def get_data_loaders(model, prefix, args):
train_transform_flip = torchvision.transforms.Compose([
model.module.get_augmentation(flip=True),
Stack(roll=("BNInc" in args.arch)),
ToTorchFormatTensor(div=("BNInc" not in args.arch)),
GroupNormalize(model.module.input_mean, model.module.input_std),
])
train_transform_nofl = torchvision.transforms.Compose([
model.module.get_augmentation(flip=False),
Stack(roll=("BNInc" in args.arch)),
ToTorchFormatTensor(div=("BNInc" not in args.arch)),
GroupNormalize(model.module.input_mean, model.module.input_std),
])
val_transform = torchvision.transforms.Compose([
GroupScale(int(model.module.scale_size)),
GroupCenterCrop(model.module.crop_size),
Stack(roll=("BNInc" in args.arch)),
ToTorchFormatTensor(div=("BNInc" not in args.arch)),
GroupNormalize(model.module.input_mean, model.module.input_std),
])
train_dataset = TSNDataSet(args.root_path,
args.train_list,
num_segments=args.num_segments,
image_tmpl=prefix,
transform=(train_transform_flip,
train_transform_nofl),
dense_sample=args.dense_sample,
dataset=args.dataset,
filelist_suffix=args.filelist_suffix,
folder_suffix=args.folder_suffix,
save_meta=args.save_meta,
always_flip=args.always_flip,
conditional_flip=args.conditional_flip,
adaptive_flip=args.adaptive_flip)
val_dataset = TSNDataSet(args.root_path,
args.val_list,
num_segments=args.num_segments,
image_tmpl=prefix,
random_shift=False,
transform=(val_transform, val_transform),
dense_sample=args.dense_sample,
dataset=args.dataset,
filelist_suffix=args.filelist_suffix,
folder_suffix=args.folder_suffix,
save_meta=args.save_meta)
train_loader = build_dataflow(train_dataset, True, args.batch_size,
args.workers, args.not_pin_memory)
val_loader = build_dataflow(val_dataset, False, args.batch_size,
args.workers, args.not_pin_memory)
return train_loader, val_loader
def get_val_loader(model):
root_path = '/home/mbc2004/datasets/Something-Something/frames/'
train_list = '/home/mbc2004/datasets/Something-Something/annotations/val_videofolder.txt'
num_segments = 8
modality = 'RGB'
dense_sample = False
batch_size = 8 #64
workers = 16
arch = 'resnet50'
prefix = '{:06d}.jpg'
print('#' * 20, 'NO FLIP!!!')
train_augmentation = torchvision.transforms.Compose(
[GroupMultiScaleCrop(224, [1, .875, .75, .66])])
return torch.utils.data.DataLoader(
TSNDataSet(root_path,
train_list,
num_segments=num_segments,
new_length=1,
modality=modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(arch not in ['BNInception', 'InceptionV3'])),
IdentityTransform(),
]),
dense_sample=dense_sample),
batch_size=batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
drop_last=True) # prevent something not % n_GPU
test_file = test_file if test_file is not None else val_list
data_loader = torch.utils.data.DataLoader(
TSNDataSet(
root_path,
test_file,
num_segments=this_test_segments,
new_length=1
if modality in ['RGB', 'RGB-flo', 'RGB-seg'] else 5,
modality=modality,
image_tmpl=prefix,
test_mode=True,
remove_missing=len(weights_list) == 1,
transform=torchvision.transforms.Compose([
cropping,
Stack(roll=(this_arch in ['BNInception', 'InceptionV3']),
mask=(modality in ['RGB-flo', 'RGB-seg'])),
ToTorchFormatTensor(
div=(this_arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std),
]),
dense_sample=args.dense_sample,
twice_sample=args.twice_sample,
dense_window=args.dense_window,
full_sample=args.full_sample,
ipn=args.dataset == 'ipn',
ipn_no_class=ipn_no_class),
batch_size=args.batch_size,
shuffle=False,
# num_workers=args.workers, pin_memory=True,
)
def main():
global args, best_prec1, TRAIN_SAMPLES
args = parser.parse_args()
num_class, args.train_list, args.val_list, args.test_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset,
args.modality)
if os.path.exists(args.test_list):
args.val_list = args.test_list
model = TSN(num_class, args.num_segments, args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift, shift_div=args.shift_div, shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local,
tin=args.tin)
crop_size = args.crop_size
scale_size = args.scale_size
input_mean = [0.485, 0.456, 0.406]
input_std = [0.229, 0.224, 0.225]
print(args.gpus)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
if os.path.isfile(args.resume_path):
print(("=> loading checkpoint '{}'".format(args.resume_path)))
checkpoint = torch.load(args.resume_path)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'], strict=False)
print(("=> loaded checkpoint '{}' (epoch {})"
.format(args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume_path)))
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
if args.random_crops == 1:
crop_aug = GroupCenterCrop(args.crop_size)
elif args.random_crops == 3:
crop_aug = GroupFullResSample(args.crop_size, args.scale_size, flip=False)
elif args.random_crops == 5:
crop_aug = GroupOverSample(args.crop_size, args.scale_size, flip=False)
else:
crop_aug = MultiGroupRandomCrop(args.crop_size, args.random_crops),
test_dataset = TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
multi_class=args.multi_class,
transform=torchvision.transforms.Compose([
GroupScale(int(args.scale_size)),
crop_aug,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]), dense_sample=args.dense_sample,
test_mode=True,
temporal_clips=args.temporal_clips)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
test(test_loader, model, args.start_epoch)
def main():
t_start = time.time()
global args, best_prec1, num_class, use_ada_framework # , model
wandb.init(
project="arnet-reproduce",
name=args.exp_header,
entity="video_channel"
)
wandb.config.update(args)
set_random_seed(args.random_seed)
use_ada_framework = args.ada_reso_skip and args.offline_lstm_last == False and args.offline_lstm_all == False and args.real_scsampler == False
if args.ablation:
logger = None
else:
if not test_mode:
logger = Logger()
sys.stdout = logger
else:
logger = None
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset,
args.data_dir)
#===
#args.val_list = args.train_list
#===
if args.ada_reso_skip:
if len(args.ada_crop_list) == 0:
args.ada_crop_list = [1 for _ in args.reso_list]
if use_ada_framework:
init_gflops_table()
model = TSN_Ada(num_class, args.num_segments,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
args=args)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(
flip=False if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
# TODO(yue) freeze some params in the policy + lstm layers
if args.freeze_policy:
for name, param in model.module.named_parameters():
if "lite_fc" in name or "lite_backbone" in name or "rnn" in name or "linear" in name:
param.requires_grad = False
if args.freeze_backbone:
