def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
categories, train_list, val_list, data_root_path, prefix = datasets_video.return_dataset(
args.root_path, args.dataset, args.modality)
num_class = len(categories)
architecture = architecture_name_parser(args.architecture)
store_name = '_'.join([
'MFF', args.dataset, args.modality, architecture,
'segment%d' % args.num_segments,
'%df1c' % args.num_motion
])
print('storing name: ' + store_name)
print("Using " + architecture + " architecture")
tsn = TSN(num_class,
args.num_segments,
args.modality,
architecture=architecture,
consensus_type=args.consensus_type,
dropout=args.dropout,
num_motion=args.num_motion,
img_feature_dim=args.img_feature_dim,
partial_bn=args.partialbn,
dataset=args.dataset,
group_norm=args.group_norm)
model = tsn.total_model
model.summary()
#TODO: group normalize for non RGBDiff or RGBFlow
transform_fn = modules[architecture].preprocess_input
# define loss function (criterion) and optimizer
if args.loss_type == 'cce':
criterion = 'categorical_crossentropy'
else:
raise ValueError("Unknown loss type")
# Create optimizer
optimizer = optimizers.SGD(lr=args.lr,
momentum=args.momentum,
decay=args.weight_decay)
if 'adam' in args.optimizer.lower():
optimizer = optimizers.Adam(lr=args.lr,
decay=args.weight_decay,
clipnorm=args.clip_gradient)
if 'rms' in args.optimizer.lower():
optimizer = optimizers.RMSprop(lr=args.lr, decay=args.weight_decay)
# #######
model.compile(optimizer, loss=criterion, metrics=['accuracy'])
if args.experiment_name:
log_dir = './logs/' + args.experiment_name
else:
log_dir = './logs'
tb = TensorBoard(log_dir=log_dir, batch_size=args.batch_size)
checkpoint = ModelCheckpoint("model/" + store_name, save_best_only=True)
train(model, args, optimizer, num_class, data_root_path, tsn.image_dim,
train_list, val_list, [tb, checkpoint], transform_fn)
def main():
global args, best_loss
args = parser.parse_args()
check_rootfolders()
root_data, train_dict, val_dict = datasets_video.return_dataset(
args.dataset, args.modality, args.view)
num_class = 1
args.store_name = '_'.join([
'TRN', args.label_name, args.dataset, args.modality, args.view,
args.arch, args.consensus_type,
'segment%d' % args.num_segments
])
print('storing name: ' + args.store_name)
img_tmpl = '{:06d}.jpg' if args.view == 'body' else 'frame_det_00_{:06d}.bmp'
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,
fix_all_weights=args.fix_all_weights)
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() # augmentation increase
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
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']
model.load_state_dict(checkpoint['state_dict'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, 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_loader = torch.utils.data.DataLoader(TSNDataSet(
root_data,
args.train_dict,
args.label_name,
num_segments=args.num_segments,
phase='Train',
new_length=data_length,
modality=args.modality,
image_tmpl=img_tmpl,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(TSNDataSet(
root_data,
args.val_dict,
args.label_name,
num_segments=args.num_segments,
phase='Validation',
new_length=data_length,
modality=args.modality,
image_tmpl=img_tmpl,
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,
])),
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()
elif args.loss_type == 'mse':
criterion = torch.nn.MSELoss().cuda()
elif args.loss_type == 'mae':
criterion = torch.nn.SmoothL1Loss().cuda()
else: # another loss is mse or mae
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)
if args.evaluate:
log_val = open(
os.path.join(args.root_log, '%s_val.csv' % args.store_name), 'w')
validate(val_loader, model, criterion, 0, log_val)
return
log_training = open(
os.path.join(args.root_log, '%s.csv' % args.store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
loss = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log_training)
# remember best prec@1 and save checkpoint
is_best = loss < best_loss
best_loss = min(loss, best_loss)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
}, is_best)
def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality)
num_class = len(categories)
args.store_name = '_'.join([
'TRN', args.dataset, args.modality, args.arch, args.consensus_type,
'segment%d' % args.num_segments
])
print('storing name: ' + args.store_name)
model = TSN(339,
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)
_, cnn = list(model.named_children())[0]
for p in cnn.parameters():
p.requires_grad = False
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()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
# remove if not transfer learning
checkpoint = torch.load('/home/ec2-user/mit_weights.pth.tar')
model.load_state_dict(checkpoint['state_dict'])
for module in list(
list(model._modules['module'].children())
[-1].children())[-1].children():
module[-1] = nn.Linear(256, num_class)
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.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
cudnn.benchmark = True
model.cuda()
# 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,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=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(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
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.Adam(model.parameters(), args.lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
8,
gamma=0.1,
last_epoch=-1)
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, '%s.csv' % args.store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
scheduler.step()
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log_training,
epoch)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
summary_writer.close()
for folder in folders_util:
if not os.path.exists(folder):
print('creating folder ' + folder)
os.mkdir(folder)
if __name__ == '__main__':
best_prec1 = 0
global args
args = parser.parse_args()
print(args)
check_rootfolders()
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality)
num_class = len(categories)
args.store_name = '_'.join([
'MFF', args.dataset, args.modality, args.arch,
'segment%d' % args.num_segments,
'%df1c' % args.num_motion
])
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
num_motion=args.num_motion,
def main():
global args, best_prec1, num_train_dataset, num_val_dataset, writer
args = parser.parse_args()
# if args.no_cudnn:
# torch.backends.cudnn.benchmark = False
# print (torch.backends.cudnn.benchmark)
# asdf
_fill_in_None_args()
_join_result_path()
check_rootfolders()
with open(os.path.join(args.result_path, 'opts.json'), 'w') as opt_file:
json.dump(vars(args), opt_file)
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality, args.root_path, args.file_type)
# print(categories, args.train_list, args.val_list, args.root_path, prefix)
num_class = len(categories)
