 1. - 0.5 * (1. + math.cos(math.pi * epoch / args.epochs)) * (1. - args.moco_m)
return m
if __name__ == '__main__':
main()