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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
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 mmcls.registry import MODELS
from ..utils import to_2tuple
from .base_backbone import BaseBackbone
class MixerBlock(BaseModule):
"""Mlp-Mixer basic block.
Basic module of `MLP-Mixer: An all-MLP Architecture for Vision
<https://arxiv.org/pdf/2105.01601.pdf>`_
Args:
num_tokens (int): The number of patched tokens
embed_dims (int): The feature dimension
tokens_mlp_dims (int): The hidden dimension for tokens FFNs
channels_mlp_dims (int): The hidden dimension for channels FFNs
drop_rate (float): Probability of an element to be zeroed
after the feed forward layer. 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.
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,
num_tokens,
embed_dims,
tokens_mlp_dims,
channels_mlp_dims,
drop_rate=0.,
drop_path_rate=0.,
num_fcs=2,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='LN'),
init_cfg=None):
super(MixerBlock, self).__init__(init_cfg=init_cfg)
self.norm1_name, norm1 = build_norm_layer(
norm_cfg, embed_dims, postfix=1)
self.add_module(self.norm1_name, norm1)
self.token_mix = FFN(
embed_dims=num_tokens,
feedforward_channels=tokens_mlp_dims,
num_fcs=num_fcs,
ffn_drop=drop_rate,
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
act_cfg=act_cfg,
add_identity=False)
self.norm2_name, norm2 = build_norm_layer(
norm_cfg, embed_dims, postfix=2)
self.add_module(self.norm2_name, norm2)
self.channel_mix = FFN(
embed_dims=embed_dims,
feedforward_channels=channels_mlp_dims,
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(MixerBlock, self).init_weights()
for m in self.token_mix.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
nn.init.normal_(m.bias, std=1e-6)
for m in self.channel_mix.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
nn.init.normal_(m.bias, std=1e-6)
def forward(self, x):
out = self.norm1(x).transpose(1, 2)
x = x + self.token_mix(out).transpose(1, 2)
x = self.channel_mix(self.norm2(x), identity=x)
return x
@MODELS.register_module()
class MlpMixer(BaseBackbone):
"""Mlp-Mixer backbone.
Pytorch implementation of `MLP-Mixer: An all-MLP Architecture for Vision
<https://arxiv.org/pdf/2105.01601.pdf>`_
Args:
arch (str | dict): MLP Mixer architecture. If use string, choose from
'small', 'base' and 'large'. If use dict, it should have below
keys:
- **embed_dims** (int): The dimensions of embedding.
- **num_layers** (int): The number of MLP blocks.
- **tokens_mlp_dims** (int): The hidden dimensions for tokens FFNs.
- **channels_mlp_dims** (int): The The hidden dimensions for
channels FFNs.
Defaults to 'base'.
img_size (int | tuple): The input image shape. Defaults to 224.
patch_size (int | tuple): The patch size in patch embedding.
Defaults to 16.
out_indices (Sequence | int): Output from which layer.
Defaults to -1, means the last layer.
drop_rate (float): Probability of an element to be zeroed.
Defaults to 0.
drop_path_rate (float): stochastic depth rate. Defaults to 0.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
act_cfg (dict): The activation config for FFNs. Default GELU.
patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict.
layer_cfgs (Sequence | dict): Configs of each mixer block layer.
Defaults to an empty dict.
init_cfg (dict, optional): Initialization config dict.
Defaults to None.
"""
arch_zoo = {
**dict.fromkeys(
['s', 'small'], {
'embed_dims': 512,
'num_layers': 8,
'tokens_mlp_dims': 256,
'channels_mlp_dims': 2048,
}),
**dict.fromkeys(
['b', 'base'], {
'embed_dims': 768,
'num_layers': 12,
'tokens_mlp_dims': 384,
'channels_mlp_dims': 3072,
}),
**dict.fromkeys(
['l', 'large'], {
'embed_dims': 1024,
'num_layers': 24,
'tokens_mlp_dims': 512,
'channels_mlp_dims': 4096,
}),
}
def __init__(self,
arch='base',
img_size=224,
patch_size=16,
out_indices=-1,
drop_rate=0.,
drop_path_rate=0.,
norm_cfg=dict(type='LN'),
act_cfg=dict(type='GELU'),
patch_cfg=dict(),
layer_cfgs=dict(),
init_cfg=None):
super(MlpMixer, 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', 'tokens_mlp_dims',
'channels_mlp_dims'
}
assert isinstance(arch, dict) and set(arch) == essential_keys, \
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.tokens_mlp_dims = self.arch_settings['tokens_mlp_dims']
self.channels_mlp_dims = self.arch_settings['channels_mlp_dims']
self.img_size = to_2tuple(img_size)
_patch_cfg = dict(
input_size=img_size,
embed_dims=self.embed_dims,
conv_type='Conv2d',
kernel_size=patch_size,
stride=patch_size,
)
_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]
if isinstance(out_indices, int):
out_indices = [out_indices]
assert isinstance(out_indices, Sequence), \
f'"out_indices" must be 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 out_indices[i] >= 0, f'Invalid out_indices {index}'
else:
assert index >= self.num_layers, f'Invalid out_indices {index}'
self.out_indices = out_indices
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(
num_tokens=num_patches,
embed_dims=self.embed_dims,
tokens_mlp_dims=self.tokens_mlp_dims,
channels_mlp_dims=self.channels_mlp_dims,
drop_rate=drop_rate,
drop_path_rate=drop_path_rate,
act_cfg=act_cfg,
norm_cfg=norm_cfg,
)
_layer_cfg.update(layer_cfgs[i])
self.layers.append(MixerBlock(**_layer_cfg))
self.norm1_name, norm1 = build_norm_layer(
norm_cfg, self.embed_dims, postfix=1)
self.add_module(self.norm1_name, norm1)
@property
def norm1(self):
return getattr(self, self.norm1_name)
def forward(self, x):
assert x.shape[2:] == self.img_size, \
"The MLP-Mixer doesn't support dynamic input shape. " \
f'Please input images with shape {self.img_size}'
x, _ = self.patch_embed(x)
outs = []
for i, layer in enumerate(self.layers):
x = layer(x)
if i == len(self.layers) - 1:
x = self.norm1(x)
if i in self.out_indices:
out = x.transpose(1, 2)
outs.append(out)
return tuple(outs)
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