for name, param in model.module.named_parameters():
if "base_model" in name:
param.requires_grad = False
if len(args.frozen_list) > 0:
for name, param in model.module.named_parameters():
for keyword in args.frozen_list:
if keyword[0] == "*":
if keyword[-1] == "*": # TODO middle
if keyword[1:-1] in name:
param.requires_grad = False
print(keyword, "->", name, "frozen")
else: # TODO suffix
if name.endswith(keyword[1:]):
param.requires_grad = False
print(keyword, "->", name, "frozen")
elif keyword[-1] == "*": # TODO prefix
if name.startswith(keyword[:-1]):
param.requires_grad = False
print(keyword, "->", name, "frozen")
else: # TODO exact word
if name == keyword:
param.requires_grad = False
print(keyword, "->", name, "frozen")
print("=" * 80)
for name, param in model.module.named_parameters():
print(param.requires_grad, "\t", name)
print("=" * 80)
for name, param in model.module.named_parameters():
print(param.requires_grad, "\t", name)
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(("=> loaded checkpoint '{}' (epoch {})"
.format(args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in args.tune_from: # new dataset
print('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
# TODO(yue) ada_model loading process
if args.ada_reso_skip:
if test_mode:
print("Test mode load from pretrained model")
the_model_path = args.test_from
if ".pth.tar" not in the_model_path:
the_model_path = ospj(the_model_path, "models", "ckpt.best.pth.tar")
model_dict = model.state_dict()
sd = load_to_sd(model_dict, the_model_path, "foo", "bar", -1, apple_to_apple=True)
model_dict.update(sd)
model.load_state_dict(model_dict)
elif args.base_pretrained_from != "":
print("Adaptively load from pretrained whole")
model_dict = model.state_dict()
sd = load_to_sd(model_dict, args.base_pretrained_from, "foo", "bar", -1, apple_to_apple=True)
model_dict.update(sd)
model.load_state_dict(model_dict)
elif len(args.model_paths) != 0:
print("Adaptively load from model_path_list")
model_dict = model.state_dict()
# TODO(yue) policy net
sd = load_to_sd(model_dict, args.policy_path, "lite_backbone", "lite_fc",
args.reso_list[args.policy_input_offset])
model_dict.update(sd)
# TODO(yue) backbones
for i, tmp_path in enumerate(args.model_paths):
base_model_index = i
new_i = i
sd = load_to_sd(model_dict, tmp_path, "base_model_list.%d" % base_model_index, "new_fc_list.%d" % new_i,
args.reso_list[i])
model_dict.update(sd)
model.load_state_dict(model_dict)
else:
if test_mode:
the_model_path = args.test_from
if ".pth.tar" not in the_model_path:
the_model_path = ospj(the_model_path, "models", "ckpt.best.pth.tar")
model_dict = model.state_dict()
sd = load_to_sd(model_dict, the_model_path, "foo", "bar", -1, apple_to_apple=True)
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.ada_reso_skip == False and args.base_pretrained_from != "":
print("Baseline: load from pretrained model")
model_dict = model.state_dict()
sd = load_to_sd(model_dict, args.base_pretrained_from, "base_model", "new_fc", 224)
if args.ignore_new_fc_weight:
print("@ IGNORE NEW FC WEIGHT !!!")
del sd["module.new_fc.weight"]
del sd["module.new_fc.bias"]
model_dict.update(sd)
model.load_state_dict(model_dict)
cudnn.benchmark = True
# Data loading code
normalize = GroupNormalize(input_mean, input_std)
data_length = 1
train_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path, args.train_list, num_segments=args.num_segments,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=False),
ToTorchFormatTensor(div=True),
normalize,
]), dense_sample=args.dense_sample,
dataset=args.dataset,
partial_fcvid_eval=args.partial_fcvid_eval,
partial_ratio=args.partial_ratio,
ada_reso_skip=args.ada_reso_skip,
reso_list=args.reso_list,
random_crop=args.random_crop,
center_crop=args.center_crop,
ada_crop_list=args.ada_crop_list,
rescale_to=args.rescale_to,
policy_input_offset=args.policy_input_offset,
save_meta=args.save_meta),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True,
drop_last=True) # prevent something not % n_GPU
val_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=False),
ToTorchFormatTensor(div=True),
normalize,
]), dense_sample=args.dense_sample,
dataset=args.dataset,
partial_fcvid_eval=args.partial_fcvid_eval,
partial_ratio=args.partial_ratio,
ada_reso_skip=args.ada_reso_skip,
reso_list=args.reso_list,
random_crop=args.random_crop,
center_crop=args.center_crop,
ada_crop_list=args.ada_crop_list,
rescale_to=args.rescale_to,
policy_input_offset=args.policy_input_offset,
save_meta=args.save_meta
),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# define loss function (criterion) and optimizer
criterion = torch.nn.CrossEntropyLoss().cuda()
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
if not test_mode:
exp_full_path = setup_log_directory(logger, args.log_dir, args.exp_header)
else:
exp_full_path = None
if not args.ablation:
if not test_mode:
with open(os.path.join(exp_full_path, 'args.txt'), 'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(log_dir=exp_full_path)
else:
tf_writer = None
else:
tf_writer = None
# TODO(yue)
map_record = Recorder()
mmap_record = Recorder()
prec_record = Recorder()
best_train_usage_str = None
best_val_usage_str = None
wandb.watch(model)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
if not args.skip_training:
set_random_seed(args.random_seed + epoch)
adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)
train_usage_str = train(train_loader, model, criterion, optimizer, epoch, logger, exp_full_path, tf_writer)
else:
train_usage_str = "No training usage stats (Eval Mode)"
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
set_random_seed(args.random_seed)
mAP, mmAP, prec1, val_usage_str, val_gflops = validate(val_loader, model, criterion, epoch, logger,
exp_full_path, tf_writer)
# remember best prec@1 and save checkpoint
map_record.update(mAP)
mmap_record.update(mmAP)
prec_record.update(prec1)
if mmap_record.is_current_best():
best_train_usage_str = train_usage_str
best_val_usage_str = val_usage_str
print('Best mAP: %.3f (epoch=%d)\t\tBest mmAP: %.3f(epoch=%d)\t\tBest Prec@1: %.3f (epoch=%d)' % (
map_record.best_val, map_record.best_at,
mmap_record.best_val, mmap_record.best_at,
prec_record.best_val, prec_record.best_at))
if args.skip_training:
break
if (not args.ablation) and (not test_mode):
tf_writer.add_scalar('acc/test_top1_best', prec_record.best_val, epoch)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': prec_record.best_val,
}, mmap_record.is_current_best(), exp_full_path)
if use_ada_framework and not test_mode:
print("Best train usage:")
print(best_train_usage_str)
print()
print("Best val usage:")
