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# https://github.com/sail-sg/poolformer/blob/main/models/poolformer.py
import torch
import torch.nn as nn
from timm.layers import DropPath, trunc_normal_
class GroupNorm(nn.GroupNorm):
"""
Group Normalization with 1 group.
Input: tensor in shape [B, C, H, W]
"""
def __init__(self, num_channels, **kwargs):
super().__init__(1, num_channels, **kwargs)
class Pooling(nn.Module):
"""
Implementation of pooling for PoolFormer
--pool_size: pooling size
"""
def __init__(self, pool_size=3):
super().__init__()
self.pool = nn.AvgPool2d(
pool_size, stride=1, padding=pool_size//2, count_include_pad=False)
def forward(self, x):
return self.pool(x) - x
class Mlp(nn.Module):
"""
Implementation of MLP with 1*1 convolutions.
Input: tensor with shape [B, C, H, W]
"""
def __init__(self, in_features, hidden_features=None,
out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
self.drop = nn.Dropout(drop)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Conv2d):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class PoolFormerBlock(nn.Module):
"""
Implementation of one PoolFormer block.
--dim: embedding dim
--pool_size: pooling size
--mlp_ratio: mlp expansion ratio
--act_layer: activation
--norm_layer: normalization
--drop: dropout rate
--drop path: Stochastic Depth,
refer to https://arxiv.org/abs/1603.09382
--init_value: LayerScale,
refer to https://arxiv.org/abs/2103.17239
"""
def __init__(self, dim, pool_size=3, mlp_ratio=4., drop=0., drop_path=0.,
init_value=1e-5, act_layer=nn.GELU, norm_layer=GroupNorm):
super().__init__()
self.norm1 = norm_layer(dim)
self.token_mixer = Pooling(pool_size=pool_size)
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
act_layer=act_layer, drop=drop)
# The following two techniques are useful to train deep PoolFormers.
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.layer_scale_1 = nn.Parameter(init_value * torch.ones((dim)), requires_grad=True)
self.layer_scale_2 = nn.Parameter(init_value * torch.ones((dim)), requires_grad=True)
def forward(self, x):
x = x + self.drop_path(
self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * self.token_mixer(self.norm1(x)))
x = x + self.drop_path(
self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * self.mlp(self.norm2(x)))
return x
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