epoch = 5
elif tag.isnumeric():
epoch = int(tag)
if args.half:
model = network_to_half(model)
model = model.cuda()
model.name = f'{args.model}_{args.tag}'
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
scale_lr = batch_size * hvd.size() / 128
scheduler_warmup = LambdaLR(
optimizer, lambda step: 1 + (scale_lr - 1) * step / len(valid_loader) / 5)
scheduler_train = MultiStepLR(optimizer, milestones=[80, 160, 200])
scheduler_train.base_lrs = [x * scale_lr for x in scheduler_train.base_lrs]
if epoch == 0:
for i in [20, 40, 60, 80, 99]:
epoch += 1
train_loader = get_split_dataloader(f'{args.dataset}/train_k{i}.csv',
width=width,
batch_size=batch_size,
transform=transform,
num_workers=num_workers)
train(model,
train_loader,
optimizer=optimizer,
epoch=epoch,
scheduler=scheduler_warmup,
half=args.half)
def main():
global args, MODELS_DIR
print args
if args.dbg:
MODELS_DIR = join(MODELS_DIR, 'dbg')
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 torch.cuda.is_available() and not args.cuda:
# print("WARNING: You have a CUDA device, so you should probably run with --cuda")
print 'CudNN:', torch.backends.cudnn.version()
print 'Run on {} GPUs'.format(torch.cuda.device_count())
cudnn.benchmark = True
is_sobel = args.arch.endswith('Sobel')
print 'is_sobel', is_sobel
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](
num_classes=args.num_clusters if args.unsupervised else 1000,
dropout7_prob=args.dropout7_prob)
model = torch.nn.DataParallel(model).cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
experiment = "{}_lr{}_{}{}".format(
args.arch, args.lr, 'unsup' if args.unsupervised else 'labels',
'_v2' if args.imagenet_version == 2 else '')
if args.unsupervised:
experiment += '{sobel_norm}_nc{nc}_l{clustering_layer}_rec{rec_epoch}{reset_fc}'.format(
sobel_norm='_normed' if args.sobel_normalized else '',
nc=args.num_clusters,
clustering_layer=args.clustering_layer,
rec_epoch=args.recluster_epoch,
reset_fc='_reset-fc' if args.reset_fc else '')
checkpoint = None
if args.output_dir is None:
args.output_dir = join(MODELS_DIR, experiment + '_' + args.exp_suffix)
if args.output_dir is not None and os.path.exists(args.output_dir):
ckpt_path = join(
args.output_dir, 'checkpoint.pth.tar'
if not args.from_best else 'model_best.pth.tar')
if not os.path.isfile(ckpt_path):
print "=> no checkpoint found at '{}'\nUsing model_best.pth.tar".format(
ckpt_path)
ckpt_path = join(args.output_dir, 'model_best.pth.tar')
if os.path.isfile(ckpt_path):
print("=> loading checkpoint '{}'".format(ckpt_path))
checkpoint = torch.load(ckpt_path)
print("=> loaded checkpoint '{}' (epoch {})".format(
ckpt_path, checkpoint['epoch']))
else:
print "=> no checkpoint found at '{}'\nUsing model_best_nmi.pth.tar".format(
ckpt_path)
ckpt_path = join(args.output_dir, 'model_best_nmi.pth.tar')
if os.path.isfile(ckpt_path):
print("=> loading checkpoint '{}'".format(ckpt_path))
checkpoint = torch.load(ckpt_path)
print("=> loaded checkpoint '{}' (epoch {})".format(
ckpt_path, checkpoint['epoch']))
else:
print "=> no checkpoint found at '{}'".format(ckpt_path)
ans = None
while ans != 'y' and ans != 'n':
ans = raw_input('Clear the dir {}? [y/n] '.format(
args.output_dir)).lower()
if ans.lower() == 'y':
shutil.rmtree(args.output_dir)
else:
print 'Just write in the same dir.'
