摘要

这篇文章告诉大家如何在DBnet中加入新的主干网络。通过这篇文章你可以学到如何将现有的主干网络加入到DBNet中，提高DBNet的检测能力

主干网络

我加入的网络是ConvNext。代码详见：

# 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_modelclass Block(nn.Module):r""" ConvNeXt Block. There are two equivalent implementations:(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 backWe use (2) as we find it slightly faster in PyTorchArgs:dim (int): Number of input channels.drop_path (float): Stochastic depth rate. Default: 0.0layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6."""def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):super().__init__()self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise convself.norm = LayerNorm(dim, eps=1e-6)self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layersself.act = nn.GELU()self.pwconv2 = nn.Linear(4 * dim, dim)self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) if layer_scale_init_value > 0 else Noneself.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()def forward(self, x):input = xx = self.dwconv(x)x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)x = self.norm(x)x = self.pwconv1(x)x = self.act(x)x = self.pwconv2(x)if self.gamma is not None:x = self.gamma * xx = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)x = input + self.drop_path(x)return xclass ConvNeXt(nn.Module):r""" ConvNeXtA PyTorch impl of : `A ConvNet for the 2020s`  -https://arxiv.org/pdf/2201.03545.pdfArgs:in_chans (int): Number of input image channels. Default: 3num_classes (int): Number of classes for classification head. Default: 1000depths (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=[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 layersstem = 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"),nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),)self.downsample_layers.append(downsample_layer)self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocksdp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] cur = 0for 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 layerself.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 xclass LayerNorm(nn.Module):r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width)."""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 = epsself.data_format = data_formatif self.data_format not in ["channels_last", "channels_first"]:raise NotImplementedError self.normalized_shape = (normalized_shape, )def forward(self, x):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 xmodel_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_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, **kwargs):model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)if pretrained:url = 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, **kwargs):model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)if pretrained:url = model_urls['convnext_small_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_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", check_hash=True)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", check_hash=True)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:url = model_urls['convnext_xlarge_22k'] if in_22k else model_urls['convnext_xlarge_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

操作过程

修改主干网络

我们把上面的代码改造成DBNet的主干网络。
DBNet的代码：https://github.com/WenmuZhou/DBNet.pytorch
将其下载下来。
然后在./models/backbone下面新建convnext.py脚本。
将上面的代码，插入进来。
然后修改ConvNeXt的 forward()函数，修改为：

 def forward(self, x):x= self.downsample_layers[0](x)x2 = self.stages[0](x)x = self.downsample_layers[1](x2)x3 = self.stages[1](x)x = self.downsample_layers[2](x3)x4 = self.stages[2](x)x = self.downsample_layers[3](x4)x5 = self.stages[3](x)return x2,x3,x4,x5

和网络图对应起来。

在 def __init__函数中增加out_channels属性

 self.out_channels=dims

将in_chans改为in_channels，这样就能和配置文件的字段对上了。

删除@register_model

接下来修改创建模型的部分，用convnext_tiny举例

def convnext_tiny(pretrained=False, **kwargs):model = ConvNeXt(depths=[2, 2, 3, 3], dims=[96, 192, 384, 768], **kwargs)if pretrained:url = 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"],strict=False)return model

原始的convnext_tiny很大，我在这里把depths做了修改，
将depths=[3, 3, 9, 3]改为depths=[2, 2, 3, 3]，设置strict为False，防止预训练权重对不上报错。
在代码的开始部分增加：

__all__ = ['convnext_tiny', 'convnext_small', 'convnext_base']

这样可以访问的方法就这三个了。

修改完成后测一下：

if __name__ == '__main__':import torchx = torch.zeros(2, 3, 640, 640)net = convnext_tiny(pretrained=False)y = net(x)for u in y:print(u.shape)print(net.out_channels)

