Source code for mmpretrain.models.necks.cae_neck
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks import DropPath
from mmcv.cnn.bricks.transformer import FFN
from mmengine.model import BaseModule
from mmengine.model.weight_init import trunc_normal_
from mmpretrain.models.backbones.beit import BEiTTransformerEncoderLayer
from mmpretrain.registry import MODELS
from ..utils import CrossMultiheadAttention
class CAETransformerRegressorLayer(BaseModule):
"""Transformer layer for the regressor of CAE.
This module is different from conventional transformer encoder layer, for
its queries are the masked tokens, but its keys and values are the
concatenation of the masked and unmasked tokens.
Args:
embed_dims (int): The feature dimension.
num_heads (int): The number of heads in multi-head attention.
feedforward_channels (int): The hidden dimension of FFNs.
Defaults: 1024.
num_fcs (int, optional): The number of fully-connected layers in
FFNs. Default: 2.
qkv_bias (bool): If True, add a learnable bias to q, k, v.
Defaults to True.
qk_scale (float, optional): Override default qk scale of
``head_dim ** -0.5`` if set. Defaults to None.
drop_rate (float): The dropout rate. Defaults to 0.0.
attn_drop_rate (float): The drop out rate for attention output weights.
Defaults to 0.
drop_path_rate (float): Stochastic depth rate. Defaults to 0.
layer_scale_init_value (float): The init value of gamma.
Defaults to 0.0.
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')``.
"""
def __init__(
self,
embed_dims: int,
num_heads: int,
feedforward_channels: int,
num_fcs: int = 2,
qkv_bias: bool = False,
qk_scale: float = None,
drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
layer_scale_init_value: float = 0.0,
act_cfg: dict = dict(type='GELU'),
norm_cfg: dict = dict(type='LN', eps=1e-6)
) -> None:
super().__init__()
# NOTE: cross attention
_, self.norm1_q_cross = build_norm_layer(
norm_cfg, embed_dims, postfix=2)
_, self.norm1_k_cross = build_norm_layer(
norm_cfg, embed_dims, postfix=2)
_, self.norm1_v_cross = build_norm_layer(
norm_cfg, embed_dims, postfix=2)
_, self.norm2_cross = build_norm_layer(norm_cfg, embed_dims, postfix=2)
self.cross_attn = CrossMultiheadAttention(
embed_dims,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop_rate,
proj_drop=drop_rate)
self.ffn = FFN(
embed_dims=embed_dims,
feedforward_channels=feedforward_channels,
num_fcs=num_fcs,
ffn_drop=drop_rate,
dropout_layer=None,
act_cfg=act_cfg,
add_identity=False)
self.drop_path = DropPath(drop_prob=drop_path_rate)
if layer_scale_init_value > 0:
self.gamma_1_cross = nn.Parameter(
layer_scale_init_value * torch.ones((embed_dims)),
requires_grad=True)
self.gamma_2_cross = nn.Parameter(
layer_scale_init_value * torch.ones((embed_dims)),
requires_grad=True)
else:
self.gamma_1_cross = nn.Parameter(
torch.ones((embed_dims)), requires_grad=False)
self.gamma_2_cross = nn.Parameter(
torch.ones((embed_dims)), requires_grad=False)
def forward(self, x_q: torch.Tensor, x_kv: torch.Tensor,
pos_q: torch.Tensor, pos_k: torch.Tensor) -> torch.Tensor:
"""Forward function."""
x = x_q + self.drop_path(self.gamma_1_cross * self.cross_attn(
self.norm1_q_cross(x_q + pos_q),
k=self.norm1_k_cross(x_kv + pos_k),
v=self.norm1_v_cross(x_kv)))
x = self.norm2_cross(x)
x = x + self.drop_path(self.gamma_2_cross * self.ffn(x))
return x
[docs]@MODELS.register_module()
class CAENeck(BaseModule):
"""Neck for CAE Pre-training.
This module construct the latent prediction regressor and the decoder
for the latent prediction and final prediction.
Args:
num_classes (int): The number of classes for final prediction. Defaults
to 8192.
embed_dims (int): The embed dims of latent feature in regressor and
decoder. Defaults to 768.
regressor_depth (int): The number of regressor blocks. Defaults to 6.
decoder_depth (int): The number of decoder blocks. Defaults to 8.
num_heads (int): The number of head in multi-head attention. Defaults
to 12.
mlp_ratio (int): The expand ratio of latent features in MLP. defaults
to 4.
qkv_bias (bool): Whether or not to use qkv bias. Defaults to True.
qk_scale (float, optional): The scale applied to the results of qk.