args.store_name = '_'.join([args.consensus_type, args.dataset, args.modality, args.arch, args.consensus_type, 'segment%d'% args.num_segments, \
'key%d'%args.key_dim, 'value%d'%args.value_dim, 'query%d'%args.query_dim, 'queryUpdateby%s'%args.query_update_method,\
'NoSoftmax%s'%args.no_softmax_on_p, 'hopMethod%s'%args.hop_method])
print('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,
key_dim=args.key_dim,
value_dim=args.value_dim,
query_dim=args.query_dim,
query_update_method=args.query_update_method,
partial_bn=not args.no_partialbn,
freezeBN_Eval=args.freezeBN_Eval,
freezeBN_Require_Grad_True=args.freezeBN_Require_Grad_True,
num_hop=args.hop,
hop_method=args.hop_method,
num_CNNs=args.num_CNNs,
no_softmax_on_p=args.no_softmax_on_p,
freezeBackbone=args.freezeBackbone,
CustomPolicy=args.CustomPolicy,
sorting=args.sorting,
MultiStageLoss=args.MultiStageLoss,
MultiStageLoss_MLP=args.MultiStageLoss_MLP,
how_to_get_query=args.how_to_get_query,
only_query=args.only_query,
CC=args.CC,
channel=args.channel,
memory_dim=args.memory_dim,
image_resolution=args.image_resolution,
how_many_objects=args.how_many_objects,
Each_Embedding=args.Each_Embedding,
Curriculum=args.Curriculum,
Curriculum_dim=args.Curriculum_dim,
lr_steps=args.lr_steps,
)
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()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
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.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
# asdf
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_data = TSNDataSet(
args.root_path,
args.train_list,
args.file_type,
num_segments=args.num_segments,
MoreAug_Rotation=args.MoreAug_Rotation,
MoreAug_ColorJitter=args.MoreAug_ColorJitter,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
phase='train',
transform1=torchvision.transforms.Compose([
train_augmentation, # GroupMultiScaleCrop[1, .875, .75, .66] AND GroupRandomHorizontalFlip
]),
transform2=torchvision.transforms.Compose([
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize, # GroupNormalize
]),
image_resolution=args.image_resolution)
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=False,
drop_last=True)
val_data = TSNDataSet(
args.root_path,
args.val_list,
args.file_type,
num_segments=args.num_segments,
MoreAug_Rotation=args.MoreAug_Rotation,
MoreAug_ColorJitter=args.MoreAug_ColorJitter,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
phase='test',
transform1=torchvision.transforms.Compose(
[GroupScale(int(scale_size)),
GroupCenterCrop(crop_size)]),
transform2=torchvision.transforms.Compose([
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
image_resolution=args.image_resolution)
val_loader = torch.utils.data.DataLoader(val_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=False,
drop_last=True)
num_train_dataset = len(train_data)
num_val_dataset = len(val_data)
# print (num_train_dataset, num_val_dataset)
# print (len(train_loader), len(val_loader))
# asdf
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss(reduce=False).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.optimizer == 'sgd':
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer == 'adam':
optimizer = torch.optim.Adam(policies,
lr=args.lr,
weight_decay=args.weight_decay)
# optimizer = torch.optim.SGD(policies,
# args.lr,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
if args.evaluate:
json_file_path = os.path.join(
args.result_path, 'results_epoch%d.json' % args.evaluation_epoch)
validate(val_loader,
model,
criterion,
0,
json_file=json_file_path,
idx2class=categories,
epoch=args.evaluation_epoch)
return
writer = SummaryWriter(args.result_path)
log_training = open(
os.path.join(args.root_log, '%s.csv' % args.store_name), 'a')
# print (count_parameters(model))
# asdf
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
json_file_path = os.path.join(args.result_path,
'results_epoch%d.json' % (epoch + 1))
# prec1 = validate(val_loader, model, criterion, (epoch + 1) * len(train_loader), log=log_training, json_file=json_file_path, idx2class=categories)
prec1 = validate(val_loader,
model,
criterion, (epoch + 1) * num_train_dataset,
log=log_training,
json_file=json_file_path,
idx2class=categories,
epoch=epoch)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
log_training.close()
writer.close()
def main():
global args, best_prec1
args = parser.parse_args()
if args.dataset == 'something-v1':
num_class = 174
elif args.dataset == 'diving48':
num_class = 48
elif args.dataset == 'ucf101':
num_class = 101
elif args.dataset == 'skating2':
num_class = 63
else:
raise ValueError('Unknown dataset ' + args.dataset)
model_dir = os.path.join('experiments', args.dataset, args.arch,
args.consensus_type + '-' + args.modality,
str(args.run_iter))
args.train_list, args.val_list, args.root_path, args.rgb_prefix = datasets_video.return_dataset(
args.dataset)
if 'something' in args.dataset:
# label transformation for left/right categories
target_transforms = {
86: 87,
87: 86,
93: 94,
94: 93,
166: 167,
167: 166
}
print('Target transformation is enabled....')
else:
target_transforms = None
if not args.resume_rgb:
if os.path.exists(model_dir):
print('Dir {} exists!!! it will be removed'.format(model_dir))
shutil.rmtree(model_dir)
os.makedirs(model_dir)
os.makedirs(os.path.join(model_dir, args.root_log))
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['flow', 'RGBDiff']:
data_length = 5
# data_length = 1
if args.resume_rgb:
if args.modality == 'RGB':
if 'gst' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='GST',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'stm' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='STM',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'tmp' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='TMP',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'tsm' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='TSM',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'ori' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='ORI',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'I3D' in args.arch:
print("!!!!!!!!!!!!!!!!!!!!!!!\n\n")
model = TemporalModel(num_class,
args.num_segments,
model='I3D',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
else:
model = TemporalModel(num_class,
args.num_segments,
model='ORI',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
if os.path.isfile(args.resume_rgb):
print(("=> loading checkpoint '{}'".format(args.resume_rgb)))
checkpoint = torch.load(args.resume_rgb)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
original_checkpoint = checkpoint['state_dict']
print(("(epoch {} ) best_prec1 : {} ".format(
checkpoint['epoch'], best_prec1)))
original_checkpoint = {
k[7:]: v
for k, v in original_checkpoint.items()
}
#model_dict = i3d_model.state_dict()
#model_dict.update(pretrained_dict)
model.load_state_dict(original_checkpoint)
print(
("=> loaded checkpoint '{}' (epoch {} ) best_prec1 : {} ".
