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:
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():
# options
parser = argparse.ArgumentParser(description="TSM testing on the full validation set")
parser.add_argument('dataset', type=str)
# may contain splits
parser.add_argument('--weights', type=str, default=None)
parser.add_argument('--test_segments', type=str, default=25)
parser.add_argument('--dense_sample', default=False, action="store_true", help='use dense sample as I3D')
parser.add_argument('--twice_sample', default=False, action="store_true", help='use twice sample for ensemble')
parser.add_argument('--full_res', default=False, action="store_true",
help='use full resolution 256x256 for test as in Non-local I3D')
parser.add_argument('--test_crops', type=int, default=1)
parser.add_argument('--coeff', type=str, default=None)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
help='number of data loading workers (default: 8)')
# for true test
parser.add_argument('--test_list', type=str, default=None)
parser.add_argument('--csv_file', type=str, default=None)
parser.add_argument('--softmax', default=False, action="store_true", help='use softmax')
parser.add_argument('--max_num', type=int, default=-1)
parser.add_argument('--input_size', type=int, default=224)
parser.add_argument('--crop_fusion_type', type=str, default='avg')
parser.add_argument('--gpus', nargs='+', type=int, default=None)
parser.add_argument('--img_feature_dim',type=int, default=256)
parser.add_argument('--num_set_segments',type=int, default=1,help='TODO: select multiply set of n-frames from a video')
parser.add_argument('--pretrain', type=str, default='imagenet')
args = parser.parse_args()
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def parse_shift_option_from_log_name(log_name):
if 'shift' in log_name:
strings = log_name.split('_')
for i, s in enumerate(strings):
if 'shift' in s:
break
return True, int(strings[i].replace('shift', '')), strings[i + 1]
else:
return False, None, None
weights_list = args.weights.split(',')
test_segments_list = [int(s) for s in args.test_segments.split(',')]
assert len(weights_list) == len(test_segments_list)
if args.coeff is None:
coeff_list = [1] * len(weights_list)
else:
coeff_list = [float(c) for c in args.coeff.split(',')]
if args.test_list is not None:
test_file_list = args.test_list.split(',')
else:
test_file_list = [None] * len(weights_list)
data_iter_list = []
net_list = []
modality_list = []
total_num = None
for this_weights, this_test_segments, test_file in zip(weights_list, test_segments_list, test_file_list):
is_shift, shift_div, shift_place = parse_shift_option_from_log_name(this_weights)
if 'RGB' in this_weights:
modality = 'RGB'
else:
modality = 'Flow'
this_arch = this_weights.split('TSM_')[1].split('_')[2]
modality_list.append(modality)
num_class, args.train_list, val_list, root_path, prefix = dataset_config.return_dataset(args.dataset,
modality)
print('=> shift: {}, shift_div: {}, shift_place: {}'.format(is_shift, shift_div, shift_place))
net = TSN(num_class, this_test_segments if is_shift else 1, modality,
base_model=this_arch,
consensus_type=args.crop_fusion_type,
img_feature_dim=args.img_feature_dim,
pretrain=args.pretrain,
is_shift=is_shift, shift_div=shift_div, shift_place=shift_place,
non_local='_nl' in this_weights,
)
if 'tpool' in this_weights:
from ops.temporal_shift import make_temporal_pool
make_temporal_pool(net.base_model, this_test_segments) # since DataParallel
checkpoint = torch.load(this_weights)
checkpoint = checkpoint['state_dict']
# base_dict = {('base_model.' + k).replace('base_model.fc', 'new_fc'): v for k, v in list(checkpoint.items())}
base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint.items())}
replace_dict = {'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
}
for k, v in replace_dict.items():
if k in base_dict:
base_dict[v] = base_dict.pop(k)
net.load_state_dict(base_dict)
input_size = net.scale_size if args.full_res else net.input_size
if args.test_crops == 1:
cropping = torchvision.transforms.Compose([
GroupScale(net.scale_size),
GroupCenterCrop(input_size),
])
elif args.test_crops == 3: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupFullResSample(input_size, net.scale_size, flip=False)
])
elif args.test_crops == 5: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, net.scale_size, flip=False)
])
elif args.test_crops == 10:
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, net.scale_size)
])
else:
raise ValueError("Only 1, 5, 10 crops are supported while we got {}".format(args.test_crops))