Defaults to None.
drop_rate (float): The dropout rate. Defaults to 0.
attn_drop_rate (float): The dropout rate in attention block. Defaults
to 0.
norm_cfg (dict): The config of normalization layer. Defaults to
dict(type='LN', eps=1e-6).
layer_scale_init_value (float, optional): The init value of gamma.
Defaults to None.
mask_tokens_num (int): The number of mask tokens. Defaults to 75.
init_cfg (dict, optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
num_classes: int = 8192,
embed_dims: int = 768,
regressor_depth: int = 6,
decoder_depth: int = 8,
num_heads: int = 12,
mlp_ratio: int = 4,
qkv_bias: bool = True,
qk_scale: float = None,
drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
norm_cfg: dict = dict(type='LN', eps=1e-6),
layer_scale_init_value: float = None,
mask_tokens_num: int = 75,
init_cfg: dict = None) -> None:
super().__init__(init_cfg=init_cfg)
self.num_features = self.embed_dim = embed_dims
self.mask_token_num = mask_tokens_num
# regressor
regressor_drop_path_rates = [
x.item()
for x in torch.linspace(0, drop_path_rate, regressor_depth)
]
self.regressors = nn.ModuleList([
CAETransformerRegressorLayer(
embed_dims=embed_dims,
num_heads=num_heads,
feedforward_channels=mlp_ratio * embed_dims,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop_rate=drop_rate,
attn_drop_rate=attn_drop_rate,
drop_path_rate=regressor_drop_path_rates[i],
norm_cfg=norm_cfg,
layer_scale_init_value=layer_scale_init_value)
for i in range(regressor_depth)
])
# decoder
decoder_drop_path_rates = [
x.item() for x in torch.linspace(0, drop_path_rate, decoder_depth)
]
self.decoders = nn.ModuleList([
BEiTTransformerEncoderLayer(
embed_dims=embed_dims,
num_heads=num_heads,
feedforward_channels=mlp_ratio * embed_dims,
layer_scale_init_value=layer_scale_init_value,
window_size=None,
# setting `use_rel_pos_bias` to False ignores the `window_size`
use_rel_pos_bias=False,
drop_rate=drop_rate,
attn_drop_rate=attn_drop_rate,
drop_path_rate=decoder_drop_path_rates[i],
norm_cfg=norm_cfg) for i in range(decoder_depth)
])
_, self.norm_regressor = build_norm_layer(
norm_cfg, embed_dims, postfix=2)
_, self.norm_decoder = build_norm_layer(
norm_cfg, embed_dims, postfix=2)
self.head = nn.Linear(
embed_dims, num_classes) if num_classes > 0 else nn.Identity()
self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dims))
[docs] def init_weights(self) -> None:
"""Initialization."""
super().init_weights()
self.apply(self._init_weights)
trunc_normal_(self.mask_token, std=0.02)
trunc_normal_(self.head.weight, std=0.02)
def _init_weights(self, m: nn.Module) -> None:
"""Initialization."""
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
[docs] def forward(
self, x_unmasked: torch.Tensor, pos_embed_masked: torch.Tensor,
pos_embed_unmasked: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Get the latent prediction and final prediction.
Args:
x_unmasked (torch.Tensor): Features of unmasked tokens.
pos_embed_masked (torch.Tensor): Position embedding of masked
tokens.
pos_embed_unmasked (torch.Tensor): Position embedding of unmasked
tokens.
Returns:
Tuple[torch.Tensor, torch.Tensor]:
- ``logits``: Final prediction.
- ``latent_pred``: Latent prediction.
"""
x_masked = self.mask_token.expand(x_unmasked.shape[0],
self.mask_token_num, -1)
# regressor
for regressor in self.regressors:
x_masked = regressor(
x_masked, torch.cat([x_unmasked, x_masked], dim=1),
pos_embed_masked,
torch.cat([pos_embed_unmasked, pos_embed_masked], dim=1))
x_masked = self.norm_regressor(x_masked)
latent_pred = x_masked
# decoder
x_masked = x_masked + pos_embed_masked
for decoder in self.decoders:
x_masked = decoder(x_masked, rel_pos_bias=None)
x_masked = self.norm_decoder(x_masked)
logits = self.head(x_masked)
return logits, latent_pred