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深度学习Tricks，第一时间送达
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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转载：https://blog.csdn.net/m0_53578855/article/details/127413249