def __init__(self, modality, checkpoint, arena_mask_path):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TSN(2,
8,
modality,
base_model='resnet18',
consensus_type='avg',
dropout=0.5,
img_feature_dim=256,
partial_bn=False,
pretrain='imagenet',
is_shift=True,
shift_div=8,
shift_place='blockres',
fc_lr5=False,
temporal_pool=False,
non_local=False)
# Get Model complexity
macs, params = get_model_complexity_info(
model, (24, 224, 224),
as_strings=True,
print_per_layer_stat=False,
verbose=True) # noqa: E128, E501
print('---{:<30} {:<8}'.format('Computational complexity: ', macs))
print('{:<30} {:<8}'.format('Number of parameters: ', params))
# Define transforms
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
self.transform = torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=False),
ToTorchFormatTensor(div=True),
GroupNormalize(input_mean, input_std),
])
# Load TSM model
model = torch.nn.DataParallel(model, device_ids=1).to(device)
model.load_state_dict(
torch.load(checkpoint, map_location=device)['state_dict'])
self.model = model
self.model.eval()
# Frame samples to be selected in a clip
self.action_names = ['explore', 'investigate']
self.rgb_sample = [2, 6, 9, 13, 17, 20, 24,
28] # [4, 12, 19, 26, 34, 41, 48, 56]
self.arena_mask = cv2.imread(arena_mask_path)
if self.arena_mask is None:
print("Arena Mask not loaded: %s" % arena_mask_path)
exit(0)
def __init__(self, modality, checkpoint):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TSN(2,
8,
modality,
base_model='resnet18',
consensus_type='avg',
dropout=0.5,
img_feature_dim=256,
partial_bn=False,
pretrain='imagenet',
is_shift=True,
shift_div=8,
shift_place='blockres',
fc_lr5=False,
temporal_pool=False,
non_local=False)
## Define transforms
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
self.transform = torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=False),
ToTorchFormatTensor(div=True),
GroupNormalize(input_mean, input_std),
])
## Load TSM model
model = torch.nn.DataParallel(model, device_ids=1).to(device)
model.load_state_dict(torch.load(checkpoint)['state_dict'])
self.model = model
self.model.eval()
self.action_names = ['explore', 'investigate']
self.rgb_sample = [4, 12, 19, 26, 34, 41, 48, 56]
def load_src_model():
model = TSN(2, 8, 'RGB',
base_model='resnet50',
consensus_type='avg',
img_feature_dim=256,
pretrain='imagenet',
is_shift=True, shift_div=8, shift_place='blockres',
non_local=False,
)
modelpath = '/nfs/volume-95-7/temporal-shift-module/checkpoint/TSM_videos_1218_RGB_resnet50_shift8_blockres_avg_segment8_e120_pr8_ext0.1/ckpt.best.pth.tar'
checkpoint = torch.load(modelpath)
checkpoint = checkpoint['state_dict']
# base_dict = {('base_model.' + k).replace('base_model.fc', 'new_fc'): v for k, v in list(checkpoint.items())}
base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint.items())}
replace_dict = {'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
}
for k, v in replace_dict.items():
if k in base_dict:
base_dict[v] = base_dict.pop(k)
model.load_state_dict(base_dict)
model.eval()
# example = torch.ones(1, 8, 3, 224, 224)
example = torch.eye(224)
example = example.expand((1, 8, 3, 224, 224))
y = model(example)
print("src_model output: ", y)
def cvt_model():
print("===> Loading model")
model = TSN(2, 8, 'RGB',
base_model='resnet50',
consensus_type='avg',
img_feature_dim=256,
pretrain='imagenet',
is_shift=True, shift_div=8, shift_place='blockres',
non_local=False,
)
modelpath = '/nfs/volume-95-7/temporal-shift-module/checkpoint/TSM_videos_1218_RGB_resnet50_shift8_blockres_avg_segment8_e120_pr8_ext0.1/ckpt.best.pth.tar'
checkpoint = torch.load(modelpath)
checkpoint = checkpoint['state_dict']
# base_dict = {('base_model.' + k).replace('base_model.fc', 'new_fc'): v for k, v in list(checkpoint.items())}
base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint.items())}
replace_dict = {'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
}
for k, v in replace_dict.items():
if k in base_dict:
base_dict[v] = base_dict.pop(k)
model.load_state_dict(base_dict)
# 模型转换，Torch Script
model.cuda()
model.eval()
example = torch.rand(1,8,3,224,224).cuda()
y = model(example)
print(y.shape)
traced_script_module = torch.jit.trace(model, example)
print(traced_script_module.code)
# traced_script_module = torch.jit.script(model)
# print(traced_script_module.code)
# output = traced_script_module(torch.rand(1, 1, 224, 224))
traced_script_module.save("tsm_with_1218.pt")
print("Export of model.pt complete!")
def load_model(weights):
global num_class
is_shift, shift_div, shift_place = parse_shift_option_from_log_name(
weights)
if 'RGB' in weights:
modality = 'RGB'
elif 'Depth' in weights:
modality = 'Depth'
else:
modality = 'Flow'
if 'concatAll' in weights:
concat = "All"
elif "concatFirst" in weights:
concat = "First"
else:
concat = ""
if 'extra' in this_weights:
extra_temporal_modeling = True
args.prune = ""
if 'conv1d' in weights:
args.crop_fusion_type = "conv1d"
else:
args.crop_fusion_type = "avg"
this_arch = weights.split('TSM_')[1].split('_')[2]
modality_list.append(modality)
num_class, args.train_list, val_list, root_path, prefix = dataset_config.return_dataset(
args.dataset, modality)
print('=> shift: {}, shift_div: {}, shift_place: {}'.format(
is_shift, shift_div, shift_place))
net = TSN(num_class,
int(args.test_segments) if is_shift else 1,
modality,
base_model=this_arch,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
pretrain=args.pretrain,
is_shift=is_shift,
shift_div=shift_div,
shift_place=shift_place,
non_local='_nl' in weights,
concat=concat,
extra_temporal_modeling=extra_temporal_modeling,
prune_list=[prune_conv1in_list, prune_conv1out_list],
is_prune=args.prune)
if 'tpool' in weights:
from ops.temporal_shift import make_temporal_pool
make_temporal_pool(net.base_model,
args.test_segments) # since DataParallel
checkpoint = torch.load(weights)
checkpoint = checkpoint['state_dict']
# base_dict = {('base_model.' + k).replace('base_model.fc', 'new_fc'): v for k, v in list(checkpoint.items())}
base_dict = {
'.'.join(k.split('.')[1:]): v
for k, v in list(checkpoint.items())
}
replace_dict = {
'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
}
for k, v in replace_dict.items():
if k in base_dict:
base_dict[v] = base_dict.pop(k)
net.load_state_dict(base_dict)
input_size = net.scale_size if args.full_res else net.input_size
if args.test_crops == 1:
cropping = torchvision.transforms.Compose([
GroupScale(net.scale_size),
GroupCenterCrop(input_size),
])
elif args.test_crops == 3: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose(
[GroupFullResSample(input_size, net.scale_size, flip=False)])
elif args.test_crops == 5: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose(
[GroupOverSample(input_size, net.scale_size, flip=False)])
elif args.test_crops == 10:
cropping = torchvision.transforms.Compose(
[GroupOverSample(input_size, net.scale_size)])
else:
raise ValueError(
"Only 1, 5, 10 crops are supported while we got {}".format(
args.test_crops))
transform = torchvision.transforms.Compose([
cropping,
Stack(roll=(this_arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(this_arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std),
])
if 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())
return is_shift, net, transform
def main():
global args, best_prec1, TRAIN_SAMPLES
args = parser.parse_args()
num_class, args.train_list, args.val_list, args.test_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset,
args.modality)
if os.path.exists(args.test_list):
args.val_list = args.test_list
model = TSN(num_class, args.num_segments, args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift, shift_div=args.shift_div, shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local,
tin=args.tin)
crop_size = args.crop_size
scale_size = args.scale_size
input_mean = [0.485, 0.456, 0.406]
input_std = [0.229, 0.224, 0.225]
print(args.gpus)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
if os.path.isfile(args.resume_path):
print(("=> loading checkpoint '{}'".format(args.resume_path)))
checkpoint = torch.load(args.resume_path)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'], strict=False)
print(("=> loaded checkpoint '{}' (epoch {})"
.format(args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume_path)))
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
if args.random_crops == 1:
crop_aug = GroupCenterCrop(args.crop_size)
elif args.random_crops == 3:
crop_aug = GroupFullResSample(args.crop_size, args.scale_size, flip=False)
elif args.random_crops == 5:
crop_aug = GroupOverSample(args.crop_size, args.scale_size, flip=False)
else:
crop_aug = MultiGroupRandomCrop(args.crop_size, args.random_crops),
test_dataset = TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
multi_class=args.multi_class,
transform=torchvision.transforms.Compose([
GroupScale(int(args.scale_size)),
crop_aug,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]), dense_sample=args.dense_sample,
test_mode=True,
temporal_clips=args.temporal_clips)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
test(test_loader, model, args.start_epoch)
def main():
global args, best_prec1
args = parser.parse_args()
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
full_arch_name = args.arch
args.store_name = '_'.join([
'TDN_', args.dataset, args.modality, full_arch_name,
args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs)
])
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.dense_sample:
args.store_name += '_dense'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
if dist.get_rank() == 0:
check_rootfolders()
logger = setup_logger(output=os.path.join(args.root_log, args.store_name),
distributed_rank=dist.get_rank(),
name=f'TDN')
logger.info('storing name: ' + args.store_name)
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
fc_lr5=(args.tune_from and args.dataset in args.tune_from))
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
for group in policies:
logger.info(
('[TDN-{}]group: {} has {} params, lr_mult: {}, decay_mult: {}'.