data_loader = torch.utils.data.DataLoader(
TSNDataSet(root_path, test_file if test_file is not None else val_list, num_segments=this_test_segments,
new_length=1 if modality == "RGB" else 5,
modality=modality,
image_tmpl=prefix,
test_mode=True,
remove_missing=len(weights_list) == 1,
transform=torchvision.transforms.Compose([
cropping,
Stack(roll=(this_arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(this_arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std),
]), dense_sample=args.dense_sample, twice_sample=args.twice_sample),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True,
)
if args.gpus is not None:
devices = [args.gpus[i] for i in range(args.workers)]
else:
devices = list(range(args.workers))
net = torch.nn.DataParallel(net.cuda())
net.eval()
data_gen = enumerate(data_loader)
if total_num is None:
total_num = len(data_loader.dataset)
else:
assert total_num == len(data_loader.dataset)
data_iter_list.append(data_gen)
net_list.append(net)
output = []
def eval_video(video_data, net, this_test_segments, modality):
net.eval()
with torch.no_grad():
i, data, label = video_data
batch_size = label.numel()
num_crop = args.test_crops
if args.dense_sample:
num_crop *= 10 # 10 clips for testing when using dense sample
if args.twice_sample:
num_crop *= 2
if modality == 'RGB':
length = 3
elif modality == 'Flow':
length = 10
elif modality == 'RGBDiff':
length = 18
else:
raise ValueError("Unknown modality "+ modality)
data_in = data.view(-1, length, data.size(2), data.size(3))
if is_shift:
data_in = data_in.view(batch_size * num_crop, this_test_segments, length, data_in.size(2), data_in.size(3))
rst = net(data_in)
rst = rst.reshape(batch_size, num_crop, -1).mean(1)
if args.softmax:
# take the softmax to normalize the output to probability
rst = F.softmax(rst, dim=1)
rst = rst.data.cpu().numpy().copy()
if net.module.is_shift:
rst = rst.reshape(batch_size, num_class)
else:
rst = rst.reshape((batch_size, -1, num_class)).mean(axis=1).reshape((batch_size, num_class))
return i, rst, label
proc_start_time = time.time()
max_num = args.max_num if args.max_num > 0 else total_num
top1 = AverageMeter()
top5 = AverageMeter()
for i, data_label_pairs in enumerate(zip(*data_iter_list)):
with torch.no_grad():
if i >= max_num:
break
this_rst_list = []
this_label = None
for n_seg, (_, (data, label)), net, modality in zip(test_segments_list, data_label_pairs, net_list, modality_list):
rst = eval_video((i, data, label), net, n_seg, modality)
this_rst_list.append(rst[1])
this_label = label
assert len(this_rst_list) == len(coeff_list)
for i_coeff in range(len(this_rst_list)):
this_rst_list[i_coeff] *= coeff_list[i_coeff]
ensembled_predict = sum(this_rst_list) / len(this_rst_list)
for p, g in zip(ensembled_predict, this_label.cpu().numpy()):
output.append([p[None, ...], g])
cnt_time = time.time() - proc_start_time
prec1, prec5 = accuracy(torch.from_numpy(ensembled_predict), this_label, topk=(1, 5))
top1.update(prec1.item(), this_label.numel())
top5.update(prec5.item(), this_label.numel())
if i % 20 == 0:
print('video {} done, total {}/{}, average {:.3f} sec/video, '
'moving Prec@1 {:.3f} Prec@5 {:.3f}'.format(i * args.batch_size, i * args.batch_size, total_num,
float(cnt_time) / (i+1) / args.batch_size, top1.avg, top5.avg))
video_pred = [np.argmax(x[0]) for x in output]
video_pred_top5 = [np.argsort(np.mean(x[0], axis=0).reshape(-1))[::-1][:5] for x in output]
video_labels = [x[1] for x in output]
if args.csv_file is not None:
print('=> Writing result to csv file: {}'.format(args.csv_file))
with open(test_file_list[0].replace('test_videofolder.txt', 'category.txt')) as f:
categories = f.readlines()
categories = [f.strip() for f in categories]
with open(test_file_list[0]) as f:
vid_names = f.readlines()
vid_names = [n.split(' ')[0] for n in vid_names]
print(vid_names)
assert len(vid_names) == len(video_pred)
if args.dataset != 'somethingv2': # only output top1
with open(args.csv_file, 'w') as f:
for n, pred in zip(vid_names, video_pred):
f.write('{};{}\n'.format(n, categories[pred]))
else:
with open(args.csv_file, 'w') as f:
for n, pred5 in zip(vid_names, video_pred_top5):
fill = [n]
for p in list(pred5):
fill.append(p)
f.write('{};{};{};{};{};{}\n'.format(*fill))
cf = confusion_matrix(video_labels, video_pred).astype(float)
np.save('cm.npy', cf)
cls_cnt = cf.sum(axis=1)
cls_hit = np.diag(cf)
cls_acc = cls_hit / cls_cnt
print(cls_acc)
upper = np.mean(np.max(cf, axis=1) / cls_cnt)
print('upper bound: {}'.format(upper))
print('-----Evaluation is finished------')
print('Class Accuracy {:.02f}%'.format(np.mean(cls_acc) * 100))
