464 lines
18 KiB
Python
464 lines
18 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
from typing import Sequence
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
from mmcv.cnn import build_norm_layer
|
|
from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
|
|
from mmengine.model import BaseModule, ModuleList
|
|
from mmengine.model.weight_init import trunc_normal_
|
|
|
|
from mmcls.registry import MODELS
|
|
from ..utils import MultiheadAttention, resize_pos_embed, to_2tuple
|
|
from .base_backbone import BaseBackbone
|
|
|
|
|
|
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. Defaults to 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.
|
|
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.
|
|
Defaluts to ``dict(type='GELU')``.
|
|
norm_cfg (dict): Config dict for normalization layer.
|
|
Defaults to ``dict(type='LN')``.
|
|
init_cfg (dict, optional): Initialization config dict.
|
|
Defaults to None.
|
|
"""
|
|
|
|
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'),
|
|
init_cfg=None):
|
|
super(TransformerEncoderLayer, self).__init__(init_cfg=init_cfg)
|
|
|
|
self.embed_dims = embed_dims
|
|
|
|
self.norm1_name, norm1 = build_norm_layer(
|
|
norm_cfg, self.embed_dims, postfix=1)
|
|
self.add_module(self.norm1_name, norm1)
|
|
|
|
self.attn = MultiheadAttention(
|
|
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)
|
|
|
|
self.norm2_name, norm2 = build_norm_layer(
|
|
norm_cfg, self.embed_dims, postfix=2)
|
|
self.add_module(self.norm2_name, norm2)
|
|
|
|
self.ffn = FFN(
|
|
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),
|
|
act_cfg=act_cfg)
|
|
|
|
@property
|
|
def norm1(self):
|
|
return getattr(self, self.norm1_name)
|
|
|
|
@property
|
|
def norm2(self):
|
|
return getattr(self, self.norm2_name)
|
|
|
|
def init_weights(self):
|
|
super(TransformerEncoderLayer, self).init_weights()
|
|
for m in self.ffn.modules():
|
|
if isinstance(m, nn.Linear):
|
|
nn.init.xavier_uniform_(m.weight)
|
|
nn.init.normal_(m.bias, std=1e-6)
|
|
|
|
def forward(self, x):
|
|
x = x + self.attn(self.norm1(x))
|
|
x = self.ffn(self.norm2(x), identity=x)
|
|
return x
|
|
|
|
|
|
@MODELS.register_module()
|
|
class VisionTransformer(BaseBackbone):
|
|
"""Vision Transformer.
|
|
|
|
A PyTorch implement of : `An Image is Worth 16x16 Words: Transformers
|
|
for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_
|
|
|
|
Args:
|
|
arch (str | dict): Vision Transformer architecture. If use string,
|
|
choose from 'small', 'base', 'large', 'deit-tiny', 'deit-small'
|
|
and 'deit-base'. If use dict, it should have below keys:
|
|
|
|
- **embed_dims** (int): The dimensions of embedding.
|
|
- **num_layers** (int): The number of transformer encoder layers.
|
|
- **num_heads** (int): The number of heads in attention modules.
|
|
- **feedforward_channels** (int): The hidden dimensions in
|
|
feedforward modules.
|
|
|
|
Defaults to 'base'.
|
|
img_size (int | tuple): The expected input image shape. Because we
|
|
support dynamic input shape, just set the argument to the most
|
|
common input image shape. Defaults to 224.
|
|
patch_size (int | tuple): The patch size in patch embedding.
|
|
Defaults to 16.
|
|
in_channels (int): The num of input channels. Defaults to 3.
|
|
out_indices (Sequence | int): Output from which stages.
|
|
Defaults to -1, means the last stage.
|
|
drop_rate (float): Probability of an element to be zeroed.
|
|
Defaults to 0.
|
|
drop_path_rate (float): stochastic depth rate. Defaults to 0.
|
|
qkv_bias (bool): Whether to add bias for qkv in attention modules.
|
|
Defaults to True.
|
|
norm_cfg (dict): Config dict for normalization layer.
|
|
Defaults to ``dict(type='LN')``.
|
|
final_norm (bool): Whether to add a additional layer to normalize
|
|
final feature map. Defaults to True.
|
|
with_cls_token (bool): Whether concatenating class token into image
|
|
tokens as transformer input. Defaults to True.
|
|
avg_token (bool): Whether or not to use the mean patch token for
|
|
classification. If True, the model will only take the average
|
|
of all patch tokens. Defaults to False.