format(args.arch, group['name'], len(group['params']),
group['lr_mult'], group['decay_mult'])))
train_augmentation = model.get_augmentation(
flip=False if 'something' in args.dataset else True)
cudnn.benchmark = True
# Data loading code
normalize = GroupNormalize(input_mean, input_std)
train_dataset = TSNDataSet(
args.dataset,
args.root_path,
args.train_list,
num_segments=args.num_segments,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
dense_sample=args.dense_sample)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler,
drop_last=True)
val_dataset = TSNDataSet(
args.dataset,
args.root_path,
args.val_list,
num_segments=args.num_segments,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
dense_sample=args.dense_sample)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
sampler=val_sampler,
drop_last=True)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
else:
raise ValueError("Unknown loss type")
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = get_scheduler(optimizer, len(train_loader), args)
model = DistributedDataParallel(model.cuda(),
device_ids=[args.local_rank],
broadcast_buffers=True,
find_unused_parameters=True)
if args.resume:
if os.path.isfile(args.resume):
logger.info(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume, map_location='cpu')
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
logger.info(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
logger.info(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
logger.info(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
logger.info('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
logger.info('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
logger.info(
'#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in args.tune_from: # new dataset
logger.info('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
with open(os.path.join(args.root_log, args.store_name, 'args.txt'),
'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(
log_dir=os.path.join(args.root_log, args.store_name))
if args.evaluate:
logger.info(("===========evaluate==========="))
val_loader.sampler.set_epoch(args.start_epoch)
prec1, prec5, val_loss = validate(val_loader, model, criterion, logger)
if dist.get_rank() == 0:
is_best = prec1 > best_prec1
best_prec1 = prec1
logger.info(("Best Prec@1: '{}'".format(best_prec1)))
save_epoch = args.start_epoch + 1
save_checkpoint(
{
'epoch': args.start_epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'prec1': prec1,
'best_prec1': best_prec1,
}, save_epoch, is_best)
return
for epoch in range(args.start_epoch, args.epochs):
train_loader.sampler.set_epoch(epoch)
train_loss, train_top1, train_top5 = train(train_loader,
model,
criterion,
optimizer,
epoch=epoch,
logger=logger,
scheduler=scheduler)
if dist.get_rank() == 0:
tf_writer.add_scalar('loss/train', train_loss, epoch)
tf_writer.add_scalar('acc/train_top1', train_top1, epoch)
tf_writer.add_scalar('acc/train_top5', train_top5, epoch)
tf_writer.add_scalar('lr', optimizer.param_groups[-1]['lr'], epoch)
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
val_loader.sampler.set_epoch(epoch)
prec1, prec5, val_loss = validate(val_loader, model, criterion,
epoch, logger)
if dist.get_rank() == 0:
tf_writer.add_scalar('loss/test', val_loss, epoch)
tf_writer.add_scalar('acc/test_top1', prec1, epoch)
tf_writer.add_scalar('acc/test_top5', prec5, epoch)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
tf_writer.add_scalar('acc/test_top1_best', best_prec1, epoch)
logger.info(("Best Prec@1: '{}'".format(best_prec1)))
tf_writer.flush()
save_epoch = epoch + 1
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'prec1': prec1,
'best_prec1': best_prec1,
}, save_epoch, is_best)
def main():
# options
parser = argparse.ArgumentParser(description="TSM testing on the full validation set")
parser.add_argument('dataset', type=str)
# may contain splits
parser.add_argument('--weights', type=str, default=None)
parser.add_argument('--test_segments', type=str, default=25)
parser.add_argument('--dense_sample', default=False, action="store_true", help='use dense sample as I3D')
parser.add_argument('--twice_sample', default=False, action="store_true", help='use twice sample for ensemble')
parser.add_argument('--full_res', default=False, action="store_true",
help='use full resolution 256x256 for test as in Non-local I3D')
parser.add_argument('--test_crops', type=int, default=1)
parser.add_argument('--coeff', type=str, default=None)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
help='number of data loading workers (default: 8)')
# for true test
parser.add_argument('--test_list', type=str, default=None)
parser.add_argument('--csv_file', type=str, default=None)
parser.add_argument('--softmax', default=False, action="store_true", help='use softmax')
parser.add_argument('--max_num', type=int, default=-1)
parser.add_argument('--input_size', type=int, default=224)
parser.add_argument('--crop_fusion_type', type=str, default='avg')
parser.add_argument('--gpus', nargs='+', type=int, default=None)
parser.add_argument('--img_feature_dim',type=int, default=256)
parser.add_argument('--num_set_segments',type=int, default=1,help='TODO: select multiply set of n-frames from a video')
parser.add_argument('--pretrain', type=str, default='imagenet')
args = parser.parse_args()
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def parse_shift_option_from_log_name(log_name):
if 'shift' in log_name:
strings = log_name.split('_')
for i, s in enumerate(strings):
if 'shift' in s:
break
return True, int(strings[i].replace('shift', '')), strings[i + 1]
else:
return False, None, None
weights_list = args.weights.split(',')
test_segments_list = [int(s) for s in args.test_segments.split(',')]
assert len(weights_list) == len(test_segments_list)
if args.coeff is None:
coeff_list = [1] * len(weights_list)
else:
coeff_list = [float(c) for c in args.coeff.split(',')]
if args.test_list is not None:
test_file_list = args.test_list.split(',')
else:
test_file_list = [None] * len(weights_list)
data_iter_list = []
net_list = []
modality_list = []
total_num = None
for this_weights, this_test_segments, test_file in zip(weights_list, test_segments_list, test_file_list):
is_shift, shift_div, shift_place = parse_shift_option_from_log_name(this_weights)
if 'RGB' in this_weights:
modality = 'RGB'
else:
modality = 'Flow'
this_arch = this_weights.split('TSM_')[1].split('_')[2]
modality_list.append(modality)
num_class, args.train_list, val_list, root_path, prefix = dataset_config.return_dataset(args.dataset,
modality)
print('=> shift: {}, shift_div: {}, shift_place: {}'.format(is_shift, shift_div, shift_place))
net = TSN(num_class, this_test_segments if is_shift else 1, modality,
base_model=this_arch,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
pretrain=args.pretrain,
is_shift=is_shift, shift_div=shift_div, shift_place=shift_place,
non_local='_nl' in this_weights,
)
if 'tpool' in this_weights:
from ops.temporal_shift import make_temporal_pool
make_temporal_pool(net.base_model, this_test_segments) # since DataParallel
checkpoint = torch.load(this_weights)
checkpoint = checkpoint['state_dict']
# base_dict = {('base_model.' + k).replace('base_model.fc', 'new_fc'): v for k, v in list(checkpoint.items())}
base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint.items())}
replace_dict = {'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
}
for k, v in replace_dict.items():
if k in base_dict:
base_dict[v] = base_dict.pop(k)
net.load_state_dict(base_dict)
input_size = net.scale_size if args.full_res else net.input_size
if args.test_crops == 1:
cropping = torchvision.transforms.Compose([
GroupScale(net.scale_size),
GroupCenterCrop(input_size),
])
elif args.test_crops == 3: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupFullResSample(input_size, net.scale_size, flip=False)
])
elif args.test_crops == 5: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, net.scale_size, flip=False)
])
elif args.test_crops == 10:
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, net.scale_size)
])
else:
raise ValueError("Only 1, 5, 10 crops are supported while we got {}".format(args.test_crops))
data_loader = torch.utils.data.DataLoader(
TSNDataSet(root_path, test_file if test_file is not None else val_list, num_segments=this_test_segments,
new_length=1 if modality == "RGB" else 5,
modality=modality,
image_tmpl=prefix,
test_mode=True,
remove_missing=len(weights_list) == 1,
transform=torchvision.transforms.Compose([
cropping,
Stack(roll=(this_arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(this_arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std),