print('Overall Prec@1 {:.02f}% Prec@5 {:.02f}%'.format(top1.avg, top5.avg))
def main():
global args, best_prec1
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:
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,
)
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)
#print(type(policies))
#print(policies)
#exit()
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()
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())
for k, v in list(sd.items()):
if k not in model_dict:
sd.pop(k)
sd.pop("module.base_model.embedding.weight")
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)
decoder = TransformerModel().cuda()
if args.decoder_resume:
decoder_chkpoint = torch.load(args.decoder_resume)
decoder.load_state_dict(decoder_chkpoint["state_dict"])
print("decoder parameters = ", decoder.parameters())
policies.append({
"params": decoder.parameters(),
"lr_mult": 5,
"decay_mult": 1,
"name": "Attndecoder_weight"
})
cudnn.benchmark = True
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# 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)
index2wordDict = trainDataloader.getIndex2wordDict()
#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.NLLLoss().cuda()
elif args.loss_type == "MSELoss":
criterion = torch.nn.MSELoss().cuda()
elif args.loss_type == "BCELoss":
#print("BCELoss")
criterion = torch.nn.BCELoss().cuda()
elif args.loss_type == "CrossEntropyLoss":
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))
#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, decoder, criterion, optimizer, epoch,
log_training, tf_writer, index2wordDict)
######
#print("268")
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader,
model,
decoder,
criterion,
epoch,
log_training,
tf_writer,
index2wordDict=index2wordDict)
# 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)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': decoder.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename="decoder")
else:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, False)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': decoder.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
},
is_best,
filename="decoder")
#break
print("test pass")
def main():
global args, best_prec1
args = parser.parse_args()
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(
args.dataset, args.modality)
full_arch_name = args.arch
if args.temporal_pool:
full_arch_name += '_tpool'
args.store_name = '_'.join([
'I3D', args.dataset, full_arch_name, 'batch{}'.format(args.batch_size),
'wd{}'.format(args.weight_decay), args.consensus_type,
'segment%d' % args.num_segments, 'e{}'.format(args.epochs),
'dropout{}'.format(args.dropout), args.pretrain,
'lr{}'.format(args.lr), '_warmup{}'.format(args.warmup)
])
if args.lr_type != 'step':
args.store_name += '_{}'.format(args.lr_type)
else:
step_str = [str(int(x)) for x in args.lr_steps]
args.store_name += '_step' + '_'.join(step_str)
if args.dense_sample:
args.store_name += '_dense'
if args.spatial_dropout:
sigmoid_layer_str = '_'.join(args.sigmoid_layer)
args.store_name += '_spatial_drop3d_{}_group{}_layer{}'.format(
args.sigmoid_thres, args.sigmoid_group, sigmoid_layer_str)
if args.sigmoid_random:
args.store_name += '_RandomSigmoid'
if args.non_local > 0:
args.store_name += '_nl'
if args.suffix is not None:
args.store_name += '_{}'.format(args.suffix)
print('storing name: ' + args.store_name)
check_rootfolders()
model = i3d(num_class,
args.num_segments,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
spatial_dropout=args.spatial_dropout,
sigmoid_thres=args.sigmoid_thres,
sigmoid_group=args.sigmoid_group,
sigmoid_random=args.sigmoid_random,
sigmoid_layer=args.sigmoid_layer,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
train_augmentation = model.get_augmentation(
flip=False
if 'something' in args.dataset or 'jester' in args.dataset else True)
model = torch.nn.DataParallel(model,
device_ids=list(range(args.gpus))).cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if args.tune_from:
print(("=> fine-tuning from '{}'".format(args.tune_from)))
sd = torch.load(args.tune_from)
sd = sd['state_dict']
model_dict = model.state_dict()
replace_dict = []
for k, v in sd.items():
if k not in model_dict and k.replace('.net', '') in model_dict:
print('=> Load after remove .net: ', k)
replace_dict.append((k, k.replace('.net', '')))
for k, v in model_dict.items():
if k not in sd and k.replace('.net', '') in sd:
print('=> Load after adding .net: ', k)
replace_dict.append((k.replace('.net', ''), k))
for k, k_new in replace_dict:
sd[k_new] = sd.pop(k)
keys1 = set(list(sd.keys()))
