(self, path: str, uuid: str)

(self, label: str)

(self, labels: List[str])

(self)

(self, num_classes: int, eps: float = 0.1, reduction: str='sum')

(self, reduction: str)

(self, metric: str)

(self, margin='soft', batch_hard=True, reduction='mean')

(self, btype: str, pretrained: bool = True, last_stride: int = 1)

(self, num_classes, feat_in: int)

(self)

(self, gate_channel, reduction_ratio=16, num_layers=1)

(self, gate_channel, reduction_ratio=16, dilation_conv_num=2, dilation_val=4)

(self, inplanes, planes, stride=1, downsample=None)

(self, inplanes, planes, stride=1, downsample=None)

(self, width, height, interpolation=3)

(self, data_source, p: int, k: int)

(self, size: int = 4)

(self, size: int = 4)

(self, size: int = 4)

(self, num_images=4)

(self, dataset, spatial_transform=None, temporal_transform=

(self, perform_x2i, perform_x2v, augment_gallery)

(self, root='/data/datasets/', min_seq_len=0)

(self, root='/data/datasets/', min_seq_len=0)

(self, root='/data/datasets/', min_seq_len=0)

(self)

(self)

(self)

(self, value: float)

(self, x: torch.Tensor)

(self, reinitl4: bool, reinitl3: bool)

(self, hparams: dict, metrics: dict)

(self, teacher_logits: torch.Tensor, student_logits: torch.Tensor)

(self, teacher_logits: torch.Tensor, student_logits: torch.Tensor)

(self, teacher_embs: torch.Tensor, student_embs: torch.Tensor)

(self, feat, id=None, pos_mask=None, neg_mask=None, mode='id', dis_func='eu', n_dis=0)

(self, x: torch.Tensor)

(self, feats)

(self, x: torch.Tensor, return_logits: bool = False)

(self, x: torch.Tensor)

(self, x)

(self, in_tensor)

(self, in_tensor)

(self, in_tensor)

(self, x)

(self, x)

(self, x)

()

(self)

()

(worker_id)

(x: torch.Tensor)

(self)

(self)

(set, n)

(self, is_teacher: bool)

(v)

(img_paths, image_loader)

(m)

(m)

(self, image: np.ndarray, epoch: int, name: str)