def main(args):
if args.apex:
if sys.version_info < (3, 0):
raise RuntimeError(
"Apex currently only supports Python 3. Aborting.")
if amp is None:
raise RuntimeError(
"Failed to import apex. Please install apex from https://www.github.com/nvidia/apex "
"to enable mixed-precision training.")
if args.output_dir:
utils.mkdir(args.output_dir)
vis = utils.Visualize(args)
utils.init_distributed_mode(args)
print(args)
print("torch version: ", torch.__version__)
print("torchvision version: ", torchvision.__version__)
device = torch.device(args.device)
torch.backends.cudnn.benchmark = True
# Data loading code
print("Loading data")
traindir = os.path.join(
args.data_path, 'train_256' if not args.fast_test else 'val_256_bob')
valdir = os.path.join(args.data_path, 'val_256_bob')
normalize = T.Normalize(mean=[0.43216, 0.394666, 0.37645],
std=[0.22803, 0.22145, 0.216989])
print("Loading training data")
st = time.time()
cache_path = _get_cache_path(traindir)
frame_transform_train = utils.make_frame_transform(args.frame_transforms)
transform_train = torchvision.transforms.Compose([
# torchvision.transforms.RandomGrayscale(p=1),
frame_transform_train,
T.ToFloatTensorInZeroOne(),
T.Resize((256, 256)),
# T.Resize((128, 171)),
# T.RandomHorizontalFlip(),
# T.GaussianBlurTransform(),
normalize,
# T.RandomCrop((112, 112))
])
def make_dataset(is_train):
_transform = transform_train if is_train else transform_test
if 'kinetics' in args.data_path.lower():
return Kinetics400(traindir if is_train else valdir,
frames_per_clip=args.clip_len,
step_between_clips=1,
transform=transform_train,
extensions=('mp4'),
frame_rate=args.frame_skip)
else:
return VideoList(
args,
is_train,
frame_gap=args.frame_skip,
transform=_transform,
# frame_transform=_frame_transform
)
if args.cache_dataset and os.path.exists(cache_path):
print("Loading dataset_train from {}".format(cache_path))
dataset, _ = torch.load(cache_path)
dataset.transform = transform_train
else:
if args.distributed:
print("It is recommended to pre-compute the dataset cache "
"on a single-gpu first, as it will be faster")
dataset = make_dataset(is_train=True)
if args.cache_dataset:
print("Saving dataset_train to {}".format(cache_path))
utils.mkdir(os.path.dirname(cache_path))
utils.save_on_master((dataset, traindir), cache_path)
if hasattr(dataset, 'video_clips'):
dataset.video_clips.compute_clips(args.clip_len, 1, frame_rate=15)
print("Took", time.time() - st)
print("Loading validation data")
cache_path = _get_cache_path(valdir)
transform_test = torchvision.transforms.Compose([
T.ToFloatTensorInZeroOne(),
# T.Resize((128, 171)),
# normalize,
# T.CenterCrop((112, 112))
T.Resize((256, 256)),
normalize
])
if args.cache_dataset and os.path.exists(cache_path):
print("Loading dataset_test from {}".format(cache_path))
dataset_test, _ = torch.load(cache_path)
dataset_test.transform = transform_test
else:
if args.distributed:
print("It is recommended to pre-compute the dataset cache "
"on a single-gpu first, as it will be faster")
# dataset_test = Kinetics400(
# valdir,
# frames_per_clip=args.clip_len,
# step_between_clips=1,
# transform=transform_test,
# extensions=('mp4')
# )
dataset_test = make_dataset(is_train=False)
if args.cache_dataset:
print("Saving dataset_test to {}".format(cache_path))
utils.mkdir(os.path.dirname(cache_path))
utils.save_on_master((dataset_test, valdir), cache_path)
if hasattr(dataset, 'video_clips'):
dataset_test.video_clips.compute_clips(args.clip_len, 1, frame_rate=15)
def make_data_sampler(is_train, dataset):
if hasattr(dataset, 'video_clips'):
_sampler = RandomClipSampler if is_train else UniformClipSampler
return _sampler(dataset.video_clips, args.clips_per_video)
else:
return torch.utils.data.sampler.RandomSampler(
dataset) if is_train else None
print("Creating data loaders")
train_sampler, test_sampler = make_data_sampler(True, dataset), \
make_data_sampler(False, dataset_test)
# train_sampler = train_sampler(dataset.video_clips, args.clips_per_video)
# test_sampler = test_sampler(dataset_test.video_clips, args.clips_per_video)
if args.distributed:
train_sampler = DistributedSampler(train_sampler)
test_sampler = DistributedSampler(test_sampler)
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=args.workers,
pin_memory=True,
collate_fn=collate_fn)
data_loader_test = torch.utils.data.DataLoader(dataset_test,
batch_size=args.batch_size,
sampler=test_sampler,
num_workers=args.workers,
pin_memory=True,
collate_fn=collate_fn)
print("Creating model")
import resnet
import timecycle as tc
# model = resnet.__dict__[args.model](pretrained=args.pretrained)
model = tc.TimeCycle(args)
# utils.compute_RF_numerical(model.resnet, torch.ones(1, 3, 1, 112, 112).numpy())
# import pdb; pdb.set_trace()
# print(utils.compute_RF_numerical(model,img_np))
model.to(device)
if args.distributed and args.sync_bn:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
criterion = nn.CrossEntropyLoss()
lr = args.lr * args.world_size
# optimizer = torch.optim.SGD(
# model.parameters(), lr=lr, momentum=args.momentum, weight_decay=args.weight_decay)
optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)
if args.apex:
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.apex_opt_level)
# convert scheduler to be per iteration, not per epoch, for warmup that lasts
# between different epochs
warmup_iters = args.lr_warmup_epochs * len(data_loader)
lr_milestones = [len(data_loader) * m for m in args.lr_milestones]
lr_scheduler = WarmupMultiStepLR(optimizer,
milestones=lr_milestones,
gamma=args.lr_gamma,
warmup_iters=warmup_iters,
warmup_factor=1e-5)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
if args.data_parallel:
model = torch.nn.parallel.DataParallel(model)
model_without_ddp = model.module
if args.resume:
checkpoint = torch.load(args.resume, map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if args.test_only:
evaluate(model, criterion, data_loader_test, device=device)
return
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
train_one_epoch(model,
criterion,
optimizer,
lr_scheduler,
data_loader,
device,
epoch,
args.print_freq,
args.apex,
vis=vis)
# evaluate(model, criterion, data_loader_test, device=device)
if args.output_dir:
checkpoint = {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'args': args
}
utils.save_on_master(
checkpoint,
os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
utils.save_on_master(
checkpoint, os.path.join(args.output_dir, 'checkpoint.pth'))
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main(args):
if args.apex and amp is None:
raise RuntimeError(
"Failed to import apex. Please install apex from https://www.github.com/nvidia/apex "
"to enable mixed-precision training.")
