Source code for mmpretrain.models.necks.milan_neck
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Union
import torch
from torch import nn
from mmpretrain.registry import MODELS
from ..backbones.vision_transformer import TransformerEncoderLayer
from ..utils import PromptMultiheadAttention
from .mae_neck import MAEPretrainDecoder
class PromptTransformerEncoderLayer(TransformerEncoderLayer):
"""Prompt Transformer Encoder Layer for MILAN.
This module is specific for the prompt encoder in MILAN. It will not update
the visible tokens from the encoder.
Args:
embed_dims (int): The feature dimension.
num_heads (int): Parallel attention heads.
feedforward_channels (int): The hidden dimension for FFNs.
drop_rate (float): Probability of an element to be zeroed
after the feed forward layer. Defaults to 0.0.
attn_drop_rate (float): The drop out rate for attention layer.
Defaults to 0.0.
drop_path_rate (float): Stochastic depth rate. Defaults to 0.0.
num_fcs (int): The number of fully-connected layers for FFNs.
Defaults to 2.
qkv_bias (bool): Enable bias for qkv if True. Defaults to True.
act_cfg (dict): The activation config for FFNs.
Defaults to ``dict(type='GELU')``.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
batch_first (bool): Key, Query and Value are shape of
(batch, n, embed_dim)
or (n, batch, embed_dim). Defaults to False.
init_cfg (dict, optional): The Config for initialization.
Defaults to None.
"""
def __init__(self,
embed_dims: int,
num_heads: int,
feedforward_channels=int,
drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
num_fcs: int = 2,
qkv_bias: bool = True,
act_cfg: dict = dict(type='GELU'),
norm_cfg: dict = dict(type='LN'),
init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
super().__init__(
embed_dims=embed_dims,
num_heads=num_heads,
feedforward_channels=feedforward_channels,
drop_rate=drop_rate,
attn_drop_rate=attn_drop_rate,
drop_path_rate=drop_path_rate,
num_fcs=num_fcs,
qkv_bias=qkv_bias,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
init_cfg=init_cfg)
self.attn = PromptMultiheadAttention(
embed_dims=embed_dims,
num_heads=num_heads,
attn_drop=attn_drop_rate,
proj_drop=drop_rate,
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
qkv_bias=qkv_bias)
def forward(self, x: torch.Tensor, visible_tokens: torch.Tensor,
ids_restore: torch.Tensor) -> torch.Tensor:
"""Forward function for `PromptMultiheadAttention`.
Args:
x (torch.Tensor): Mask token features with shape N x L_m x C.
visible_tokens (torch.Tensor): The visible tokens features from
encoder with shape N x L_v x C.
ids_restore (torch.Tensor): The ids of all tokens in the original
image with shape N x L.
Returns:
torch Tensor: Output features with shape N x L x C.
"""
x = x + self.attn(self.norm1(x), visible_tokens, ids_restore)
x = self.ffn(self.norm2(x), identity=x)
return x
[docs]@MODELS.register_module()
class MILANPretrainDecoder(MAEPretrainDecoder):
"""Prompt decoder for MILAN.
This decoder is used in MILAN pretraining, which will not update these
visible tokens from the encoder.
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.
predict_feature_dim (int): The dimension of the feature to be
predicted. Defaults to 512.
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.
"""
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,
predict_feature_dim: int = 512,
mlp_ratio: int = 4,
norm_cfg: dict = dict(type='LN', eps=1e-6),
init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
super().__init__(
num_patches=num_patches,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
decoder_embed_dim=decoder_embed_dim,
decoder_depth=decoder_depth,
decoder_num_heads=decoder_num_heads,
mlp_ratio=mlp_ratio,
norm_cfg=norm_cfg,
init_cfg=init_cfg)
# map the dim of features from decoder to the dim compatible with
# that of CLIP
self.decoder_pred = nn.Linear(
decoder_embed_dim, predict_feature_dim, bias=True)
# use prompt transformer encoder layer, instead of the conventional
# transformer encoder layer
self.decoder_blocks = nn.ModuleList([
PromptTransformerEncoderLayer(
decoder_embed_dim,
decoder_num_heads,
int(mlp_ratio * decoder_embed_dim),
qkv_bias=True,
norm_cfg=norm_cfg) for _ in range(decoder_depth)
])
[docs] def forward(self, x: torch.Tensor, ids_restore: torch.Tensor,
ids_keep: torch.Tensor,
ids_dump: torch.Tensor) -> torch.Tensor:
"""Forward function.
Args:
x (torch.Tensor): The input features, which is of shape (N, L, C).
ids_restore (torch.Tensor): The indices to restore these tokens
to the original image.
ids_keep (torch.Tensor): The indices of tokens to be kept.
ids_dump (torch.Tensor): The indices of tokens to be masked.
Returns:
torch.Tensor: The reconstructed features, which is of shape
(N, L, 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
# split mask tokens and visible tokens
visible_tokens = torch.cat([
x[:, :1, :],
torch.gather(
x[:, 1:, :],
dim=1,
index=ids_keep.unsqueeze(-1).repeat(1, 1, x.shape[-1]))
],
dim=1)
x = torch.gather(
x[:, 1:, :],
dim=1,
index=ids_dump.unsqueeze(-1).repeat(1, 1, x.shape[-1]))
for blk in self.decoder_blocks:
x = blk(x, visible_tokens, ids_restore)
# full sequence recovery
x_ = torch.cat([visible_tokens[:, 1:, :], x], dim=1)
x_ = torch.gather(
x_,
dim=1,
index=ids_restore.unsqueeze(-1).repeat(1, 1,
x.shape[-1])) # unshuffle
x = torch.cat([visible_tokens[:, :1, :], x_], dim=1)
x = self.decoder_norm(x)
# predictor projection
x = self.decoder_pred(x)
return x