print(best_val_usage_str)
print("Finished in %.4f seconds\n" % (time.time() - t_start))
def main():
global args, best_prec1
args = parser.parse_args()
print("------------------------------------")
print("Environment Versions:")
print("- Python: {}".format(sys.version))
print("- PyTorch: {}".format(torch.__version__))
print("- TorchVison: {}".format(torchvision.__version__))
args_dict = args.__dict__
print("------------------------------------")
print(args.arch + " Configurations:")
for key in args_dict.keys():
print("- {}: {}".format(key, args_dict[key]))
print("------------------------------------")
print(args.mode)
if args.dataset == 'ucf101':
num_class = 101
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'hmdb51':
num_class = 51
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'kinetics':
num_class = 400
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'something':
num_class = 174
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'somethingv2':
num_class = 174
rgb_read_format = "img_{:05d}.jpg"
elif args.dataset == 'NTU_RGBD':
num_class = 120
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'tinykinetics':
num_class = 150
rgb_read_format = "{:05d}.jpg"
else:
raise ValueError('Unknown dataset ' + args.dataset)
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
partial_bn=not args.no_partialbn,
non_local=args.non_local)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
# Optimizer s also support specifying per-parameter options.
# To do this, pass in an iterable of dict s.
# Each of them will define a separate parameter group,
# and should contain a params key, containing a list of parameters belonging to it.
# Other keys should match the keyword arguments accepted by the optimizers,
# and will be used as optimization options for this group.
policies = model.get_optim_policies(args.dataset)
train_augmentation = model.get_augmentation()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
model_dict = model.state_dict()
if args.arch == "resnet50":
new_state_dict = {} #model_dict
div = False
roll = True
elif args.arch == "resnet34":
pretrained_dict = {}
new_state_dict = {} #model_dict
for k, v in model_dict.items():
if ('fc' not in k):
new_state_dict.update({k: v})
div = False
roll = True
elif (args.arch[:3] == "TCM"):
pretrained_dict = {}
new_state_dict = {} #model_dict
for k, v in model_dict.items():
if ('fc' not in k):
new_state_dict.update({k: v})
div = True
roll = False
if args.resume:
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 1
train_loader = torch.utils.data.DataLoader(
TSNDataSet(
"",
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
mode=args.mode,
image_tmpl=args.rgb_prefix + rgb_read_format if args.modality
in ["RGB", "RGBDiff"] else args.flow_prefix + rgb_read_format,
img_start_idx=args.img_start_idx,
transform=torchvision.transforms.Compose([
GroupScale((240, 320)),
# GroupScale(int(scale_size)),
train_augmentation,
Stack(roll=roll),
ToTorchFormatTensor(div=div),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(
TSNDataSet(
"",
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
mode=args.mode,
image_tmpl=args.rgb_prefix + rgb_read_format if args.modality
in ["RGB", "RGBDiff"] else args.flow_prefix + rgb_read_format,
img_start_idx=args.img_start_idx,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale((240, 320)),
# GroupScale((224)),
# GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=roll),
ToTorchFormatTensor(div=div),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
else:
raise ValueError("Unknown loss type")
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'],
group['decay_mult'])))
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=args.nesterov)
output_list = []
if args.evaluate:
prec1, score_tensor = validate(val_loader,
model,
criterion,
temperature=100)
output_list.append(score_tensor)
save_validation_score(output_list, filename='score.pt')
print("validation score saved in {}".format('/'.join(
(args.val_output_folder, 'score_inf5.pt'))))
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
temperature = train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1, score_tensor = validate(val_loader,
model,
criterion,
temperature=temperature)
output_list.append(score_tensor)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
output_best = 'Best Prec@1: %.3f\n' % (best_prec1)
print(output_best)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
# save validation score
save_validation_score(output_list)
print("validation score saved in {}".format('/'.join(
(args.val_output_folder, 'score.pt'))))
raise ValueError(
"Only 1, 5, 10 crops are supported while we got {}".format(
args.test_crops))
data_loader = torch.utils.data.DataLoader(
TSNDataSet(
root_path, [test_file] if test_file is not None else val_list,
num_segments=this_test_segments,
new_length=[new_length],
modality=[modality],
image_tmpl=prefix,
test_mode=True,
remove_missing=len(weights_list) == 1,
transform=torchvision.transforms.Compose([
cropping,
Stack(roll=(this_arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(this_arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std)
if modality != 'RGBDiff' else IdentityTransform(),
]),
dense_sample=args.dense_sample,
dense_length=args.dense_length,
dense_number=args.dense_number,
twice_sample=args.twice_sample,
random_sample=args.random_sample),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
def main():
# options
parser = argparse.ArgumentParser(description="TSM testing on the full validation set")
parser.add_argument('dataset', type=str)
# may contain splits
parser.add_argument('--weights', type=str, default=None)
parser.add_argument('--test_segments', type=str, default=25)
parser.add_argument('--dense_sample', default=False, action="store_true", help='use dense sample as I3D')
parser.add_argument('--twice_sample', default=False, action="store_true", help='use twice sample for ensemble')
parser.add_argument('--full_res', default=False, action="store_true",
help='use full resolution 256x256 for test as in Non-local I3D')
parser.add_argument('--test_crops', type=int, default=1)
parser.add_argument('--coeff', type=str, default=None)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
help='number of data loading workers (default: 8)')
# for true test
parser.add_argument('--test_list', type=str, default=None)
parser.add_argument('--csv_file', type=str, default=None)
parser.add_argument('--softmax', default=False, action="store_true", help='use softmax')
parser.add_argument('--max_num', type=int, default=-1)
parser.add_argument('--input_size', type=int, default=224)
