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