]), dense_sample=args.dense_sample, twice_sample=args.twice_sample),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True,
)
if args.gpus is not None:
devices = [args.gpus[i] for i in range(args.workers)]
else:
devices = list(range(args.workers))
net = torch.nn.DataParallel(net.cuda())
net.eval()
data_gen = enumerate(data_loader)
if total_num is None:
total_num = len(data_loader.dataset)
else:
assert total_num == len(data_loader.dataset)
data_iter_list.append(data_gen)
net_list.append(net)
output = []
def eval_video(video_data, net, this_test_segments, modality):
net.eval()
with torch.no_grad():
i, data, label = video_data
batch_size = label.numel()
num_crop = args.test_crops
if args.dense_sample:
num_crop *= 10 # 10 clips for testing when using dense sample
if args.twice_sample:
num_crop *= 2
if modality == 'RGB':
length = 3
elif modality == 'Flow':
length = 10
elif modality == 'RGBDiff':
length = 18
else:
raise ValueError("Unknown modality "+ modality)
data_in = data.view(-1, length, data.size(2), data.size(3))
if is_shift:
data_in = data_in.view(batch_size * num_crop, this_test_segments, length, data_in.size(2), data_in.size(3))
rst = net(data_in)
rst = rst.reshape(batch_size, num_crop, -1).mean(1)
if args.softmax:
# take the softmax to normalize the output to probability
rst = F.softmax(rst, dim=1)
rst = rst.data.cpu().numpy().copy()
if net.module.is_shift:
rst = rst.reshape(batch_size, num_class)
else:
rst = rst.reshape((batch_size, -1, num_class)).mean(axis=1).reshape((batch_size, num_class))
return i, rst, label
proc_start_time = time.time()
max_num = args.max_num if args.max_num > 0 else total_num
top1 = AverageMeter()
top5 = AverageMeter()
for i, data_label_pairs in enumerate(zip(*data_iter_list)):
with torch.no_grad():
if i >= max_num:
break
this_rst_list = []
this_label = None
for n_seg, (_, (data, label)), net, modality in zip(test_segments_list, data_label_pairs, net_list, modality_list):
rst = eval_video((i, data, label), net, n_seg, modality)
this_rst_list.append(rst[1])
this_label = label
assert len(this_rst_list) == len(coeff_list)
for i_coeff in range(len(this_rst_list)):
this_rst_list[i_coeff] *= coeff_list[i_coeff]
ensembled_predict = sum(this_rst_list) / len(this_rst_list)
for p, g in zip(ensembled_predict, this_label.cpu().numpy()):
output.append([p[None, ...], g])
cnt_time = time.time() - proc_start_time
prec1, prec5 = accuracy(torch.from_numpy(ensembled_predict), this_label, topk=(1, 5))
top1.update(prec1.item(), this_label.numel())
top5.update(prec5.item(), this_label.numel())
if i % 20 == 0:
print('video {} done, total {}/{}, average {:.3f} sec/video, '
'moving Prec@1 {:.3f} Prec@5 {:.3f}'.format(i * args.batch_size, i * args.batch_size, total_num,
float(cnt_time) / (i+1) / args.batch_size, top1.avg, top5.avg))
video_pred = [np.argmax(x[0]) for x in output]
video_pred_top5 = [np.argsort(np.mean(x[0], axis=0).reshape(-1))[::-1][:5] for x in output]
video_labels = [x[1] for x in output]
if args.csv_file is not None:
print('=> Writing result to csv file: {}'.format(args.csv_file))
with open(test_file_list[0].replace('test_videofolder.txt', 'category.txt')) as f:
categories = f.readlines()
categories = [f.strip() for f in categories]
with open(test_file_list[0]) as f:
vid_names = f.readlines()
vid_names = [n.split(' ')[0] for n in vid_names]
print(vid_names)
assert len(vid_names) == len(video_pred)
if args.dataset != 'somethingv2': # only output top1
with open(args.csv_file, 'w') as f:
for n, pred in zip(vid_names, video_pred):
f.write('{};{}\n'.format(n, categories[pred]))
else:
with open(args.csv_file, 'w') as f:
for n, pred5 in zip(vid_names, video_pred_top5):
fill = [n]
for p in list(pred5):
fill.append(p)
f.write('{};{};{};{};{};{}\n'.format(*fill))
cf = confusion_matrix(video_labels, video_pred).astype(float)
np.save('cm.npy', cf)
cls_cnt = cf.sum(axis=1)
cls_hit = np.diag(cf)
cls_acc = cls_hit / cls_cnt
print(cls_acc)
upper = np.mean(np.max(cf, axis=1) / cls_cnt)
print('upper bound: {}'.format(upper))
print('-----Evaluation is finished------')
print('Class Accuracy {:.02f}%'.format(np.mean(cls_acc) * 100))
print('Overall Prec@1 {:.02f}% Prec@5 {:.02f}%'.format(top1.avg, top5.avg))
def get_tsm(num_classes=3, pretrain_set='kinetics'):
if pretrain_set == 'kinetics':
base_model = "resnet50"
this_weights = "pretrained/TSM_kinetics_RGB_resnet50_shift8_blockres_avg_segment8_e100_dense_nl.pth"
original_num_classes = 400
non_local = True
print("Using kinetics")
else:
base_model = "resnet101"
this_weights = "pretrained/TSM_somethingv2_RGB_resnet101_shift8_blockres_avg_segment8_e45.pth"
# base_model = "resnet50"
# this_weights = "pretrained/TSM_somethingv2_RGB_resnet50_shift8_blockres_avg_segment8_e45.pth"
original_num_classes = 174
non_local = False
modality = "RGB"
segments = 8
consensus_type = "avg"
img_feature_dim = 256
pretrain = True
is_shift = True
shift_div = 8
shift_place = "blockres"
net = TSN(
original_num_classes,
segments,
modality,
base_model=base_model,
consensus_type=consensus_type,
img_feature_dim=img_feature_dim,
pretrain=pretrain,
is_shift=is_shift,
shift_div=shift_div,
shift_place=shift_place,
non_local=non_local,
)
checkpoint = torch.load(this_weights)
checkpoint = checkpoint['state_dict']
base_dict = {
'.'.join(k.split('.')[1:]): v
for k, v in list(checkpoint.items())
}
replace_dict = {
'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
}
for k, v in replace_dict.items():
if k in base_dict:
base_dict[v] = base_dict.pop(k)
net.load_state_dict(base_dict)
#
# for param in net.parameters():
# param.requires_grad = False
#
# for param in net.base_model.layer4.parameters():
# param.requires_grad = True
net.new_fc = torch.nn.Linear(2048, num_classes)
return net
def main_worker(gpu, ngpus_per_node, args):
global best_acc1
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=rank)
else:
rank = 0
# create model
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset, args.modality)
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div, args.shift_place)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join(
['TSM', args.dataset, args.modality, full_arch_name, args.consensus_type, 'segment%d' % args.num_segments,
'e{}'.format(args.epochs)])
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.dense_sample:
args.store_name += '_dense'
if args.non_local > 0:
args.store_name += '_nl'
args.store_name += '_lr{}'.format(args.lr)
args.store_name += '_wd{:.1e}'.format(args.weight_decay)
args.store_name += '_do{}'.format(args.dropout)
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
check_rootfolders(args, rank)
model = TSN(num_class, args.num_segments, args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift, shift_div=args.shift_div, shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local)
# first synchronization of initial weights
# sync_initial_weights(model, args.rank, args.world_size)
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
if rank == 0:
print(model)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(flip=False if 'something' in args.dataset or 'jester' in args.dataset else True)
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have on a node
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
optimizer = torch.optim.SGD(policies, args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
else:
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
if args.start_epoch == 1:
args.start_epoch = checkpoint['epoch'] + 1
best_acc1 = checkpoint['best_acc1']
# if args.gpu is not None:
# # best_acc1 may be from a checkpoint from a different GPU
# best_acc1 = best_acc1.to(args.gpu)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
train_dataset = TSNDataSet(args.dataset, args.root_path, args.train_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize]),
dense_sample=args.dense_sample)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, drop_last=True, sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(
TSNDataSet(args.dataset, args.root_path, args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]), dense_sample=args.dense_sample),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.evaluate:
validate(val_loader, model, criterion, args)
return
log_training = open(os.path.join(args.root_model, args.store_name, 'log.csv'), 'w')
with open(os.path.join(args.root_model, args.store_name, 'args.txt'), 'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(log_dir=os.path.join(args.root_model, args.store_name))
for epoch in range(args.start_epoch, args.epochs+1):
if args.distributed:
train_sampler.set_epoch(epoch)
# adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps, args)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training, tf_writer, args, rank)
if rank % ngpus_per_node == 0:
save_checkpoint({
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}, False, args, rank)
if epoch % 5 == 0 and rank % ngpus_per_node == 0:
save_checkpoint({
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
}, False, args, rank, e=epoch)
# evaluate on validation set
is_best = False
if epoch % args.eval_freq == 0 or epoch == args.epochs:
acc1 = validate(val_loader, model, criterion, epoch, args, rank, log_training, tf_writer)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
if not args.multiprocessing_distributed or (args.multiprocessing_distributed and rank % ngpus_per_node == 0):
save_checkpoint({
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}, is_best, args, rank)
has_tam = parse_shift_option_from_log_name(this_weights)
if 'RGB' in this_weights:
modality = 'RGB'
else:
modality = 'Flow'
this_arch = this_weights.split('/')[-2].split('_')[2]
modality_list.append(modality)
num_class, args.train_list, val_list, root_path, prefix = dataset_config.return_dataset(
args.dataset, modality)
print('=> TAM : {}, {} sampling'.format(has_tam, args.sample))
net = TSN(
num_class,
this_test_segments if has_tam else 1,
modality,
base_model=this_arch,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
pretrain=args.pretrain,
tam=True,
non_local='_nl' in this_weights,
)
sf_ckpt = torch.load(this_weights, map_location=torch.device('cpu'))
sf_weights = sf_ckpt['state_dict']
tam_ckpt = net.state_dict()
# print(base_dict.keys())
# exit()
base_dict = {}
for k, v in sf_weights.items():
if 'self_conv.conv_f' in k:
k = k.replace('self_conv.conv_f', 'tam.G')
def main():
# settings
global args, best_prec1
args = parser.parse_args()
n_class, args.train_list, args.val_list, args.test_list, prefix = dataset_config.dataset(
)
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}'.format(args.shift_div)
args.store_name = '_'.join(
['tsm', full_arch_name,
'segment%d' % args.num_segments])
print('storing name: ' + args.store_name)
check_rootfolders(args.root_log, args.root_model, args.store_name)
# tsn model added temporal shift module
model = TSN(n_class,
args.num_segments,
base_model=args.arch,
dropout=args.dropout,
partial_bn=not args.no_partialbn,
is_shift=args.shift,
shift_div=args.shift_div)
# preprocessing for input
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(flip=False)
# optimizer
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# cuda and cudnn
try:
model = nn.DataParallel(model).cuda()
except:
model = model.cuda()
cudnn.benchmark = True
# data loader
normalize = GroupNormalize(input_mean, input_std)
train_loader = torch.utils.data.DataLoader(TSNDataSet(
args.train_list,
num_segments=args.num_segments,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=False),
ToTorchFormatTensor(div=True), normalize
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=False,
drop_last=True)
val_loader = torch.utils.data.DataLoader(TSNDataSet(
args.val_list,
num_segments=args.num_segments,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=False),
ToTorchFormatTensor(div=True), normalize
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=False)
test_loader = torch.utils.data.DataLoader(TSNDataSet(
args.test_list,
num_segments=args.num_segments,
image_tmpl=prefix,
random_shift=False,
test_mode=True,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=False),
ToTorchFormatTensor(div=True), normalize
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=False)
# loss function
criterion = 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'])))
# tensorboard
time_stamp = "{0:%Y-%m-%dT%H-%M-%S/}".format(datetime.now())
# train
if args.mode == 'train':
log_training = open(
os.path.join(args.root_log, args.store_name, time_stamp,
'log.csv'), 'w')
tf_writer = SummaryWriter(
'{}/{}/'.format(args.root_log, args.store_name) + time_stamp)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps, args.lr,
args.weight_decay)
train(train_loader, model, criterion, optimizer, epoch,
log_training, tf_writer)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0:
prec1 = validate(val_loader, model, criterion, epoch,
log_training, tf_writer)
# remember best precision and save checkpoint
is_best = prec1 >= best_prec1
best_prec1 = max(prec1, best_prec1)
output_best = 'Best Prec@1: %.2f\n' % (best_prec1)
print(output_best)
log_training.write(output_best + '\n')
log_training.flush()
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, is_best, args.root_model, args.store_name)
tf_writer.close()
# test
checkpoint = '%s/%s/ckpt.best.pth.tar' % (args.root_model, args.store_name)
test(test_loader, model, checkpoint, time_stamp)
extra_temporal_modeling = extra_temporal_modeling,
prune_list = [prune_conv1in_list, prune_conv1out_list],
is_prune = args.prune,
)
'''
net = TSN(
num_class,
this_test_segments if is_shift else 1,
modality,
base_model=this_arch,
new_length=2 if args.data_fuse else None,
consensus_type=args.crop_fusion_type,
#dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
#partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift,
shift_div=args.shift_div,
shift_place=args.shift_place,
#fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
#temporal_pool=args.temporal_pool,
non_local='_nl' in this_weights,
concat=concat,
extra_temporal_modeling=extra_temporal_modeling,
prune_list=[prune_conv1in_list, prune_conv1out_list],
is_prune=args.prune,
)
print(net)
#print(args.shift)
#exit()
if 'tpool' in this_weights:
from ops.temporal_shift import make_temporal_pool
thickness)
return image
if __name__ == '__main__':
args = parser.parse_args()
arch = 'resnet50'
tsn = TSN(len(action_to_idx),
args.num_segments,
'RGB',
base_model=arch,
consensus_type='avg',
dropout=0.5,
img_feature_dim=256,
partial_bn=False,
pretrain='imagenet',
is_shift=True,
shift_div=8,
shift_place='blockres',
fc_lr5=False,
temporal_pool=False,
non_local=False).to(args.device)
model = torch.nn.DataParallel(tsn, device_ids=None).to(args.device)
sd = torch.load(args.model,
map_location=torch.device(args.device))['state_dict']
model.load_state_dict(sd)
model.eval()
meta = pd.DataFrame(columns=['action', 'time_start', 'time_end'])
def main():
global args, best_prec1, least_loss
least_loss = 1000
args = parser.parse_args()
if os.path.exists(os.path.join(args.root_log, "error.log")):
os.remove(os.path.join(args.root_log, "error.log"))
logging.basicConfig(
level=logging.DEBUG,
filename=os.path.join(args.root_log, "error.log"),
filemode='a',
format=
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
# log_handler = open(os.path.join(args.root_log,"error.log"),"w")
# sys.stdout = log_handler
if args.root_path:
num_class, args.train_list, args.val_list, _, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
args.train_list = os.path.join(args.root_log,
"kf1_train_anno_lijun_iod.json")
args.test_list = os.path.join(args.root_log,
"kf1_test_anno_lijun_iod.json")
else:
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div,
args.shift_place)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join([
'TSA', args.dataset, args.modality, full_arch_name,
args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs)
])
# if args.pretrain != 'imagenet':
# args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.dense_sample:
args.store_name += '_dense'
if args.non_local > 0:
args.store_name += '_nl'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
check_rootfolders()
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift,
shift_div=args.shift_div,
shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local,
is_TSA=args.tsa,
is_sTSA=args.stsa,
is_tTSA=args.ttsa,
shift_diff=args.shift_diff,
shift_groups=args.shift_groups,
is_ME=args.me,
is_3D=args.is_3D,
cfg_file=args.cfg_file)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
# policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(
flip=False
if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
if args.optimizer == "sgd":
if args.lr_scheduler:
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer == "adam":
params = get_vmz_fine_tuning_parameters(model,
args.vmz_tune_last_k_layer)
optimizer = torch.optim.Adam(params,
args.lr,
weight_decay=args.weight_decay)
else:
raise RuntimeError("not supported optimizer")
if args.lr_scheduler:
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, args.lr_steps, args.lr_scheduler_gamma)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
