Source code for mmseg.models.backbones.beit
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.utils.weight_init import (constant_init, kaiming_init,
trunc_normal_)
from mmcv.runner import BaseModule, ModuleList, _load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.utils import _pair as to_2tuple
from mmseg.utils import get_root_logger
from ..builder import BACKBONES
from ..utils import PatchEmbed
from .vit import TransformerEncoderLayer as VisionTransformerEncoderLayer
try:
from scipy import interpolate
except ImportError:
interpolate = None
class BEiTAttention(BaseModule):
"""Window based multi-head self-attention (W-MSA) module with relative
position bias.
Args:
embed_dims (int): Number of input channels.
num_heads (int): Number of attention heads.
window_size (tuple[int]): The height and width of the window.
bias (bool): The option to add leanable bias for q, k, v. If bias is
True, it will add leanable bias. If bias is 'qv_bias', it will only
add leanable bias for q, v. If bias is False, it will not add bias
for q, k, v. Default to 'qv_bias'.
qk_scale (float | None, optional): Override default qk scale of
head_dim ** -0.5 if set. Default: None.
attn_drop_rate (float): Dropout ratio of attention weight.
Default: 0.0
proj_drop_rate (float): Dropout ratio of output. Default: 0.
init_cfg (dict | None, optional): The Config for initialization.
Default: None.
"""
def __init__(self,
embed_dims,
num_heads,
window_size,
bias='qv_bias',
qk_scale=None,
attn_drop_rate=0.,
proj_drop_rate=0.,
init_cfg=None,
**kwargs):
super().__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
self.num_heads = num_heads
head_embed_dims = embed_dims // num_heads
self.bias = bias
self.scale = qk_scale or head_embed_dims**-0.5
qkv_bias = bias
if bias == 'qv_bias':
self._init_qv_bias()
qkv_bias = False
self.window_size = window_size
self._init_rel_pos_embedding()
self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop_rate)
self.proj = nn.Linear(embed_dims, embed_dims)
self.proj_drop = nn.Dropout(proj_drop_rate)
def _init_qv_bias(self):
self.q_bias = nn.Parameter(torch.zeros(self.embed_dims))
self.v_bias = nn.Parameter(torch.zeros(self.embed_dims))
def _init_rel_pos_embedding(self):
Wh, Ww = self.window_size
# cls to token & token 2 cls & cls to cls
self.num_relative_distance = (2 * Wh - 1) * (2 * Ww - 1) + 3
# relative_position_bias_table shape is (2*Wh-1 * 2*Ww-1 + 3, nH)
self.relative_position_bias_table = nn.Parameter(
torch.zeros(self.num_relative_distance, self.num_heads))
# get pair-wise relative position index for
# each token inside the window
coords_h = torch.arange(Wh)
coords_w = torch.arange(Ww)
# coords shape is (2, Wh, Ww)
coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
# coords_flatten shape is (2, Wh*Ww)
coords_flatten = torch.flatten(coords, 1)
relative_coords = (
coords_flatten[:, :, None] - coords_flatten[:, None, :])
# relative_coords shape is (Wh*Ww, Wh*Ww, 2)
relative_coords = relative_coords.permute(1, 2, 0).contiguous()
# shift to start from 0
relative_coords[:, :, 0] += Wh - 1
relative_coords[:, :, 1] += Ww - 1
relative_coords[:, :, 0] *= 2 * Ww - 1
relative_position_index = torch.zeros(
size=(Wh * Ww + 1, ) * 2, dtype=relative_coords.dtype)
# relative_position_index shape is (Wh*Ww, Wh*Ww)
relative_position_index[1:, 1:] = relative_coords.sum(-1)
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
self.register_buffer('relative_position_index',
relative_position_index)
def init_weights(self):
trunc_normal_(self.relative_position_bias_table, std=0.02)
def forward(self, x):
"""
Args:
x (tensor): input features with shape of (num_windows*B, N, C).
"""
B, N, C = x.shape
if self.bias == 'qv_bias':
k_bias = torch.zeros_like(self.v_bias, requires_grad=False)
qkv_bias = torch.cat((self.q_bias, k_bias, self.v_bias))
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
else:
qkv = self.qkv(x)
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
if self.relative_position_bias_table is not None:
Wh = self.window_size[0]
Ww = self.window_size[1]
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.view(-1)].view(
Wh * Ww + 1, Wh * Ww + 1, -1)
relative_position_bias = relative_position_bias.permute(
2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class BEiTTransformerEncoderLayer(VisionTransformerEncoderLayer):
"""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.