|
|
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
|
-1 means not freezing any parameters. Defaults to -1.
|
|
output_cls_token (bool): Whether output the cls_token. If set True,
|
|
``with_cls_token`` must be True. Defaults to True.
|
|
interpolate_mode (str): Select the interpolate mode for position
|
|
embeding vector resize. Defaults to "bicubic".
|
|
patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict.
|
|
layer_cfgs (Sequence | dict): Configs of each transformer layer in
|
|
encoder. Defaults to an empty dict.
|
|
init_cfg (dict, optional): Initialization config dict.
|
|
Defaults to None.
|
|
"""
|
|
arch_zoo = {
|
|
**dict.fromkeys(
|
|
['s', 'small'], {
|
|
'embed_dims': 768,
|
|
'num_layers': 8,
|
|
'num_heads': 8,
|
|
'feedforward_channels': 768 * 3,
|
|
}),
|
|
**dict.fromkeys(
|
|
['b', 'base'], {
|
|
'embed_dims': 768,
|
|
'num_layers': 12,
|
|
'num_heads': 12,
|
|
'feedforward_channels': 3072
|
|
}),
|
|
**dict.fromkeys(
|
|
['l', 'large'], {
|
|
'embed_dims': 1024,
|
|
'num_layers': 24,
|
|
'num_heads': 16,
|
|
'feedforward_channels': 4096
|
|
}),
|
|
**dict.fromkeys(
|
|
['h', 'huge'],
|
|
{
|
|
# The same as the implementation in MAE
|
|
# <https://arxiv.org/abs/2111.06377>
|
|
'embed_dims': 1280,
|
|
'num_layers': 32,
|
|
'num_heads': 16,
|
|
'feedforward_channels': 5120
|
|
}),
|
|
**dict.fromkeys(
|
|
['eva-g', 'eva-giant'],
|
|
{
|
|
# The implementation in EVA
|
|
# <https://arxiv.org/abs/2211.07636>
|
|
'embed_dims': 1408,
|
|
'num_layers': 40,
|
|
'num_heads': 16,
|
|
'feedforward_channels': 6144
|
|
}),
|
|
**dict.fromkeys(
|
|
['deit-t', 'deit-tiny'], {
|
|
'embed_dims': 192,
|
|
'num_layers': 12,
|
|
'num_heads': 3,
|
|
'feedforward_channels': 192 * 4
|
|
}),
|
|
**dict.fromkeys(
|
|
['deit-s', 'deit-small'], {
|
|
'embed_dims': 384,
|
|
'num_layers': 12,
|
|
'num_heads': 6,
|
|
'feedforward_channels': 384 * 4
|
|
}),
|
|
**dict.fromkeys(
|
|
['deit-b', 'deit-base'], {
|
|
'embed_dims': 768,
|
|
'num_layers': 12,
|
|
'num_heads': 12,
|
|
'feedforward_channels': 768 * 4
|
|
}),
|
|
}
|
|
# Some structures have multiple extra tokens, like DeiT.
|
|
num_extra_tokens = 1 # cls_token
|
|
|
|
def __init__(self,
|
|
arch='base',
|
|
img_size=224,
|
|
patch_size=16,
|
|
in_channels=3,
|
|
out_indices=-1,
|
|
drop_rate=0.,
|
|
drop_path_rate=0.,
|
|
qkv_bias=True,
|
|
norm_cfg=dict(type='LN', eps=1e-6),
|
|
final_norm=True,
|
|
with_cls_token=True,
|
|
avg_token=False,
|
|
frozen_stages=-1,
|
|
output_cls_token=True,
|
|
interpolate_mode='bicubic',
|
|
patch_cfg=dict(),
|
|
layer_cfgs=dict(),
|
|
pre_norm=False,
|
|
init_cfg=None):
|
|
super(VisionTransformer, self).__init__(init_cfg)
|
|
|
|
if isinstance(arch, str):
|
|
arch = arch.lower()
|
|
assert arch in set(self.arch_zoo), \
|
|
f'Arch {arch} is not in default archs {set(self.arch_zoo)}'
|
|
self.arch_settings = self.arch_zoo[arch]
|
|
else:
|
|
essential_keys = {
|
|
'embed_dims', 'num_layers', 'num_heads', 'feedforward_channels'
|
|
}
|
|
assert isinstance(arch, dict) and essential_keys <= set(arch), \
|
|
f'Custom arch needs a dict with keys {essential_keys}'
|
|
self.arch_settings = arch
|
|
|
|
self.embed_dims = self.arch_settings['embed_dims']
|
|
self.num_layers = self.arch_settings['num_layers']
|
|
self.img_size = to_2tuple(img_size)
|
|
|
|
# Set patch embedding
|
|
_patch_cfg = dict(
|
|
in_channels=in_channels,
|
|
input_size=img_size,
|
|
embed_dims=self.embed_dims,
|
|
conv_type='Conv2d',
|
|
kernel_size=patch_size,
|
|
stride=patch_size,
|
|
bias=not pre_norm, # disable bias if pre_norm is used(e.g., CLIP)
