YOLOv9有效改进|使用动态蛇形卷积Dynamic Snake Convolution

专栏介绍：YOLOv9改进系列 | 包含深度学习最新创新，主力高效涨点！！！

一、改进点介绍

使用ICCV2023中的动态蛇形卷积替换YOLOv9网络中的Conv模块。

二、Dynamic Snake Convolution模块详解

2.1 模块简介

应用场景：适合具有细长微弱的局部结构特征与复杂多变的全局形态特征的场景。

三、 Dynamic Snake Convolution模块使用教程

3.1 Dynamic Snake Convolution模块的代码

import torch
import torch.nn as nn
from conv import Conv__all__ = ['DySnakeConv']class DySnakeConv(nn.Module):def __init__(self, inc, ouc, k=3) -> None:super().__init__()c_ = ouc // 3 // 16 * 16self.conv_0 = Conv(inc, ouc - 2 *c_, k)self.conv_x = DSConv(inc, c_, 0, k)self.conv_y = DSConv(inc, c_, 1, k)def forward(self, x):return torch.cat([self.conv_0(x), self.conv_x(x), self.conv_y(x)], dim=1)class DSConv(nn.Module):def __init__(self, in_ch, out_ch, morph, kernel_size=3, if_offset=True, extend_scope=1):"""The Dynamic Snake Convolution:param in_ch: input channel:param out_ch: output channel:param kernel_size: the size of kernel:param extend_scope: the range to expand (default 1 for this method):param morph: the morphology of the convolution kernel is mainly divided into two typesalong the x-axis (0) and the y-axis (1) (see the paper for details):param if_offset: whether deformation is required, if it is False, it is the standard convolution kernel"""super(DSConv, self).__init__()# use the <offset_conv> to learn the deformable offsetself.offset_conv = nn.Conv2d(in_ch, 2 * kernel_size, 3, padding=1)self.bn = nn.BatchNorm2d(2 * kernel_size)self.kernel_size = kernel_size# two types of the DSConv (along x-axis and y-axis)self.dsc_conv_x = nn.Conv2d(in_ch,out_ch,kernel_size=(kernel_size, 1),stride=(kernel_size, 1),padding=0,)self.dsc_conv_y = nn.Conv2d(in_ch,out_ch,kernel_size=(1, kernel_size),stride=(1, kernel_size),padding=0,)self.gn = nn.GroupNorm(out_ch // 4, out_ch)self.act = Conv.default_actself.extend_scope = extend_scopeself.morph = morphself.if_offset = if_offsetdef forward(self, f):offset = self.offset_conv(f)offset = self.bn(offset)# We need a range of deformation between -1 and 1 to mimic the snake's swingoffset = torch.tanh(offset)input_shape = f.shapedsc = DSC(input_shape, self.kernel_size, self.extend_scope, self.morph)deformed_feature = dsc.deform_conv(f, offset, self.if_offset)if self.morph == 0:x = self.dsc_conv_x(deformed_feature.type(f.dtype))x = self.gn(x)x = self.act(x)return xelse:x = self.dsc_conv_y(deformed_feature.type(f.dtype))x = self.gn(x)x = self.act(x)return x# Core code, for ease of understanding, we mark the dimensions of input and output next to the code
class DSC(object):def __init__(self, input_shape, kernel_size, extend_scope, morph):self.num_points = kernel_sizeself.width = input_shape[2]self.height = input_shape[3]self.morph = morphself.extend_scope = extend_scope  # offset (-1 ~ 1) * extend_scope# define feature map shape"""B: Batch size  C: Channel  W: Width  H: Height"""self.num_batch = input_shape[0]self.num_channels = input_shape[1]"""input: offset [B,2*K,W,H]  K: Kernel size (2*K: 2D image, deformation contains <x_offset> and <y_offset>)output_x: [B,1,W,K*H]   coordinate mapoutput_y: [B,1,K*W,H]   coordinate map"""def _coordinate_map_3D(self, offset, if_offset):device = offset.device# offsety_offset, x_offset = torch.split(offset, self.num_points, dim=1)y_center = torch.arange(0, self.width).repeat([self.height])y_center = y_center.reshape(self.height, self.width)y_center = y_center.permute(1, 0)y_center = y_center.reshape([-1, self.width, self.height])y_center = y_center.repeat([self.num_points, 1, 1]).float()y_center = y_center.unsqueeze(0)x_center = torch.arange(0, self.height).repeat([self.width])x_center = x_center.reshape(self.width, self.height)x_center = x_center.permute(0, 1)x_center = x_center.reshape([-1, self.width, self.height])x_center = x_center.repeat([self.num_points, 1, 1]).float()x_center = x_center.unsqueeze(0)if self.morph == 0:"""Initialize the kernel and flatten the kernely: only need 0x: -num_points//2 ~ num_points//2 (Determined by the kernel size)!!! The related PPT will be submitted later, and the PPT will contain the whole changes of each step"""y = torch.linspace(0, 0, 1)x = torch.linspace(-int(self.num_points // 2),int(self.num_points // 2),int(self.num_points),)y, x = torch.meshgrid(y, x, indexing = 'ij')y_spread = y.reshape(-1, 1)x_spread = x.reshape(-1, 1)y_grid = y_spread.repeat([1, self.width * self.height])y_grid = y_grid.reshape([self.num_points, self.width, self.height])y_grid = y_grid.unsqueeze(0)  # [B*K*K, W,H]x_grid = x_spread.repeat([1, self.width * self.height])x_grid = x_grid.reshape([self.num_points, self.width, self.height])x_grid = x_grid.unsqueeze(0)  # [B*K*K, W,H]y_new = y_center + y_gridx_new = x_center + x_gridy_new = y_new.repeat(self.num_batch, 1, 1, 1).to(device)x_new = x_new.repeat(self.num_batch, 1, 1, 1).to(device)y_offset_new = y_offset.detach().clone()if if_offset:y_offset = y_offset.permute(1, 0, 2, 3)y_offset_new = y_offset_new.permute(1, 0, 2, 3)center = int(self.num_points // 2)# The center position remains unchanged and the rest of the positions begin to swing# This part is quite simple. The main idea is that "offset is an iterative process"y_offset_new[center] = 0for index in range(1, center):y_offset_new[center + index] = (y_offset_new[center + index - 1] + y_offset[center + index])y_offset_new[center - index] = (y_offset_new[center - index + 1] + y_offset[center - index])y_offset_new = y_offset_new.permute(1, 0, 2, 3).to(device)y_new = y_new.add(y_offset_new.mul(self.extend_scope))y_new = y_new.reshape([self.num_batch, self.num_points, 1, self.width, self.height])y_new = y_new.permute(0, 3, 1, 4, 2)y_new = y_new.reshape([self.num_batch, self.num_points * self.width, 1 * self.height])x_new = x_new.reshape([self.num_batch, self.num_points, 1, self.width, self.height])x_new = x_new.permute(0, 3, 1, 4, 2)x_new = x_new.reshape([self.num_batch, self.num_points * self.width, 1 * self.height])return y_new, x_newelse:"""Initialize the kernel and flatten the kernely: -num_points//2 ~ num_points//2 (Determined by the kernel size)x: only need 0"""y = torch.linspace(-int(self.num_points // 2),int(self.num_points // 2),int(self.num_points),)x = torch.linspace(0, 0, 1)y, x = torch.meshgrid(y, x, indexing = 'ij')y_spread = y.reshape(-1, 1)x_spread = x.reshape(-1, 1)y_grid = y_spread.repeat([1, self.width * self.height])y_grid = y_grid.reshape([self.num_points, self.width, self.height])y_grid = y_grid.unsqueeze(0)x_grid = x_spread.repeat([1, self.width * self.height])x_grid = x_grid.reshape([self.num_points, self.width, self.height])x_grid = x_grid.unsqueeze(0)y_new = y_center + y_gridx_new = x_center + x_gridy_new = y_new.repeat(self.num_batch, 1, 1, 1)x_new = x_new.repeat(self.num_batch, 1, 1, 1)y_new = y_new.to(device)x_new = x_new.to(device)x_offset_new = x_offset.detach().clone()if if_offset:x_offset = x_offset.permute(1, 0, 2, 3)x_offset_new = x_offset_new.permute(1, 0, 2, 3)center = int(self.num_points // 2)x_offset_new[center] = 0for index in range(1, center):x_offset_new[center + index] = (x_offset_new[center + index - 1] + x_offset[center + index])x_offset_new[center - index] = (x_offset_new[center - index + 1] + x_offset[center - index])x_offset_new = x_offset_new.permute(1, 0, 2, 