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metaformer_baselines.py
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metaformer_baselines.py
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# Copyright 2022 Garena Online Private Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
MetaFormer baselines including IdentityFormer, RandFormer, PoolFormerV2,
ConvFormer and CAFormer.
Some implementations are modified from timm (https://github.com/rwightman/pytorch-image-models).
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers.helpers import to_2tuple
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': 1.0, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head',
**kwargs
}
default_cfgs = {
'identityformer_s12': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_s12.pth'),
'identityformer_s24': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_s24.pth'),
'identityformer_s36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_s36.pth'),
'identityformer_m36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_m36.pth'),
'identityformer_m48': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/identityformer/identityformer_m48.pth'),
'randformer_s12': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_s12.pth'),
'randformer_s24': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_s24.pth'),
'randformer_s36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_s36.pth'),
'randformer_m36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_m36.pth'),
'randformer_m48': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/randformer/randformer_m48.pth'),
'poolformerv2_s12': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_s12.pth'),
'poolformerv2_s24': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_s24.pth'),
'poolformerv2_s36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_s36.pth'),
'poolformerv2_m36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_m36.pth'),
'poolformerv2_m48': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/poolformerv2/poolformerv2_m48.pth'),
'convformer_s18': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18.pth'),
'convformer_s18_384': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18_384.pth',
input_size=(3, 384, 384)),
'convformer_s18_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18_in21ft1k.pth'),
'convformer_s18_384_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18_384_in21ft1k.pth',
input_size=(3, 384, 384)),
'convformer_s18_in21k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s18_in21k.pth',
num_classes=21841),
'convformer_s36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36.pth'),
'convformer_s36_384': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36_384.pth',
input_size=(3, 384, 384)),
'convformer_s36_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36_in21ft1k.pth'),
'convformer_s36_384_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36_384_in21ft1k.pth',
input_size=(3, 384, 384)),
'convformer_s36_in21k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_s36_in21k.pth',
num_classes=21841),
'convformer_m36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36.pth'),
'convformer_m36_384': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36_384.pth',
input_size=(3, 384, 384)),
'convformer_m36_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36_in21ft1k.pth'),
'convformer_m36_384_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36_384_in21ft1k.pth',
input_size=(3, 384, 384)),
'convformer_m36_in21k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_m36_in21k.pth',
num_classes=21841),
'convformer_b36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36.pth'),
'convformer_b36_384': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36_384.pth',
input_size=(3, 384, 384)),
'convformer_b36_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36_in21ft1k.pth'),
'convformer_b36_384_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36_384_in21ft1k.pth',
input_size=(3, 384, 384)),
'convformer_b36_in21k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/convformer/convformer_b36_in21k.pth',
num_classes=21841),
'caformer_s18': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18.pth'),
'caformer_s18_384': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18_384.pth',
input_size=(3, 384, 384)),
'caformer_s18_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18_in21ft1k.pth'),
'caformer_s18_384_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18_384_in21ft1k.pth',
input_size=(3, 384, 384)),
'caformer_s18_in21k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s18_in21k.pth',
num_classes=21841),
'caformer_s36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36.pth'),
'caformer_s36_384': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36_384.pth',
input_size=(3, 384, 384)),
'caformer_s36_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36_in21ft1k.pth'),
'caformer_s36_384_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36_384_in21ft1k.pth',
input_size=(3, 384, 384)),
'caformer_s36_in21k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_s36_in21k.pth',
num_classes=21841),
'caformer_m36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36.pth'),
'caformer_m36_384': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36_384.pth',
input_size=(3, 384, 384)),
'caformer_m36_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36_in21ft1k.pth'),
'caformer_m36_384_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36_384_in21ft1k.pth',
input_size=(3, 384, 384)),
'caformer_m36_in21k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36_in21k.pth',
num_classes=21841),
'caformer_b36': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36.pth'),
'caformer_b36_384': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36_384.pth',
input_size=(3, 384, 384)),
'caformer_b36_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36_in21ft1k.pth'),
'caformer_b36_384_in21ft1k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36_384_in21ft1k.pth',
input_size=(3, 384, 384)),
'caformer_b36_in21k': _cfg(
url='https://huggingface.co/sail/dl/resolve/main/caformer/caformer_b36_in21k.pth',
num_classes=21841),
}
class Downsampling(nn.Module):
"""
Downsampling implemented by a layer of convolution.