format(args.resume_rgb, checkpoint['epoch'], best_prec1)))
else:
raise ValueError("=> no checkpoint found at '{}'".format(
args.resume_rgb))
else:
if args.modality == 'flow':
if 'I3D' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='I3D',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi,
modality='flow',
new_length=data_length)
elif args.modality == 'RGB':
if 'gst' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='GST',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'stm' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='STM',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'tmp' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='TMP',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'tsm' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='TSM',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'ori' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='ORI',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
elif 'I3D' in args.arch:
model = TemporalModel(num_class,
args.num_segments,
model='I3D',
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
else:
model = TemporalModel(num_class,
args.num_segments,
model='ORI',
backbone=args.arch + '_ori',
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms,
resi=args.resi)
cudnn.benchmark = True
writer = SummaryWriter(model_dir)
# Data loading code
args.store_name = '_'.join([
args.dataset, args.arch, args.consensus_type,
'segment%d' % args.num_segments
])
print('storing name: ' + args.store_name)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = get_optim_policies(model)
train_augmentation = get_augmentation(mode='train')
val_trans = get_augmentation(mode='val')
normalize = GroupNormalize(input_mean, input_std)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
if args.dataset == 'diving48':
args.root_path = args.root_path + '/train'
train_loader = torch.utils.data.DataLoader(VideoDataset(
args.root_path,
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=args.rgb_prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
dataset=args.dataset),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
print("trainloader.type = {}".format(type(train_loader)))
if args.dataset == 'diving48':
args.root_path = args.root_path[:-6] + '/test'
val_loader = torch.utils.data.DataLoader(VideoDataset(
args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=args.rgb_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,
]),
dataset=args.dataset),
batch_size=1,
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)
if args.evaluate:
log_test = open('test_not.csv', 'w')
validate(val_loader, model, criterion, log_test)
os.remove(log_test)
return
if args.lr_scheduler == 'cos_warmup':
lr_scheduler_clr = CosineAnnealingLR.WarmupCosineLR(
optimizer=optimizer,
milestones=[args.warmup, args.epochs],
warmup_iters=args.warmup,
min_ratio=1e-7)
elif args.lr_scheduler == 'lr_step_warmup':
lr_scheduler_clr = CosineAnnealingLR.WarmupStepLR(
optimizer=optimizer,
milestones=[args.warmup] +
[args.epochs - 30, args.epochs - 10, args.epochs],
warmup_iters=args.warmup)
elif args.lr_scheduler == 'lr_step':
lr_scheduler_clr = torch.optim.lr_scheduler.MultiStepLR(
optimizer, args.lr_steps, 0.1)
if args.resume_rgb:
for epoch in range(0, args.start_epoch):
optimizer.step()
lr_scheduler_clr.step()
log_training = open(
os.path.join(model_dir, args.root_log, '%s.csv' % args.store_name),
'a')
for epoch in range(args.start_epoch, args.epochs):
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch + 1)
train(train_loader,
model,
criterion,
optimizer,
epoch,
log_training,
writer=writer)
lr_scheduler_clr.step()
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader,
model,
criterion,
log_training,
writer=writer,
epoch=epoch)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'lr': optimizer.param_groups[-1]['lr'],
}, is_best, model_dir)
print('best_prec1: {}'.format(best_prec1))
else:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'lr': optimizer.param_groups[-1]['lr'],
}, False, model_dir)
self.avg = self.sum / self.count
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
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
"emmanuelle", "RGBFlow")
num_class = len(categories)
originalNet = TSN(
27,
args.test_segments if args.consensus_type in ['MLP'] else 1,
"RGBFlow",
base_model=args.arch,
consensus_type=args.consensus_type,
img_feature_dim=args.img_feature_dim,
)
torch.save(originalNet, "emmanuelle.pth")
emmanuelleNet = originalNet.base_model
print(
def __init__(self,
num_segments,
modality,
lr =0.001,
loss_type = 'nll', # cross entropy
weight_decay=5e-4, #weight_decay: L2 penalty #default
lr_steps =[30, 60], # epochs to decay learning rate by 10
momentum= 0.9,
gpus= None,
clip_gradient =20,
new_length=None,
base_model="resnet50",
dropout=0.7,
img_feature_dim=256, #The dimensionality of the features used for relational reasoning.
partial_bn=True,
consensus_type= 'TRN', # MTRN
dataset = 'epic',
batch_size= 1,
workers= 2,
resume = None, # pretained model (path)
epochs= None,
start_epoch = None, #
ifprintmodel= 0, # print the model structure
print_freq =1,
eval_freq =1,
):
self.num_segments= num_segments
self.modality= modality
self.base_model= base_model
self.new_length= new_length
self.img_feature_dim= img_feature_dim
self.consensus_type= consensus_type
self.dataset= dataset
self.resume = resume
self.epochs = epochs
self.start_epoch= start_epoch
self.lr= lr
self.loss_type= loss_type
self.weight_decay = weight_decay
self.lr_steps= lr_steps
self.momentum= momentum
self.partial_bn= partial_bn
self.dropout= dropout
self.batch_size = batch_size
self.workers= workers
self.gpus= gpus
self.eval_freq= eval_freq
self.print_freq= print_freq
self.num_class, self.train_list, self.val_list, self.root_path, self.prefix = datasets_video.return_dataset(self.dataset, self.modality)
self.store_name = '_'.join(['TRN', self.dataset, self.modality, self.base_model, self.consensus_type, 'segment%d'% self.num_segments, 'K%d'% self.new_length])
self.best_prec1= 0
self.clip_gradient= clip_gradient
self.model = TSN(self.num_class, self.num_segments, self.modality,
new_length= self.new_length,
base_model= self.base_model,
consensus_type= self.consensus_type,
dropout=self.dropout,
img_feature_dim= self.img_feature_dim,
partial_bn= self.partial_bn)
self.crop_size = self.model.crop_size
self.scale_size = self.model.scale_size
self.input_mean = self.model.input_mean
self.input_std = self.model.input_std
self.model_policies = self.model.get_optim_policies()
self.augmentation= self.model.get_augmentation()
print('we have {} GPUs found'.format(torch.cuda.device_count()))
self.model = torch.nn.DataParallel(self.model #, device_ids=self.gpus
).cuda()
print(f'''
+-------------------------------------------------------+
num_class : {self.num_class}
modality : {self.modality}
base_model : {self.base_model}
new_length : {self.new_length}
consensus_type : {self.consensus_type}
img_feature_dim : {self.img_feature_dim}
resume : {self.resume}
epochs : {self.epochs }
start_epoch : {self.start_epoch }
lr : {self.lr }
loss_type : {self.loss_type }
weight_decay : {self.weight_decay }
lr_steps : {self.lr_steps }
momentum : {self.momentum }
partial_bn : {self.partial_bn}
clip_gradient : {self.clip_gradient }
dropout : {self.dropout}
batch_size : {self.batch_size}
workers : {self.workers}
gpus : {self.gpus } ( no use now)
eval_freq : {self.eval_freq }
print_freq : {self.print_freq }
crop_size : {self.crop_size}
scale_size : {self.scale_size}
+-------------------------------------------------------+
construct a network named : {self.store_name}''')
#---- checkpoint------load model ----
if self.resume:
if os.path.isfile(self.resume):
print(("=> loading checkpoint '{}'".format(self.resume)))
checkpoint = torch.load(self.resume)
self.start_epoch = checkpoint['epoch']
self.best_prec1 = checkpoint['best_prec1']
self.model.load_state_dict(checkpoint['state_dict'])
print(("=> loaded checkpoint '{}' (epoch {}) (epochs={})"
.format(self.resume, checkpoint['epoch'], self.epochs)))
else:
print(("=> no checkpoint found at '{}'".format(self.resume)))
cudnn.benchmark = True
# Data loading code
if self.modality != 'RGBDiff':
self.normalize = GroupNormalize(self.input_mean, self.input_std)
else:
self.normalize = IdentityTransform()
#------- define loss function (criterion) and optimizer-------
if self.loss_type == 'nll':
self.criterion = torch.nn.CrossEntropyLoss().cuda()
else:
raise ValueError("Unknown loss type")
#---------=========describe parameters:========= ----------------------
print('*'*20,'TSN parameters:')
Tools.parameter_desc(self.model, ifprint= ifprintmodel)
#------parameter way2-----
print('-'*30)
for group in self.model_policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'], group['decay_mult'])))
toal_params=0
for p in self.model_policies:
# print('-'*10,'{} ( num: {})'.format(p['name'],len(p['params'])))
for i, param in enumerate(p['params']):
toal_params+= param.size().numel()