# raise IOError("=> no checkpoint found at '{}'".format(ckpt_path))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
print 'Output dir:', args.output_dir
start_epoch = 0
best_score = 0
best_nmi = 0
if checkpoint is not None:
start_epoch = checkpoint['epoch']
if 'best_score' in checkpoint:
best_score = checkpoint['best_score']
else:
print 'WARNING! NO best "score_found" in checkpoint!'
best_score = 0
if 'nmi' in checkpoint:
print 'Current NMI/GT:', checkpoint['nmi']
if 'best_nmi' in checkpoint:
best_nmi = checkpoint['best_nmi']
print 'Best NMI/GT:', best_nmi
print 'Best score:', best_score
if 'cur_score' in checkpoint:
print 'Current score:', checkpoint['cur_score']
model.load_state_dict(checkpoint['state_dict'])
print 'state dict loaded'
optimizer.load_state_dict(checkpoint['optimizer'])
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
param_group['initial_lr'] = args.lr
logger = SummaryWriter(log_dir=args.output_dir)
### Data loading ###
num_gt_classes = 1000
split_dirs = {
'train':
join(args.data,
'train' if args.imagenet_version == 1 else 'train_256'),
'val':
join(
args.data, 'val' if args.imagenet_version == 1 else 'val_256'
) # we get lower accuracy with cal_256, probably because of jpeg compression
}
dataset_indices = dict()
for key in ['train', 'val']:
index_path = join(args.data,
os.path.basename(split_dirs[key]) + '_index.json')
if os.path.exists(index_path):
with open(index_path) as json_file:
dataset_indices[key] = json.load(json_file)
else:
print 'Indexing ' + key
dataset_indices[key] = index_imagenet(split_dirs[key], index_path)
assert dataset_indices['train']['class_to_idx'] == \
dataset_indices['val']['class_to_idx']
if args.dbg:
max_images = 1000
print 'DBG: WARNING! Trauncate train datset to {} images'.format(
max_images)
dataset_indices['train']['samples'] = dataset_indices['train'][
'samples'][:max_images]
dataset_indices['val']['samples'] = dataset_indices['val'][
'samples'][:max_images]
num_workers = args.workers # if args.unsupervised else max(1, args.workers / 2)
print '[TRAIN]...'
if args.unsupervised:
train_loader_gt = create_data_loader(
split_dirs['train'],
dataset_indices['train'],
is_sobel,
sobel_normalized=args.sobel_normalized,
aug='random_crop_flip',
shuffle='shuffle' if not args.fast_dataflow else 'shuffle_buffer',
num_workers=num_workers,
use_fast_dataflow=args.fast_dataflow,
buffer_size=args.buffer_size)
eval_gt_aug = '10_crop'
val_loader_gt = create_data_loader(
split_dirs['val'],
dataset_indices['val'],
is_sobel,
sobel_normalized=args.sobel_normalized,
aug=eval_gt_aug,
batch_size=26, # WARNING. Decrease the batch size because of Memory
shuffle='shuffle',
num_workers=num_workers,
use_fast_dataflow=False,
buffer_size=args.buffer_size)
else:
train_loader = create_data_loader(
split_dirs['train'],
dataset_indices['train'],
is_sobel,
sobel_normalized=args.sobel_normalized,
aug='random_crop_flip',
shuffle='shuffle' if not args.fast_dataflow else 'shuffle_buffer',
num_workers=num_workers,
use_fast_dataflow=args.fast_dataflow,
buffer_size=args.buffer_size)
print '[VAL]...'
# with GT labels!
val_loader = create_data_loader(
split_dirs['val'],
dataset_indices['val'],
is_sobel,
sobel_normalized=args.sobel_normalized,
aug='central_crop',
batch_size=args.batch_size,
shuffle='shuffle' if not args.fast_dataflow else None,
num_workers=num_workers,
use_fast_dataflow=args.fast_dataflow,
buffer_size=args.buffer_size)
###############################################################################
# StepLR(optimizer, step_size=args.decay_step, gamma=args.decay_gamma)
if args.scheduler == 'multi_step':
scheduler = MultiStepLR(optimizer,
milestones=[30, 60, 80],
gamma=args.decay_gamma)
elif args.scheduler == 'multi_step2':
scheduler = MultiStepLR(optimizer,
milestones=[50, 100],
gamma=args.decay_gamma)
elif args.scheduler == 'cyclic':
print 'Using Cyclic LR!'