if args.output_dir:
utils.mkdir(args.output_dir)
utils.init_distributed_mode(args)
print(args)
print("torch version: ", torch.__version__)
print("torchvision version: ", torchvision.__version__)
device = torch.device(args.device)
torch.backends.cudnn.benchmark = True
# Data loading code
print("Loading data")
traindir = os.path.join(args.data_path, args.train_dir)
valdir = os.path.join(args.data_path, args.val_dir)
normalize = T.Normalize(mean=[0.43216, 0.394666, 0.37645],
std=[0.22803, 0.22145, 0.216989])
print("Loading training data")
st = time.time()
cache_path = _get_cache_path(traindir)
transform_train = torchvision.transforms.Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 171)),
T.RandomHorizontalFlip(), normalize,
T.RandomCrop((112, 112))
])
if args.cache_dataset and os.path.exists(cache_path):
print("Loading dataset_train from {}".format(cache_path))
dataset, _ = torch.load(cache_path)
dataset.transform = transform_train
else:
if args.distributed:
print("It is recommended to pre-compute the dataset cache "
"on a single-gpu first, as it will be faster")
dataset = torchvision.datasets.Kinetics400(
traindir,
frames_per_clip=args.clip_len,
step_between_clips=1,
transform=transform_train,
frame_rate=15)
if args.cache_dataset:
print("Saving dataset_train to {}".format(cache_path))
utils.mkdir(os.path.dirname(cache_path))
utils.save_on_master((dataset, traindir), cache_path)
print("Took", time.time() - st)
print("Loading validation data")
cache_path = _get_cache_path(valdir)
transform_test = torchvision.transforms.Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 171)), normalize,
T.CenterCrop((112, 112))
])
if args.cache_dataset and os.path.exists(cache_path):
print("Loading dataset_test from {}".format(cache_path))
dataset_test, _ = torch.load(cache_path)
dataset_test.transform = transform_test
else:
if args.distributed:
print("It is recommended to pre-compute the dataset cache "
"on a single-gpu first, as it will be faster")
dataset_test = torchvision.datasets.Kinetics400(
valdir,
frames_per_clip=args.clip_len,
step_between_clips=1,
transform=transform_test,
frame_rate=15)
if args.cache_dataset:
print("Saving dataset_test to {}".format(cache_path))
utils.mkdir(os.path.dirname(cache_path))
utils.save_on_master((dataset_test, valdir), cache_path)
print("Creating data loaders")
train_sampler = RandomClipSampler(dataset.video_clips,
args.clips_per_video)
test_sampler = UniformClipSampler(dataset_test.video_clips,
args.clips_per_video)
if args.distributed:
train_sampler = DistributedSampler(train_sampler)
test_sampler = DistributedSampler(test_sampler)
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=args.workers,
pin_memory=True,
collate_fn=collate_fn)
data_loader_test = torch.utils.data.DataLoader(dataset_test,
batch_size=args.batch_size,
sampler=test_sampler,
num_workers=args.workers,
pin_memory=True,
collate_fn=collate_fn)
print("Creating model")
model = torchvision.models.video.__dict__[args.model](
pretrained=args.pretrained)
model.to(device)
if args.distributed and args.sync_bn:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
criterion = nn.CrossEntropyLoss()
lr = args.lr * args.world_size
optimizer = torch.optim.SGD(model.parameters(),
lr=lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.apex:
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.apex_opt_level)
# convert scheduler to be per iteration, not per epoch, for warmup that lasts
# between different epochs
warmup_iters = args.lr_warmup_epochs * len(data_loader)
lr_milestones = [len(data_loader) * m for m in args.lr_milestones]
lr_scheduler = WarmupMultiStepLR(optimizer,
milestones=lr_milestones,
gamma=args.lr_gamma,
warmup_iters=warmup_iters,
warmup_factor=1e-5)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
if args.resume:
checkpoint = torch.load(args.resume, map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if args.test_only:
evaluate(model, criterion, data_loader_test, device=device)
return
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
train_one_epoch(model, criterion, optimizer, lr_scheduler, data_loader,
device, epoch, args.print_freq, args.apex)
evaluate(model, criterion, data_loader_test, device=device)
if args.output_dir:
checkpoint = {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'args': args
}
utils.save_on_master(
checkpoint,
os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
utils.save_on_master(
checkpoint, os.path.join(args.output_dir, 'checkpoint.pth'))
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))