# if args.lr_scheduler:
# scheduler.load_state_dict(checkpoint["lr_scheduler"])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
logging.info(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
logging.error(
("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
# sd = {k:v for k, v in sd.items() if k in keys2}
sd = {k: v for k, v in sd.items() if k in keys2}
if args.dataset not in args.tune_from: # new dataset
print('=> New dataset, do not load fc weights')
sd = {
k: v
for k, v in sd.items()
if 'fc' not in k and "projection" not in k
}
if args.modality == 'Flow' and 'Flow' not in args.tune_from:
sd = {k: v for k, v in sd.items() if 'conv1.weight' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality in ['RGB', "PoseAction"]:
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
if not args.shuffle:
train_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path,
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch
in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
ToTorchFormatTensor(
div=(args.arch
not in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True) # prevent something not % n_GPU
val_loader = torch.utils.data.DataLoader(TSNDataSet(
args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(scale_size),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
ToTorchFormatTensor(div=(args.arch
not in ['BNInception',
'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D",
"X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# for group in policies:
# print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
# group['name'], len(group['params']), group['lr_mult'], group['decay_mult'])))
if args.evaluate:
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
elif args.loss_type == "bce":
criterion = torch.nn.BCEWithLogitsLoss().cuda()
elif args.loss_type == "wbce":
class_weight, pos_weight = prep_weight(args.train_list)
criterion = WeightedBCEWithLogitsLoss(class_weight, pos_weight)
else:
raise ValueError("Unknown loss type")
val_loader = torch.utils.data.DataLoader(TSNDataSet(
args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(scale_size),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
ToTorchFormatTensor(div=(args.arch
not in ['BNInception',
'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D",
"X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample,
analysis=True),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
test(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, args.store_name, 'log.csv'), 'w')
with open(os.path.join(args.root_log, args.store_name, 'args.txt'),
'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(
log_dir=os.path.join(args.root_log, args.store_name))
print(model)
logging.info(model)
for epoch in range(args.start_epoch, args.epochs):
logging.info("Train Epoch {}/{} starts, estimated time 5832s".format(
str(epoch), str(args.epochs)))
# update data_loader
if args.shuffle:
gen_label(args.prop_path,
args.label_path,
args.trn_name,
args.train_list,
args.neg_rate,
STR=False)
gen_label(args.prop_path,
args.label_path,
args.tst_name,
args.val_list,
args.test_rate,
STR=False)
train_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path,
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch
in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D",
"X3D"])),
ToTorchFormatTensor(
div=(args.arch
not in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
val_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(scale_size),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch
in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D",
"X3D"])),
ToTorchFormatTensor(
div=(args.arch
not in ['BNInception', 'InceptionV3']),
inc_dim=(args.arch in ["R2plus1D", "X3D"])),
normalize,
]),
dense_sample=args.dense_sample,
all_sample=args.all_sample),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
print(train_loader)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
elif args.loss_type == "bce":
criterion = torch.nn.BCEWithLogitsLoss().cuda()
elif args.loss_type == "wbce":
class_weight, pos_weight = prep_weight(args.train_list)
criterion = WeightedBCEWithLogitsLoss(class_weight, pos_weight)
else:
raise ValueError("Unknown loss type")
if not args.lr_scheduler:
adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)
train(train_loader, model, criterion, optimizer, epoch,
log_training, tf_writer)
else:
train(train_loader, model, criterion, optimizer, epoch,
log_training, tf_writer)
scheduler.step()
# train for one epoch
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
logging.info(
"Test Epoch {}/{} starts, estimated time 13874s".format(
str(epoch // args.eval_freq),
str(args.epochs / args.eval_freq)))
if args.loss_type == "wbce":
# class_weight,pos_weight = prep_weight(args.val_list)
criterion = torch.nn.BCEWithLogitsLoss().cuda()
lossm = validate(val_loader, model, criterion, epoch, log_training,
tf_writer)
# remember best prec@1 and save checkpoint
is_best = lossm < least_loss
least_loss = min(lossm, least_loss)
tf_writer.add_scalar('lss/test_top1_best', least_loss, epoch)
output_best = 'Best Loss: %.3f\n' % (lossm)
logging.info(output_best)
log_training.write(output_best + '\n')
log_training.flush()
if args.lr_scheduler:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': least_loss,
'lr_scheduler': scheduler,
}, is_best, epoch)
else:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': least_loss,
}, is_best, epoch)
for i, s in enumerate(strings):
if 'shift' in s:
break
return True, int(strings[i].replace('shift', '')), strings[i + 1]
else:
return False, None, None
is_shift, shift_div, shift_place = parse_shift_option_from_log_name(this_weights)
print(is_shift, shift_div, shift_place)
with torch.cuda.device(0):
net = TSN(2, 1, 'RGB',
base_model=this_arch,
consensus_type='avg',
img_feature_dim='225',
#pretrain=args.pretrain,
is_shift=is_shift, shift_div=shift_div, shift_place=shift_place,
non_local='_nl' in this_weights,
)
macs, params = get_model_complexity_info(net, (1,3, 224, 224), as_strings=True,print_per_layer_stat=False, verbose=False)
print("Using ptflops")
print('{:<30} {:<8}'.format('Computational complexity: ', macs))
print('{:<30} {:<8}'.format('Number of parameters: ', params))
from thop import profile
model = net = TSN(2, 1, 'RGB',
base_model=this_arch,
consensus_type='avg',
img_feature_dim='225',
#pretrain=args.pretrain,
def doInferecing(cap, args, GPU_FLAG):
# switch between archs based on selected arch
if args.get("arch") == "mobilenetv2":
this_weights = "checkpoint/TSM_ucfcrime_RGB_mobilenetv2_shift8_blockres_avg_segment8_e25/ckpt.best.pth.tar"
else:
this_weights = "checkpoint/TSM_ucfcrime_RGB_resnet50_shift8_blockres_avg_segment8_e25/ckpt.best.pth.tar"
is_shift, shift_div, shift_place = parse_shift_option_from_log_name(
this_weights)
modality = "RGB"
if "RGB" in this_weights:
modality = "RGB"
# Get dataset categories.
categories = ["Normal Activity", "Abnormal Activity"]
num_class = len(categories)
this_arch = args.get("arch")
print("[INFO] >> Model loading weights from disk!!")
net = TSN(
num_class,
1,
modality,
base_model=this_arch,
consensus_type="avg",
img_feature_dim="225",
# pretrain=args.pretrain,
is_shift=is_shift,
shift_div=shift_div,
shift_place=shift_place,
non_local="_nl" in this_weights,
)
# See GPU_FLAG to check where to load the weights on CPU or GPU
if GPU_FLAG == "y":
checkpoint = torch.load(this_weights)
else:
checkpoint = torch.load(this_weights, map_location=torch.device("cpu"))
checkpoint = checkpoint["state_dict"]
base_dict = {
".".join(k.split(".")[1:]): v
for k, v in list(checkpoint.items())
}
replace_dict = {
"base_model.classifier.weight": "new_fc.weight",
"base_model.classifier.bias": "new_fc.bias",
}
for k, v in replace_dict.items():
if k in base_dict:
base_dict[v] = base_dict.pop(k)
net.load_state_dict(base_dict)
print("\n[INFO] >> Model loading Successfull")
if GPU_FLAG == "y":
net.cuda().eval()
skip_frames = 2
summary(net, (1, 3, 224, 224))
else:
net.eval()
skip_frames = 4
transform = torchvision.transforms.Compose([
Stack(roll=(this_arch in ["BNInception", "InceptionV3"])),
ToTorchFormatTensor(
div=(this_arch not in ["BNInception", "InceptionV3"])),
GroupNormalize(net.input_mean, net.input_std),
])
WINDOW_NAME = "Real-Time Video Action Recognition"
# set a lower resolution for speed up
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)
# env variables
full_screen = False
cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
cv2.resizeWindow(WINDOW_NAME, 640, 480)
cv2.moveWindow(WINDOW_NAME, 0, 0)
cv2.setWindowTitle(WINDOW_NAME, WINDOW_NAME)
t = None
i_frame = -1
count = 0
imageName = 0
# variable to hold writer object
writer = None
c = 0
print("Ready!")
