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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Sequence, Tuple, Union
import torch
from mmpretrain.models import HiViT, VisionTransformer
from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from ..utils import build_2d_sincos_position_embedding
from .base import BaseSelfSupervisor
@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.
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
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,
out_type: str = 'raw',
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,
out_type=out_type,
with_cls_token=True,
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]
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)
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
def forward(
self,
x: torch.Tensor,
mask: Optional[bool] = True
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""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.
Args:
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:
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.
"""
if mask is None or False:
return super().forward(x)
else:
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)
@MODELS.register_module()
class MAE(BaseSelfSupervisor):
"""MAE.
Implementation of `Masked Autoencoders Are Scalable Vision Learners
<https://arxiv.org/abs/2111.06377>`_.
"""
def extract_feat(self, inputs: torch.Tensor):
return self.backbone(inputs, mask=None)
def loss(self, inputs: torch.Tensor, data_samples: List[DataSample],
**kwargs) -> Dict[str, torch.Tensor]:
"""The forward function in training.
Args:
inputs (torch.Tensor): The input images.
data_samples (List[DataSample]): All elements required
during the forward function.
Returns:
Dict[str, torch.Tensor]: A dictionary of loss components.
"""
# ids_restore: the same as that in original repo, which is used
# to recover the original order of tokens in decoder.
latent, mask, ids_restore = self.backbone(inputs)
pred = self.neck(latent, ids_restore)
loss = self.head.loss(pred, inputs, mask)
losses = dict(loss=loss)
return losses
@MODELS.register_module()
class MAEHiViT(HiViT):
"""HiViT for MAE pre-training.
A PyTorch implement of: `HiViT: A Simple and More Efficient Design
of Hierarchical Vision Transformer <https://arxiv.org/abs/2205.14949>`_.
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
Defaults to 4, to downsample 4x at the first stage
inner_patches (int): The inner patches within a token
Defaults to 4
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')``.
ape (bool): the absolute position embedding
rpe (bool): the relative position embedding
Defaults to False
layer_scale_init_value (float): the layer scale init value
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,
inner_patches: int = 4,
out_indices: Union[list, int] = [23],
drop_rate: float = 0.0,
drop_path_rate: float = 0.0,
norm_cfg: dict = dict(type='LN', eps=1e-6),
ape: bool = True,
rpe: bool = False,
layer_scale_init_value: float = 0.0,
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,
inner_patches=inner_patches,
out_indices=out_indices,
drop_rate=drop_rate,
drop_path_rate=drop_path_rate,
norm_cfg=norm_cfg,
ape=ape,
rpe=rpe,
layer_scale_init_value=layer_scale_init_value,
init_cfg=init_cfg)
self.pos_embed.requires_grad = False
self.mask_ratio = mask_ratio
self.num_patches = self.patch_embed.num_patches
def init_weights(self) -> None:
"""Initialize position embedding, patch embedding."""
super().apply(self._init_weights)
pos_embed = build_2d_sincos_position_embedding(
int(self.num_patches**.5),
self.pos_embed.shape[-1],
cls_token=False)
self.pos_embed.data.copy_(pos_embed.float())
w = self.patch_embed.proj.weight.data
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
def masking_id(
self, batch_size,
mask_ratio) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Generate the mask for MAE Pre-training.
Args:
batch_size: The batch size of input data
mask_ratio: The mask ratio of total patches.
Defaults to 0.75.
Returns:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: the ids
for the tokens retained, the ids to restore original image,
and the mask
"""
N, L = batch_size, self.pos_embed.size(1)
len_keep = int(L * (1 - mask_ratio))
noise = torch.rand(
N, L, device=self.pos_embed.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]
# generate the binary mask: 0 is keep, 1 is remove
mask = torch.ones([N, L], device=self.pos_embed.device)
mask[:, :ids_keep.size(1)] = 0
# unshuffle to get the binary mask
mask = torch.gather(mask, dim=1, index=ids_restore)
return ids_keep, ids_restore, mask
def forward(
self,
x: torch.Tensor,
mask: Optional[bool] = True
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""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.
Args:
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:
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.
"""
if mask is None or False:
return super().forward(x)
else:
B, C, H, W = x.shape
ids_keep, ids_restore, mask = self.masking_id(B, self.mask_ratio)
x = self.patch_embed(x)
x = torch.gather(
x,
dim=1,
index=ids_keep[:, :, None, None,
None].expand(-1, -1, *x.shape[2:]))
for blk in self.blocks[:-self.num_main_blocks]:
x = blk(x)
x = x[..., 0, 0, :]
if self.ape:
pos_embed = self.interpolate_pos_encoding(x, H, W)
pos_embed = torch.gather(
pos_embed.expand(B, -1, -1),
dim=1,
index=ids_keep[:, :, None].expand(-1, -1,
pos_embed.shape[2]),
)
x = x + pos_embed
x = self.pos_drop(x)
for blk in self.blocks[-self.num_main_blocks:]:
x = blk(x)
return (x, mask, ids_restore)
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