parser.add_argument('--crop_fusion_type', type=str, default='avg')
parser.add_argument('--gpus', nargs='+', type=int, default=None)
parser.add_argument('--img_feature_dim',type=int, default=256)
parser.add_argument('--num_set_segments',type=int, default=1,help='TODO: select multiply set of n-frames from a video')
parser.add_argument('--pretrain', type=str, default='imagenet')
args = parser.parse_args()
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def parse_shift_option_from_log_name(log_name):
if 'shift' in log_name:
strings = log_name.split('_')
for i, s in enumerate(strings):
if 'shift' in s:
break
return True, int(strings[i].replace('shift', '')), strings[i + 1]
else:
return False, None, None
weights_list = args.weights.split(',')
test_segments_list = [int(s) for s in args.test_segments.split(',')]
assert len(weights_list) == len(test_segments_list)
if args.coeff is None:
coeff_list = [1] * len(weights_list)
else:
coeff_list = [float(c) for c in args.coeff.split(',')]
if args.test_list is not None:
test_file_list = args.test_list.split(',')
else:
test_file_list = [None] * len(weights_list)
data_iter_list = []
net_list = []
modality_list = []
total_num = None
for this_weights, this_test_segments, test_file in zip(weights_list, test_segments_list, test_file_list):
is_shift, shift_div, shift_place = parse_shift_option_from_log_name(this_weights)
if 'RGB' in this_weights:
modality = 'RGB'
else:
modality = 'Flow'
this_arch = this_weights.split('TSM_')[1].split('_')[2]
modality_list.append(modality)
num_class, args.train_list, val_list, root_path, prefix = dataset_config.return_dataset(args.dataset,
modality)
print('=> shift: {}, shift_div: {}, shift_place: {}'.format(is_shift, shift_div, shift_place))
net = TSN(num_class, this_test_segments if is_shift else 1, modality,
base_model=this_arch,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
pretrain=args.pretrain,
is_shift=is_shift, shift_div=shift_div, shift_place=shift_place,
non_local='_nl' in this_weights,
)
if 'tpool' in this_weights:
from ops.temporal_shift import make_temporal_pool
make_temporal_pool(net.base_model, this_test_segments) # since DataParallel
checkpoint = torch.load(this_weights)
checkpoint = checkpoint['state_dict']
# base_dict = {('base_model.' + k).replace('base_model.fc', 'new_fc'): v for k, v in list(checkpoint.items())}
base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint.items())}
replace_dict = {'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
}
for k, v in replace_dict.items():
if k in base_dict:
base_dict[v] = base_dict.pop(k)
net.load_state_dict(base_dict)
input_size = net.scale_size if args.full_res else net.input_size
if args.test_crops == 1:
cropping = torchvision.transforms.Compose([
GroupScale(net.scale_size),
GroupCenterCrop(input_size),
])
elif args.test_crops == 3: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupFullResSample(input_size, net.scale_size, flip=False)
])
elif args.test_crops == 5: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, net.scale_size, flip=False)
])
elif args.test_crops == 10:
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, net.scale_size)
])
else:
raise ValueError("Only 1, 5, 10 crops are supported while we got {}".format(args.test_crops))
data_loader = torch.utils.data.DataLoader(
TSNDataSet(root_path, test_file if test_file is not None else val_list, num_segments=this_test_segments,
new_length=1 if modality == "RGB" else 5,
modality=modality,
image_tmpl=prefix,
test_mode=True,
remove_missing=len(weights_list) == 1,
transform=torchvision.transforms.Compose([
cropping,
Stack(roll=(this_arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(this_arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std),
]), dense_sample=args.dense_sample, twice_sample=args.twice_sample),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True,
)
if args.gpus is not None:
devices = [args.gpus[i] for i in range(args.workers)]
else:
devices = list(range(args.workers))
net = torch.nn.DataParallel(net.cuda())
net.eval()
data_gen = enumerate(data_loader)
if total_num is None:
total_num = len(data_loader.dataset)
else:
assert total_num == len(data_loader.dataset)
data_iter_list.append(data_gen)
net_list.append(net)
output = []
def eval_video(video_data, net, this_test_segments, modality):
net.eval()
with torch.no_grad():
i, data, label = video_data
batch_size = label.numel()
num_crop = args.test_crops
if args.dense_sample:
num_crop *= 10 # 10 clips for testing when using dense sample
if args.twice_sample:
num_crop *= 2
if modality == 'RGB':
length = 3
elif modality == 'Flow':
length = 10
elif modality == 'RGBDiff':
length = 18
else:
raise ValueError("Unknown modality "+ modality)
data_in = data.view(-1, length, data.size(2), data.size(3))
if is_shift:
data_in = data_in.view(batch_size * num_crop, this_test_segments, length, data_in.size(2), data_in.size(3))
rst = net(data_in)
rst = rst.reshape(batch_size, num_crop, -1).mean(1)
if args.softmax:
# take the softmax to normalize the output to probability
rst = F.softmax(rst, dim=1)
rst = rst.data.cpu().numpy().copy()
if net.module.is_shift:
rst = rst.reshape(batch_size, num_class)
else:
rst = rst.reshape((batch_size, -1, num_class)).mean(axis=1).reshape((batch_size, num_class))
return i, rst, label
proc_start_time = time.time()
max_num = args.max_num if args.max_num > 0 else total_num
top1 = AverageMeter()
top5 = AverageMeter()
for i, data_label_pairs in enumerate(zip(*data_iter_list)):
with torch.no_grad():
if i >= max_num:
break
this_rst_list = []
this_label = None
for n_seg, (_, (data, label)), net, modality in zip(test_segments_list, data_label_pairs, net_list, modality_list):
rst = eval_video((i, data, label), net, n_seg, modality)
this_rst_list.append(rst[1])
this_label = label
assert len(this_rst_list) == len(coeff_list)
for i_coeff in range(len(this_rst_list)):
this_rst_list[i_coeff] *= coeff_list[i_coeff]
ensembled_predict = sum(this_rst_list) / len(this_rst_list)
for p, g in zip(ensembled_predict, this_label.cpu().numpy()):
output.append([p[None, ...], g])
cnt_time = time.time() - proc_start_time
prec1, prec5 = accuracy(torch.from_numpy(ensembled_predict), this_label, topk=(1, 5))
top1.update(prec1.item(), this_label.numel())
top5.update(prec5.item(), this_label.numel())
if i % 20 == 0:
print('video {} done, total {}/{}, average {:.3f} sec/video, '
'moving Prec@1 {:.3f} Prec@5 {:.3f}'.format(i * args.batch_size, i * args.batch_size, total_num,
float(cnt_time) / (i+1) / args.batch_size, top1.avg, top5.avg))
video_pred = [np.argmax(x[0]) for x in output]
video_pred_top5 = [np.argsort(np.mean(x[0], axis=0).reshape(-1))[::-1][:5] for x in output]