"""
def __init__(self, in_channels, out_channels,
kernel_size, stride=1, padding=0,
pre_norm=None, post_norm=None, pre_permute=False):
super().__init__()
self.pre_norm = pre_norm(in_channels) if pre_norm else nn.Identity()
self.pre_permute = pre_permute
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,
stride=stride, padding=padding)
self.post_norm = post_norm(out_channels) if post_norm else nn.Identity()
def forward(self, x):
x = self.pre_norm(x)
if self.pre_permute:
# if take [B, H, W, C] as input, permute it to [B, C, H, W]
x = x.permute(0, 3, 1, 2)
x = self.conv(x)
x = x.permute(0, 2, 3, 1) # [B, C, H, W] -> [B, H, W, C]
x = self.post_norm(x)
return x
class Scale(nn.Module):
"""
Scale vector by element multiplications.
"""
def __init__(self, dim, init_value=1.0, trainable=True):
super().__init__()
self.scale = nn.Parameter(init_value * torch.ones(dim), requires_grad=trainable)
def forward(self, x):
return x * self.scale
class SquaredReLU(nn.Module):
"""
Squared ReLU: https://arxiv.org/abs/2109.08668
"""
def __init__(self, inplace=False):
super().__init__()
self.relu = nn.ReLU(inplace=inplace)
def forward(self, x):
return torch.square(self.relu(x))
class StarReLU(nn.Module):
"""
StarReLU: s * relu(x) ** 2 + b
"""
def __init__(self, scale_value=1.0, bias_value=0.0,
scale_learnable=True, bias_learnable=True,
mode=None, inplace=False):
super().__init__()
self.inplace = inplace
self.relu = nn.ReLU(inplace=inplace)
self.scale = nn.Parameter(scale_value * torch.ones(1),
requires_grad=scale_learnable)
self.bias = nn.Parameter(bias_value * torch.ones(1),
requires_grad=bias_learnable)
def forward(self, x):
return self.scale * self.relu(x)**2 + self.bias
class Attention(nn.Module):
"""
Vanilla self-attention from Transformer: https://arxiv.org/abs/1706.03762.
Modified from timm.
"""
def __init__(self, dim, head_dim=32, num_heads=None, qkv_bias=False,
attn_drop=0., proj_drop=0., proj_bias=False, **kwargs):
super().__init__()
self.head_dim = head_dim
self.scale = head_dim ** -0.5
self.num_heads = num_heads if num_heads else dim // head_dim
if self.num_heads == 0:
self.num_heads = 1
self.attention_dim = self.num_heads * self.head_dim
self.qkv = nn.Linear(dim, self.attention_dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(self.attention_dim, dim, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, H, W, C = x.shape
N = H * W
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, H, W, self.attention_dim)
x = self.proj(x)
x = self.proj_drop(x)
return x
class RandomMixing(nn.Module):
def __init__(self, num_tokens=196, **kwargs):
super().__init__()
self.random_matrix = nn.parameter.Parameter(
data=torch.softmax(torch.rand(num_tokens, num_tokens), dim=-1),
requires_grad=False)
def forward(self, x):
B, H, W, C = x.shape
x = x.reshape(B, H*W, C)
x = torch.einsum('mn, bnc -> bmc', self.random_matrix, x)
x = x.reshape(B, H, W, C)
return x
class LayerNormGeneral(nn.Module):
r""" General LayerNorm for different situations.
Args:
affine_shape (int, list or tuple): The shape of affine weight and bias.
Usually the affine_shape=C, but in some implementation, like torch.nn.LayerNorm,
the affine_shape is the same as normalized_dim by default.
To adapt to different situations, we offer this argument here.
normalized_dim (tuple or list): Which dims to compute mean and variance.
scale (bool): Flag indicates whether to use scale or not.
bias (bool): Flag indicates whether to use scale or not.
We give several examples to show how to specify the arguments.
LayerNorm (https://arxiv.org/abs/1607.06450):
For input shape of (B, *, C) like (B, N, C) or (B, H, W, C),
affine_shape=C, normalized_dim=(-1, ), scale=True, bias=True;
For input shape of (B, C, H, W),
affine_shape=(C, 1, 1), normalized_dim=(1, ), scale=True, bias=True.
