Source code for mmpretrain.models.necks.mae_neck
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmengine.model import BaseModule
from mmpretrain.registry import MODELS
from ..backbones.vision_transformer import TransformerEncoderLayer
from ..utils import build_2d_sincos_position_embedding
[docs]@MODELS.register_module()
class MAEPretrainDecoder(BaseModule):
"""Decoder for MAE Pre-training.
Some of the code is borrowed from `https://github.com/facebookresearch/mae`. # noqa
Args:
num_patches (int): The number of total patches. Defaults to 196.
patch_size (int): Image patch size. Defaults to 16.
in_chans (int): The channel of input image. Defaults to 3.
embed_dim (int): Encoder's embedding dimension. Defaults to 1024.
decoder_embed_dim (int): Decoder's embedding dimension.
Defaults to 512.
decoder_depth (int): The depth of decoder. Defaults to 8.
decoder_num_heads (int): Number of attention heads of decoder.
Defaults to 16.
mlp_ratio (int): Ratio of mlp hidden dim to decoder's embedding dim.
Defaults to 4.
norm_cfg (dict): Normalization layer. Defaults to LayerNorm.
init_cfg (Union[List[dict], dict], optional): Initialization config
dict. Defaults to None.
Example:
>>> from mmpretrain.models import MAEPretrainDecoder
>>> import torch
>>> self = MAEPretrainDecoder()
>>> self.eval()
>>> inputs = torch.rand(1, 50, 1024)
>>> ids_restore = torch.arange(0, 196).unsqueeze(0)
>>> level_outputs = self.forward(inputs, ids_restore)
>>> print(tuple(level_outputs.shape))
(1, 196, 768)
"""
def __init__(self,
num_patches: int = 196,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 1024,
decoder_embed_dim: int = 512,
decoder_depth: int = 8,
decoder_num_heads: int = 16,
mlp_ratio: int = 4,
norm_cfg: dict = dict(type='LN', eps=1e-6),
predict_feature_dim: Optional[float] = None,
init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
super().__init__(init_cfg=init_cfg)
self.num_patches = num_patches
# used to convert the dim of features from encoder to the dim
# compatible with that of decoder
self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)
self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
# create new position embedding, different from that in encoder
# and is not learnable
self.decoder_pos_embed = nn.Parameter(
torch.zeros(1, self.num_patches + 1, decoder_embed_dim),
requires_grad=False)
self.decoder_blocks = nn.ModuleList([
TransformerEncoderLayer(
decoder_embed_dim,
decoder_num_heads,
int(mlp_ratio * decoder_embed_dim),
qkv_bias=True,
norm_cfg=norm_cfg) for _ in range(decoder_depth)
])
self.decoder_norm_name, decoder_norm = build_norm_layer(
norm_cfg, decoder_embed_dim, postfix=1)
self.add_module(self.decoder_norm_name, decoder_norm)
# Used to map features to pixels
if predict_feature_dim is None:
predict_feature_dim = patch_size**2 * in_chans
self.decoder_pred = nn.Linear(
decoder_embed_dim, predict_feature_dim, bias=True)
[docs] def init_weights(self) -> None:
"""Initialize position embedding and mask token of MAE decoder."""
super().init_weights()
decoder_pos_embed = build_2d_sincos_position_embedding(
int(self.num_patches**.5),
self.decoder_pos_embed.shape[-1],
cls_token=True)
self.decoder_pos_embed.data.copy_(decoder_pos_embed.float())
torch.nn.init.normal_(self.mask_token, std=.02)
@property
def decoder_norm(self):
"""The normalization layer of decoder."""
return getattr(self, self.decoder_norm_name)
[docs] def forward(self, x: torch.Tensor,
ids_restore: torch.Tensor) -> torch.Tensor:
"""The forward function.
The process computes the visible patches' features vectors and the mask
tokens to output feature vectors, which will be used for
reconstruction.
Args:
x (torch.Tensor): hidden features, which is of shape
B x (L * mask_ratio) x C.
ids_restore (torch.Tensor): ids to restore original image.
Returns:
torch.Tensor: The reconstructed feature vectors, which is of
shape B x (num_patches) x C.
"""
# embed tokens
x = self.decoder_embed(x)
# append mask tokens to sequence
mask_tokens = self.mask_token.repeat(
x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)
x_ = torch.gather(
x_,
dim=1,
index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))
x = torch.cat([x[:, :1, :], x_], dim=1)
# add pos embed
x = x + self.decoder_pos_embed
# apply Transformer blocks
for blk in self.decoder_blocks:
x = blk(x)
x = self.decoder_norm(x)
# predictor projection
x = self.decoder_pred(x)
# remove cls token
x = x[:, 1:, :]
return x
[docs]@MODELS.register_module()
class ClsBatchNormNeck(BaseModule):
"""Normalize cls token across batch before head.
This module is proposed by MAE, when running linear probing.
Args:
input_features (int): The dimension of features.
affine (bool): a boolean value that when set to ``True``, this module
has learnable affine parameters. Defaults to False.
eps (float): a value added to the denominator for numerical stability.
Defaults to 1e-6.
init_cfg (Dict or List[Dict], optional): Config dict for weight
initialization. Defaults to None.
"""
def __init__(self,
input_features: int,
affine: bool = False,
eps: float = 1e-6,
init_cfg: Optional[Union[dict, List[dict]]] = None) -> None:
super().__init__(init_cfg)
self.bn = nn.BatchNorm1d(input_features, affine=affine, eps=eps)
[docs] def forward(
self,
inputs: Tuple[List[torch.Tensor]]) -> Tuple[List[torch.Tensor]]:
"""The forward function."""
# Only apply batch norm to cls_token
inputs = [self.bn(input_) for input_ in inputs]
return tuple(inputs)