# if i< 5 :
# print(param.size(), param.size().numel())
# elif i==5 :
# print('...')
print('*'*20, 'count from policies, total parameters: {:,}'.format(toal_params))
print('TRN initialised \n')
args.second_model_path = os.path.join('model', args.second_model_path)
if args.dataset == 'ucf101':
num_class = 101
args.train_list = '../temporal-segment-networks/data/ucf101_rgb_train_split_1.txt'
args.val_list = '../temporal-segment-networks/data/ucf101_rgb_val_split_1.txt'
args.root_path = '/'
with open('video_datasets/ucf101/classInd.txt', 'r') as f:
content = f.readlines()
class_to_name = {
int(line.strip().split(' ')[0]) - 1: line.strip().split(' ')[1]
for line in content
}
prefix = 'image_{:05d}.jpg'
else:
categories, args.train_list, args.val_list, args.root_path, prefix = \
datasets_video.return_dataset(args.dataset, args.modality)
class_to_name = {
i: name.replace(' ', '-')
for i, name in enumerate(categories)
}
num_class = len(categories)
print(class_to_name)
# input('...')
first_model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=0.8,
img_feature_dim=args.img_feature_dim)
def main():
logger.auto_set_dir()
global args, best_prec1
import argparse
parser = argparse.ArgumentParser(description="PyTorch implementation of Temporal Segment Networks")
parser.add_argument('--dataset', type=str,default="something", choices=['something', 'jester', 'moments'])
parser.add_argument('--modality', type=str, default="RGB", choices=['RGB', 'Flow'])
parser.add_argument('--train_list', type=str, default="")
parser.add_argument('--val_list', type=str, default="")
parser.add_argument('--root_path', type=str, default="")
parser.add_argument('--store_name', type=str, default="")
# ========================= Model Configs ==========================
parser.add_argument('--arch', type=str, default="BNInception")
parser.add_argument('--num_segments', type=int, default=3)
parser.add_argument('--consensus_type', type=str, default='avg')
parser.add_argument('--k', type=int, default=3)
parser.add_argument('--dropout', '--do', default=0.8, type=float,
metavar='DO', help='dropout ratio (default: 0.5)')
parser.add_argument('--loss_type', type=str, default="nll",
choices=['nll'])
parser.add_argument('--img_feature_dim', default=256, type=int, help="the feature dimension for each frame")
# ========================= Learning Configs ==========================
parser.add_argument('--epochs', default=120, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('-b', '--batch_size', default=128, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--lr_steps', default=[50, 100], type=float, nargs="+",
metavar='LRSteps', help='epochs to decay learning rate by 10')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
metavar='W', help='weight decay (default: 5e-4)')
parser.add_argument('--clip-gradient', '--gd', default=20, type=float,
metavar='W', help='gradient norm clipping (default: disabled)')
parser.add_argument('--no_partialbn', '--npb', default=False, action="store_true")
# ========================= Monitor Configs ==========================
parser.add_argument('--print-freq', '-p', default=20, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--eval-freq', '-ef', default=5, type=int,
metavar='N', help='evaluation frequency (default: 5)')
# ========================= Runtime Configs ==========================
parser.add_argument('-j', '--workers', default=30, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--snapshot_pref', type=str, default="")
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('--gpu', type=str, default='4')
parser.add_argument('--flow_prefix', default="", type=str)
parser.add_argument('--root_log', type=str, default='log')
parser.add_argument('--root_model', type=str, default='model')
parser.add_argument('--root_output', type=str, default='output')
args = parser.parse_args()
args.consensus_type = "TRN"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
device_ids = [int(id) for id in args.gpu.split(',')]
assert len(device_ids) >1, "TRN must run with GPU_num > 1"
args.root_log = logger.get_logger_dir()
args.root_model = logger.get_logger_dir()
args.root_output = logger.get_logger_dir()
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(args.dataset, args.modality)
num_class = len(categories)
args.store_name = '_'.join(['TRN', args.dataset, args.modality, args.arch, args.consensus_type, 'segment%d'% args.num_segments])
print('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)
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()
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)#TODO, , device_ids=[int(id) for id in args.gpu.split(',')]
if torch.cuda.is_available():
model.cuda()
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.evaluate, 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_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,
])),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=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(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception','InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception','InceptionV3'])),
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:
logger.info('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)
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(os.path.join(args.root_log, '%s.csv' % args.store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion, (epoch + 1) * len(train_loader), log_training)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
def main():
check_rootfolders()
global best_prec1
if args.run_for == 'train':
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality)
elif args.run_for == 'test':
categories, args.test_list, args.root_path, prefix = datasets_video.return_data(
args.dataset, args.modality)
num_class = len(categories)
args.store_name = '_'.join([
'STModeling', args.dataset, args.modality, args.arch,
args.consensus_type,
'segment%d' % args.num_segments
])
print('storing name: ' + args.store_name)
model = TSN(num_class, args)
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()
policies = model.get_optim_policies()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
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']
# best_prec1 = 0
model.load_state_dict(checkpoint['state_dict'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
#print(model)
cudnn.benchmark = True
# Data loading code
if ((args.modality != 'RGBDiff') | (args.modality != 'RGBFlow')):
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
elif args.modality == 'RGBFlow':
data_length = args.num_motion
if args.run_for == 'train':
train_loader = torch.utils.data.DataLoader(TSNDataSet(
"/home/machine/PROJECTS/OTHER/DATASETS/kussaster/data",
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
dataset=args.dataset,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3']),
isRGBFlow=(args.modality == 'RGBFlow')),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=False)
val_loader = torch.utils.data.DataLoader(TSNDataSet(
"/home/machine/PROJECTS/OTHER/DATASETS/kussaster/data",
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
dataset=args.dataset,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3']),
isRGBFlow=(args.modality == 'RGBFlow')),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=False)
# 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)
if args.consensus_type == 'DNDF':
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(params, lr=args.lr, weight_decay=1e-5)
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, '%s.csv' % args.store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
if not args.consensus_type == 'DNDF':
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch,
log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log_training)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
elif args.run_for == 'test':
print("=> loading checkpoint '{}'".format(args.root_weights))
checkpoint = torch.load(args.root_weights)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
model.cuda().eval()
print("=> loaded checkpoint ")
test_loader = torch.utils.data.DataLoader(TSNDataSet(
"/home/machine/PROJECTS/OTHER/DATASETS/kussaster/data",
args.test_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
dataset=args.dataset,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3']),