while cap.isOpened():
i_frame += 1
hasFrame, img = cap.read() # (480, 640, 3) 0 ~ 255
if hasFrame:
img_tran = transform([Image.fromarray(img).convert("RGB")])
if (i_frame % skip_frames == 0
): # skip every other frame to obtain a suitable frame rate
t1 = time.time()
if GPU_FLAG == "y":
input1 = (img_tran.view(
-1, 3, img_tran.size(1),
img_tran.size(2)).unsqueeze(0).cuda())
else:
input1 = img_tran.view(-1, 3, img_tran.size(1),
img_tran.size(2)).unsqueeze(0)
input = input1
with torch.no_grad():
logits = net(input)
h_x = torch.mean(F.softmax(logits, 1), dim=0).data
print(
"<<< [INFO] >>> PROB - | Normal: {:.2f}".format(
h_x[0]),
"| Abnormal: {:.2f} |".format(h_x[1]),
"Frames Rendered-",
count,
)
pr, li = h_x.sort(0, True)
probs = pr.tolist()
idx = li.tolist()
# print(probs)
t2 = time.time()
print(
"<<< [INFO] >>>",
"EVENT - |",
categories[idx[0]],
" Prob: {:.2f}| ".format(probs[0]),
"\n",
)
current_time = t2 - t1
img = cv2.resize(img, (640, 480))
height, width, _ = img.shape
if categories[idx[0]] == "Abnormal Activity":
R = 255
G = 0
Abnormality = True
tempThres = probs[0]
c += 1
maxAbnormalProb.append(float(probs[0]))
else:
R = 0
G = 255
Abnormality = False
cv2.putText(
img,
"EVENT: " + categories[idx[0]],
(20, int(height / 16)),
cv2.FONT_HERSHEY_SIMPLEX,
0.7,
(0, int(G), int(R)),
2,
)
cv2.putText(
img,
"Confidence: {0:.2f}%".format(probs[0] * 100, "%"),
(20, int(height - 420)),
cv2.FONT_HERSHEY_SIMPLEX,
0.7,
(0, int(G), int(R)),
2,
)
fps = 1 / current_time
# if args.get('f',True):
FpsList.append(float(fps))
maxFps = max(FpsList)
estFps = sum(FpsList) / len(FpsList)
# else:
# maxFps=-1
# estFps=-1
cv2.putText(
img,
"FPS: {0:.1f}".format(fps),
(width - 150, int(height / 16)),
cv2.FONT_HERSHEY_SIMPLEX,
0.7,
(0, 255, 255),
2,
)
if writer is None:
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
(H, W) = img.shape[:2]
path = "./appData/Anoamly_Clips/"
name = len(glob.glob(path + "*.avi"))
getVidName = path + "Abnormal_Event_{0}.avi".format(name + 1)
writer = cv2.VideoWriter(getVidName, fourcc, 30.0, (W, H),
True)
# Saving Anaomlous Event Image and Clip
if Abnormality:
writer.write(img)
# record stat every two seconds if exists
if c % 60 == 0:
getStatsOfAbnormalActivity()
# if tempThres > 0.75:
path = "./appData/Anoamly_Images/"
index = len(glob.glob(path + "*.jpg"))
# imageName = getFileName(path+'.jpg')
imageName = path + "Abnormal_Event_{0}.jpg".format(index +
1)
cv2.imwrite(imageName, img)
cv2.imshow(WINDOW_NAME, img)
key = cv2.waitKey(1)
if key & 0xFF == ord("q") or key == 27: # exit
break
elif key == ord("F") or key == ord("f"): # full screen
print("Changing full screen option!")
full_screen = not full_screen
if full_screen:
print("Setting FS!!!")
cv2.setWindowProperty(WINDOW_NAME, cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
else:
cv2.setWindowProperty(WINDOW_NAME, cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_NORMAL)
# resetting time for next frame
if t is None:
t = time.time()
else:
nt = time.time()
count += 1
t = nt
else:
# Uncomment below lines to run code unfinitely and comment cap.release and writer,release
# i_frame = 0
# cap.set(cv2.CAP_PROP_POS_FRAMES,0)
cap.release()
writer.release()
cv2.destroyAllWindows()
# Clearing Variables for re-running
# estFps=None
# maxAbnormalProb.clear()
# maxFps=None
# Calculating total execution time
execTime = time.time() - startime
print()
# Display Results
print("<<< [INFO] >>> Total Abnormal Probs : ", len(maxAbnormalProb))
print("<<< [INFO] >>> Max Abnormality Prob : {:.2f}".format(
max(maxAbnormalProb)))
print("<<< [INFO] >>> Avg Abnormality Prob : {:.2f}".format(
sum(maxAbnormalProb) / len(maxAbnormalProb)))
print("<<< [INFO] >>> Max FPS achieved : {:.1f}".format(maxFps))
print("<<< [INFO] >>> Averge Estimated FPS : {:.1f}".format(estFps))
print("<<< [INFO] >>> Total Infernece Time : {:.2f} seconds".format(
execTime))
def main():
global args, best_prec1
global crop_size
args = parser.parse_args()
num_class, train_list, val_list, args.root_path, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
num_class = 1
if args.train_list == "":
args.train_list = train_list
if args.val_list == "":
args.val_list = val_list
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div,
args.shift_place)
if args.concat != "":
full_arch_name += '_concat{}'.format(args.concat)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join([
'TSM', args.dataset, args.modality, full_arch_name,
args.consensus_type,
'lr%.5f' % args.lr,
'dropout%.2f' % args.dropout,
'wd%.5f' % args.weight_decay,
'batch%d' % args.batch_size,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs)
])
if args.data_fuse:
args.store_name += '_fuse'
if args.extra_temporal_modeling:
args.store_name += '_extra'
if args.tune_from is not None:
args.store_name += '_finetune'
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.dense_sample:
args.store_name += '_dense'
if args.non_local > 0:
args.store_name += '_nl'
if args.clipnums:
#pass
args.store_name += "_clip{}".format(args.clipnums)
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
check_rootfolders()
if args.prune in ['input', 'inout'] and args.tune_from:
sd = torch.load(args.tune_from)
sd = sd['state_dict']
sd = input_dim_L2distance(sd, args.shift_div)
model = TSN(
num_class,
args.num_segments,
args.modality,
base_model=args.arch,
new_length=2 if args.data_fuse else None,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift,
shift_div=args.shift_div,
shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local,
concat=args.concat,
extra_temporal_modeling=args.extra_temporal_modeling,
prune_list=[prune_conv1in_list, prune_conv1out_list],
is_prune=args.prune,
)
#model = torch.load("/home/ubuntu/backup_kevin/myownTSM_git/checkpoint/TSM_youcook_RGB_resnet50_shift8_blockres_concatAll_conv1d_lr0.00025_dropout0.70_wd0.00050_batch16_segment8_e20_finetune_slice_v1_clipnum500/ckpt_"+str(1)+".pth.tar")
print(model)
#summary(model, torch.zeros((16, 24, 224, 224)))
#exit(1)
if args.dataset == 'ucf101': #twice sample & full resolution
twice_sample = True
crop_size = model.scale_size #256 x 256
else:
twice_sample = False
crop_size = model.crop_size #224 x 224
crop_size = 256
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies(args.concat)
train_augmentation = model.get_augmentation(
flip=False if 'something' in args.dataset or 'jester' in args.dataset
or 'nvgesture' in args.dataset else True)
model = torch.nn.DataParallel(model).cuda()
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
tune_from_list = args.tune_from.split(',')
sd = torch.load(tune_from_list[0])
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, v in model_dict.items():
if k not in sd and k.replace('.prune', '') in sd:
print('=> Load after adding .prune: ', k)
replace_dict.append((k.replace('.prune', ''), k))
if args.prune in ['input', 'inout']:
sd = adjust_para_shape_prunein(sd, model_dict)
if args.prune in ['output', 'inout']:
sd = adjust_para_shape_pruneout(sd, model_dict)
if args.concat != "" and "concat" not in tune_from_list[0]:
sd = adjust_para_shape_concat(sd, model_dict)
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in tune_from_list[0]: # new dataset
print('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
if args.modality != 'Flow' and 'Flow' in tune_from_list[0]:
sd = {k: v for k, v in sd.items() if 'conv1.weight' not in k}
#print(sd.keys())
#print("*"*50)
#print(model_dict.keys())
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality in ['RGB']:
data_length = 1
elif args.modality in ['Depth']:
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
'''
dataRoot = r"/home/share/YouCook/downloadVideo"
for dirPath, dirnames, filenames in os.walk(dataRoot):
for filename in filenames:
print(os.path.join(dirPath,filename) +"is {}".format("exist" if os.path.isfile(os.path.join(dirPath,filename))else "NON"))
train_data = torchvision.io.read_video(os.path.join(dirPath,filename),start_pts=0,end_pts=1001, )
tmp = torchvision.io.read_video_timestamps(os.path.join(dirPath,filename),)
print(tmp)
print(len(tmp[0]))
print(train_data[0].size())
exit()
exit()
'''
'''
train_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path, args.train_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]), dense_sample=args.dense_sample, data_fuse = args.data_fuse),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True,
drop_last=True) # prevent something not % n_GPU
val_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]), dense_sample=args.dense_sample, twice_sample=twice_sample, data_fuse = args.data_fuse),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
'''
#global trainDataloader, valDataloader, train_loader, val_loader
trainDataloader = YouCookDataSetRcg(args.root_path, args.train_list,train=True,inputsize=crop_size,hasPreprocess = False,\
clipnums=args.clipnums,
hasWordIndex = True,)
valDataloader = YouCookDataSetRcg(args.root_path, args.val_list,val=True,inputsize=crop_size,hasPreprocess = False,\
clipnums=args.clipnums,
hasWordIndex = True,)
#print(trainDataloader._getMode())
#print(valDataloader._getMode())
#exit()
train_loader = torch.utils.data.DataLoader(trainDataloader,
#shuffle=True,
)
val_loader = torch.utils.data.DataLoader(valDataloader)
#print(train_loader is val_loader)
#print(trainDataloader._getMode())
#print(valDataloader._getMode())
#print(trainDataloader._getMode())
#print(valDataloader._getMode())
#print(len(train_loader))
#exit()
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
elif args.loss_type == "MSELoss":
criterion = torch.nn.MSELoss().cuda()