video_labels = [x[1] for x in output]
if args.csv_file is not None:
print('=> Writing result to csv file: {}'.format(args.csv_file))
with open(test_file_list[0].replace('test_videofolder.txt', 'category.txt')) as f:
categories = f.readlines()
categories = [f.strip() for f in categories]
with open(test_file_list[0]) as f:
vid_names = f.readlines()
vid_names = [n.split(' ')[0] for n in vid_names]
print(vid_names)
assert len(vid_names) == len(video_pred)
if args.dataset != 'somethingv2': # only output top1
with open(args.csv_file, 'w') as f:
for n, pred in zip(vid_names, video_pred):
f.write('{};{}\n'.format(n, categories[pred]))
else:
with open(args.csv_file, 'w') as f:
for n, pred5 in zip(vid_names, video_pred_top5):
fill = [n]
for p in list(pred5):
fill.append(p)
f.write('{};{};{};{};{};{}\n'.format(*fill))
cf = confusion_matrix(video_labels, video_pred).astype(float)
np.save('cm.npy', cf)
cls_cnt = cf.sum(axis=1)
cls_hit = np.diag(cf)
cls_acc = cls_hit / cls_cnt
print(cls_acc)
upper = np.mean(np.max(cf, axis=1) / cls_cnt)
print('upper bound: {}'.format(upper))
print('-----Evaluation is finished------')
print('Class Accuracy {:.02f}%'.format(np.mean(cls_acc) * 100))
print('Overall Prec@1 {:.02f}% Prec@5 {:.02f}%'.format(top1.avg, top5.avg))
def main():
global args, best_prec1
args = parser.parse_args()
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset,
args.modality)
model = TSN(num_class, args.num_segments, args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift, shift_div=args.shift_div, shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local,
cca3d = args.cca3d
)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(flip=False if 'something' in args.dataset or 'jester' in args.dataset else True)
model = model.cuda()
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume,map_location='cuda:0')
parallel_state_dict = checkpoint['state_dict']
cpu_state_dict={}
for k,v in parallel_state_dict.items():
cpu_state_dict[k[len('module.'):]] = v
model.load_state_dict(cpu_state_dict)
print(("=> loaded checkpoint '{}' (epoch {})".format(args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
preprocess=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])
val_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform = preprocess,
dense_sample=args.dense_sample),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=False)
norm_param = (input_mean, input_std)
cam_process(val_loader, model,norm_param)
def main_worker(gpu, ngpus_per_node, args):
global best_acc1
args.gpu = gpu
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
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=rank)
else:
rank = 0
# create model
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset, args.modality)
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div, args.shift_place)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join(
['TSM', args.dataset, args.modality, full_arch_name, args.consensus_type, 'segment%d' % args.num_segments,
'e{}'.format(args.epochs)])
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.dense_sample:
args.store_name += '_dense'
if args.non_local > 0:
args.store_name += '_nl'
args.store_name += '_lr{}'.format(args.lr)
args.store_name += '_wd{:.1e}'.format(args.weight_decay)
args.store_name += '_do{}'.format(args.dropout)
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
check_rootfolders(args, rank)
model = TSN(num_class, args.num_segments, args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift, shift_div=args.shift_div, shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local)
# first synchronization of initial weights
# sync_initial_weights(model, args.rank, args.world_size)
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
if rank == 0:
print(model)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(flip=False if 'something' in args.dataset or 'jester' in args.dataset else True)
if args.distributed:
# 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 on a node
args.batch_size = int(args.batch_size / ngpus_per_node)
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)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
optimizer = torch.optim.SGD(policies, args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# 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)
if args.start_epoch == 1:
args.start_epoch = checkpoint['epoch'] + 1
best_acc1 = checkpoint['best_acc1']
# if args.gpu is not None:
# # best_acc1 may be from a checkpoint from a different GPU
# best_acc1 = best_acc1.to(args.gpu)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
train_dataset = TSNDataSet(args.dataset, args.root_path, args.train_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize]),
dense_sample=args.dense_sample)
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, drop_last=True, sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(
TSNDataSet(args.dataset, args.root_path, args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]), dense_sample=args.dense_sample),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.evaluate:
validate(val_loader, model, criterion, args)
return
log_training = open(os.path.join(args.root_model, args.store_name, 'log.csv'), 'w')
with open(os.path.join(args.root_model, args.store_name, 'args.txt'), 'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(log_dir=os.path.join(args.root_model, args.store_name))
for epoch in range(args.start_epoch, args.epochs+1):
if args.distributed:
train_sampler.set_epoch(epoch)
# adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps, args)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training, tf_writer, args, rank)
if rank % ngpus_per_node == 0:
save_checkpoint({
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}, False, args, rank)
if epoch % 5 == 0 and rank % ngpus_per_node == 0:
save_checkpoint({
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
}, False, args, rank, e=epoch)
# evaluate on validation set
is_best = False
if epoch % args.eval_freq == 0 or epoch == args.epochs:
acc1 = validate(val_loader, model, criterion, epoch, args, rank, log_training, tf_writer)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
if not args.multiprocessing_distributed or (args.multiprocessing_distributed and rank % ngpus_per_node == 0):
save_checkpoint({
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}, is_best, args, rank)
[GroupOverSample(input_size, net.scale_size)])
else:
raise ValueError(
"Only 1, 5, 10 crops are supported while we got {}".format(
args.test_crops))
data_loader = torch.utils.data.DataLoader(
TSNDataSet(