|
|
)
|
|
_patch_cfg.update(patch_cfg)
|
|
self.patch_embed = PatchEmbed(**_patch_cfg)
|
|
self.patch_resolution = self.patch_embed.init_out_size
|
|
num_patches = self.patch_resolution[0] * self.patch_resolution[1]
|
|
|
|
# Set cls token
|
|
if output_cls_token:
|
|
assert with_cls_token is True, f'with_cls_token must be True if' \
|
|
f'set output_cls_token to True, but got {with_cls_token}'
|
|
self.with_cls_token = with_cls_token
|
|
self.output_cls_token = output_cls_token
|
|
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dims))
|
|
|
|
# Set position embedding
|
|
self.interpolate_mode = interpolate_mode
|
|
self.pos_embed = nn.Parameter(
|
|
torch.zeros(1, num_patches + self.num_extra_tokens,
|
|
self.embed_dims))
|
|
self._register_load_state_dict_pre_hook(self._prepare_pos_embed)
|
|
|
|
self.drop_after_pos = nn.Dropout(p=drop_rate)
|
|
|
|
if isinstance(out_indices, int):
|
|
out_indices = [out_indices]
|
|
assert isinstance(out_indices, Sequence), \
|
|
f'"out_indices" must by a sequence or int, ' \
|
|
f'get {type(out_indices)} instead.'
|
|
for i, index in enumerate(out_indices):
|
|
if index < 0:
|
|
out_indices[i] = self.num_layers + index
|
|
assert 0 <= out_indices[i] <= self.num_layers, \
|
|
f'Invalid out_indices {index}'
|
|
self.out_indices = out_indices
|
|
|
|
# stochastic depth decay rule
|
|
dpr = np.linspace(0, drop_path_rate, self.num_layers)
|
|
|
|
self.layers = ModuleList()
|
|
if isinstance(layer_cfgs, dict):
|
|
layer_cfgs = [layer_cfgs] * self.num_layers
|
|
for i in range(self.num_layers):
|
|
_layer_cfg = dict(
|
|
embed_dims=self.embed_dims,
|
|
num_heads=self.arch_settings['num_heads'],
|
|
feedforward_channels=self.
|
|
arch_settings['feedforward_channels'],
|
|
drop_rate=drop_rate,
|
|
drop_path_rate=dpr[i],
|
|
qkv_bias=qkv_bias,
|
|
norm_cfg=norm_cfg)
|
|
_layer_cfg.update(layer_cfgs[i])
|
|
self.layers.append(TransformerEncoderLayer(**_layer_cfg))
|
|
|
|
self.frozen_stages = frozen_stages
|
|
if pre_norm:
|
|
_, norm_layer = build_norm_layer(
|
|
norm_cfg, self.embed_dims, postfix=1)
|
|
else:
|
|
norm_layer = nn.Identity()
|
|
self.add_module('pre_norm', norm_layer)
|
|
|
|
self.final_norm = final_norm
|
|
if final_norm:
|
|
self.norm1_name, norm1 = build_norm_layer(
|
|
norm_cfg, self.embed_dims, postfix=1)
|
|
self.add_module(self.norm1_name, norm1)
|
|
|
|
self.avg_token = avg_token
|
|
if avg_token:
|
|
self.norm2_name, norm2 = build_norm_layer(
|
|
norm_cfg, self.embed_dims, postfix=2)
|
|
self.add_module(self.norm2_name, norm2)
|
|
# freeze stages only when self.frozen_stages > 0
|
|
if self.frozen_stages > 0:
|
|
self._freeze_stages()
|
|
|
|
@property
|
|
def norm1(self):
|
|
return getattr(self, self.norm1_name)
|
|
|
|
@property
|
|
def norm2(self):
|
|
return getattr(self, self.norm2_name)
|
|
|
|
def init_weights(self):
|
|
super(VisionTransformer, self).init_weights()
|
|
|
|
if not (isinstance(self.init_cfg, dict)
|
|
and self.init_cfg['type'] == 'Pretrained'):
|
|
if self.pos_embed is not None:
|
|
trunc_normal_(self.pos_embed, std=0.02)
|
|
|
|
def _prepare_pos_embed(self, state_dict, prefix, *args, **kwargs):
|
|
name = prefix + 'pos_embed'
|
|
if name not in state_dict.keys():
|
|
return
|
|
|
|
ckpt_pos_embed_shape = state_dict[name].shape
|
|
if self.pos_embed.shape != ckpt_pos_embed_shape:
|
|
from mmengine.logging import MMLogger
|
|
logger = MMLogger.get_current_instance()
|
|
logger.info(
|
|
f'Resize the pos_embed shape from {ckpt_pos_embed_shape} '
|
|
f'to {self.pos_embed.shape}.')