Modified LayerNorm (https://arxiv.org/abs/2111.11418)
that is idental to partial(torch.nn.GroupNorm, num_groups=1):
For input shape of (B, N, C),
affine_shape=C, normalized_dim=(1, 2), scale=True, bias=True;
For input shape of (B, H, W, C),
affine_shape=C, normalized_dim=(1, 2, 3), scale=True, bias=True;
For input shape of (B, C, H, W),
affine_shape=(C, 1, 1), normalized_dim=(1, 2, 3), scale=True, bias=True.
For the several metaformer baslines,
IdentityFormer, RandFormer and PoolFormerV2 utilize Modified LayerNorm without bias (bias=False);
ConvFormer and CAFormer utilizes LayerNorm without bias (bias=False).
"""
def __init__(self, affine_shape=None, normalized_dim=(-1, ), scale=True,
bias=True, eps=1e-5):
super().__init__()
self.normalized_dim = normalized_dim
self.use_scale = scale
self.use_bias = bias
self.weight = nn.Parameter(torch.ones(affine_shape)) if scale else None
self.bias = nn.Parameter(torch.zeros(affine_shape)) if bias else None
self.eps = eps
def forward(self, x):
c = x - x.mean(self.normalized_dim, keepdim=True)
s = c.pow(2).mean(self.normalized_dim, keepdim=True)
x = c / torch.sqrt(s + self.eps)
if self.use_scale:
x = x * self.weight
if self.use_bias:
x = x + self.bias
return x
class LayerNormWithoutBias(nn.Module):
"""
Equal to partial(LayerNormGeneral, bias=False) but faster,
because it directly utilizes otpimized F.layer_norm
"""
def __init__(self, normalized_shape, eps=1e-5, **kwargs):
super().__init__()
self.eps = eps
self.bias = None
if isinstance(normalized_shape, int):
normalized_shape = (normalized_shape,)
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.normalized_shape = normalized_shape
def forward(self, x):
return F.layer_norm(x, self.normalized_shape, weight=self.weight, bias=self.bias, eps=self.eps)
class SepConv(nn.Module):
r"""
Inverted separable convolution from MobileNetV2: https://arxiv.org/abs/1801.04381.
"""
def __init__(self, dim, expansion_ratio=2,
act1_layer=StarReLU, act2_layer=nn.Identity,
bias=False, kernel_size=7, padding=3,
**kwargs, ):
super().__init__()
med_channels = int(expansion_ratio * dim)
self.pwconv1 = nn.Linear(dim, med_channels, bias=bias)
self.act1 = act1_layer()
self.dwconv = nn.Conv2d(
med_channels, med_channels, kernel_size=kernel_size,
padding=padding, groups=med_channels, bias=bias) # depthwise conv
self.act2 = act2_layer()
self.pwconv2 = nn.Linear(med_channels, dim, bias=bias)
def forward(self, x):
x = self.pwconv1(x)
x = self.act1(x)
x = x.permute(0, 3, 1, 2)
x = self.dwconv(x)
x = x.permute(0, 2, 3, 1)
x = self.act2(x)
x = self.pwconv2(x)
return x
class Pooling(nn.Module):
"""
Implementation of pooling for PoolFormer: https://arxiv.org/abs/2111.11418
Modfiled for [B, H, W, C] input
"""
def __init__(self, pool_size=3, **kwargs):
super().__init__()
self.pool = nn.AvgPool2d(
pool_size, stride=1, padding=pool_size//2, count_include_pad=False)
def forward(self, x):
y = x.permute(0, 3, 1, 2)
y = self.pool(y)
y = y.permute(0, 2, 3, 1)
return y - x
class Mlp(nn.Module):
""" MLP as used in MetaFormer models, eg Transformer, MLP-Mixer, PoolFormer, MetaFormer baslines and related networks.
Mostly copied from timm.