isRGBFlow=(args.modality == 'RGBFlow')),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=False)
# cam = cv2.VideoCapture(0)
# cam.set(cv2.CAP_PROP_FPS, 48)
# for i, (input, _) in enumerate(test_loader):
# with torch.no_grad():
# input_var = torch.autograd.Variable(input)
#
# ret, frame = cam.read()
# frame_map = np.full((280, 640, 3), 0, np.uint8)
# frame_map = frame
# print(frame_map)
# while (True):
# bg = np.full((480, 1200, 3), 15, np.uint8)
# bg[:480, :640] = frame
#
# font = cv2.FONT_HERSHEY_SIMPLEX
# # cv2.rectangle(bg, (128, 48), (640 - 128, 480 - 48), (0, 255, 0), 3)
#
# cv2.imshow('preview', bg)
#
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
test(test_loader, model, categories)
def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
args.dataset = "thumos"
args.modality = "RGB"
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
'thumos', args.modality)
num_class = len(categories)
args.store_name = '_'.join([
'TRN', args.dataset, args.modality, args.arch, args.consensus_type,
'segment%d' % args.num_segments
])
print('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)
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()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
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.evaluate, 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_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,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=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(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
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)
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, '%s.csv' % args.store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log_training)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
###############################################################################
# ALL LINES AFTER THIS REPRESENT NEW CODE WRITTEN TO TRAIN THE FEW-SHOT MODEL #
###############################################################################
for i in range(10):
print("TRAINING FEW-SHOT MODEL")
num_fs_class = 14 # number of few shot classes
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
'thumos-fs', args.modality) # load few-shot dataset
# modify the fully connected layers to fit our new task with 14 classes
fs_model = model
fs_model.module.consensus.classifier = nn.Sequential(
nn.ReLU(), nn.Linear(in_features=768, out_features=512, bias=True),
nn.ReLU(),
nn.Linear(in_features=512, out_features=num_fs_class,
bias=True)).cuda()
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,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=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(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.evaluate:
validate(val_loader, fs_model, criterion, 0)
return
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)
args.store_name = '_'.join([
'fs_TRN', args.dataset, args.modality, args.arch, args.consensus_type,
'segment%d' % args.num_segments
])
print('storing name: ' + args.store_name)
best_prec1 = 0
log_fs_training = open(
os.path.join(args.root_log, '%s.csv' % "fs-logging"), 'w')
for epoch in range(args.start_epoch, args.epochs):
torch.cuda.empty_cache()
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, fs_model, criterion, optimizer, epoch,
log_fs_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, fs_model, criterion,
(epoch + 1) * len(train_loader), log_fs_training)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': fs_model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename='fs_checkpoint.pth.tar')
def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality)
num_class = len(categories)
print("num_class: " + str(num_class))
args.store_name = '_'.join([
'TRN', args.dataset, args.modality, args.arch, args.consensus_type,
'segment%d' % args.num_segments
])
print('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)
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()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
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.evaluate, 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 == 'Flow' or args.modality == 'RGB':
data_length = 1
elif args.modality in ['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,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
print("Creating val_loader:")
print("args.root_path: " + str(args.root_path))
print("args.val_list: " + str(args.val_list))
print("args.num_segments: " + str(args.num_segments))
print("data_length: " + str(data_length))
print("modality: " + str(args.modality))
print("prefix: " + str(prefix))
print("scale_size: " + str(int(scale_size)))
print("crop_size: " + str(crop_size))
print("args.arch: " + str(args.arch))
print("args.batch_size: " + str(args.batch_size))
print("args.workers: " + str(args.workers))
print("")
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,
])),
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)
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
git_log_output = subprocess.run(
[
'git', 'log', '-n1',
'--pretty=format:commit: %h%nauthor: %an%n%s%n%b'
],
stdout=subprocess.PIPE).stdout.decode('utf-8').split('\n')
git_diff_output = subprocess.run(
['git', 'diff'], stdout=subprocess.PIPE).stdout.decode('utf-8')
if args.exp_name == '':
exp_name_match = re.match(r'experiment: *(.+)', git_log_output[2])
if exp_name_match is None:
print(
'Experiment name required:\n'
' current commit subject does not specify an experiment, and\n'
' --experiment_name was not specified')
sys.exit(0)
args.exp_name = exp_name_match.group(1)
print(f'experiment name: {args.exp_name}')
time = str(datetime.now().strftime("%Y-%m-%d-%H-%M-%S"))
exp_dir_path = os.path.join(args.root_log, args.exp_name, time)
log_file_path = os.path.join(exp_dir_path, f'{args.store_name}.csv')
print("log_file_path:")
print(log_file_path)
os.makedirs(exp_dir_path)
log_training = open(log_file_path, 'w')
# store information about git status
git_info_path = os.path.join(exp_dir_path, 'experiment_info.txt')
with open(git_info_path, 'w') as f:
f.write('\n'.join(git_log_output))
f.write('\n\n' + ('=' * 80) + '\n')
f.write(git_diff_output)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log_training)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best, time)
def main():
#*************************Processing Data**************************
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
# 对Something-something数据集进行预处理,将.txt文件读入内存
categories, train_list, val_list, root_path, prefix = datasets_video.return_dataset(
args.dataset, args.root_path)
num_class = len(categories)
if args.dataset == 'somethingv1' or args.dataset == 'somethingv2':
# label transformation for left/right categories
# please refer to labels.json file in sometingv2 for detail.
target_transforms = {
86: 87,
87: 86,
93: 94,
94: 93,
166: 167,
167: 166
}
else:
target_transforms = None
#****************************Create Model***************************
model = getattr(CSN, args.arch)(num_class,
target_transforms=target_transforms,
mode=args.mode)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = get_optim_policies(model)
train_augmentation = model.get_augmentation()
# ***************************Data loading code****************************
normalize = GroupNormalize(input_mean, input_std)
train_loader = torch.utils.data.DataLoader(VideoDataSet(
root_path,
train_list,
num_segments=args.num_segments,
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,
])),
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(VideoDataSet(
root_path,
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=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
#**************************Training config**************************
device = 'cuda'
if torch.cuda.is_available():
devices = ['cuda:' + id for id in args.gpus.split(',')]
if len(devices) > 1:
model = torch.nn.DataParallel(model,
device_ids=devices) # 使用单机多卡进行训练
else:
device = 'cpu'
model = model.to(device)
if args.resume: # 用于中断训练后继续训练
if os.path.isfile(args.resume): # 用指定的检查点进行训练
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.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
cudnn.benchmark = True
# define loss function (criterion) and optimizer
criterion = torch.nn.CrossEntropyLoss().cuda() # 交叉熵损失
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)
