def get(args, Part_class_list):
""" Entry point. Call this function to get all Charades dataloaders """
normalize = trasmy.Norm_my(mean=[114.77, 107.74, 99.48],
std=[1, 1, 1]) # for resnet 3D
train_file = args.train_file
val_file = args.val_file
args.data = '/VIDEO_DATA/Charades_v1_rgb'
train_dataset = Charades(args.data,
'train',
train_file,
args.cache,
transform=transforms.Compose([
trasmy.IMG_resize(args.inputsize,
args.inputsize),
transforms.ToTensor(), normalize
]),
label_list=Part_class_list)
val_dataset = Charades(args.data,
'val',
val_file,
args.cache,
transform=transforms.Compose([
trasmy.IMG_resize(240, 240),
trasmy.CenterCrop(args.inputsize),
transforms.ToTensor(), normalize
]),
label_list=Part_class_list)
valvideo_dataset = Charades(args.data,
'val_video',
val_file,
args.cache,
transform=transforms.Compose([
trasmy.IMG_resize(240, 240),
trasmy.CenterCrop(args.inputsize),
transforms.ToTensor(), normalize
]),
label_list=Part_class_list)
return train_dataset, val_dataset, valvideo_dataset
def get_train(args, Part_class_list):
""" Entry point. Call this function to get all Charades dataloaders """
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_file = args.train_file
args.data = '/VIDEO_DATA/Charades_v1_rgb'
train_dataset = Charades(args.data,
'train',
train_file,
args.cache,
transform=transforms.Compose([
trasmy.IMG_resize(args.inputsize,
args.inputsize),
transforms.ToTensor(), normalize
]),
label_list=Part_class_list,
vdlist=args.vdlist,
args=args)
return train_dataset
def get_train(args, Part_class_list):
""" Entry point. Call this function to get all Charades dataloaders """
normalize = trasmy.Norm_my(mean=[114.77, 107.74, 99.48],
std=[1, 1, 1]) # for resnet 3D
train_file = args.train_file
args.data = '/VIDEO_DATA/Charades_v1_rgb'
train_dataset = Charades(args.data,
'train',
train_file,
args.cache,
transform=transforms.Compose([
trasmy.IMG_resize(args.inputsize,
args.inputsize),
trasmy.To_Tensor_My(), normalize
]),
label_list=Part_class_list,
vdlist=args.vdlist,
args=args)
return train_dataset
def get_val(args, Part_class_list):
""" Entry point. Call this function to get all Charades dataloaders """
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
val_file = os.path.join(os.getcwd(), '../../..',
'./data/data_csv/Charades_v1_test.csv')
args.data = '/VIDEO_DATA/Charades_v1_rgb'
resize_shape = args.inputsize + 16
val_dataset = Charades(args.data,
'val',
val_file,
args.cache,
transform=transforms.Compose([
trasmy.IMG_resize(resize_shape, resize_shape),
trasmy.CenterCrop(args.inputsize),
transforms.ToTensor(), normalize
]),
label_list=Part_class_list,
vdlist=args.vdlist,
args=args)
return val_dataset
def get_val(args, Part_class_list):
""" Entry point. Call this function to get all Charades dataloaders """
normalize = trasmy.Norm_my(mean=[114.77, 107.74, 99.48],
std=[1, 1, 1]) # for resnet 3D
val_file = os.path.join(os.getcwd(), '../../..',
'./data/data_csv/Charades_v1_test.csv')
args.data = '/VIDEO_DATA/Charades_v1_rgb'
resize_shape = args.inputsize + 16
val_dataset = Charades(args.data,
'val',
val_file,
args.cache,
transform=transforms.Compose([
trasmy.IMG_resize(resize_shape, resize_shape),
trasmy.CenterCrop(args.inputsize),
trasmy.To_Tensor_My(), normalize
]),
label_list=Part_class_list,
vdlist=args.vdlist,
args=args)
return val_dataset