val_list,
num_segments=args.test_segments,
new_length=1 if args.modality == "RGB" else 5,
modality=args.modality,
image_tmpl=prefix,
test_mode=True,
remove_missing=True,
multi_clip_test=args.multi_clip_test,
transform=torchvision.transforms.Compose([
cropping,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std),
]),
dense_sample=args.dense_sample,
),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
if args.gpus is not None:
def main():
global args, best_prec1, least_loss
least_loss = 1000
args = parser.parse_args()
if os.path.exists(os.path.join(args.root_log, "error.log")):
os.remove(os.path.join(args.root_log, "error.log"))
logging.basicConfig(
level=logging.DEBUG,
filename=os.path.join(args.root_log, "error.log"),
filemode='a',
format=
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
# log_handler = open(os.path.join(args.root_log,"error.log"),"w")
# sys.stdout = log_handler
if args.root_path:
num_class, args.train_list, args.val_list, _, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
args.train_list = os.path.join(args.root_log,
"kf1_train_anno_lijun_iod.json")
args.test_list = os.path.join(args.root_log,
"kf1_test_anno_lijun_iod.json")
else:
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div,
args.shift_place)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join([
'TSA', args.dataset, args.modality, full_arch_name,
args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs)
])
# if args.pretrain != 'imagenet':
# args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.dense_sample:
args.store_name += '_dense'
if args.non_local > 0:
args.store_name += '_nl'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
check_rootfolders()
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift,
shift_div=args.shift_div,
shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local,
is_TSA=args.tsa,
is_sTSA=args.stsa,
is_tTSA=args.ttsa,
shift_diff=args.shift_diff,
shift_groups=args.shift_groups,
is_ME=args.me,
is_3D=args.is_3D,
cfg_file=args.cfg_file)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
# policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(
flip=False
if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
if args.optimizer == "sgd":
if args.lr_scheduler:
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer == "adam":
params = get_vmz_fine_tuning_parameters(model,
args.vmz_tune_last_k_layer)
optimizer = torch.optim.Adam(params,
args.lr,
weight_decay=args.weight_decay)
else:
raise RuntimeError("not supported optimizer")
if args.lr_scheduler:
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, args.lr_steps, args.lr_scheduler_gamma)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
# if args.lr_scheduler:
# scheduler.load_state_dict(checkpoint["lr_scheduler"])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
logging.info(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
logging.error(
("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
# sd = {k:v for k, v in sd.items() if k in keys2}
sd = {k: v for k, v in sd.items() if k in keys2}
if args.dataset not in args.tune_from: # new dataset
print('=> New dataset, do not load fc weights')
sd = {
k: v
for k, v in sd.items()
if 'fc' not in k and "projection" not in k
}
if args.modality == 'Flow' and 'Flow' not in args.tune_from:
sd = {k: v for k, v in sd.items() if 'conv1.weight' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality in ['RGB', "PoseAction"]:
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
if not args.shuffle:
train_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path,
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch
in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
ToTorchFormatTensor(
div=(args.arch
not in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True) # prevent something not % n_GPU
val_loader = torch.utils.data.DataLoader(TSNDataSet(
args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(scale_size),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
ToTorchFormatTensor(div=(args.arch
not in ['BNInception',
'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D",
"X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# for group in policies:
# print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
# group['name'], len(group['params']), group['lr_mult'], group['decay_mult'])))
if args.evaluate:
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
elif args.loss_type == "bce":
criterion = torch.nn.BCEWithLogitsLoss().cuda()
elif args.loss_type == "wbce":
class_weight, pos_weight = prep_weight(args.train_list)
criterion = WeightedBCEWithLogitsLoss(class_weight, pos_weight)
else:
raise ValueError("Unknown loss type")
val_loader = torch.utils.data.DataLoader(TSNDataSet(
args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(scale_size),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
ToTorchFormatTensor(div=(args.arch
not in ['BNInception',
'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D",
"X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample,
analysis=True),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
test(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, args.store_name, 'log.csv'), 'w')
with open(os.path.join(args.root_log, args.store_name, 'args.txt'),
'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(
log_dir=os.path.join(args.root_log, args.store_name))
print(model)
logging.info(model)
for epoch in range(args.start_epoch, args.epochs):
logging.info("Train Epoch {}/{} starts, estimated time 5832s".format(
str(epoch), str(args.epochs)))
# update data_loader
if args.shuffle:
gen_label(args.prop_path,
args.label_path,
args.trn_name,
args.train_list,
args.neg_rate,
STR=False)
gen_label(args.prop_path,
args.label_path,
args.tst_name,
args.val_list,
args.test_rate,
STR=False)
train_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path,
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch
in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D",
"X3D"])),
ToTorchFormatTensor(
div=(args.arch
not in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
val_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(scale_size),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch
in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D",
"X3D"])),
ToTorchFormatTensor(
div=(args.arch
not in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
print(train_loader)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
elif args.loss_type == "bce":
criterion = torch.nn.BCEWithLogitsLoss().cuda()
elif args.loss_type == "wbce":
class_weight, pos_weight = prep_weight(args.train_list)
