【CVPR2022 ConvNeXt网络结构详解】 A ConvNet for the 2020s
FAIR ConvNext主体网络结构代码详解，提供多个模型版本以满足不同应用场景的使用。
paper：https://arxiv.org/pdf/2201.03545.pdf
Source Code：GitHub - facebookresearch/ConvNeXt: Code release for ConvNeXt model
url中提供ImageNet 1K以及22K的权重链接，大家合理使用即可。具体性能表现见如上述github链接中所示。
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
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
#---------------------------------------------------------------------------------#
# LayerNorm 支持两种形式channels_last (default) or channels_first
# channels_last 对应具有形状的输入(batch_size, height, width, channels)
# channels_first 对应具有形状的输入(batch_size, channels, height, width)
# 我们这里默认为channels_last
#---------------------------------------------------------------------------------#
class LayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.data_format = data_format
self.normalized_shape = (normalized_shape, )
if self.data_format not in ["channels_last", "channels_first"]:
raise NotImplementedError
def forward(self, x):
#-----------------------------------------------#
# 当为默认形式的时候我们直接调用torch自带的layer_norm
#-----------------------------------------------#
if self.data_format == "channels_last":
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
elif self.data_format == "channels_first":
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
#--------------------------------------------------------------------------------------------------------------#
# ConvNeXt Block有两种等效的实现:
# (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
# (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
# 代码中使用（2），因为这个在PyTorch中稍微快一点
# args:
# dim (int): Number of input channels.
# drop_path (float): Stochastic depth rate. Default: 0.0
# layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
#--------------------------------------------------------------------------------------------------------------#
class Block(nn.Module):
def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
super().__init__()
#--------------------------#
# 7x7的逐层卷积
#--------------------------#
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
self.norm = LayerNorm(dim, eps=1e-6)
#--------------------------#
# 利用全连接层代替1x1卷积
#--------------------------#
self.pwconv1 = nn.Linear(dim, 4 * dim)
self.act = nn.GELU()
#--------------------------#
# 利用全连接层代替1x1卷积
#--------------------------#
self.pwconv2 = nn.Linear(4 * dim, dim)
#--------------------------#
# 加入缩放系数
#--------------------------#
self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) if layer_scale_init_value > 0 else None
#--------------------------#
# 加入Drop_path正则化
#--------------------------#
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
input = x
#--------------------------#
# 7x7的逐层卷积
#--------------------------#
x = self.dwconv(x)
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.norm(x)
#--------------------------#
# 利用全连接层代替1x1卷积
#--------------------------#
x = self.pwconv1(x)
x = self.act(x)
#--------------------------#
# 利用全连接层代替1x1卷积
#--------------------------#
x = self.pwconv2(x)
#--------------------------#
# 加入缩放系数
#--------------------------#
if self.gamma is not None:
x = self.gamma * x
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
#--------------------------#
# 加入Drop_path正则化
#--------------------------#
x = input + self.drop_path(x)
return x
#--------------------------------------------------------------------------------------------------------#
# ConvNeXt
# Args:
# in_chans (int): Number of input image channels. Default: 3
# num_classes (int): Number of classes for classification head. Default: 1000
# depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
# dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
# drop_path_rate (float): Stochastic depth rate. Default: 0.
# layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
# head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
#-----------------------------------------------------------------------------------------------------=---#
class ConvNeXt(nn.Module):
def __init__(
self, in_chans=3, num_classes=1000, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.,):
super().__init__()
self.downsample_layers = nn.ModuleList()
#--------------------------------------------------#
# bs, 3, 224, 224 -> bs, 96, 56, 56
#--------------------------------------------------#
stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
)
self.downsample_layers.append(stem)
#--------------------------------------------------#
# 定义三次下采样的过程
# 利用步长为2x2，卷积核大小为2x2的卷积进行下采样
#--------------------------------------------------#
for i in range(3):
downsample_layer = nn.Sequential(
LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
)
self.downsample_layers.append(downsample_layer)
#--------------------------------------------------#
# 根据深度的不同,定义不同的drop率
#--------------------------------------------------#
self.stages = nn.ModuleList()
dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
cur = 0
#--------------------------------------------------#
# 整个ConvNeXt除了Stem外,存在四个Stage
# 每个Stage里面是多个ConvNeXt Block的堆叠
#--------------------------------------------------#
for i in range(4):
stage = nn.Sequential(
*[Block(dim=dims[i], drop_path=dp_rates[cur + j], layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
)
self.stages.append(stage)
cur += depths[i]
self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer
self.head = nn.Linear(dims[-1], num_classes)
self.apply(self._init_weights)
self.head.weight.data.mul_(head_init_scale)
self.head.bias.data.mul_(head_init_scale)
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
trunc_normal_(m.weight, std=.02)
nn.init.constant_(m.bias, 0)
def forward_features(self, x):
for i in range(4):
x = self.downsample_layers[i](x)
x = self.stages[i](x)
return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C)
def forward(self, x):
x = self.forward_features(x)
out = self.head(x)
return out
model_urls = {
"convnext_tiny_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
"convnext_small_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",
"convnext_base_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",
"convnext_large_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",
"convnext_tiny_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth",
"convnext_small_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth",
"convnext_base_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth",
"convnext_large_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth",
"convnext_xlarge_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth",
}
@register_model
def convnext_tiny(pretrained=False, in_22k=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
if pretrained:
url = model_urls['convnext_tiny_22k'] if in_22k else model_urls['convnext_tiny_1k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
model.load_state_dict(checkpoint["model"])
return model
@register_model
def convnext_small(pretrained=False, in_22k=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
if pretrained:
url = model_urls['convnext_small_22k'] if in_22k else model_urls['convnext_small_1k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
model.load_state_dict(checkpoint["model"])
return model
@register_model
def convnext_base(pretrained=False, in_22k=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
if pretrained:
url = model_urls['convnext_base_22k'] if in_22k else model_urls['convnext_base_1k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
model.load_state_dict(checkpoint["model"])
return model
@register_model
def convnext_large(pretrained=False, in_22k=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
if pretrained:
url = model_urls['convnext_large_22k'] if in_22k else model_urls['convnext_large_1k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
model.load_state_dict(checkpoint["model"])
return model
@register_model
def convnext_xlarge(pretrained=False, in_22k=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
if pretrained:
assert in_22k, "only ImageNet-22K pre-trained ConvNeXt-XL is available; please set in_22k=True"
url = model_urls['convnext_xlarge_22k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")
model.load_state_dict(checkpoint["model"])
return model