mmselfsup.models.backbones.mae_vit 源代码
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Sequence, Tuple, Union
import torch
from mmcls.models import VisionTransformer
from mmselfsup.registry import MODELS
from ..utils import build_2d_sincos_position_embedding
[文档]@MODELS.register_module()
class MAEViT(VisionTransformer):
"""Vision Transformer for MAE pre-training.
A PyTorch implement of: `An Image is Worth 16x16 Words: Transformers
for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_.
This module implements the patch masking in MAE and initialize the
position embedding with sine-cosine position embedding.
Args:
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.
output_cls_token (bool): Whether output the cls_token. If set True,
`with_cls_token` must be True. Defaults to True.
interpolate_mode (str): Select the interpolate mode for position
embeding vector resize. Defaults to "bicubic".
patch_cfg (dict): Configs of patch embeding. 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.
"""
def __init__(self,
arch: Union[str, dict] = 'b',
img_size: int = 224,
patch_size: int = 16,
out_indices: Union[Sequence, int] = -1,
drop_rate: float = 0,
drop_path_rate: float = 0,
norm_cfg: dict = dict(type='LN', eps=1e-6),
final_norm: bool = True,
output_cls_token: bool = True,
interpolate_mode: str = 'bicubic',
patch_cfg: dict = dict(),
layer_cfgs: dict = dict(),
mask_ratio: float = 0.75,
init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
super().__init__(
arch=arch,
img_size=img_size,
patch_size=patch_size,
out_indices=out_indices,
drop_rate=drop_rate,
drop_path_rate=drop_path_rate,
norm_cfg=norm_cfg,
final_norm=final_norm,
output_cls_token=output_cls_token,
interpolate_mode=interpolate_mode,
patch_cfg=patch_cfg,
layer_cfgs=layer_cfgs,
init_cfg=init_cfg)
# position embedding is not learnable during pretraining
self.pos_embed.requires_grad = False
self.mask_ratio = mask_ratio
self.num_patches = self.patch_resolution[0] * self.patch_resolution[1]
[文档] def init_weights(self) -> None:
"""Initialize position embedding, patch embedding and cls token."""
super().init_weights()
pos_embed = build_2d_sincos_position_embedding(
int(self.num_patches**.5),
self.pos_embed.shape[-1],
cls_token=True)
self.pos_embed.data.copy_(pos_embed.float())
w = self.patch_embed.projection.weight.data
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
torch.nn.init.normal_(self.cls_token, std=.02)
[文档] def random_masking(
self,
x: torch.Tensor,
mask_ratio: float = 0.75
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Generate the mask for MAE Pre-training.
Args:
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:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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.
"""
N, L, D = x.shape # batch, length, dim
len_keep = int(L * (1 - mask_ratio))
noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
# sort noise for each sample
ids_shuffle = torch.argsort(
noise, dim=1) # ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
x_masked = torch.gather(
x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
# generate the binary mask: 0 is keep, 1 is remove
mask = torch.ones([N, L], device=x.device)
mask[:, :len_keep] = 0
# unshuffle to get the binary mask
mask = torch.gather(mask, dim=1, index=ids_restore)
return x_masked, mask, ids_restore
[文档] def forward(
self, x: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Generate features for masked images.
This function generates mask and masks some patches randomly and get
the hidden features for visible patches.
Args:
x (torch.Tensor): Input images, which is of shape B x C x H x W.
Returns:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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.
"""
B = x.shape[0]
x = self.patch_embed(x)[0]
# add pos embed w/o cls token
x = x + self.pos_embed[:, 1:, :]
# masking: length -> length * mask_ratio
x, mask, ids_restore = self.random_masking(x, self.mask_ratio)
# append cls token
cls_token = self.cls_token + self.pos_embed[:, :1, :]
cls_tokens = cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
for _, layer in enumerate(self.layers):
x = layer(x)
# Use final norm
x = self.norm1(x)
return (x, mask, ids_restore)