criterion = WeightedBCEWithLogitsLoss(class_weight, pos_weight)
else:
raise ValueError("Unknown loss type")
if not args.lr_scheduler:
adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)
train(train_loader, model, criterion, optimizer, epoch,
log_training, tf_writer)
else:
train(train_loader, model, criterion, optimizer, epoch,
log_training, tf_writer)
scheduler.step()
# train for one epoch
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
logging.info(
"Test Epoch {}/{} starts, estimated time 13874s".format(
str(epoch // args.eval_freq),
str(args.epochs / args.eval_freq)))
if args.loss_type == "wbce":
# class_weight,pos_weight = prep_weight(args.val_list)
criterion = torch.nn.BCEWithLogitsLoss().cuda()
lossm = validate(val_loader, model, criterion, epoch, log_training,
tf_writer)
# remember best prec@1 and save checkpoint
is_best = lossm < least_loss
least_loss = min(lossm, least_loss)
tf_writer.add_scalar('lss/test_top1_best', least_loss, epoch)
output_best = 'Best Loss: %.3f\n' % (lossm)
logging.info(output_best)
log_training.write(output_best + '\n')
log_training.flush()
if args.lr_scheduler:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': least_loss,
'lr_scheduler': scheduler,
}, is_best, epoch)
else:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': least_loss,
}, is_best, epoch)
def main():
global args, best_prec1
args = parser.parse_args()
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
full_arch_name = args.arch
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join([
'I3D', args.dataset, full_arch_name, 'batch{}'.format(args.batch_size),
'wd{}'.format(args.weight_decay), args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs),
'dropout{}'.format(args.dropout), args.pretrain,
'lr{}'.format(args.lr), '_warmup{}'.format(args.warmup)
])
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
else:
step_str = [str(int(x)) for x in args.lr_steps]
args.store_name += '_step' + '_'.join(step_str)
if args.dense_sample:
args.store_name += '_dense'
if args.spatial_dropout:
sigmoid_layer_str = '_'.join(args.sigmoid_layer)
args.store_name += '_spatial_drop3d_{}_group{}_layer{}'.format(
args.sigmoid_thres, args.sigmoid_group, sigmoid_layer_str)
if args.sigmoid_random:
args.store_name += '_RandomSigmoid'
if args.non_local > 0:
args.store_name += '_nl'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
check_rootfolders()
model = i3d(num_class,
args.num_segments,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
spatial_dropout=args.spatial_dropout,
sigmoid_thres=args.sigmoid_thres,
sigmoid_group=args.sigmoid_group,
sigmoid_random=args.sigmoid_random,
sigmoid_layer=args.sigmoid_layer,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
train_augmentation = model.get_augmentation(
flip=False
if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model,
device_ids=list(range(args.gpus))).cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in args.tune_from: # new dataset
print('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
if args.modality == 'Flow' and 'Flow' not in args.tune_from:
sd = {k: v for k, v in sd.items() if 'conv1.weight' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
normalize = GroupNormalize(input_mean, input_std)
train_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path,
args.train_list,
num_segments=args.num_segments,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
GroupScale((256, 340)),
train_augmentation,
Stack('3D'),
ToTorchFormatTensor(),
normalize,
]),
dense_sample=args.dense_sample),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True) # prevent something not % n_GPU
val_loader = torch.utils.data.DataLoader(TSNDataSet(
args.root_path,
args.val_list,
num_segments=args.num_segments,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack('3D'),
ToTorchFormatTensor(),
normalize,
]),
dense_sample=args.dense_sample),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.BCEWithLogitsLoss().cuda()
else:
raise ValueError("Unknown loss type")
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, args.store_name, 'log.csv'), 'w')
with open(os.path.join(args.root_log, args.store_name, 'args.txt'),
'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(
log_dir=os.path.join(args.root_log, args.store_name))
for epoch in range(args.start_epoch, args.epochs):
# adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training,
tf_writer, args.warmup, args.lr_type, args.lr_steps)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion, epoch, log_training,
tf_writer)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
tf_writer.add_scalar('acc/test_mAP_best', best_prec1, epoch)
output_best = 'Best mAP: %.3f\n' % (best_prec1)
print(output_best)
log_training.write(output_best + '\n')
log_training.flush()
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, is_best)
[GroupOverSample(input_size, net.scale_size)])
else:
raise ValueError(
"Only 1, 5, 10 crops are supported while we got {}".format(
args.test_crops))
data_loader = torch.utils.data.DataLoader(
TSNDataSet(
root_path,
test_file if test_file is not None else val_list,
num_segments=this_test_segments,
new_length=1 if modality == "RGB" else 5,
modality=modality,
image_tmpl=prefix,
test_mode=True,
remove_missing=len(weights_list) == 1,
transform=torchvision.transforms.Compose([
cropping,
Stack(roll=(this_arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(this_arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std),
]),
dense_sample=args.dense_sample,
twice_sample=args.twice_sample),
batch_size=args.batch_size,
shuffle=False,
# num_workers=args.workers, pin_memory=True,
)
if args.gpus is not None:
devices = [args.gpus[i] for i in range(args.workers)]
def main():
global args, best_prec1
args = parser.parse_args()
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
full_arch_name = args.arch
args.store_name = '_'.join([
'TDN_', args.dataset, args.modality, full_arch_name,
args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs)
])
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.dense_sample:
args.store_name += '_dense'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
if dist.get_rank() == 0:
check_rootfolders()
logger = setup_logger(output=os.path.join(args.root_log, args.store_name),
distributed_rank=dist.get_rank(),
name=f'TDN')
logger.info('storing name: ' + args.store_name)
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
fc_lr5=(args.tune_from and args.dataset in args.tune_from))
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
for group in policies:
logger.info(
('[TDN-{}]group: {} has {} params, lr_mult: {}, decay_mult: {}'.