elif args.loss_type == "BCELoss":
#print("BCELoss")
criterion = torch.nn.BCELoss().cuda()
else:
raise ValueError("Unknown loss type")
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'],
group['decay_mult'])))
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, args.store_name, 'log.csv'), 'w')
with open(os.path.join(args.root_log, args.store_name, 'args.txt'),
'w') as f:
f.write(str(args))
#print(os.path.join(args.root_log, args.store_name, 'args.txt'))
#exit()
tf_writer = SummaryWriter(
log_dir=os.path.join(args.root_log, args.store_name))
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)
#print("265")
# train for one epoch
######
#print(trainDataloader._getMode())
#print(valDataloader._getMode())
train(train_loader, model, criterion, optimizer, epoch, log_training,
tf_writer)
######
#print("268")
# evaluate on validation set
#model = model.load_state_dict(torch.load("/home/ubuntu/backup_kevin/myownTSM_git/checkpoint/TSM_youcook_RGB_resnet50_shift8_blockres_concatAll_conv1d_lr0.00025_dropout0.70_wd0.00050_batch16_segment8_e20_finetune_slice_v1_clipnum500/ckpt_"+str(epoch+1)+".pth.tar"))
#if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
if False:
prec1 = validate(val_loader, model, criterion, epoch, log_training,
tf_writer)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
tf_writer.add_scalar('acc/test_top1_best', best_prec1, epoch)
output_best = 'Best Prec@1: %.3f\n' % (best_prec1)
#print(output_best)
log_training.write(output_best + '\n')
log_training.flush()
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, is_best)
else:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, False)
#break
print("test pass")
modality = 'RGB'
else:
modality = 'Flow'
this_arch = this_weights.split('TSM_')[1].split('_')[2]
modality_list.append(modality)
num_class, args.train_list, val_list, root_path, prefix = dataset_config.return_dataset(
args.dataset, modality)
print('=> shift: {}, shift_div: {}, shift_place: {}'.format(
is_shift, shift_div, shift_place))
net = TSN(
num_class,
this_test_segments if is_shift else 1,
modality,
base_model=this_arch,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
pretrain=args.pretrain,
is_shift=is_shift,
shift_div=shift_div,
shift_place=shift_place,
non_local='_nl' in this_weights,
)
if 'tpool' in this_weights:
from ops.temporal_shift import make_temporal_pool
make_temporal_pool(net.base_model,
this_test_segments) # since DataParallel
checkpoint = torch.load(this_weights)
checkpoint = checkpoint['state_dict']
total_num = None
for this_weights, this_test_segments, test_file in zip(weights_list,
test_segments_list,
test_file_list):
has_tam, modality, backbone = parse_shift_option_from_log_name(
this_weights)
modality_list.append(modality)
num_class, args.train_list, val_list, root_path, prefix = dataset_config.return_dataset(
args.dataset, modality)
print('=> TAM : {}, {} dense'.format(has_tam, args.sample))
net = TSN(
num_class,
this_test_segments if has_tam else 1,
modality,
base_model=backbone,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
tam=has_tam,
non_local='_nl' in this_weights,
)
checkpoint = torch.load(this_weights, map_location='cpu')
checkpoint = checkpoint['state_dict']
base_dict = {}
for k, v in list(checkpoint.items()):
if k.startswith('module'):
base_dict['.'.join(k.split('.')[1:])] = v
else:
base_dict[k] = v
net.load_state_dict(base_dict)
def main():
global args, best_prec1
args = parser.parse_args()
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset,
args.modality)
model = TSN(num_class, args.num_segments, args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift, shift_div=args.shift_div, shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local,
cca3d = args.cca3d
)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(flip=False if 'something' in args.dataset or 'jester' in args.dataset else True)
model = model.cuda()
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume,map_location='cuda:0')
parallel_state_dict = checkpoint['state_dict']
cpu_state_dict={}
for k,v in parallel_state_dict.items():
cpu_state_dict[k[len('module.'):]] = v
model.load_state_dict(cpu_state_dict)
print(("=> loaded checkpoint '{}' (epoch {})".format(args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
preprocess=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])
val_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform = preprocess,
dense_sample=args.dense_sample),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=False)
norm_param = (input_mean, input_std)
cam_process(val_loader, model,norm_param)
rst = rst.reshape(
(batch_size, -1, num_class)).mean(axis=1).reshape(
(batch_size, num_class))
return rst
num_class, args.train_list, val_list, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
net = TSN(
num_class,
args.test_segments,
args.modality,
base_model=args.arch,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
pretrain=args.pretrain,
is_shift=args.shift,
shift_div=args.shift_div,
shift_place=args.shift_place,
)
# import pdb; pdb.set_trace()
'''
checkpoint = torch.load(args.weight)
checkpoint = checkpoint['state_dict']
base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint.items())}
replace_dict = {
'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
def main():
global args, best_prec1
args = parser.parse_args()
#num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset,
# args.modality)
num_class = 21
args.train_list = "/home/jzwang/code/Video_3D/movienet/data/movie/movie_train.txt"
args.val_list = "/home/jzwang/code/Video_3D/movienet/data/movie/movie_test.txt"
args.root_path = ""
prefix = "frame_{:04d}.jpg"
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div, args.shift_place)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join(
['TSM', args.dataset, args.modality, full_arch_name, args.consensus_type, 'segment%d' % args.num_segments,
'e{}'.format(args.epochs)])
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.dense_sample:
args.store_name += '_dense'
if args.non_local > 0:
args.store_name += '_nl'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
#check_rootfolders()
model = TSN(num_class, args.num_segments, args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift, shift_div=args.shift_div, shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(flip=False if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
#best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(("=> loaded checkpoint '{}' (epoch {})"
.format(args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in args.tune_from: # new dataset
print('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
if args.modality == 'Flow' and 'Flow' not in args.tune_from:
sd = {k: v for k, v in sd.items() if 'conv1.weight' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
train_loader = torch.utils.data.DataLoader(
TSNDataSetMovie("", args.train_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl="frame_{:04d}.jpg" if args.modality in ["RGB", "RGBDiff"] else args.flow_prefix+"{}_{:05d}.jpg",
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=args.arch == 'BNInception'),
ToTorchFormatTensor(div=args.arch != 'BNInception'),
normalize,
])),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
TSNDataSetMovie("", args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl="frame_{:04d}.jpg" if args.modality in ["RGB", "RGBDiff"] else args.flow_prefix+"{}_{:05d}.jpg",
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=args.arch == 'BNInception'),
ToTorchFormatTensor(div=args.arch != 'BNInception'),
normalize,
])),
batch_size=int(args.batch_size/2), shuffle=False,
num_workers=args.workers, pin_memory=True)
# define loss function (criterion) and optimizer
criterion = torch.nn.BCEWithLogitsLoss().cuda()
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'], group['decay_mult'])))
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
zero_time = time.time()
best_map = 0
print ('Start training...')
for epoch in range(args.start_epoch, args.epochs):
valloss, mAP, wAP, output_mtx = validate(val_loader, model, criterion)
adjust_learning_rate(optimizer, epoch, args.lr_steps)
np.save("testnew.npy", output_mtx)
print("saving down")
# train for one epoch
start_time = time.time()
trainloss = train(train_loader, model, criterion, optimizer, epoch)
print('Traing loss %4f Epoch %d'% (trainloss, epoch))
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
valloss, mAP, wAP, output_mtx = validate(val_loader, model, criterion)
end_time = time.time()
epoch_time = end_time - start_time
total_time = end_time - zero_time
print ('Total time used: %s Epoch %d time uesd: %s'%(
str(datetime.timedelta(seconds=int(total_time))),
epoch, str(datetime.timedelta(seconds=int(epoch_time)))))
print ('Train loss: {0:.4f} val loss: {1:.4f} mAP: {2:.4f} wAP: {3:.4f}'.format(
trainloss, valloss, mAP, wAP))
# evaluate on validation set
is_best = mAP > best_map
#if mAP > best_map:
#best_map = mAP
# checkpoint_name = "%04d_%s" % (epoch+1, "checkpoint.pth.tar")
checkpoint_name = "best_checkpoint.pth.tar"
save_checkpoint({
'epoch': epoch+1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, is_best, epoch)
np.save("testnew.npy", output_mtx)
print("saving down")
with open(args.record_path, 'a') as file:
file.write('Epoch:[{0}]'
'Train loss: {1:.4f} val loss: {2:.4f} map: {3:.4f}\n'.format(
epoch+1, trainloss, valloss, mAP))
print ('************ Done!... ************')
data_iter_list = []
net_list = []
modality_list = args.modalities.split(',')
arch_list = args.archs.split('.')