#******************************Training**********************************
if args.evaluate:
prec1 = validate(val_loader, model, criterion, 0)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': args.start_epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename='evaluate')
return
# 模型存储的名字
global store_name
store_name = '_'.join([
args.type, args.dataset, args.arch,
'segment%d' % args.num_segments, args.store_name
])
log('storing name: ' + store_name, file=log_stream)
for epoch in range(args.start_epoch, args.epochs):
log("********************************\n", file=log_stream)
log("EPOCH：" + str(epoch + 1) + "\n", file=log_stream)
# adjust learning rate
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename=str(epoch + 1))
log("********************************\n", file=log_stream)
def get_pred(video_path, caption_path, opt):
# options
parser = argparse.ArgumentParser(
description="TRN testing on the full validation set")
# parser.add_argument('dataset', type=str, choices=['something','jester','moments','charades'])
# parser.add_argument('modality', type=str, choices=['RGB', 'Flow', 'RGBDiff'])
parser.add_argument('--dataset', type=str, default='somethingv2')
parser.add_argument('--modality', type=str, default='RGB')
parser.add_argument(
'--weights',
type=str,
default=
'model/TRN_somethingv2_RGB_BNInception_TRNmultiscale_segment8_best.pth.tar'
)
parser.add_argument('--arch', type=str, default="BNInception")
parser.add_argument('--save_scores', type=str, default=None)
parser.add_argument('--test_segments', type=int, default=8)
parser.add_argument('--max_num', type=int, default=-1)
parser.add_argument('--test_crops', type=int, default=10)
parser.add_argument('--input_size', type=int, default=224)
parser.add_argument('--crop_fusion_type',
type=str,
default='TRNmultiscale',
choices=['avg', 'TRN', 'TRNmultiscale'])
parser.add_argument('-j',
'--workers',
default=4,
type=int,
metavar='N',
help='number of data loading workers (default: 4)')
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('--softmax', type=int, default=0)
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)
prob, pred = output.topk(maxk, 1, True, True)
prob = prob.t().data.numpy().squeeze()
pred = pred.t().data.numpy().squeeze()
return prob, pred
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality, opt)
num_class = len(categories)
net = TSN(num_class,
args.test_segments
if args.crop_fusion_type in ['TRN', 'TRNmultiscale'] else 1,
args.modality,
base_model=args.arch,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
opt=opt)
try:
checkpoint = torch.load(args.weights)
except:
args.weights = os.path.join(opt.project_root, 'scripts/Eval/',
args.weights)
checkpoint = torch.load(args.weights)
print("model epoch {} best prec@1: {}".format(checkpoint['epoch'],
checkpoint['best_prec1']))
base_dict = {
'.'.join(k.split('.')[1:]): v
for k, v in list(checkpoint['state_dict'].items())
}
net.load_state_dict(base_dict)
if args.test_crops == 1:
cropping = torchvision.transforms.Compose([
GroupScale(net.scale_size),
GroupCenterCrop(net.input_size),
])
elif args.test_crops == 10:
cropping = torchvision.transforms.Compose(
[GroupOverSample(net.input_size, net.scale_size)])
else:
raise ValueError(
"Only 1 and 10 crops are supported while we got {}".format(
args.test_crops))
data_loader = torch.utils.data.DataLoader(TSNDataSet(
video_path,
caption_path,
num_segments=args.test_segments,
new_length=1 if args.modality == "RGB" else 5,
modality=args.modality,
image_tmpl=prefix,
test_mode=True,
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),
])),
batch_size=1,
shuffle=False,
num_workers=args.workers * 2,
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(devices[0]), device_ids=devices)
net = torch.nn.DataParallel(net.cuda())
net.eval()
data_gen = enumerate(data_loader)
output = []
def eval_video(video_data):
i, data, label = video_data
num_crop = args.test_crops
if args.modality == 'RGB':
length = 3
elif args.modality == 'Flow':
length = 10
elif args.modality == 'RGBDiff':
length = 18
else:
raise ValueError("Unknown modality " + args.modality)
input_var = torch.autograd.Variable(data.view(-1, length, data.size(2),
data.size(3)),
volatile=True)
rst = net(input_var)
if args.softmax == 1:
# take the softmax to normalize the output to probability
rst = F.softmax(rst)
rst = rst.data.cpu().numpy().copy()
if args.crop_fusion_type in ['TRN', 'TRNmultiscale']:
rst = rst.reshape(-1, 1, num_class)
else:
rst = rst.reshape((num_crop, args.test_segments,
num_class)).mean(axis=0).reshape(
(args.test_segments, 1, num_class))
return i, rst, label[0]
max_num = args.max_num if args.max_num > 0 else len(data_loader.dataset)
prob_all, pred_all = [], []
for i, (data, label) in data_gen:
if i >= max_num:
break
rst = eval_video((i, data, label))
output.append(rst[1:])
prob, pred = accuracy(torch.from_numpy(np.mean(rst[1], axis=0)),
label,
topk=(1, 174))
prob_all.append(prob)
pred_all.append(pred)
return prob_all, pred_all
def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
categories, train_list, val_list, root_path, prefix = datasets_video.return_dataset(
args.dataset, args.root_path)
num_class = len(categories)
global store_name
store_name = '_'.join([
args.type, args.dataset, args.arch,
'segment%d' % args.num_segments, args.store_name
])
print(('storing name: ' + store_name))
if args.dataset == 'somethingv1' or args.dataset == 'somethingv2':
# label transformation for left/right categories
# please refer to labels.json file in sometingv2 for detail.
target_transforms = {
86: 87,
87: 86,
93: 94,
94: 93,
166: 167,
167: 166
}
else:
target_transforms = None
model = TemporalModel(num_class,
args.num_segments,
model=args.type,
backbone=args.arch,
alpha=args.alpha,
beta=args.beta,
dropout=args.dropout,
target_transforms=target_transforms)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = get_optim_policies(model)
train_augmentation = model.get_augmentation()
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
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.module.load_state_dict(checkpoint['state_dict'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
cudnn.benchmark = True
# Data loading code
normalize = GroupNormalize(input_mean, input_std)
train_loader = torch.utils.data.DataLoader(VideoDataSet(
root_path,
train_list,
num_segments=args.num_segments,
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,
])),
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(VideoDataSet(
root_path,
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=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
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()
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)
if args.evaluate:
prec1 = validate(val_loader, model, criterion, 0)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': args.start_epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
return
log_training = open(
os.path.join(args.checkpoint_dir, 'log', '%s.csv' % store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
# adjust learning rate
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log_training)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
def main_charades():
global args, best_prec1, use_gpu
use_gpu = torch.cuda.is_available()
categories, args.train_list, args.val_list, args.train_num_list, args.val_num_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality, args.root_path)
num_class = len(categories)
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]
train_dataset = TSNDataSet(
args.root_path,
args.train_list,
args.train_num_list,
num_class=num_class,
num_segments=args.num_segments,
new_length=args.data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
GroupMultiScaleCrop(crop_size, [1.0, 0.875, 0.75, 0.66, 0.5],
max_distort=2),
GroupRandomHorizontalFlip(is_flow=False),
Stack(roll=False),
ToTorchFormatTensor(div=True),
GroupNormalize(input_mean, input_std),
ChangeToCTHW(modality=args.modality)
]))
val_dataset = TSNDataSet(args.root_path,
args.val_list,
args.val_num_list,
num_class=num_class,
num_segments=args.num_segments,
new_length=args.data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=False),
ToTorchFormatTensor(div=True),
GroupNormalize(input_mean, input_std),
ChangeToCTHW(modality=args.modality)
]))
# model = modelfile.InceptionI3d(num_class, in_channels=3)
model = modelfile.gcn_i3d(