format(args.arch, group['name'], len(group['params']),
group['lr_mult'], group['decay_mult'])))
train_augmentation = model.get_augmentation(
flip=False if 'something' in args.dataset else True)
cudnn.benchmark = True
# Data loading code
normalize = GroupNormalize(input_mean, input_std)
train_dataset = TSNDataSet(
args.dataset,
args.root_path,
args.train_list,
num_segments=args.num_segments,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
dense_sample=args.dense_sample)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler,
drop_last=True)
val_dataset = TSNDataSet(
args.dataset,
args.root_path,
args.val_list,
num_segments=args.num_segments,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
dense_sample=args.dense_sample)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
sampler=val_sampler,
drop_last=True)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
else:
raise ValueError("Unknown loss type")
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = get_scheduler(optimizer, len(train_loader), args)
model = DistributedDataParallel(model.cuda(),
device_ids=[args.local_rank],
broadcast_buffers=True,
find_unused_parameters=True)
if args.resume:
if os.path.isfile(args.resume):
logger.info(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume, map_location='cpu')
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
logger.info(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
logger.info(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
logger.info(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
logger.info('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
logger.info('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
logger.info(
'#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in args.tune_from: # new dataset
logger.info('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
with open(os.path.join(args.root_log, args.store_name, 'args.txt'),
'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(
log_dir=os.path.join(args.root_log, args.store_name))
if args.evaluate:
logger.info(("===========evaluate==========="))
val_loader.sampler.set_epoch(args.start_epoch)
prec1, prec5, val_loss = validate(val_loader, model, criterion, logger)
if dist.get_rank() == 0:
is_best = prec1 > best_prec1
best_prec1 = prec1
logger.info(("Best Prec@1: '{}'".format(best_prec1)))
save_epoch = args.start_epoch + 1
save_checkpoint(
{
'epoch': args.start_epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'prec1': prec1,
'best_prec1': best_prec1,
}, save_epoch, is_best)
return
for epoch in range(args.start_epoch, args.epochs):
train_loader.sampler.set_epoch(epoch)
train_loss, train_top1, train_top5 = train(train_loader,
model,
criterion,
optimizer,
epoch=epoch,
logger=logger,
scheduler=scheduler)
if dist.get_rank() == 0:
tf_writer.add_scalar('loss/train', train_loss, epoch)
tf_writer.add_scalar('acc/train_top1', train_top1, epoch)
tf_writer.add_scalar('acc/train_top5', train_top5, epoch)
tf_writer.add_scalar('lr', optimizer.param_groups[-1]['lr'], epoch)
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
val_loader.sampler.set_epoch(epoch)
prec1, prec5, val_loss = validate(val_loader, model, criterion,
epoch, logger)
if dist.get_rank() == 0:
tf_writer.add_scalar('loss/test', val_loss, epoch)
tf_writer.add_scalar('acc/test_top1', prec1, epoch)
tf_writer.add_scalar('acc/test_top5', prec5, epoch)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
tf_writer.add_scalar('acc/test_top1_best', best_prec1, epoch)
logger.info(("Best Prec@1: '{}'".format(best_prec1)))
tf_writer.flush()
save_epoch = epoch + 1
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'prec1': prec1,
'best_prec1': best_prec1,
}, save_epoch, is_best)
def main():
global args, best_prec1
args = parser.parse_args()
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div,
args.shift_place)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join([
'TSM', args.dataset, args.modality, full_arch_name,
args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs)
])
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.dense_sample:
args.store_name += '_dense'
if args.non_local > 0:
args.store_name += '_nl'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
check_rootfolders()
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift,
shift_div=args.shift_div,
shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(
flip=False
if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in args.tune_from: # new dataset
print('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
if args.modality == 'Flow' and 'Flow' not in args.tune_from:
sd = {k: v for k, v in sd.items() if 'conv1.weight' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
train_loader = torch.utils.data.DataLoader(
TSNDataSet(
args.root_path,
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
dense_sample=args.dense_sample),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True) # prevent something not % n_GPU
val_loader = torch.utils.data.DataLoader(TSNDataSet(
args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
dense_sample=args.dense_sample),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
else:
raise ValueError("Unknown loss type")
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'],
group['decay_mult'])))
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, args.store_name, 'log.csv'), 'w')
with open(os.path.join(args.root_log, args.store_name, 'args.txt'),
'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(
log_dir=os.path.join(args.root_log, args.store_name))
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training,
tf_writer)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion, epoch, log_training,
tf_writer)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
tf_writer.add_scalar('acc/test_top1_best', best_prec1, epoch)
output_best = 'Best Prec@1: %.3f\n' % (best_prec1)
print(output_best)
log_training.write(output_best + '\n')
log_training.flush()
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, is_best)
def main():
global args, best_prec1
args = parser.parse_args()
#num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset,
# args.modality)
num_class = 21
args.train_list = "/home/jzwang/code/Video_3D/movienet/data/movie/movie_val_new.txt"
args.val_list = "/home/jzwang/code/RGB-FLOW/movie_new/data/datanew/movie_val_new.txt"
args.root_path = ""
prefix = "frame_{:04d}.jpg"
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div,
args.shift_place)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join([
'TSM', args.dataset, args.modality, full_arch_name,
args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs)
])
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.dense_sample:
args.store_name += '_dense'
if args.non_local > 0:
args.store_name += '_nl'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
#check_rootfolders()
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift,
shift_div=args.shift_div,
shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(
flip=False
if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
#best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in args.tune_from: # new dataset
print('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
if args.modality == 'Flow' and 'Flow' not in args.tune_from:
sd = {k: v for k, v in sd.items() if 'conv1.weight' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
train_loader = torch.utils.data.DataLoader(TSNDataSet(
"",
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl="frame_{:04d}.jpg" if args.modality in ["RGB", "RGBDiff"]
else args.flow_prefix + "{}_{:05d}.jpg",
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=args.arch == 'BNInception'),
ToTorchFormatTensor(div=args.arch != 'BNInception'),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(TSNDataSet(
"",
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl="frame_{:04d}.jpg" if args.modality in ["RGB", "RGBDiff"]
else args.flow_prefix + "{}_{:05d}.jpg",
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=args.arch == 'BNInception'),
ToTorchFormatTensor(div=args.arch != 'BNInception'),
normalize,
])),
batch_size=int(16),
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and optimizer
criterion = torch.nn.BCEWithLogitsLoss().cuda()
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'],
group['decay_mult'])))
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
zero_time = time.time()
best_map = 0
print('Start training...')
for epoch in range(args.start_epoch, args.epochs):
#adjust_learning_rate(optimizer, epoch, args.lr_steps)
#if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
valloss, mAP, output_mtx = validate(val_loader, model, criterion)
end_time = time.time()
epoch_time = end_time - start_time
total_time = end_time - zero_time
print('Total time used: %s Epoch %d time uesd: %s' %
(str(datetime.timedelta(seconds=int(total_time))), epoch,
str(datetime.timedelta(seconds=int(epoch_time)))))
print('Train loss: {0:.4f} val loss: {1:.4f} mAP: {2:.4f}'.format(
trainloss, valloss, mAP))
# evaluate on validation set
is_best = mAP > best_map
#if mAP > best_map:
#best_map = mAP
# checkpoint_name = "%04d_%s" % (epoch+1, "checkpoint.pth.tar")
checkpoint_name = "best_checkpoint.pth.tar"
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, is_best, epoch)
np.save("val1.npy", output_mtx)
with open(args.record_path, 'a') as file:
file.write(
'Epoch:[{0}]'
'Train loss: {1:.4f} val loss: {2:.4f} map: {3:.4f}\n'.format(
epoch + 1, trainloss, valloss, mAP))
print('************ Done!... ************')