keys2 = set(list(model_dict.keys()))
set_diff = (keys1 - keys2) | (keys2 - keys1)
print('#### Notice: keys that failed to load: {}'.format(set_diff))
if args.dataset not in args.tune_from: # new dataset
print('=> New dataset, do not load fc weights')
sd = {k: v for k, v in sd.items() if 'fc' not in k}
if args.modality == 'Flow' and 'Flow' not in args.tune_from:
sd = {k: v for k, v in sd.items() if 'conv1.weight' not in k}
model_dict.update(sd)
model.load_state_dict(model_dict)
if args.temporal_pool and not args.resume:
make_temporal_pool(model.module.base_model, args.num_segments)
cudnn.benchmark = True
# Data loading code
normalize = GroupNormalize(input_mean, input_std)
train_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path,
args.train_list,
num_segments=args.num_segments,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
GroupScale((256, 340)),
train_augmentation,
Stack('3D'),
ToTorchFormatTensor(),
normalize,
]),
dense_sample=args.dense_sample),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True) # prevent something not % n_GPU
val_loader = torch.utils.data.DataLoader(TSNDataSet(
args.root_path,
args.val_list,
num_segments=args.num_segments,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack('3D'),
ToTorchFormatTensor(),
normalize,
]),
dense_sample=args.dense_sample),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.BCEWithLogitsLoss().cuda()
else:
raise ValueError("Unknown loss type")
if args.evaluate:
validate(val_loader, model, criterion, 0)
return
log_training = open(
os.path.join(args.root_log, args.store_name, 'log.csv'), 'w')
with open(os.path.join(args.root_log, args.store_name, 'args.txt'),
'w') as f:
f.write(str(args))
tf_writer = SummaryWriter(
log_dir=os.path.join(args.root_log, args.store_name))
for epoch in range(args.start_epoch, args.epochs):
# adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training,
tf_writer, args.warmup, args.lr_type, args.lr_steps)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion, epoch, log_training,
tf_writer)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
tf_writer.add_scalar('acc/test_mAP_best', best_prec1, epoch)
output_best = 'Best mAP: %.3f\n' % (best_prec1)
print(output_best)
log_training.write(output_best + '\n')
log_training.flush()
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1,
}, is_best)
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)
def v_train(train_loader, val_loader, model, num_class, vnet, criterion,
valcriterion, optimizer, epoch, log, tf_writer):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
val_loader_iter = iter(val_loader)
if args.no_partialbn:
model.module.partialBN(False)
else:
model.module.partialBN(True)
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
target = target.cuda()
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
vnet_temp = VNet(1, 100, 1).cuda()
optimizer_vnet_temp = torch.optim.Adam(vnet_temp.params(),
1e-3,
weight_decay=1e-4)
vnet_temp.load_state_dict(vnet.state_dict())
v_model = v_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,
print_spec=False)
v_model = torch.nn.DataParallel(v_model, device_ids=args.gpus).cuda()
if args.temporal_pool and not args.resume:
make_temporal_pool(v_model.module.base_model, args.num_segments)
v_model.load_state_dict(model.state_dict())
# compute output
output = v_model(input_var)
# loss = criterion(output, target_var)
cost = criterion(output, target_var)
cost_v = torch.reshape(cost, (-1, 1))
v_lambda = vnet_temp(cost_v.data)
l_f_v = torch.sum(cost_v * v_lambda) / len(cost_v)
v_model.zero_grad()
grads = torch.autograd.grad(l_f_v, (v_model.module.params()),
create_graph=True)
# to be modified
v_lr = args.lr * ((0.1**int(epoch >= 80)) * (0.1**int(epoch >= 100)))
v_model.module.update_params(lr_inner=v_lr, source_params=grads)
del grads
# phase 2. pixel weights step
try:
inputs_val, targets_val = next(val_loader_iter) # 拿一个val set图片
except StopIteration:
val_loader_iter = iter(val_loader)
inputs_val, targets_val = next(val_loader_iter)
# inputs_val, targets_val = sample_val['image'], sample_val['label']
inputs_val, targets_val = inputs_val.cuda(), targets_val.cuda()
y_g_hat = v_model(inputs_val)
l_g_meta = valcriterion(y_g_hat, targets_val) # val loss
optimizer_vnet_temp.zero_grad()
l_g_meta.backward()
optimizer_vnet_temp.step()
vnet.load_state_dict(vnet_temp.state_dict())
# phase 1. network weight step (w)
output = model(input_var)
cost = criterion(output, target)
cost_v = torch.reshape(cost, (-1, 1))
with torch.no_grad():
v_new = vnet(cost_v)