3).to(device)x_new = x_new.add(x_offset_new.mul(self.extend_scope))y_new = y_new.reshape([self.num_batch, 1, self.num_points, self.width, self.height])y_new = y_new.permute(0, 3, 1, 4, 2)y_new = y_new.reshape([self.num_batch, 1 * self.width, self.num_points * self.height])x_new = x_new.reshape([self.num_batch, 1, self.num_points, self.width, self.height])x_new = x_new.permute(0, 3, 1, 4, 2)x_new = x_new.reshape([self.num_batch, 1 * self.width, self.num_points * self.height])return y_new, x_new"""input: input feature map [N,C,D,W,H]；coordinate map [N,K*D,K*W,K*H] output: [N,1,K*D,K*W,K*H]  deformed feature map"""def _bilinear_interpolate_3D(self, input_feature, y, x):device = input_feature.devicey = y.reshape([-1]).float()x = x.reshape([-1]).float()zero = torch.zeros([]).int()max_y = self.width - 1max_x = self.height - 1# find 8 grid locationsy0 = torch.floor(y).int()y1 = y0 + 1x0 = torch.floor(x).int()x1 = x0 + 1# clip out coordinates exceeding feature map volumey0 = torch.clamp(y0, zero, max_y)y1 = torch.clamp(y1, zero, max_y)x0 = torch.clamp(x0, zero, max_x)x1 = torch.clamp(x1, zero, max_x)input_feature_flat = input_feature.flatten()input_feature_flat = input_feature_flat.reshape(self.num_batch, self.num_channels, self.width, self.height)input_feature_flat = input_feature_flat.permute(0, 2, 3, 1)input_feature_flat = input_feature_flat.reshape(-1, self.num_channels)dimension = self.height * self.widthbase = torch.arange(self.num_batch) * dimensionbase = base.reshape([-1, 1]).float()repeat = torch.ones([self.num_points * self.width * self.height]).unsqueeze(0)repeat = repeat.float()base = torch.matmul(base, repeat)base = base.reshape([-1])base = base.to(device)base_y0 = base + y0 * self.heightbase_y1 = base + y1 * self.height# top rectangle of the neighbourhood volumeindex_a0 = base_y0 - base + x0index_c0 = base_y0 - base + x1# bottom rectangle of the neighbourhood volumeindex_a1 = base_y1 - base + x0index_c1 = base_y1 - base + x1# get 8 grid valuesvalue_a0 = input_feature_flat[index_a0.type(torch.int64)].to(device)value_c0 = input_feature_flat[index_c0.type(torch.int64)].to(device)value_a1 = input_feature_flat[index_a1.type(torch.int64)].to(device)value_c1 = input_feature_flat[index_c1.type(torch.int64)].to(device)# find 8 grid locationsy0 = torch.floor(y).int()y1 = y0 + 1x0 = torch.floor(x).int()x1 = x0 + 1# clip out coordinates exceeding feature map volumey0 = torch.clamp(y0, zero, max_y + 1)y1 = torch.clamp(y1, zero, max_y + 1)x0 = torch.clamp(x0, zero, max_x + 1)x1 = torch.clamp(x1, zero, max_x + 1)x0_float = x0.float()x1_float = x1.float()y0_float = y0.float()y1_float = y1.float()vol_a0 = ((y1_float - y) * (x1_float - x)).unsqueeze(-1).to(device)vol_c0 = ((y1_float - y) * (x - x0_float)).unsqueeze(-1).to(device)vol_a1 = ((y - y0_float) * (x1_float - x)).unsqueeze(-1).to(device)vol_c1 = ((y - y0_float) * (x - x0_float)).unsqueeze(-1).to(device)outputs = (value_a0 * vol_a0 + value_c0 * vol_c0 + value_a1 * vol_a1 +value_c1 * vol_c1)if self.morph == 0:outputs = outputs.reshape([self.num_batch,self.num_points * self.width,1 * self.height,self.num_channels,])outputs = outputs.permute(0, 3, 1, 2)else:outputs = outputs.reshape([self.num_batch,1 * self.width,self.num_points * self.height,self.num_channels,])outputs = outputs.permute(0, 3, 1, 2)return outputsdef deform_conv(self, input, offset, if_offset):y, x = self._coordinate_map_3D(offset, if_offset)deformed_feature = self._bilinear_interpolate_3D(input, y, x)return deformed_featureif __name__ == "__main__":model = DySnakeConv(32, 32)print(model(torch.zeros(2, 32, 640, 320)).shape)