完整代码：

# 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__all__ = ['convnext_tiny', 'convnext_small', 'convnext_base']class Block(nn.Module):r""" ConvNeXt Block. There are two equivalent implementations:(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 backWe use (2) as we find it slightly faster in PyTorchArgs:dim (int): Number of input channels.drop_path (float): Stochastic depth rate. Default: 0.0layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6."""def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):super().__init__()self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)  # depthwise convself.norm = LayerNorm(dim, eps=1e-6)self.pwconv1 = nn.Linear(dim, 4 * dim)  # pointwise/1x1 convs, implemented with linear layersself.act = nn.GELU()self.pwconv2 = nn.Linear(4 * dim, dim)self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),requires_grad=True) if layer_scale_init_value > 0 else Noneself.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()def forward(self, x):input = xx = self.dwconv(x)x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)x = self.norm(x)x = self.pwconv1(x)x = self.act(x)x = self.pwconv2(x)if self.gamma is not None:x = self.gamma * xx = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)x = input + self.drop_path(x)return xclass ConvNeXt(nn.Module):r""" ConvNeXtA PyTorch impl of : `A ConvNet for the 2020s`  -https://arxiv.org/pdf/2201.03545.pdfArgs:in_chans (int): Number of input image channels. Default: 3num_classes (int): Number of classes for classification head. Default: 1000depths (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_channels=3, num_classes=1000,depths=[3, 3, 3, 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()  # stem and 3 intermediate downsampling conv layersstem = nn.Sequential(nn.Conv2d(in_channels, 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"),nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),)self.downsample_layers.append(downsample_layer)self.stages = nn.ModuleList()  # 4 feature resolution stages, each consisting of multiple residual blocksdp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]cur = 0for 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 layerself.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)self.out_channels=[96,192,384,768]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.downsample_layers[0](x)x2 = self.stages[0](x)x = self.downsample_layers[1](x2)x3 = self.stages[1](x)x = self.downsample_layers[2](x3)x4 = self.stages[2](x)x = self.downsample_layers[3](x4)x5 = self.stages[3](x)return x2,x3,x4,x5class LayerNorm(nn.Module):r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.The ordering of the dimensions in the inputs. channels_last corresponds to inputs withshape (batch_size, height, width, channels) while channels_first corresponds to inputswith shape (batch_size, channels, height, width)."""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 = epsself.data_format = data_formatif self.data_format not in ["channels_last", "channels_first"]:raise NotImplementedErrorself.normalized_shape = (normalized_shape,)def forward(self, x):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 xmodel_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_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",
}def convnext_tiny(pretrained=False, **kwargs):model = ConvNeXt(depths=[2, 2, 3, 3], dims=[96, 192, 384, 768], **kwargs)if pretrained:url = 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"],strict=False)return modeldef convnext_small(pretrained=False, **kwargs):model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)if pretrained:url = model_urls['convnext_small_1k']checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)model.load_state_dict(checkpoint["model"])return modeldef 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", check_hash=True)model.load_state_dict(checkpoint["model"])return modeldef 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", check_hash=True)model.load_state_dict(checkpoint["model"])return modeldef convnext_xlarge(pretrained=False, in_22k=False, **kwargs):model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)if pretrained:url = model_urls['convnext_xlarge_22k'] if in_22k else model_urls['convnext_xlarge_1k']checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)model.load_state_dict(checkpoint["model"])return modelif __name__ == '__main__':import torchx = torch.zeros(2, 3, 640, 640)net = convnext_tiny(pretrained=False)y = net(x)for u in y:print(u.shape)print(net.out_channels)

将主干网络加到配置列表

修改完成后，在__init__.py导入convnext，在support_backbone列表中增加convnext_tiny字段，如下图：

接下来在./config文件夹中增加convnext的配置文件，命名为：icdar2015_convnext_FPN_DBhead_polyLR.yaml
从其他的配置文件中复制一份参数，
然后将backbone的type修改为 convnext_tiny

完整代码：

name: DBNet
base: ['config/icdar2015.yaml']
arch:type: Modelbackbone:type: convnext_tinypretrained: trueneck:type: FPNinner_channels: 384head:type: DBHeadout_channels: 2k: 50
post_processing:type: SegDetectorRepresenterargs:thresh: 0.3box_thresh: 0.7max_candidates: 1000unclip_ratio: 1.5 # from paper
metric:type: QuadMetricargs:is_output_polygon: false
loss:type: DBLossalpha: 1beta: 10ohem_ratio: 3
optimizer:type: Adamargs:lr: 0.001weight_decay: 0amsgrad: true
lr_scheduler:type: WarmupPolyLRargs:warmup_epoch: 3
trainer:seed: 2epochs: 1200log_iter: 10show_images_iter: 50resume_checkpoint: ''finetune_checkpoint: ''output_dir: outputtensorboard: true
dataset:train:dataset:args:data_path:- ./datasets/train.txtimg_mode: RGBloader:batch_size: 2shuffle: truepin_memory: truenum_workers: 1collate_fn: ''validate:dataset:args:data_path:- ./datasets/test.txtpre_processes:- type: ResizeShortSizeargs:short_size: 736resize_text_polys: falseimg_mode: RGBloader:batch_size: 1shuffle: truepin_memory: falsenum_workers: 1collate_fn: ICDARCollectFN

然后就可以运行了。

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如何在DBNet中加入新的主干网络

摘要

主干网络

操作过程

修改主干网络

将主干网络加到配置列表

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