loss = torch.sum(cost_v * v_new) / len(cost_v)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# compute gradient and do SGD step
# loss.backward()
if args.clip_gradient is not None:
total_norm = clip_grad_norm_(model.parameters(),
args.clip_gradient)
# optimizer.step()
# optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
output = ('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5,
lr=optimizer.param_groups[-1]['lr'] * 0.1)) # TODO
print(output, end=" ")
for n, p in vnet.named_params(vnet):
print("vnet param: ", n, p[0].item())
break
log.write(output + '\n')
log.flush()
tf_writer.add_scalar('loss/train', losses.avg, epoch)
tf_writer.add_scalar('acc/train_top1', top1.avg, epoch)
tf_writer.add_scalar('acc/train_top5', top5.avg, epoch)
tf_writer.add_scalar('lr', optimizer.param_groups[-1]['lr'], epoch)
def __init__(self, weightPath, segments, crops, fullSize=True):
self.weightPath = weightPath
self.segments = segments
self.crops = crops
self.fullSize = fullSize
self.is_shift, shift_div, shift_place = parse_shift_option_from_log_name(self.weightPath)
if 'RGB' in self.weightPath:
self.modality = 'RGB'
else:
self.modality = 'Flow'
this_arch = self.weightPath.split('TSM_')[1].split('_')[2]
self.num_class = 400
print('=> shift: {}, shift_div: {}, shift_place: {}'.format(self.is_shift, shift_div, shift_place))
self.net = TSN(self.num_class, self.segments if self.is_shift else 1, self.modality,
base_model=this_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 self.weightPath,
)
if 'tpool' in self.weightPath:
from ops.temporal_shift import make_temporal_pool
make_temporal_pool(self.net.base_model, self.segments) # since DataParallel
checkpoint = torch.load(self.weightPath)
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)
self.net.load_state_dict(base_dict)
input_size = self.net.scale_size if self.fullSize else self.net.input_size
if self.crops == 1:
cropping = torchvision.transforms.Compose([
GroupScale(self.net.scale_size),
GroupCenterCrop(input_size),
])
elif self.crops == 3: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupFullResSample(input_size, self.net.scale_size, flip=False)
])
elif self.crops == 5: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, self.net.scale_size, flip=False)
])
elif self.crops == 10:
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, self.net.scale_size)
])
else:
raise ValueError("Only 1, 5, 10 crops are supported while we got {}".format(self.crops))
self.transform = torchvision.transforms.Compose([
cropping,
Stack(roll=(this_arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(this_arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(self.net.input_mean, self.net.input_std)])
if self.modality == 'RGB':
self.length = 3
elif self.modality == 'Flow':
self.length = 10
elif self.modality == 'RGBDiff':
self.length = 18
self.net = self.net.cuda()
self.net.eval()
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!... ************')
def main():
global args, best_prec1
args = parser.parse_args()
num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset,
args.modality)
model = TSN(num_class, args.num_segments, args.modality,
base_model=args.arch,
consensus_type=args.consensus_type,
dropout=args.dropout,
img_feature_dim=args.img_feature_dim,
partial_bn=not args.no_partialbn,
pretrain=args.pretrain,
is_shift=args.shift, shift_div=args.shift_div, shift_place=args.shift_place,
fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
temporal_pool=args.temporal_pool,
non_local=args.non_local,
cca3d = args.cca3d
)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation(flip=False if 'something' in args.dataset or 'jester' in args.dataset else True)
model = model.cuda()
if args.resume:
if args.temporal_pool: # early temporal pool so that we can load the state_dict
make_temporal_pool(model.module.base_model, args.num_segments)
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume,map_location='cuda:0')
parallel_state_dict = checkpoint['state_dict']
cpu_state_dict={}
for k,v in parallel_state_dict.items():
cpu_state_dict[k[len('module.'):]] = v
model.load_state_dict(cpu_state_dict)
print(("=> loaded checkpoint '{}' (epoch {})".format(args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
preprocess=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])
val_loader = torch.utils.data.DataLoader(
TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform = preprocess,
dense_sample=args.dense_sample),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=False)
norm_param = (input_mean, input_std)
cam_process(val_loader, model,norm_param)
def 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
