comp_weights = weights
reg_weights = weights
rel_props = score_pickle_list[0][vid][0]
return rel_props, \
merge_part(arrays, 1, act_weights), \
merge_part(arrays, 2, comp_weights), \
merge_part(arrays, 3, reg_weights)
print('Merge detection scores from {} sources...'.format(
len(score_pickle_list)))
detection_scores = {k: merge_scores(k) for k in score_pickle_list[0]}
print('Done.')
dataset = SSNDataSet("", test_prop_file, verbose=False)
dataset_detections = [dict() for i in range(num_class)]
if args.cls_scores:
print('Using classifier scores from {}'.format(args.cls_scores))
cls_score_pc = pickle.load(open(args.cls_scores, 'rb'), encoding='bytes')
cls_score_dict = {
os.path.splitext(os.path.basename(k.decode('utf-8')))[0]: v
for k, v in cls_score_pc.items()
}
else:
cls_score_dict = None
# generate detection results
def gen_detection_results(video_id, score_tp):
def main():
global args, best_loss
args = parser.parse_args()
dataset_configs = get_configs(args.dataset)
num_class = dataset_configs['num_class']
stpp_configs = tuple(dataset_configs['stpp'])
sampling_configs = dataset_configs['sampling']
model = SSN(num_class,
args.num_aug_segments,
args.num_body_segments,
args.num_aug_segments,
args.modality,
base_model=args.arch,
dropout=args.dropout,
stpp_cfg=stpp_configs,
bn_mode=args.bn_mode)
if args.init_weights:
if os.path.isfile(args.init_weights):
print(("=> loading pretrained weigths '{}'".format(
args.init_weights)))
wd = torch.load(args.init_weights)
model.base_model.load_state_dict(wd['state_dict'])
print(
("=> loaded init weights from '{}'".format(args.init_weights)))
else:
print(
("=> no weights file found at '{}'".format(args.init_weights)))
elif args.kinetics_pretrain:
model_url = dataset_configs['kinetics_pretrain'][args.arch][
args.modality]
model.base_model.load_state_dict(
model_zoo.load_url(model_url)['state_dict'])
print(("=> loaded init weights from '{}'".format(model_url)))
else:
# standard ImageNet pretraining
if args.modality == 'Flow':
model_url = dataset_configs['flow_init'][args.arch]
model.base_model.load_state_dict(
model_zoo.load_url(model_url)['state_dict'])
print(("=> loaded flow init weights from '{}'".format(model_url)))
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
if args.resume:
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_loss = checkpoint['best_loss']
model.load_state_dict(checkpoint['state_dict'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
cudnn.benchmark = True
pin_memory = (args.modality == 'RGB')
# 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
else:
raise ValueError("unknown modality {}".format(args.modality))
train_prop_file = 'data/{}_proposal_list.txt'.format(
dataset_configs['train_list'])
val_prop_file = 'data/{}_proposal_list.txt'.format(
dataset_configs['val_list'])
train_loader = torch.utils.data.DataLoader(
SSNDataSet(
"",
train_prop_file,
epoch_multiplier=args.training_epoch_multiplier,
new_length=data_length,
modality=args.modality,
exclude_empty=True,
**sampling_configs,
aug_seg=args.num_aug_segments,
body_seg=args.num_body_segments,
image_tmpl="img_{:05d}.jpg" if args.modality in ["RGB", "RGBDiff"]
else args.flow_prefix + "{}_{:05d}.jpg",
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True) # in training we drop the last incomplete minibatch
val_loader = torch.utils.data.DataLoader(SSNDataSet(
"",
val_prop_file,
new_length=data_length,
modality=args.modality,
exclude_empty=True,
**sampling_configs,
aug_seg=args.num_aug_segments,
body_seg=args.num_body_segments,
image_tmpl="img_{:05d}.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 in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
reg_stats=train_loader.dataset.stats),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory)
activity_criterion = torch.nn.CrossEntropyLoss().cuda()
completeness_criterion = CompletenessLoss().cuda()
regression_criterion = ClassWiseRegressionLoss().cuda()
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'],
group['decay_mult'])))
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.evaluate:
validate(val_loader, model, activity_criterion, completeness_criterion,
regression_criterion, 0)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, activity_criterion, completeness_criterion,
regression_criterion, optimizer, epoch)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
loss = validate(val_loader, model, activity_criterion,
completeness_criterion, regression_criterion,
(epoch + 1) * len(train_loader))
# remember best prec@1 and save checkpoint
is_best = loss < best_loss
best_loss = min(loss, best_loss)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'reg_stats': torch.from_numpy(train_loader.dataset.stats)
},
is_best,
foldername=args.save_path,
filename="checkpoint_{}.pth".format(epoch))
print('======================================================')
print(epoch, is_best, loss, best_loss)
print('======================================================')
reg_weights = weights
rel_props = score_pickle_list[0][vid][0]
return (
rel_props,
merge_part(arrays, 1, act_weights),
merge_part(arrays, 2, comp_weights),
merge_part(arrays, 3, reg_weights),
)
print(("Merge detection scores from {} sources...".format(len(score_pickle_list))))
detection_scores = {k: merge_scores(k) for k in score_pickle_list[0]}
print("Done.")