"""
def __init__(self, dim, mlp_ratio=4, out_features=None, act_layer=StarReLU, drop=0., bias=False, **kwargs):
super().__init__()
in_features = dim
out_features = out_features or in_features
hidden_features = int(mlp_ratio * in_features)
drop_probs = to_2tuple(drop)
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class MlpHead(nn.Module):
""" MLP classification head
"""
def __init__(self, dim, num_classes=1000, mlp_ratio=4, act_layer=SquaredReLU,
norm_layer=nn.LayerNorm, head_dropout=0., bias=True):
super().__init__()
hidden_features = int(mlp_ratio * dim)
self.fc1 = nn.Linear(dim, hidden_features, bias=bias)
self.act = act_layer()
self.norm = norm_layer(hidden_features)
self.fc2 = nn.Linear(hidden_features, num_classes, bias=bias)
self.head_dropout = nn.Dropout(head_dropout)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.norm(x)
x = self.head_dropout(x)
x = self.fc2(x)
return x
class MetaFormerBlock(nn.Module):
"""
Implementation of one MetaFormer block.
"""
def __init__(self, dim,
token_mixer=nn.Identity, mlp=Mlp,
norm_layer=nn.LayerNorm,
drop=0., drop_path=0.,
layer_scale_init_value=None, res_scale_init_value=None
):
super().__init__()
self.norm1 = norm_layer(dim)
self.token_mixer = token_mixer(dim=dim, drop=drop)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.layer_scale1 = Scale(dim=dim, init_value=layer_scale_init_value) \
if layer_scale_init_value else nn.Identity()
self.res_scale1 = Scale(dim=dim, init_value=res_scale_init_value) \
if res_scale_init_value else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = mlp(dim=dim, drop=drop)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.layer_scale2 = Scale(dim=dim, init_value=layer_scale_init_value) \
if layer_scale_init_value else nn.Identity()
self.res_scale2 = Scale(dim=dim, init_value=res_scale_init_value) \
if res_scale_init_value else nn.Identity()
def forward(self, x):
x = self.res_scale1(x) + \
self.layer_scale1(
self.drop_path1(
self.token_mixer(self.norm1(x))
)
)
x = self.res_scale2(x) + \
self.layer_scale2(
self.drop_path2(
self.mlp(self.norm2(x))
)
)
return x
r"""
downsampling (stem) for the first stage is a layer of conv with k7, s4 and p2
downsamplings for the last 3 stages is a layer of conv with k3, s2 and p1
DOWNSAMPLE_LAYERS_FOUR_STAGES format: [Downsampling, Downsampling, Downsampling, Downsampling]
use `partial` to specify some arguments
"""
DOWNSAMPLE_LAYERS_FOUR_STAGES = [partial(Downsampling,
kernel_size=7, stride=4, padding=2,
post_norm=partial(LayerNormGeneral, bias=False, eps=1e-6)
)] + \
[partial(Downsampling,
kernel_size=3, stride=2, padding=1,
pre_norm=partial(LayerNormGeneral, bias=False, eps=1e-6), pre_permute=True
)]*3
class MetaFormer(nn.Module):
r""" MetaFormer
A PyTorch impl of : `MetaFormer Baselines for Vision` -
https://arxiv.org/abs/2210.13452
Args:
in_chans (int): Number of input image channels. Default: 3.
num_classes (int): Number of classes for classification head. Default: 1000.
depths (list or tuple): Number of blocks at each stage. Default: [2, 2, 6, 2].
dims (int): Feature dimension at each stage. Default: [64, 128, 320, 512].
downsample_layers: (list or tuple): Downsampling layers before each stage.
token_mixers (list, tuple or token_fcn): Token mixer for each stage. Default: nn.Identity.
mlps (list, tuple or mlp_fcn): Mlp for each stage. Default: Mlp.
norm_layers (list, tuple or norm_fcn): Norm layers for each stage. Default: partial(LayerNormGeneral, eps=1e-6, bias=False).
drop_path_rate (float): Stochastic depth rate. Default: 0.
head_dropout (float): dropout for MLP classifier. Default: 0.
layer_scale_init_values (list, tuple, float or None): Init value for Layer Scale. Default: None.
None means not use the layer scale. Form: https://arxiv.org/abs/2103.17239.
res_scale_init_values (list, tuple, float or None): Init value for Layer Scale. Default: [None, None, 1.0, 1.0].
None means not use the layer scale. From: https://arxiv.org/abs/2110.09456.
output_norm: norm before classifier head. Default: partial(nn.LayerNorm, eps=1e-6).
head_fn: classification head. Default: nn.Linear.
