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import math
import warnings
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention
from mmengine.logging import print_log
from mmengine.model import BaseModule, ModuleList
from mmengine.model.weight_init import (constant_init, kaiming_init,
trunc_normal_)
from mmengine.runner.checkpoint import CheckpointLoader, load_state_dict
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.utils import _pair as to_2tuple
from mmpl.registry import MODELS
@MODELS.register_module()
class TransformerEncoderLayer(BaseModule):
"""Implements one encoder layer in Vision Transformer.
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. Default: 0.0.
attn_drop_rate (float): The drop out rate for attention layer.
Default: 0.0.
drop_path_rate (float): stochastic depth rate. Default 0.0.
num_fcs (int): The number of fully-connected layers for FFNs.
Default: 2.
qkv_bias (bool): enable bias for qkv if True. Default: True
act_cfg (dict): The activation config for FFNs.
Default: dict(type='GELU').
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN').
batch_first (bool): Key, Query and Value are shape of
(batch, n, embed_dim)
or (n, batch, embed_dim). Default: True.
with_cp (bool): Use checkpoint or not. Using checkpoint will save
some memory while slowing down the training speed. Default: False.
"""
def __init__(self,
embed_dims,
num_heads,
feedforward_channels,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
num_fcs=2,
qkv_bias=True,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='LN'),
batch_first=True,
attn_cfg=dict(),
ffn_cfg=dict(),
with_cp=False):
super().__init__()
self.norm1_name, norm1 = build_norm_layer(
norm_cfg, embed_dims, postfix=1)
self.add_module(self.norm1_name, norm1)
attn_cfg.update(
dict(
embed_dims=embed_dims,
num_heads=num_heads,
attn_drop=attn_drop_rate,
proj_drop=drop_rate,
batch_first=batch_first,
bias=qkv_bias))
self.build_attn(attn_cfg)
self.norm2_name, norm2 = build_norm_layer(
norm_cfg, embed_dims, postfix=2)
self.add_module(self.norm2_name, norm2)
ffn_cfg.update(
dict(
embed_dims=embed_dims,
feedforward_channels=feedforward_channels,
num_fcs=num_fcs,
ffn_drop=drop_rate,
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate)
if drop_path_rate > 0 else None,
act_cfg=act_cfg))
self.build_ffn(ffn_cfg)
self.with_cp = with_cp
def build_attn(self, attn_cfg):
self.attn = MultiheadAttention(**attn_cfg)
def build_ffn(self, ffn_cfg):
self.ffn = FFN(**ffn_cfg)
@property
def norm1(self):
return getattr(self, self.norm1_name)
@property
def norm2(self):
return getattr(self, self.norm2_name)
def forward(self, x):
def _inner_forward(x):
x = self.attn(self.norm1(x), identity=x)
x = self.ffn(self.norm2(x), identity=x)
return x
if self.with_cp and x.requires_grad:
x = cp.checkpoint(_inner_forward, x)
else:
x = _inner_forward(x)
return x
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