def __init__(self,
h5_file,
test_txt,
segment_len,
ten_crop=False,
height=128,
width=171,
crop_size=112):
self.h5_path = h5_file
self.keys = sorted(list(h5py.File(self.h5_path, 'r').keys()))
self.test_dict = read_testing_txt(test_txt)
self.segment_len = segment_len
self.ten_crop = ten_crop
self.crop_size = crop_size
self.height = height
self.width = width
self.test_dict_annotation()
self.mean = [90.25, 97.66, 101.41]
self.std = [1, 1, 1]
if ten_crop:
# self.resize_aug=transforms.Resize([256,340])
# self.ten_crop_aug=transforms.TenCropTensor(self.crop_size)
self.ten_crop_aug = transforms.Compose([
transforms.Resize([self.height, self.width]),
transforms.ClipToTensor(div_255=False),
transforms.Normalize(mean=self.mean, std=self.std),
transforms.TenCropTensor(self.crop_size)
])
self.transforms = transforms.Compose([
transforms.Resize([self.height, self.width]),
transforms.CenterCrop(self.crop_size),
transforms.ClipToTensor(div_255=False),
transforms.Normalize(mean=self.mean, std=self.std)
])
self.dataset_len = len(h5py.File(self.h5_path, 'r')[self.keys[0]][:])
def __init__(self,
h5_file,
test_txt,
test_mask_dir,
segment_len=16,
ten_crop=False,
height=256,
width=340,
crop_size=224):
self.h5_path = h5_file
# self.keys = sorted(list(h5py.File(self.h5_path, 'r').keys()))
self.test_txt = test_txt
self.segment_len = segment_len
self.ten_crop = ten_crop
self.crop_size = crop_size
self.test_mask_dir = test_mask_dir
self.mean = [128, 128, 128]
self.std = [128, 128, 128]
self.height = height
self.width = width
self.test_dict_annotation()
if ten_crop:
self.ten_crop_aug = transforms.Compose([
transforms.Resize([self.height, self.width]),
transforms.ClipToTensor(div_255=False),
transforms.Normalize(mean=self.mean, std=self.std),
transforms.TenCropTensor(self.crop_size)
])
self.transforms = transforms.Compose([
transforms.Resize([240, 320]),
# transforms.CenterCrop(self.crop_size),
transforms.ClipToTensor(div_255=False),
transforms.Normalize(mean=self.mean, std=self.std)
])
self.dataset_len = len(h5py.File(self.h5_path, 'r')[self.keys[0]][:])
def __init__(self,
h5_file,
train_txt,
pseudo_labels,
clip_num=8,
segment_len=16,
type='Normal',
rgb_diff=False,
hard_label=False,
score_segment_len=16,
continuous_sampling=False):
self.h5_path = h5_file
self.pseudo_labels = np.load(pseudo_labels, allow_pickle=True).tolist()
self.keys = sorted(list(h5py.File(self.h5_path, 'r').keys()))
self.clip_num = clip_num
self.dataset_len = len(h5py.File(self.h5_path, 'r')[self.keys[0]][:])
self.segment_len = segment_len
self.rgb_diff = rgb_diff
self.hard_label = hard_label
self.score_segment_len = score_segment_len
self.continuous_sampling = continuous_sampling
self.train_txt = train_txt
# self.mean =torch.from_numpy(np.load('/mnt/sdd/jiachang/c3d_train01_16_128_171_mean.npy'))
self.mean = [90.25, 97.66, 101.41]
self.std = [1, 1, 1]
self.get_vid_names_dict()
self.type = type
if self.type == 'Normal':
self.selected_keys = list(self.norm_vid_names_dict.keys())
self.selected_dict = self.norm_vid_names_dict
else:
self.selected_keys = list(self.abnorm_vid_names_dict.keys())
self.selected_dict = self.abnorm_vid_names_dict
self.transforms = transforms.Compose([
transforms.Resize((128, 171)),
# transforms.RandomCrop((112,112)),
transforms.MultiScaleCrop(112, [1.0, 0.8],
max_distort=1,
fix_crop=True),
transforms.RandomHorizontalFlip(),
transforms.RandomGrayScale(),
transforms.ClipToTensor(div_255=False),
transforms.Normalize(self.mean, self.std)
])