"""
def __init__(self, in_chans=3, num_classes=1000,
depths=[2, 2, 6, 2],
dims=[64, 128, 320, 512],
downsample_layers=DOWNSAMPLE_LAYERS_FOUR_STAGES,
token_mixers=nn.Identity,
mlps=Mlp,
norm_layers=partial(LayerNormWithoutBias, eps=1e-6), # partial(LayerNormGeneral, eps=1e-6, bias=False),
drop_path_rate=0.,
head_dropout=0.0,
layer_scale_init_values=None,
res_scale_init_values=[None, None, 1.0, 1.0],
output_norm=partial(nn.LayerNorm, eps=1e-6),
head_fn=nn.Linear,
**kwargs,
):
super().__init__()
self.num_classes = num_classes
if not isinstance(depths, (list, tuple)):
depths = [depths] # it means the model has only one stage
if not isinstance(dims, (list, tuple)):
dims = [dims]
num_stage = len(depths)
self.num_stage = num_stage
if not isinstance(downsample_layers, (list, tuple)):
downsample_layers = [downsample_layers] * num_stage
down_dims = [in_chans] + dims
self.downsample_layers = nn.ModuleList(
[downsample_layers[i](down_dims[i], down_dims[i+1]) for i in range(num_stage)]
)
if not isinstance(token_mixers, (list, tuple)):
token_mixers = [token_mixers] * num_stage
if not isinstance(mlps, (list, tuple)):
mlps = [mlps] * num_stage
if not isinstance(norm_layers, (list, tuple)):
norm_layers = [norm_layers] * num_stage
dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
if not isinstance(layer_scale_init_values, (list, tuple)):
layer_scale_init_values = [layer_scale_init_values] * num_stage
if not isinstance(res_scale_init_values, (list, tuple)):
res_scale_init_values = [res_scale_init_values] * num_stage
self.stages = nn.ModuleList() # each stage consists of multiple metaformer blocks
cur = 0
for i in range(num_stage):
stage = nn.Sequential(
*[MetaFormerBlock(dim=dims[i],
token_mixer=token_mixers[i],
mlp=mlps[i],
norm_layer=norm_layers[i],
drop_path=dp_rates[cur + j],
layer_scale_init_value=layer_scale_init_values[i],
res_scale_init_value=res_scale_init_values[i],
) for j in range(depths[i])]
)
self.stages.append(stage)
cur += depths[i]
self.norm = output_norm(dims[-1])
if head_dropout > 0.0:
self.head = head_fn(dims[-1], num_classes, head_dropout=head_dropout)
else:
self.head = head_fn(dims[-1], num_classes)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def no_weight_decay(self):
return {'norm'}
def forward_features(self, x):
for i in range(self.num_stage):
x = self.downsample_layers[i](x)
x = self.stages[i](x)
return self.norm(x.mean([1, 2])) # (B, H, W, C) -> (B, C)
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
@register_model
def identityformer_s12(pretrained=False, **kwargs):
model = MetaFormer(
depths=[2, 2, 6, 2],
dims=[64, 128, 320, 512],
token_mixers=nn.Identity,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['identityformer_s12']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def identityformer_s24(pretrained=False, **kwargs):
model = MetaFormer(
depths=[4, 4, 12, 4],
dims=[64, 128, 320, 512],
token_mixers=nn.Identity,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['identityformer_s24']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def identityformer_s36(pretrained=False, **kwargs):
model = MetaFormer(
depths=[6, 6, 18, 6],
dims=[64, 128, 320, 512],
token_mixers=nn.Identity,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['identityformer_s36']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def identityformer_m36(pretrained=False, **kwargs):
model = MetaFormer(
depths=[6, 6, 18, 6],
dims=[96, 192, 384, 768],
token_mixers=nn.Identity,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['identityformer_m36']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def identityformer_m48(pretrained=False, **kwargs):
model = MetaFormer(
depths=[8, 8, 24, 8],
dims=[96, 192, 384, 768],
token_mixers=nn.Identity,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['identityformer_m48']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def randformer_s12(pretrained=False, **kwargs):
model = MetaFormer(
depths=[2, 2, 6, 2],
dims=[64, 128, 320, 512],
token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)],
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['randformer_s12']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def randformer_s24(pretrained=False, **kwargs):
model = MetaFormer(
depths=[4, 4, 12, 4],
dims=[64, 128, 320, 512],
token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)],