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.
bias (bool): The option to add leanable bias for q, k, v. If bias is
True, it will add leanable bias. If bias is 'qv_bias', it will only
add leanable bias for q, v. If bias is False, it will not add bias
for q, k, v. Default to 'qv_bias'.
act_cfg (dict): The activation config for FFNs.
Default: dict(type='GELU').
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN').
window_size (tuple[int], optional): The height and width of the window.
Default: None.
init_values (float, optional): Initialize the values of BEiTAttention
and FFN with learnable scaling. Default: None.
"""
def __init__(self,
embed_dims,
num_heads,
feedforward_channels,
attn_drop_rate=0.,
drop_path_rate=0.,
num_fcs=2,
bias='qv_bias',
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='LN'),
window_size=None,
attn_cfg=dict(),
ffn_cfg=dict(add_identity=False),
init_values=None):
attn_cfg.update(dict(window_size=window_size, qk_scale=None))
super(BEiTTransformerEncoderLayer, self).__init__(
embed_dims=embed_dims,
num_heads=num_heads,
feedforward_channels=feedforward_channels,
attn_drop_rate=attn_drop_rate,
drop_path_rate=0.,
drop_rate=0.,
num_fcs=num_fcs,
qkv_bias=bias,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
attn_cfg=attn_cfg,
ffn_cfg=ffn_cfg)
# NOTE: drop path for stochastic depth, we shall see if
# this is better than dropout here
dropout_layer = dict(type='DropPath', drop_prob=drop_path_rate)
self.drop_path = build_dropout(
dropout_layer) if dropout_layer else nn.Identity()
self.gamma_1 = nn.Parameter(
init_values * torch.ones((embed_dims)), requires_grad=True)
self.gamma_2 = nn.Parameter(
init_values * torch.ones((embed_dims)), requires_grad=True)
def build_attn(self, attn_cfg):
self.attn = BEiTAttention(**attn_cfg)
def forward(self, x):
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
x = x + self.drop_path(self.gamma_2 * self.ffn(self.norm2(x)))
return x
[docs]@BACKBONES.register_module()
class BEiT(BaseModule):
"""BERT Pre-Training of Image Transformers.
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.
qv_bias (bool): Enable bias for qv if True. Default: True.
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 BEiTAttention and FFN
with learnable scaling.
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,
qv_bias=True,
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(BEiT, self).__init__(init_cfg=init_cfg)
if isinstance(img_size, int):
img_size = to_2tuple(img_size)
elif isinstance(img_size, tuple):
if len(img_size) == 1:
img_size = to_2tuple(img_size[0])
assert len(img_size) == 2, \
f'The size of image should have length 1 or 2, ' \
f'but got {len(img_size)}'
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be set at the same time'
if isinstance(pretrained, str):
warnings.warn('DeprecationWarning: pretrained is deprecated, '
'please use "init_cfg" instead')
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
elif pretrained is not None:
raise TypeError('pretrained must be a str or None')
self.in_channels = in_channels
self.img_size = img_size
self.patch_size = patch_size
self.norm_eval = norm_eval
self.pretrained = pretrained
self.num_layers = num_layers
self.embed_dims = embed_dims
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
self.attn_drop_rate = attn_drop_rate
self.drop_path_rate = drop_path_rate
self.num_fcs = num_fcs
self.qv_bias = qv_bias
self.act_cfg = act_cfg
self.norm_cfg = norm_cfg
self.patch_norm = patch_norm
self.init_values = init_values
self.window_size = (img_size[0] // patch_size,
img_size[1] // patch_size)
self.patch_shape = self.window_size
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))
self._build_patch_embedding()
self._build_layers()
if isinstance(out_indices, int):
if out_indices == -1:
out_indices = num_layers - 1
self.out_indices = [out_indices]
elif isinstance(out_indices, list) or isinstance(out_indices, tuple):
self.out_indices = out_indices
else:
raise TypeError('out_indices must be type of int, list or tuple')
self.final_norm = final_norm
if final_norm:
self.norm1_name, norm1 = build_norm_layer(
norm_cfg, embed_dims, postfix=1)
self.add_module(self.norm1_name, norm1)
def _build_patch_embedding(self):
"""Build patch embedding layer."""
self.patch_embed = PatchEmbed(
in_channels=self.in_channels,
embed_dims=self.embed_dims,
conv_type='Conv2d',
kernel_size=self.patch_size,
stride=self.patch_size,
padding=0,
norm_cfg=self.norm_cfg if self.patch_norm else None,
init_cfg=None)
def _build_layers(self):
"""Build transformer encoding layers."""
