Source code for mmseg.models.backbones.mae
# Copyright (c) OpenMMLab. All rights reserved.import math
import math
import torch
import torch.nn as nn
from mmcv.cnn.utils.weight_init import (constant_init, kaiming_init,
trunc_normal_)
from mmcv.runner import ModuleList, _load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmseg.utils import get_root_logger
from ..builder import BACKBONES
from .beit import BEiT, BEiTAttention, BEiTTransformerEncoderLayer
class MAEAttention(BEiTAttention):
"""Multi-head self-attention with relative position bias used in MAE.
This module is different from ``BEiTAttention`` by initializing the
relative bias table with zeros.
"""
def init_weights(self):
"""Initialize relative position bias with zeros."""
# As MAE initializes relative position bias as zeros and this class
# inherited from BEiT which initializes relative position bias
# with `trunc_normal`, `init_weights` here does
# nothing and just passes directly
pass
class MAETransformerEncoderLayer(BEiTTransformerEncoderLayer):
"""Implements one encoder layer in Vision Transformer.
This module is different from ``BEiTTransformerEncoderLayer`` by replacing
``BEiTAttention`` with ``MAEAttention``.
"""
def build_attn(self, attn_cfg):
self.attn = MAEAttention(**attn_cfg)
[docs]@BACKBONES.register_module()
class MAE(BEiT):
"""VisionTransformer with support for patch.
Args:
img_size (int | tuple): Input image size. Default: 224.
patch_size (int): The patch size. Default: 16.
in_channels (int): Number of input channels. Default: 3.
embed_dims (int): embedding dimension. Default: 768.
num_layers (int): depth of transformer. Default: 12.
num_heads (int): number of attention heads. Default: 12.
mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
Default: 4.
out_indices (list | tuple | int): Output from which stages.
Default: -1.
attn_drop_rate (float): The drop out rate for attention layer.
Default 0.0
drop_path_rate (float): stochastic depth rate. Default 0.0.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN')
act_cfg (dict): The activation config for FFNs.
Default: dict(type='GELU').
patch_norm (bool): Whether to add a norm in PatchEmbed Block.
Default: False.
final_norm (bool): Whether to add a additional layer to normalize
final feature map. Default: False.
num_fcs (int): The number of fully-connected layers for FFNs.
Default: 2.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only. Default: False.
pretrained (str, optional): model pretrained path. Default: None.
init_values (float): Initialize the values of Attention and FFN
with learnable scaling. Defaults to 0.1.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None.
"""
def __init__(self,
img_size=224,
patch_size=16,
in_channels=3,
embed_dims=768,
num_layers=12,
num_heads=12,
mlp_ratio=4,
out_indices=-1,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_cfg=dict(type='LN'),
act_cfg=dict(type='GELU'),
patch_norm=False,
final_norm=False,
num_fcs=2,
norm_eval=False,
pretrained=None,
init_values=0.1,
init_cfg=None):
super(MAE, self).__init__(
img_size=img_size,
patch_size=patch_size,
in_channels=in_channels,
embed_dims=embed_dims,
num_layers=num_layers,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
out_indices=out_indices,
qv_bias=False,
attn_drop_rate=attn_drop_rate,
drop_path_rate=drop_path_rate,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
patch_norm=patch_norm,
final_norm=final_norm,
num_fcs=num_fcs,
norm_eval=norm_eval,
pretrained=pretrained,
init_values=init_values,
init_cfg=init_cfg)
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))
self.num_patches = self.patch_shape[0] * self.patch_shape[1]
self.pos_embed = nn.Parameter(
torch.zeros(1, self.num_patches + 1, embed_dims))
def _build_layers(self):
dpr = [
x.item()
for x in torch.linspace(0, self.drop_path_rate, self.num_layers)
]
self.layers = ModuleList()
for i in range(self.num_layers):
self.layers.append(
MAETransformerEncoderLayer(
embed_dims=self.embed_dims,
num_heads=self.num_heads,
feedforward_channels=self.mlp_ratio * self.embed_dims,
attn_drop_rate=self.attn_drop_rate,
drop_path_rate=dpr[i],
num_fcs=self.num_fcs,
bias=True,
act_cfg=self.act_cfg,
norm_cfg=self.norm_cfg,
window_size=self.patch_shape,
init_values=self.init_values))
[docs] def fix_init_weight(self):
"""Rescale the initialization according to layer id.
This function is copied from https://github.com/microsoft/unilm/blob/master/beit/modeling_pretrain.py. # noqa: E501
Copyright (c) Microsoft Corporation
Licensed under the MIT License
"""
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.layers):
rescale(layer.attn.proj.weight.data, layer_id + 1)
rescale(layer.ffn.layers[1].weight.data, layer_id + 1)
[docs] def init_weights(self):
def _init_weights(m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
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)
self.apply(_init_weights)
self.fix_init_weight()
if (isinstance(self.init_cfg, dict)
and self.init_cfg.get('type') == 'Pretrained'):
logger = get_root_logger()
checkpoint = _load_checkpoint(
self.init_cfg['checkpoint'], logger=logger, map_location='cpu')
state_dict = self.resize_rel_pos_embed(checkpoint)
state_dict = self.resize_abs_pos_embed(state_dict)
self.load_state_dict(state_dict, False)
elif self.init_cfg is not None:
super(MAE, self).init_weights()
else:
# We only implement the 'jax_impl' initialization implemented at
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501
# Copyright 2019 Ross Wightman
# Licensed under the Apache License, Version 2.0 (the "License")
trunc_normal_(self.cls_token, std=.02)
for n, m in self.named_modules():
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
if 'ffn' in n:
nn.init.normal_(m.bias, mean=0., std=1e-6)
else:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
kaiming_init(m, mode='fan_in', bias=0.)
elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
constant_init(m, val=1.0, bias=0.)
def resize_abs_pos_embed(self, state_dict):
if 'pos_embed' in state_dict:
pos_embed_checkpoint = state_dict['pos_embed']
embedding_size = pos_embed_checkpoint.shape[-1]
num_extra_tokens = self.pos_embed.shape[-2] - self.num_patches
# height (== width) for the checkpoint position embedding
orig_size = int(
(pos_embed_checkpoint.shape[-2] - num_extra_tokens)**0.5)
# height (== width) for the new position embedding
new_size = int(self.num_patches**0.5)
# class_token and dist_token are kept unchanged
if orig_size != new_size:
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size,
embedding_size).permute(
0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens,
size=(new_size, new_size),
mode='bicubic',
align_corners=False)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
state_dict['pos_embed'] = new_pos_embed
return state_dict
[docs] def forward(self, inputs):
B = inputs.shape[0]
x, hw_shape = self.patch_embed(inputs)
# stole cls_tokens impl from Phil Wang, thanks
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x = x + self.pos_embed
outs = []
for i, layer in enumerate(self.layers):
x = layer(x)
if i == len(self.layers) - 1:
if self.final_norm:
x = self.norm1(x)
if i in self.out_indices:
out = x[:, 1:]
B, _, C = out.shape
out = out.reshape(B, hw_shape[0], hw_shape[1],
C).permute(0, 3, 1, 2).contiguous()
outs.append(out)
return tuple(outs)