num_class=num_class,
t=0.4,
adj_file=
'./data/Charades_v1/gcn_info/class_graph_conceptnet_context_0.8.pkl',
word_file='./data/Charades_v1/gcn_info/class_word.pkl')
# define loss function (criterion)
criterion = nn.MultiLabelSoftMarginLoss()
# define optimizer
params = get_config_optim(model,
lr=args.lr,
weight_decay=args.weight_decay)
# params = get_optim_fix_conv(model, lr=args.lr, weight_decay=args.weight_decay)
# optimizer = torch.optim.SGD(params, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
optimizer = torch.optim.Adam(params, eps=1e-8)
state = {
'batch_size': args.batch_size,
'val_batch_size': args.val_batch_size,
'image_size': args.image_size,
'max_epochs': args.epochs,
'evaluate': args.evaluate,
'resume': args.resume,
'num_classes': num_class
}
state['difficult_examples'] = False
state['print_freq'] = args.print_freq
state['save_model_path'] = args.save_model_path
state['log_path'] = args.log_path
state['logname'] = args.logname
state['workers'] = args.workers
state['epoch_step'] = args.epoch_step
state['lr'] = args.lr
state['device_ids'] = list(range(torch.cuda.device_count()))
if args.evaluate:
state['evaluate'] = True
mapengine = engine.GCNMultiLabelMAPEngine(
state, inp_file='./data/Charades_v1/gcn_info/class_word.pkl')
mapengine.learning(model, criterion, train_dataset, val_dataset, optimizer)
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
args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset)
if args.dataset == 'something-v1':
num_class = 174
args.rgb_prefix = ''
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'diving48':
num_class = 48
args.rgb_prefix = 'frames'
rgb_read_format = "{:05d}.jpg"
else:
raise ValueError('Unknown dataset ' + args.dataset)
net = VideoModel(num_class=num_class,
num_segments=args.test_segments,
modality=args.modality,
def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality)
num_class = len(categories)
args.store_name = '_'.join([
'STSNN', args.dataset, args.modality, args.arch,
'group%d' % args.num_segments,
'%df1c' % args.num_motion
])
print('storing name: ' + args.store_name)
model = STSNN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
num_motion=args.num_motion,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
dataset=args.dataset)
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()
policies = model.get_optim_policies()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
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.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
print(model)
cudnn.benchmark = True
# Data loading code
if ((args.modality != 'RGBDiff') | (args.modality != 'RGBFlow')):
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
elif args.modality == 'RGBFlow':
data_length = args.num_motion
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,
dataset=args.dataset,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3']),
isRGBFlow=(args.modality == 'RGBFlow')),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=False)
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,
dataset=args.dataset,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3']),
isRGBFlow=(args.modality == 'RGBFlow')),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=False)
# 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)
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, '%s.csv' % args.store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log_training)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
categories, args.train_list, args.val_list, args.test_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality)
num_class = len(categories)
args.store_name = '_'.join([
'TRN', args.dataset, args.modality, args.arch, args.consensus_type,
'segment%d' % args.num_segments
])
print('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)
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()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
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.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
cudnn.benchmark = True
# Data loading code
# Four types of input modalities for two-stream ConvNets (one stream spatial and the other temporal): a single RGB image, stacked RGB difference,
# stacked optical flow field, and stacked warped optical flow field; the spatial stream ConvNet operates on a single RGB images,
# and the temporal stream ConvNet takes a stack of consecutive optical flow fields as input.
# A single RGB image usually encodes static appearance at a specific time point and lacks the contextual information about previous and next frames.
# RGB difference between two consecutive frames describe the appearance change, which may correspond to the motion salient region.
# Optical flow fields may not concentrate on the human action; the warped optical flow suppresses the background motion and makes motion concentrate
# on the actor.
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
# Division between train and val set
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'])
), # Batch-Normalization-Inception, InceptionV3: evolution of InceptionV2 of GoogleNet
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=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(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
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)
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, '%s.csv' % args.store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log_training)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
def main():
finetuning = False
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
if args.dataset == 'something-v1':
num_class = 174
args.rgb_prefix = ''
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'diving48':
num_class = 48
args.rgb_prefix = 'frames'
rgb_read_format = "{:05d}.jpg"
else:
raise ValueError('Unknown dataset ' + args.dataset)
model_dir = os.path.join('experiments', args.dataset, args.arch,
args.consensus_type + '-' + args.modality,
str(args.run_iter))
if not args.resume:
if os.path.exists(model_dir):
print('Dir {} exists!!!'.format(model_dir))
sys.exit()
else:
os.makedirs(model_dir)
os.makedirs(os.path.join(model_dir, args.root_log))
writer = SummaryWriter(model_dir)
args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset)
if 'something' in args.dataset:
# label transformation for left/right categories
target_transforms = {
86: 87,
87: 86,
93: 94,
94: 93,
166: 167,
167: 166
}
print('Target transformation is enabled....')
else:
target_transforms = None
args.store_name = '_'.join([
args.dataset, args.arch, args.consensus_type,
'segment%d' % args.num_segments
])
print('storing name: ' + args.store_name)
model = VideoModel(num_class=num_class,
modality=args.modality,
num_segments=args.num_segments,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
partial_bn=not args.no_partialbn,
gsm=args.gsm,
target_transform=target_transforms)
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()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
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.evaluate, 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_loader = torch.utils.data.DataLoader(VideoDataset(
args.root_path,
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=args.rgb_prefix + rgb_read_format,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(VideoDataset(
args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=args.rgb_prefix + rgb_read_format,
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,
])),
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)
lr_scheduler_clr = CosineAnnealingLR.WarmupCosineLR(
optimizer=optimizer,
milestones=[args.warmup, args.epochs],
warmup_iters=args.warmup,
min_ratio=1e-7)
if args.resume:
for epoch in range(0, args.start_epoch):
lr_scheduler_clr.step()
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(model_dir, args.root_log, '%s.csv' % args.store_name),
'a')
for epoch in range(args.start_epoch, args.epochs):
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch + 1)
train_prec1 = train(train_loader,
model,
criterion,
optimizer,
epoch,
log_training,
writer=writer)
lr_scheduler_clr.step()
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader,
model,
criterion, (epoch + 1) * len(train_loader),
log_training,
writer=writer,
