MAEViT

class mmpretrain.models.selfsup.MAEViT(arch='b', img_size=224, patch_size=16, out_indices=-1, drop_rate=0, drop_path_rate=0, norm_cfg={'eps': 1e-06, 'type': 'LN'}, final_norm=True, out_type='raw', interpolate_mode='bicubic', patch_cfg={}, layer_cfgs={}, mask_ratio=0.75, init_cfg=None)

Vision Transformer for MAE pre-training.

A PyTorch implement of: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. This module implements the patch masking in MAE and initialize the position embedding with sine-cosine position embedding.

Parameters:

• arch (str | dict) – Vision Transformer architecture Default: ‘b’

• img_size (int | tuple) – Input image size

• patch_size (int | tuple) – The patch size

• out_indices (Sequence | int) – Output from which stages. Defaults to -1, means the last stage.

• drop_rate (float) – Probability of an element to be zeroed. Defaults to 0.

• drop_path_rate (float) – stochastic depth rate. Defaults to 0.

• norm_cfg (dict) – Config dict for normalization layer. Defaults to dict(type='LN').

• final_norm (bool) – Whether to add a additional layer to normalize final feature map. Defaults to True.

• out_type (str) –

  The type of output features. Please choose from

  • "cls_token": The class token tensor with shape (B, C).

  • "featmap": The feature map tensor from the patch tokens with shape (B, C, H, W).

  • "avg_featmap": The global averaged feature map tensor with shape (B, C).

  • "raw": The raw feature tensor includes patch tokens and class tokens with shape (B, L, C).

  It only works without input mask. Defaults to "avg_featmap".

• interpolate_mode (str) – Select the interpolate mode for position embedding vector resize. Defaults to “bicubic”.

• patch_cfg (dict) – Configs of patch embedding. Defaults to an empty dict.

• layer_cfgs (Sequence | dict) – Configs of each transformer layer in encoder. Defaults to an empty dict.

• mask_ratio (bool) – The ratio of total number of patches to be masked. Defaults to 0.75.

• init_cfg (Union[List[dict], dict], optional) – Initialization config dict. Defaults to None.

forward(x, mask=True)

Generate features for masked images.

The function supports two kind of forward behaviors. If the mask is True, the function will generate mask to masking some patches randomly and get the hidden features for visible patches, which means the function will be executed as masked imagemodeling pre-training; if the mask is None or False, the forward function will call super().forward(), which extract features from images without mask.

Parameters:

• x (torch.Tensor) – Input images, which is of shape B x C x H x W.

• mask (bool, optional) – To indicate whether the forward function generating mask or not.

Returns:

Hidden features, mask and the ids to restore original image.

• x (torch.Tensor): hidden features, which is of shape B x (L * mask_ratio) x C.

• mask (torch.Tensor): mask used to mask image.

• ids_restore (torch.Tensor): ids to restore original image.

Return type:

Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

init_weights()

Initialize position embedding, patch embedding and cls token.

random_masking(x, mask_ratio=0.75)

Generate the mask for MAE Pre-training.

Parameters:

• x (torch.Tensor) – Image with data augmentation applied, which is of shape B x L x C.

• mask_ratio (float) – The mask ratio of total patches. Defaults to 0.75.

Returns:

masked image, mask and the ids to restore original image.

• x_masked (torch.Tensor): masked image.

• mask (torch.Tensor): mask used to mask image.

• ids_restore (torch.Tensor): ids to restore original image.

Return type:

Tuple[torch.Tensor, torch.Tensor, torch.Tensor]