def cd3_extartion(video_parth, device=None):
batch_size = 1
train_frame_interval = 2
clip_length = 16
single_load = True #should not matter
home = os.getcwd()
pretrained_3d = home + "/c3d-pytorch-master/c3d.pickle"
if device == None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#Load clips
print("doing train loader")
train_loader = VideoIterTrain(dataset_path=None,
annotation_path=video_parth,
clip_length=clip_length,
frame_stride=train_frame_interval,
video_transform=build_transforms(),
name='train',
return_item_subpath=False,
single_load=single_load)
print("train loader done, train_iter now")
train_iter = torch.utils.data.DataLoader(
train_loader,
batch_size=batch_size,
shuffle=False,
num_workers=32, # 4, # change this part accordingly
pin_memory=True)
#Possesing with CD3
print("Now loading the data to C3D netowr")
network = C3D(pretrained=pretrained_3d)
network.to(device)
if not os.path.exists(features_dir):
os.mkdir(features_dir)
features_writer = FeaturesWriter()
dir_list = []
for i_batch, (data, target, sampled_idx, dirs,
vid_names) in tqdm(enumerate(train_iter)):
with torch.no_grad():
outputs = network(data.cuda())
for i, (dir, vid_name, start_frame) in enumerate(
zip(dirs, vid_names,
sampled_idx.cpu().numpy())):
dir_list.append([dir, vid_name])
dir = os.path.join(features_dir, dir)
features_writer.write(feature=outputs[i],
video_name=vid_name,
idx=start_frame,
dir=dir)
features_writer.dump()
return dir_list
def main():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args = parser.parse_args()
set_logger(log_file=args.log_file, debug_mode=args.debug_mode)
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
cudnn.benchmark = True
train_loader = VideoIterTrain(
dataset_path=args.dataset_path,
annotation_path=args.annotation_path,
clip_length=args.clip_length,
frame_stride=args.train_frame_interval,
video_transform=build_transforms(),
name='train',
return_item_subpath=False,
)
train_iter = torch.utils.data.DataLoader(
train_loader,
batch_size=args.batch_size,
shuffle=False,
num_workers=32, # 4, # change this part accordingly
pin_memory=True)
val_loader = VideoIterTrain(
dataset_path=args.dataset_path,
annotation_path=args.annotation_path_test,
clip_length=args.clip_length,
frame_stride=args.val_frame_interval,
video_transform=build_transforms(),
name='val',
return_item_subpath=False,
)
val_iter = torch.utils.data.DataLoader(
val_loader,
batch_size=args.batch_size,
shuffle=False,
num_workers=32, # 4, # change this part accordingly
pin_memory=True)
network = C3D(pretrained=args.pretrained_3d)
network.to(device)
if not path.exists(features_dir):
mkdir(features_dir)
features_writer = FeaturesWriter()
for i_batch, (data, target, sampled_idx, dirs,
vid_names) in tqdm(enumerate(train_iter)):
with torch.no_grad():
outputs = network(data.cuda())
for i, (dir, vid_name, start_frame) in enumerate(
zip(dirs, vid_names,
sampled_idx.cpu().numpy())):
dir = path.join(features_dir, dir)
features_writer.write(feature=outputs[i],
video_name=vid_name,
start_frame=start_frame,
dir=dir)
features_writer.dump()
features_writer = FeaturesWriter()
for i_batch, (data, target, sampled_idx, dirs,
vid_names) in tqdm(enumerate(val_iter)):
with torch.no_grad():
outputs = network(data.cuda())
for i, (dir, vid_name, start_frame) in enumerate(
zip(dirs, vid_names,
sampled_idx.cpu().numpy())):
dir = path.join(features_dir, dir)
features_writer.write(feature=outputs[i],
video_name=vid_name,
start_frame=start_frame,
dir=dir)
features_writer.dump()
def main():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args = parser.parse_args()
set_logger(log_file=args.log_file, debug_mode=args.debug_mode)
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
cudnn.benchmark = True
mean = [124 / 255, 117 / 255, 104 / 255]
std = [1 / (.0167 * 255)] * 3
normalize = transforms.Normalize(mean=mean, std=std)
train_loader = VideoIterTrain(
dataset_path=args.dataset_path,
annotation_path=args.annotation_path,
clip_length=args.clip_length,
frame_interval=args.train_frame_interval,
video_transform=transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomCrop((224, 224)),
transforms.ToTensor(),
normalize,
]),
name='train',
return_item_subpath=False,
)
train_iter = torch.utils.data.DataLoader(
train_loader,
batch_size=args.batch_size,
shuffle=False,
num_workers=32, # 4, # change this part accordingly
pin_memory=True)
val_loader = VideoIterTrain(
dataset_path=args.dataset_path,
annotation_path=args.annotation_path_test,
clip_length=args.clip_length,
frame_interval=args.val_frame_interval,
video_transform=transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomCrop((224, 224)),
transforms.ToTensor(),
normalize,
]),
name='val',
return_item_subpath=False,
)
val_iter = torch.utils.data.DataLoader(
val_loader,
batch_size=args.batch_size,
shuffle=False,
num_workers=32, # 4, # change this part accordingly
pin_memory=True)
network = C3D(pretrained=args.pretrained_3d)
network.to(device)
if not path.exists(features_dir):
mkdir(features_dir)
features_writer = FeaturesWriter()
for i_batch, (data, target, sampled_idx, dirs,
vid_names) in tqdm(enumerate(train_iter)):
data = data.to(device)
with torch.no_grad():
input_var = torch.autograd.Variable(data)
outputs = network(input_var)
for i, (dir, vid_name, start_frame) in enumerate(
zip(dirs, vid_names,
sampled_idx.cpu().numpy())):
dir = path.join(features_dir, dir)
features_writer.write(feature=outputs[i],
video_name=vid_name,
start_frame=start_frame,
dir=dir)
features_writer.dump()
features_writer = FeaturesWriter()
for i_batch, (data, target, sampled_idx, dirs,
vid_names) in tqdm(enumerate(val_iter)):
data = data.to(device)
with torch.no_grad():
input_var = torch.autograd.Variable(data)
outputs = network(input_var)
for i, (dir, vid_name, start_frame) in enumerate(
zip(dirs, vid_names,
sampled_idx.cpu().numpy())):
dir = path.join(features_dir, dir)
features_writer.write(feature=outputs[i],
video_name=vid_name,
start_frame=start_frame,
dir=dir)
features_writer.dump()