epoch=epoch)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'current_prec1': prec1,
'lr': optimizer.param_groups[-1]['lr'],
}, is_best, model_dir)
else:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'current_prec1': train_prec1,
'lr': optimizer.param_groups[-1]['lr'],
}, False, model_dir)
def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(args.dataset, args.modality)
num_class = len(categories)
args.store_name = '_'.join(['TRN', args.dataset, args.modality, args.arch, args.consensus_type, 'segment%d'% args.num_segments])
print('storing name: ' + args.store_name)
model = TSN(2, 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)
checkpoint = torch.load('pretrain/TRN_somethingv2_RGB_BNInception_TRNmultiscale_segment8_best.pth.tar', map_location='cpu')
base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items())}
for key in ['consensus.fc_fusion_scales.6.3.bias', 'consensus.fc_fusion_scales.5.3.bias',
'consensus.fc_fusion_scales.4.3.bias',
'consensus.fc_fusion_scales.3.3.bias', 'consensus.fc_fusion_scales.2.3.bias',
'consensus.fc_fusion_scales.1.3.bias',
'consensus.fc_fusion_scales.0.3.bias', 'consensus.fc_fusion_scales.6.3.weight',
'consensus.fc_fusion_scales.5.3.weight',
'consensus.fc_fusion_scales.4.3.weight', 'consensus.fc_fusion_scales.3.3.weight',
'consensus.fc_fusion_scales.2.3.weight',
'consensus.fc_fusion_scales.1.3.weight', 'consensus.fc_fusion_scales.0.3.weight']:
del base_dict[key]
# print(base_dict)
model.load_state_dict(base_dict, strict=False)
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()
model = torch.nn.DataParallel(model).cuda()
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.evaluate, 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_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,
])),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=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(int(scale_size)),
# GroupCenterCrop(crop_size),
# Stack(roll=(args.arch in ['BNInception','InceptionV3'])),
# ToTorchFormatTensor(div=(args.arch not in ['BNInception','InceptionV3'])),
# 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':
weight = torch.ones([2]).cuda()
weight[0] = 1.2
pos_weight = torch.ones([2]).cuda()
#pos_weight[0] = 2
criterion = torch.nn.BCEWithLogitsLoss(weight = weight, pos_weight=pos_weight).cuda()
#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,
0.0001,
momentum=args.momentum,
weight_decay=args.weight_decay)
# if args.evaluate:
# validate(val_loader, model, criterion, 0)
# return
log_training = open(os.path.join(args.root_log, '%s.csv' % args.store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
torch.save(model.state_dict(), 'checkpoint_bce_20_w12_{}.pth.tar'.format(epoch))
torch.save(model.state_dict(), 'checkpoint_bce_20_w12_{}.pth'.format(epoch))
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
if not args.test:
categories, args.train_list, args.val_list, args.root_path, prefix = datasets_video.return_dataset(
args.dataset, args.modality)
else:
categories, args.test_list, args.root_path, prefix = datasets_video.return_dataset(
'SHGDTuples', args.modality)
num_class = len(categories)
args.store_name = '_'.join([
args.dataset, args.modality, args.arch,
'segment%d' % args.num_segments
])
print('storing name: ' + args.store_name)
model = MFF(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
dataset=args.dataset)
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()
policies = model.get_optim_policies()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
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.test, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.pretrained:
if args.arch == 'squeezenet1_1':
name = 'module.base_model.0.weight'
else:
name = 'module.base_model.features.0.0.weight'
if os.path.isfile(args.pretrained):
pretrained_dict = torch.load(args.pretrained)
pretrained_state_dict = pretrained_dict['state_dict']
#for name, param in pretrained_state_dict.items():
#if 'base_model' in name:
#print(name)
pretrained_state_dict = {
k: v
for k, v in pretrained_state_dict.items()
if 'module.consensus.classifier.3.' not in k
}
model_dict = model.state_dict()
weight_conv_t = pretrained_state_dict[name]
else:
print(("=> no pretrained model checkpoint found at '{}'".format(
args.pretrained)))
if args.modality == 'IRD':
# make the first conv from 3 chann to 2 chann (average the sum of 3 chann)
weight_conv_t = weight_conv_t.sum(1)
weight_conv_t = weight_conv_t.unsqueeze(1)
weight_conv_t = weight_conv_t.mean(1)
weight_conv_t = torch.stack((weight_conv_t, weight_conv_t), 1)
pretrained_state_dict[name] = weight_conv_t
model_dict.update(pretrained_state_dict)
print("Converted the first conv layer to 2 channels.")
if args.modality == 'IR' or args.modality == 'D':
# make the first conv from 3 chann to 1 chann (average the sum of 3 chann)
weight_conv_t = weight_conv_t.sum(1)
weight_conv_t = weight_conv_t.unsqueeze(1)
weight_conv_t = weight_conv_t.mean(1)
weight_conv_t = weight_conv_t.unsqueeze(1)
pretrained_state_dict[name] = weight_conv_t
model_dict.update(pretrained_state_dict)
print("Converted the first conv layer to 1 channel.")
model.load_state_dict(model_dict)
print("=> loaded pretrained model checkpoint '{}'".format(
args.pretrained))
print(model)
## to print the number of trainable paramters in the network
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params_num = sum([np.prod(p.size()) for p in model_parameters])
print('Total number of parameters:' + str(params_num))
cudnn.benchmark = True
# Data loading code
normalize = GroupNormalize(input_mean, input_std)
if args.dataset == 'SHGD':
from SHGD import DataSet
if args.dataset == 'jester':
from Jester import DataSet
if not args.test:
train_loader = torch.utils.data.DataLoader(DataSet(
args.root_path,
args.train_list,
num_segments=args.num_segments,
modality=args.modality,
image_tmpl=prefix,
dataset=args.dataset,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(),
ToTorchFormatTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=False)
val_loader = torch.utils.data.DataLoader(DataSet(
args.root_path,
args.val_list,
num_segments=args.num_segments,
modality=args.modality,
image_tmpl=prefix,
dataset=args.dataset,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(),
ToTorchFormatTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=False)
else:
test_loader = torch.utils.data.DataLoader(DataSet(
args.root_path,
args.test_list,
num_segments=args.num_segments,
modality=args.modality,
image_tmpl=prefix,
dataset=args.dataset,
random_shift=False,
test_mode=True,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(),
ToTorchFormatTensor(),
normalize,
])),
batch_size=1,
shuffle=False,
num_workers=args.workers,
pin_memory=False)
if args.test:
if not args.resume:
print('Please give a path to a trained model for testing.')
sys.exit()
else:
test(args.start_epoch, test_loader, model, args)
return
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
elif args.loss_type == 'nll' and num_class == 13:
# give the "No gesture/Hand up/Hand down less weight than the other classes. No:4420 Hand up:2280 Hand Down:2190 Others:228
weights = [1, 1, 1, 1 / 10, 1 / 10, 1 / 20, 1, 1, 1, 1, 1, 1, 1]
class_weights = torch.Tensor(weights).cuda()
criterion = torch.nn.CrossEntropyLoss(weight=class_weights)
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)
log_training = open(
os.path.join(args.root_log, '%s.csv' % args.store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion,
(epoch + 1) * len(train_loader), log_training,
num_class)
#remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
images.extend(seg_imgs)
if p < record.num_frames:
p += 1
process_data = self.transform(images) # transformation changes 8, 224, 244 to 24, 224, 224,
return process_data, record.label
def __len__(self):
return len(self.utterance_list)
if __name__ == "__main__":
import datasets_video
from transforms import *
import torchvision
root_path, train_dict, val_dict = datasets_video.return_dataset('omg', 'RGB','face')
label_name = 'arousal'
num_segments = 8
dataset = TSNDataSet(root_path, train_dict, label_name, num_segments=num_segments, phase='Train',
new_length=1,
modality='RGB',
image_tmpl='frame_det_00_{:06d}.bmp',
transform=torchvision.transforms.Compose([
GroupScale(256),
GroupRandomCrop(224),
Stack(True),
ToTorchFormatTensor(),
GroupNormalize(
mean=[.485, .456, .406],
std=[.229, .224, .225]),