cyclic_lr = CyclicLr(start_epoch if args.reset_lr else 0,
init_lr=args.lr,
num_epochs_per_cycle=args.cycle,
epochs_pro_decay=args.decay_step,
lr_decay_factor=args.decay_gamma)
scheduler = LambdaLR(optimizer, lr_lambda=cyclic_lr)
scheduler.base_lrs = list(
map(lambda group: 1.0, optimizer.param_groups))
elif args.scheduler == 'step':
step_lr = StepMinLr(start_epoch if args.reset_lr else 0,
init_lr=args.lr,
epochs_pro_decay=args.decay_step,
lr_decay_factor=args.decay_gamma,
min_lr=args.min_lr)
scheduler = LambdaLR(optimizer, lr_lambda=step_lr)
scheduler.base_lrs = list(
map(lambda group: 1.0, optimizer.param_groups))
else:
assert False, 'wrong scheduler: ' + args.scheduler
print 'scheduler.base_lrs=', scheduler.base_lrs
logger.add_scalar('data/batch_size', args.batch_size, start_epoch)
save_epoch = 50
if not args.unsupervised:
validate_epoch = 1
else:
validate_epoch = 50
labels_holder = {
} # utility container to save labels from the previous clustering step
last_lr = 100500
for epoch in range(start_epoch, args.epochs):
nmi_gt = None
if epoch == start_epoch:
if not args.unsupervised:
validate(val_loader,
model,
criterion,
epoch - 1,
logger=logger)
# elif start_epoch == 0:
# print 'validate_gt_linear'
# validate_gt_linear(train_loader_gt, val_loader_gt, num_gt_classes,
# model, args.eval_layer, criterion, epoch - 1, lr=0.01,
# num_train_epochs=2,
# logger=logger, tag='val_gt_{}_{}'.format(args.eval_layer, eval_gt_aug))
if args.unsupervised and (epoch == start_epoch
or epoch % args.recluster_epoch == 0):
train_loader, nmi_gt = unsupervised_clustering_step(
epoch, model, is_sobel, args.sobel_normalized, split_dirs,
dataset_indices, num_workers, labels_holder, logger,
args.fast_dataflow)
if args.reset_fc:
model.module.reset_fc8()
try:
with open(join(args.output_dir, 'labels_holder.json'),
'w') as f:
for k in labels_holder.keys():
labels_holder[k] = np.asarray(
labels_holder[k]).tolist()
json.dump(labels_holder, f)
except Exception as e:
print e
scheduler.step(epoch=epoch)
if last_lr != scheduler.get_lr()[0]:
last_lr = scheduler.get_lr()[0]
print 'LR := {}'.format(last_lr)
logger.add_scalar('data/lr', scheduler.get_lr()[0], epoch)
logger.add_scalar('data/v', args.imagenet_version, epoch)
logger.add_scalar('data/weight_decay', args.weight_decay, epoch)
logger.add_scalar('data/dropout7_prob', args.dropout7_prob, epoch)
top1_avg, top5_avg, loss_avg = \
train(train_loader, model, criterion, optimizer,
epoch, args.epochs,
log_iter=100, logger=logger)
if (epoch + 1) % validate_epoch == 0:
# evaluate on validation set
if not args.unsupervised:
score = validate(val_loader,
model,
criterion,
epoch,
logger=logger)
else:
score = validate_gt_linear(
train_loader_gt,
val_loader_gt,
num_gt_classes,
model,
args.eval_layer,
criterion,
epoch,
lr=0.01,
num_train_epochs=args.epochs_train_linear,
logger=logger,
tag='val_gt_{}_{}'.format(args.eval_layer, eval_gt_aug))
# remember best prec@1 and save checkpoint
is_best = score > best_score
best_score = max(score, best_score)
best_ckpt_suffix = ''
else:
score = None
if nmi_gt is not None and nmi_gt > best_nmi:
best_nmi = nmi_gt
best_ckpt_suffix = '_nmi'
is_best = True
else:
is_best = False
best_ckpt_suffix = ''
if (epoch + 1) % save_epoch == 0:
filepath = join(args.output_dir,
'checkpoint-{:05d}.pth.tar'.format(epoch + 1))
else:
filepath = join(args.output_dir, 'checkpoint.pth.tar')
save_dict = {
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_score': best_score,
'top1_avg_accuracy_train': top1_avg,
'optimizer': optimizer.state_dict(),
}
if nmi_gt is not None:
save_dict['nmi'] = nmi_gt
save_dict['best_nmi'] = best_nmi
if score is not None:
save_dict['cur_score'] = score
save_checkpoint(save_dict,
is_best=is_best,
filepath=filepath,
best_suffix=best_ckpt_suffix)