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main_pixel_finetuning.py
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main_pixel_finetuning.py
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# 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 math
import os
import shutil
import time
from logging import getLogger
import apex
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torchvision.transforms as transforms
from apex.parallel.LARC import LARC
import src.pseudo_transforms as custom_transforms
import src.resnet as resnet_models
from options import getOption
from src.singlecropdataset import PseudoLabelDataset
from src.utils import (AverageMeter, accuracy, fix_random_seeds,
init_distributed_mode, initialize_exp,
restart_from_checkpoint)
logger = getLogger()
parser = getOption()
def main():
global args
args = parser.parse_args()
init_distributed_mode(args)
fix_random_seeds(args.seed)
logger, training_stats = initialize_exp(args, 'epoch', 'loss')
# build data
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = PseudoLabelDataset(
args.data_path,
custom_transforms.Compose([
custom_transforms.RandomResizedCropSemantic(224),
custom_transforms.RandomHorizontalFlipSemantic(),
custom_transforms.ToTensorSemantic(),
normalize,
]),
pseudo_path=args.pseudo_path,
)
sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(train_dataset,
sampler=sampler,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
logger.info('Building data done with {} images loaded.'.format(
len(train_dataset)))
# build model
model = resnet_models.__dict__[args.arch](hidden_mlp=0,
output_dim=0,
nmb_prototypes=0,
num_classes=args.num_classes,
train_mode='finetune')
# loading pretrained weights
checkpoint = torch.load(args.pretrained,
map_location='cpu')['state_dict']
for k in list(checkpoint.keys()):
if k.startswith('module.'):
checkpoint[k[len('module.'):]] = checkpoint[k]
del checkpoint[k]
msg = model.load_state_dict(checkpoint, strict=False)
logger.info("Loaded pretrained weights '{}' with missing {}".format(
args.pretrained, msg.missing_keys))
# synchronize batch norm layers
if args.sync_bn == 'pytorch':
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
elif args.sync_bn == 'apex':
# with apex syncbn we sync bn per group
# because it speeds up computation
# compared to global syncbn
process_group = apex.parallel.create_syncbn_process_group(
args.syncbn_process_group_size)
model = apex.parallel.convert_syncbn_model(model,
process_group=process_group)
# copy model to GPU
model = model.cuda()
if args.rank == 0:
logger.info(model)
logger.info('Building model done.')
# build optimizer
optimizer = torch.optim.SGD(
model.parameters(),
lr=args.base_lr,
momentum=0.9,
weight_decay=args.wd,
)
optimizer = LARC(optimizer=optimizer, trust_coefficient=0.001, clip=False)
warmup_lr_schedule = np.linspace(args.start_warmup, args.base_lr,
len(train_loader) * args.warmup_epochs)
iters = np.arange(len(train_loader) * (args.epochs - args.warmup_epochs))
cosine_lr_schedule = \
np.array([args.final_lr + 0.5 * (args.base_lr - args.final_lr) * (
1 +
math.cos(
math.pi * t /
(len(train_loader) * (args.epochs - args.warmup_epochs))
)
)
for t in iters])
lr_schedule = np.concatenate((warmup_lr_schedule, cosine_lr_schedule))
logger.info('Building optimizer done.')
# init mixed precision
if args.use_fp16:
model, optimizer = apex.amp.initialize(model,
optimizer,
opt_level='O1')
logger.info('Initializing mixed precision done.')
# wrap model
model = nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu_to_work_on])
# optionally resume from a checkpoint
to_restore = {'epoch': 0}
restart_from_checkpoint(
os.path.join(args.dump_path, 'checkpoint.pth.tar'),
run_variables=to_restore,
state_dict=model,
optimizer=optimizer,
amp=apex.amp,
)
start_epoch = to_restore['epoch']
# loss function
criterion = nn.CrossEntropyLoss()
cudnn.benchmark = True
for epoch in range(start_epoch, args.epochs):
# train the network for one epoch
logger.info('============ Starting epoch %i ... ============' % epoch)
# set sampler
train_loader.sampler.set_epoch(epoch)
# train the network
scores = train(train_loader, model, optimizer, criterion, epoch,
lr_schedule)
training_stats.update(scores)
# save checkpoints
if args.rank == 0:
save_dict = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
if args.use_fp16:
save_dict['amp'] = apex.amp.state_dict()
torch.save(
save_dict,
os.path.join(args.dump_path, 'checkpoint.pth.tar'),
)
if epoch % args.checkpoint_freq == 0 or epoch == args.epochs - 1:
shutil.copyfile(
os.path.join(args.dump_path, 'checkpoint.pth.tar'),
os.path.join(args.dump_checkpoints,
'ckp-' + str(epoch) + '.pth'),
)
def train(train_loader, model, optimizer, criterion, epoch, lr_schedule):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc = AverageMeter()
model.train()
end = time.time()
for it, (inputs, labels) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
# update learning rate
iteration = epoch * len(train_loader) + it
for param_group in optimizer.param_groups:
param_group['lr'] = lr_schedule[iteration]
# ============ forward step ... ============
inputs = inputs.cuda()
labels = labels.cuda()
labels = labels[:, 1, :, :] * 256 + labels[:, 0, :, :]
labels = labels.long()
output = model(inputs)
labels = F.interpolate(labels.float().unsqueeze(1),
scale_factor=0.5,
mode='nearest').long().squeeze(1)
output = F.interpolate(output,
size=(labels.shape[1], labels.shape[2]),
mode='bilinear')
c = output.shape[1]
loss = criterion(output, labels)
(acc1, ) = accuracy(
output.permute(0, 2, 3, 1).contiguous().view(-1, c),
labels.view(-1))
# ============ backward and optim step ... ============
optimizer.zero_grad()
if args.use_fp16:
with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
# ============ misc ... ============
losses.update(loss.item(), inputs[0].size(0))
acc.update(acc1.item(), inputs[0].size(0))
batch_time.update(time.time() - end)
end = time.time()
if args.rank == 0 and it % 50 == 0:
logger.info('Epoch: [{0}][{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {acc1.val:.2f} ({acc1.avg:.2f})\t'
'Lr: {lr:.4f}'.format(
epoch,
it,
batch_time=batch_time,
data_time=data_time,
loss=losses,
lr=optimizer.optim.param_groups[0]['lr'],
acc1=acc,
))
return (epoch, losses.avg)
if __name__ == '__main__':
main()