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(
BEiTTransformerEncoderLayer(
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='qv_bias' if self.qv_bias else False,
act_cfg=self.act_cfg,
norm_cfg=self.norm_cfg,
window_size=self.window_size,
init_values=self.init_values))
@property
def norm1(self):
return getattr(self, self.norm1_name)
def _geometric_sequence_interpolation(self, src_size, dst_size, sequence,
num):
"""Get new sequence via geometric sequence interpolation.
Args:
src_size (int): Pos_embedding size in pre-trained model.
dst_size (int): Pos_embedding size in the current model.
sequence (tensor): The relative position bias of the pretrain
model after removing the extra tokens.
num (int): Number of attention heads.
Returns:
new_sequence (tensor): Geometric sequence interpolate the
pre-trained relative position bias to the size of
the current model.
"""
def geometric_progression(a, r, n):
return a * (1.0 - r**n) / (1.0 - r)
# Here is a binary function.
left, right = 1.01, 1.5
while right - left > 1e-6:
q = (left + right) / 2.0
gp = geometric_progression(1, q, src_size // 2)
if gp > dst_size // 2:
right = q
else:
left = q
# The position of each interpolated point is determined
# by the ratio obtained by dichotomy.
dis = []
cur = 1
for i in range(src_size // 2):
dis.append(cur)
cur += q**(i + 1)
r_ids = [-_ for _ in reversed(dis)]
x = r_ids + [0] + dis
y = r_ids + [0] + dis
t = dst_size // 2.0
dx = np.arange(-t, t + 0.1, 1.0)
dy = np.arange(-t, t + 0.1, 1.0)
# Interpolation functions are being executed and called.
new_sequence = []
for i in range(num):
z = sequence[:, i].view(src_size, src_size).float().numpy()
f = interpolate.interp2d(x, y, z, kind='cubic')
new_sequence.append(
torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(sequence))
new_sequence = torch.cat(new_sequence, dim=-1)
return new_sequence
[docs] def resize_rel_pos_embed(self, checkpoint):
"""Resize relative pos_embed weights.
This function is modified from
https://github.com/microsoft/unilm/blob/master/beit/semantic_segmentation/mmcv_custom/checkpoint.py. # noqa: E501
Copyright (c) Microsoft Corporation
Licensed under the MIT License
Args:
checkpoint (dict): Key and value of the pretrain model.
Returns:
state_dict (dict): Interpolate the relative pos_embed weights
in the pre-train model to the current model size.
"""
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
all_keys = list(state_dict.keys())
for key in all_keys:
if 'relative_position_index' in key:
state_dict.pop(key)
# In order to keep the center of pos_bias as consistent as
# possible after interpolation, and vice versa in the edge
# area, the geometric sequence interpolation method is adopted.
if 'relative_position_bias_table' in key:
rel_pos_bias = state_dict[key]
src_num_pos, num_attn_heads = rel_pos_bias.size()
dst_num_pos, _ = self.state_dict()[key].size()
dst_patch_shape = self.patch_shape
if dst_patch_shape[0] != dst_patch_shape[1]:
raise NotImplementedError()
# Count the number of extra tokens.
num_extra_tokens = dst_num_pos - (
dst_patch_shape[0] * 2 - 1) * (
dst_patch_shape[1] * 2 - 1)
src_size = int((src_num_pos - num_extra_tokens)**0.5)
dst_size = int((dst_num_pos - num_extra_tokens)**0.5)
if src_size != dst_size:
extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
new_rel_pos_bias = self._geometric_sequence_interpolation(
src_size, dst_size, rel_pos_bias, num_attn_heads)
new_rel_pos_bias = torch.cat(
(new_rel_pos_bias, extra_tokens), dim=0)
state_dict[key] = new_rel_pos_bias
return state_dict
[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)
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)
self.load_state_dict(state_dict, False)
elif self.init_cfg is not None:
super(BEiT, 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.)
[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)
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:
# Remove class token and reshape token for decoder head
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)
[docs] def train(self, mode=True):
super(BEiT, self).train(mode)
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, nn.LayerNorm):
m.eval()