目标检测算法——YOLOv5/YOLOv7改进之结合ConvNeXt结构（纯卷积|超越Swin）
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论文题目：A ConvNet for the 2020s
论文地址：https://arxiv.org/abs/2201.03545
源代码：https://github.com/facebookresearch/ConvNeXt
纯卷积主干网络！可与大火的分层视觉Transformer竞争！多个任务性能超越Swin！
MetaAI在论文A ConvNet for the 2020s中, 从ResNet出发并借鉴Swin Transformer提出了一种新的 CNN 模型：ConvNeXt，其效果无论在图像分类还是检测分割任务上均能超过Swin Transformer，而且ConvNeXt和vision transformer一样具有类似的scalability（随着数据量和模型大小增加，性能同比提升）。
ConvNeXt 从原始的 ResNet 出发，逐步加入swin transform 的 trick，来改进模型。论文中适用 ResNet模型：ResNet50和ResNet200。其中ResNet50和Swin-T有类似的FLOPs（4G vs 4.5G），而ResNet200和Swin-B有类似的FLOPs（15G）。首先做的改进是调整训练策略，然后是模型设计方面的递进优化：宏观设计->ResNeXt化->改用Inverted bottleneck->采用large kernel size->微观设计。由于模型性能和FLOPs强相关，所以在优化过程中尽量保持FLOPs的稳定。
相关代码：
class ConvNeXt(nn.Module):
r""" ConvNeXt
A PyTorch impl of : `A ConvNet for the 2020s` -
https://arxiv.org/pdf/2201.03545.pdf
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.
"""
def __init__(self, in_chans=3, num_classes=1000,
depths=[3, 3, 9, 3], dims: list = [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() # stem and 3 intermediate downsampling conv layers
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)
for i in range(3):
downsample_layer = nn.Sequential(
LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
# 下采样
nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
)
self.downsample_layers.append(downsample_layer)
# 4 feature resolution stages, each consisting of multiple residual blocks
self.stages = nn.ModuleList()
dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
cur = 0
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)
x = self.head(x)
return x通过借鉴Swin Transformer的设计来逐步地改进模型。论文共选择了两个不同大小的ResNet模型：ResNet50和ResNet200，其中ResNet50和Swin-T有类似的FLOPs（4G vs 4.5G），而ResNet200和Swin-B有类似的FLOPs（15G）。首先做的改进是调整训练策略，然后是模型设计方面的递进优化：宏观设计>ResNeXt化>改用Inverted bottleneck>采用large kernel size>微观设计。由于模型性能和FLOPs强相关，所以在优化过程中尽量保持FLOPs的稳定。 ConVNeXt 这篇文章，通过借鉴 Swin TransForm 精心构建的 tricks，卷积在图像领域反超 Transformerer。
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