dataset = SSNDataSet("", test_prop_file, verbose=False)
dataset_detections = [dict() for i in range(num_class)]
if args.cls_scores:
print(("Using classifier scores from {}".format(args.cls_scores)))
cls_score_pc = pickle.load(open(args.cls_scores, "rb"), encoding="bytes")
cls_score_dict = {
os.path.splitext(os.path.basename(k.decode("utf-8")))[0]: v
for k, v in list(cls_score_pc.items())
}
else:
cls_score_dict = None
# generate detection results
".".join(k.split(".")[1:]): v
for k, v in list(checkpoint["state_dict"].items())
}
stats = checkpoint["reg_stats"].numpy()
dataset = SSNDataSet(
"",
test_prop_file,
new_length=data_length,
modality=args.modality,
aug_seg=2,
body_seg=5,
image_tmpl="img_{:05d}.jpg" if args.modality in ["RGB", "RGBDiff"] else
args.flow_pref + "{}_{:05d}.jpg",
test_mode=True,
test_interval=args.frame_interval,
transform=torchvision.transforms.Compose([
cropping,
Stack(roll=(args.arch in ["BNInception", "InceptionV3"])),
ToTorchFormatTensor(
div=(args.arch not in ["BNInception", "InceptionV3"])),
GroupNormalize(net.input_mean, net.input_std),
]),
reg_stats=stats,
verbose=False,
)
index_queue = ctx.Queue()
result_queue = ctx.Queue()
workers = [
ctx.Process(
def main():
global args, best_loss
args = parser.parse_args()
dataset_configs = get_configs(args.dataset)
num_class = dataset_configs['num_class']
stpp_configs = tuple(dataset_configs['stpp']) #TODO
sampling_configs = dataset_configs['sampling']
base_model = 'p3d'
model = SSN(num_class,
args.num_aug_segments,
args.num_body_segments,
args.num_aug_segments,
args.modality,
base_model=base_model,
dropout=args.dropout,
stpp_cfg=stpp_configs,
bn_mode=args.bn_mode)
weights_file = 'ssn_activitynet1.2_BNInception_rgb_epoch-2_checkpoint.pth.tar'
weights = torch.load(weights_file)['state_dict']
weights = {'.'.join(k.split('.')[1:]): v for k, v in list(weights.items())}
model.load_state_dict(weights)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
train_augmentation = model.get_augmentation()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
cudnn.benchmark = True
pin_memory = (args.modality == 'RGB')
# 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
else:
raise ValueError("unknown modality {}".format(args.modality))
train_prop_file = 'data/{}_proposal_list.txt'.format(
dataset_configs['train_list'])
val_prop_file = 'data/{}_proposal_list.txt'.format(
dataset_configs['test_list'])
train_loader = torch.utils.data.DataLoader(
SSNDataSet(
"",
train_prop_file,
epoch_multiplier=args.training_epoch_multiplier,
new_length=data_length,
modality=args.modality,
exclude_empty=True,
**sampling_configs,
aug_seg=args.num_aug_segments,
body_seg=args.num_body_segments,
image_tmpl="{:05d}.jpg" if args.modality in ["RGB", "RGBDiff"] else
args.flow_prefix + "{}_{:05d}.jpg",
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True) # in training we drop the last incomplete minibatch
val_loader = torch.utils.data.DataLoader(SSNDataSet(
"",
val_prop_file,
new_length=data_length,
modality=args.modality,
exclude_empty=True,
**sampling_configs,
aug_seg=args.num_aug_segments,
body_seg=args.num_body_segments,
image_tmpl="{:05d}.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 in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(
div=(args.arch not in ['BNInception', 'InceptionV3'])),
normalize,
]),
reg_stats=train_loader.dataset.stats),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory)
activity_criterion = torch.nn.CrossEntropyLoss().cuda()
completeness_criterion = CompletenessLoss().cuda()
regression_criterion = ClassWiseRegressionLoss().cuda()
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'],
group['decay_mult'])))
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.evaluate:
validate(val_loader, model, activity_criterion, completeness_criterion,
regression_criterion, 0)
return
# exit()
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, activity_criterion, completeness_criterion,
regression_criterion, optimizer, epoch)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
loss = validate(val_loader, model, activity_criterion,
completeness_criterion, regression_criterion,
(epoch + 1) * len(train_loader))
# remember best prec@1 and save checkpoint
is_best = loss < best_loss
best_loss = min(loss, best_loss)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'reg_stats': torch.from_numpy(train_loader.dataset.stats)
}, is_best)
arrays = [pc[vid] for pc in score_pickle_list]
act_weights = weights
comp_weights = weights
reg_weights = weights
rel_props = score_pickle_list[0][vid][0]
return rel_props, \
merge_part(arrays, 1, act_weights), \
merge_part(arrays, 2, comp_weights), \
merge_part(arrays, 3, reg_weights)
print('Merge detection scores from {} sources...'.format(len(score_pickle_list)))
detection_scores = {k: merge_scores(k) for k in score_pickle_list[0]}
print('Done.')
dataset = SSNDataSet("", test_prop_file, verbose=False)
dataset_detections = [dict() for i in range(num_class)]
if args.cls_scores:
print('Using classifier scores from {}'.format(args.cls_scores))
cls_score_pc = pickle.load(open(args.cls_scores, 'rb'), encoding='bytes')
cls_score_dict = {os.path.splitext(os.path.basename(k.decode('utf-8')))[0]:v for k, v in cls_score_pc.items()}
else:
cls_score_dict = None
# generate detection results
def gen_detection_results(video_id, score_tp):
if len(score_tp[0].shape) == 3:
rel_prop = np.squeeze(score_tp[0], 0)