total_num = None
for this_weights, this_test_segments, test_file, modality, this_arch in zip(
weights_list, test_segments_list, test_file_list, modality_list,
arch_list):
num_class, args.train_list, val_list, root_path, prefix = dataset_config.return_dataset(
args.dataset, modality)
net = TSN(num_class,
this_test_segments,
modality,
base_model=this_arch,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
pretrain=args.pretrain)
checkpoint = torch.load(this_weights)
try:
net.load_state_dict(checkpoint['state_dict'])
except:
checkpoint = checkpoint['state_dict']
base_dict = {
'.'.join(k.split('.')[1:]): v
for k, v in list(checkpoint.items())
}
replace_dict = {
'base_model.classifier.weight': 'new_fc.weight',
def __init__(self,
checkpoint_file,
num_classes,
max_length=8,
trim_net=False,
checkpoint_is_model=False,
bottleneck_size=128):
self.is_shift = None
self.net = None
self.arch = None
self.num_classes = num_classes
self.max_length = max_length
self.bottleneck_size = bottleneck_size
#self.feature_idx = feature_idx
self.transform = None
self.CNN_FEATURE_COUNT = [256, 512, 1024, 2048]
# input variables
this_test_segments = self.max_length
test_file = None
#model variables
self.is_shift, shift_div, shift_place = True, 8, 'blockres'
self.arch = 'resnet101'
modality = 'RGB'
# dataset variables
num_class, train_list, val_list, root_path, prefix = dataset_config.return_dataset(
'somethingv2', modality)
print('=> shift: {}, shift_div: {}, shift_place: {}'.format(
self.is_shift, shift_div, shift_place))
# define model
net = TSN(
num_class,
this_test_segments if self.is_shift else 1,
modality,
base_model=self.arch,
consensus_type='avg',
img_feature_dim=256,
pretrain='imagenet',
is_shift=self.is_shift,
shift_div=shift_div,
shift_place=shift_place,
non_local='_nl' in checkpoint_file,
)
'''
The checkpoint file appears to be an entire TSMBackBone Object. this needs to be
handled acordingly. Either find a way to convert it back to a weights file or maniuplate it
to work with the system.
'''
# load checkpoint file
checkpoint = torch.load(checkpoint_file)
'''
#include
print("self.bottleneck_size:", self.bottleneck_size, type(self.bottleneck_size))
net.base_model.avgpool = nn.Sequential(
nn.Conv2d(2048, self.bottleneck_size, (1,1)),
nn.ReLU(inplace=True),
#nn.AdaptiveAvgPool2d(output_size=1)
)
if(not trim_net):
print("no trim")
net.new_fc = nn.Linear(self.bottleneck_size, 174)
else:
print("trim")
net.consensus = nn.Identity()
net.new_fc = nn.Identity()
net.base_model.fc = nn.Identity() # sets the dropout value to None
print(net)
# Combine network together so that the it can have parameters set correctly
# I think, I'm not 100% what this code section actually does and I don't have
# the time to figure it out right now
#print("checkpoint------------------------")
#print(checkpoint)
'''
if (checkpoint_is_model):
checkpoint = checkpoint.net.state_dict()
else:
checkpoint = checkpoint['state_dict']
base_dict = {
'.'.join(k.split('.')[1:]): v
for k, v in list(checkpoint.items())
}
'''
#include
replace_dict = {'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
}
for k, v in replace_dict.items():
if v in base_dict:
base_dict.pop(v)
if k in base_dict:
base_dict.pop(k)
#base_dict[v] = base_dict.pop(k)
'''
net.load_state_dict(base_dict, strict=False)
# define image modifications
self.transform = torchvision.transforms.Compose([
torchvision.transforms.Compose([
GroupScale(net.scale_size),
GroupCenterCrop(net.scale_size),
]),
#torchvision.transforms.Compose([ GroupFullResSample(net.scale_size, net.scale_size, flip=False) ]),
Stack(roll=(self.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(self.arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std),
])
# place net onto GPU and finalize network
self.model = net
net = torch.nn.DataParallel(net.cuda())
net.eval()
# network variable
self.net = net
# loss variable (used for generating gradients when ranking)
if (not trim_net):
self.loss = torch.nn.CrossEntropyLoss().cuda()
def main():
global args, best_prec1
args = parser.parse_args()
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div,
args.shift_place)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join([
'TSM', args.dataset, args.modality, full_arch_name,
args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs)
])
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.dense_sample:
args.store_name += '_dense'
if args.non_local > 0:
args.store_name += '_nl'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
check_rootfolders()
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift,
shift_div=args.shift_div,
shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(
flip=False
if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in args.tune_from: # new dataset
print('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
if args.modality == 'Flow' and 'Flow' not in args.tune_from:
sd = {k: v for k, v in sd.items() if 'conv1.weight' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
train_loader = torch.utils.data.DataLoader(
TSNDataSet(
args.root_path,
args.train_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
dense_sample=args.dense_sample),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True) # prevent something not % n_GPU
val_loader = torch.utils.data.DataLoader(TSNDataSet(
args.root_path,
args.val_list,
num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
dense_sample=args.dense_sample),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
else:
raise ValueError("Unknown loss type")
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'],
group['decay_mult'])))
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, args.store_name, 'log.csv'), 'w')
with open(os.path.join(args.root_log, args.store_name, 'args.txt'),
'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(
log_dir=os.path.join(args.root_log, args.store_name))
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training,
tf_writer)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion, epoch, log_training,
tf_writer)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
tf_writer.add_scalar('acc/test_top1_best', best_prec1, epoch)
output_best = 'Best Prec@1: %.3f\n' % (best_prec1)
print(output_best)
log_training.write(output_best + '\n')
log_training.flush()
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, is_best)
def main():
global args, best_prec1
args = parser.parse_args()
num_class = opts.num_class
full_arch_name = args.arch
if args.shift:
full_arch_name += '_shift{}_{}'.format(args.shift_div,
args.shift_place)
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join([
'TSM', args.dataset, args.modality, full_arch_name,
args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs)
])
if args.pretrain != 'imagenet':
args.store_name += '_{}'.format(args.pretrain)
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
if args.non_local > 0:
args.store_name += '_nl'
print('storing name: ' + args.store_name)
# check_rootfolders()
model = TSN(num_class,
args.num_segments,
args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_size=args.img_size,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift,
shift_div=args.shift_div,
shift_place=args.shift_place,
fc_lr5=True,
temporal_pool=args.temporal_pool,
non_local=args.non_local)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(
flip=False
if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
model.apply(weights_init)
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
# best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'], strict=False)
# optimizer.load_state_dict(checkpoint['optimizer'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
fr_r = open(opts.NUM_LABEL_R, 'r+')
w2n = eval(fr_r.read())
fr_r.close()
fr = open(opts.NUM_LABEL, 'r+')
n2w = eval(fr.read())
fr.close()
train_loader, val_loader, test_loader = None, None, None
if args.mode != 'test':
lip_dict, video_list = opts.file_deal(opts.TRAIN_DATA, w2n)
train_num = int(len(video_list) * 0.95)
train_loader = torch.utils.data.DataLoader(TSNDataSet(
opts.TRAIN_DATA,
args.mode,
num_segments=args.num_segments,
img_size=args.img_size,
lip_dict=lip_dict,
video_list=video_list[:train_num],
transform=torchvision.transforms.Compose([
train_augmentation,
GroupMultiScaleCrop(args.img_size, [1, .875, .75, .66]),
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(
opts.TRAIN_DATA,
args.mode,
num_segments=args.num_segments,
img_size=args.img_size,
lip_dict=lip_dict,
video_list=video_list[train_num:],
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)
else:
lip_dict, video_list = opts.file_deal(opts.TEST_DATA, w2n)
test_loader = torch.utils.data.DataLoader(TSNDataSet_infer(
opts.TEST_DATA,
num_segments=args.num_segments,
img_size=args.img_size,
lip_dict=lip_dict,
video_list=video_list,
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'])))
if args.mode == 'test':
if args.sub == 'sub':
inference(test_loader, model, n2w)
else:
inferencefusion(test_loader, model, n2w)
return
# 开始训练
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion, epoch, n2w)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
output_best = 'Best Prec@1: %.3f\n' % (best_prec1)
print(output_best)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, is_best)