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['randformer_s24']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def randformer_s36(pretrained=False, **kwargs):
model = MetaFormer(
depths=[6, 6, 18, 6],
dims=[64, 128, 320, 512],
token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)],
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['randformer_s36']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def randformer_m36(pretrained=False, **kwargs):
model = MetaFormer(
depths=[6, 6, 18, 6],
dims=[96, 192, 384, 768],
token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)],
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['randformer_m36']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def randformer_m48(pretrained=False, **kwargs):
model = MetaFormer(
depths=[8, 8, 24, 8],
dims=[96, 192, 384, 768],
token_mixers=[nn.Identity, nn.Identity, RandomMixing, partial(RandomMixing, num_tokens=49)],
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['randformer_m48']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def poolformerv2_s12(pretrained=False, **kwargs):
model = MetaFormer(
depths=[2, 2, 6, 2],
dims=[64, 128, 320, 512],
token_mixers=Pooling,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['poolformerv2_s12']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def poolformerv2_s24(pretrained=False, **kwargs):
model = MetaFormer(
depths=[4, 4, 12, 4],
dims=[64, 128, 320, 512],
token_mixers=Pooling,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['poolformerv2_s24']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def poolformerv2_s36(pretrained=False, **kwargs):
model = MetaFormer(
depths=[6, 6, 18, 6],
dims=[64, 128, 320, 512],
token_mixers=Pooling,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['poolformerv2_s36']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def poolformerv2_m36(pretrained=False, **kwargs):
model = MetaFormer(
depths=[6, 6, 18, 6],
dims=[96, 192, 384, 768],
token_mixers=Pooling,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['poolformerv2_m36']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def poolformerv2_m48(pretrained=False, **kwargs):
model = MetaFormer(
depths=[8, 8, 24, 8],
dims=[96, 192, 384, 768],
token_mixers=Pooling,
norm_layers=partial(LayerNormGeneral, normalized_dim=(1, 2, 3), eps=1e-6, bias=False),
**kwargs)
model.default_cfg = default_cfgs['poolformerv2_m48']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def convformer_s18(pretrained=False, **kwargs):
model = MetaFormer(
depths=[3, 3, 9, 3],
dims=[64, 128, 320, 512],
token_mixers=SepConv,
head_fn=MlpHead,
**kwargs)
model.default_cfg = default_cfgs['convformer_s18']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def convformer_s18_384(pretrained=False, **kwargs):
model = MetaFormer(
depths=[3, 3, 9, 3],
dims=[64, 128, 320, 512],
token_mixers=SepConv,
head_fn=MlpHead,
**kwargs)
model.default_cfg = default_cfgs['convformer_s18_384']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def convformer_s18_in21ft1k(pretrained=False, **kwargs):
model = MetaFormer(
depths=[3, 3, 9, 3],
dims=[64, 128, 320, 512],
token_mixers=SepConv,
head_fn=MlpHead,
**kwargs)
model.default_cfg = default_cfgs['convformer_s18_in21ft1k']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def convformer_s18_384_in21ft1k(pretrained=False, **kwargs):
model = MetaFormer(
depths=[3, 3, 9, 3],
dims=[64, 128, 320, 512],
token_mixers=SepConv,
head_fn=MlpHead,
**kwargs)
model.default_cfg = default_cfgs['convformer_s18_384_in21ft1k']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def convformer_s18_in21k(pretrained=False, **kwargs):
model = MetaFormer(
depths=[3, 3, 9, 3],
dims=[64, 128, 320, 512],
token_mixers=SepConv,
head_fn=MlpHead,
**kwargs)
model.default_cfg = default_cfgs['convformer_s18_in21k']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def convformer_s36(pretrained=False, **kwargs):
model = MetaFormer(
depths=[3, 12, 18, 3],
dims=[64, 128, 320, 512],
token_mixers=SepConv,
head_fn=MlpHead,
**kwargs)
model.default_cfg = default_cfgs['convformer_s36']
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
url= model.default_cfg['url'], map_location="cpu", check_hash=True)
model.load_state_dict(state_dict)
return model
@register_model
def convformer_s36_384(pretrained=False, **kwargs):
model = MetaFormer(
depths=[3, 12, 18, 3],