|
|
|
|
ckpt_pos_embed_shape = to_2tuple(
|
|
int(np.sqrt(ckpt_pos_embed_shape[1] - self.num_extra_tokens)))
|
|
pos_embed_shape = self.patch_embed.init_out_size
|
|
|
|
state_dict[name] = resize_pos_embed(state_dict[name],
|
|
ckpt_pos_embed_shape,
|
|
pos_embed_shape,
|
|
self.interpolate_mode,
|
|
self.num_extra_tokens)
|
|
|
|
@staticmethod
|
|
def resize_pos_embed(*args, **kwargs):
|
|
"""Interface for backward-compatibility."""
|
|
return resize_pos_embed(*args, **kwargs)
|
|
|
|
def _freeze_stages(self):
|
|
# freeze position embedding
|
|
if self.pos_embed is not None:
|
|
self.pos_embed.requires_grad = False
|
|
# set dropout to eval model
|
|
self.drop_after_pos.eval()
|
|
# freeze patch embedding
|
|
self.patch_embed.eval()
|
|
for param in self.patch_embed.parameters():
|
|
param.requires_grad = False
|
|
# freeze cls_token
|
|
self.cls_token.requires_grad = False
|
|
# freeze layers
|
|
for i in range(1, self.frozen_stages + 1):
|
|
m = self.layers[i - 1]
|
|
m.eval()
|
|
for param in m.parameters():
|
|
param.requires_grad = False
|
|
# freeze the last layer norm
|
|
if self.frozen_stages == len(self.layers) and self.final_norm:
|
|
self.norm1.eval()
|
|
for param in self.norm1.parameters():
|
|
param.requires_grad = False
|
|
|
|
def forward(self, x):
|
|
B = x.shape[0]
|
|
x, patch_resolution = self.patch_embed(x)
|
|
|
|
# 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 + resize_pos_embed(
|
|
self.pos_embed,
|
|
self.patch_resolution,
|
|
patch_resolution,
|
|
mode=self.interpolate_mode,
|
|
num_extra_tokens=self.num_extra_tokens)
|
|
x = self.drop_after_pos(x)
|
|
|
|
x = self.pre_norm(x)
|
|
if not self.with_cls_token:
|
|
# Remove class token for transformer encoder input
|
|
x = x[:, 1:]
|
|
|
|
outs = []
|
|
for i, layer in enumerate(self.layers):
|
|
x = layer(x)
|
|
|
|
if i == len(self.layers) - 1 and self.final_norm:
|
|
x = self.norm1(x)
|
|
|
|
if i in self.out_indices:
|
|
B, _, C = x.shape
|
|
if self.with_cls_token:
|
|
patch_token = x[:, 1:].reshape(B, *patch_resolution, C)
|
|
patch_token = patch_token.permute(0, 3, 1, 2)
|
|
cls_token = x[:, 0]
|
|
else:
|
|
patch_token = x.reshape(B, *patch_resolution, C)
|
|
patch_token = patch_token.permute(0, 3, 1, 2)
|
|
cls_token = None
|
|
if self.avg_token:
|
|
patch_token = patch_token.permute(0, 2, 3, 1)
|
|
patch_token = patch_token.reshape(
|
|
B, patch_resolution[0] * patch_resolution[1],
|
|
C).mean(dim=1)
|
|
patch_token = self.norm2(patch_token)
|
|
if self.output_cls_token:
|
|
out = [patch_token, cls_token]
|
|
else:
|
|
out = patch_token
|
|
outs.append(out)
|
|
|
|
return tuple(outs)
|