3.2 在YOlO v9中的添加教程

阅读YOLOv9添加模块教程或使用下文操作

1. 将YOLOv9工程中models下common.py文件中的最下行增加模块的代码。

2. 将YOLOv9工程中models下yolo.py文件中的第681行（可能因版本变化而变化）增加以下代码。

            RepNCSPELAN4, SPPELAN, DySnakeConv}:

3.3 运行配置文件

# YOLOv9
# Powered bu https://blog.csdn.net/StopAndGoyyy
# parameters
nc: 80  # number of classes
depth_multiple: 1  # model depth multiple
width_multiple: 1  # layer channel multiple
#activation: nn.LeakyReLU(0.1)
#activation: nn.ReLU()# anchors
anchors: 3# YOLOv9 backbone
backbone:[[-1, 1, Silence, []],  # conv down[-1, 1, Conv, [64, 3, 2]],  # 1-P1/2# conv down[-1, 1, Conv, [128, 3, 2]],  # 2-P2/4# elan-1 block[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 3# avg-conv down[-1, 1, ADown, [256]],  # 4-P3/8# elan-2 block[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 5# avg-conv down[-1, 1, ADown, [512]],  # 6-P4/16# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 7# avg-conv down[-1, 1, ADown, [512]],  # 8-P5/32# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 9]# YOLOv9 head
head:[# elan-spp block[-1, 1, SPPELAN, [512, 256]],  # 10# up-concat merge[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 7], 1, Concat, [1]],  # cat backbone P4# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 13# up-concat merge[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 5], 1, Concat, [1]],  # cat backbone P3# elan-2 block[-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 16 (P3/8-small)# avg-conv-down merge[-1, 1, ADown, [256]],[[-1, 13], 1, Concat, [1]],  # cat head P4# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 19 (P4/16-medium)# avg-conv-down merge[-1, 1, ADown, [512]],[[-1, 10], 1, Concat, [1]],  # cat head P5# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 22 (P5/32-large)# multi-level reversible auxiliary branch# routing[5, 1, CBLinear, [[256]]], # 23[7, 1, CBLinear, [[256, 512]]], # 24[9, 1, CBLinear, [[256, 512, 512]]], # 25# conv down[0, 1, Conv, [64, 3, 2]],  # 26-P1/2# conv down[-1, 1, Conv, [128, 3, 2]],  # 27-P2/4# elan-1 block[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 28# avg-conv down fuse[-1, 1, ADown, [256]],  # 29-P3/8[[23, 24, 25, -1], 1, CBFuse, [[0, 0, 0]]], # 30  # elan-2 block[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 31# avg-conv down fuse[-1, 1, ADown, [512]],  # 32-P4/16[[24, 25, -1], 1, CBFuse, [[1, 1]]], # 33 # elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 34# avg-conv down fuse[-1, 1, ADown, [512]],  # 35-P5/32[[25, -1], 1, CBFuse, [[2]]], # 36# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 37[-1, 1, DySnakeConv, [512]],  # 38# detection head# detect[[31, 34, 38, 16, 19, 22], 1, DualDDetect, [nc]],  # DualDDetect(A3, A4, A5, P3, P4, P5)]