#!/usr/bin/env python3 # -*- coding:utf-8 -*- # Copyright (c) Megvii, Inc. and its affiliates. import torch from torch import nn class SiLU(nn.Module): @staticmethod def forward(x): return x * torch.sigmoid(x) def get_activation(name="silu", inplace=True): if name == "silu": module = SiLU() elif name == "relu": module = nn.ReLU(inplace=inplace) elif name == "lrelu": module = nn.LeakyReLU(0.1, inplace=inplace) else: raise AttributeError("Unsupported act type: {}".format(name)) return module class Focus(nn.Module): def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="silu"): super().__init__() self.conv = BaseConv(in_channels * 4, out_channels, ksize, stride, act=act) def forward(self, x): patch_top_left = x[..., ::2, ::2] patch_bot_left = x[..., 1::2, ::2] patch_top_right = x[..., ::2, 1::2] patch_bot_right = x[..., 1::2, 1::2] x = torch.cat((patch_top_left, patch_bot_left, patch_top_right, patch_bot_right,), dim=1,) return self.conv(x) class BaseConv(nn.Module): def __init__(self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu"): super().__init__() pad = (ksize - 1) // 2 self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=ksize, stride=stride, padding=pad, groups=groups, bias=bias) self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.03) self.act = get_activation(act, inplace=True) def forward(self, x): return self.act(self.bn(self.conv(x))) def fuseforward(self, x): return self.act(self.conv(x)) class DWConv(nn.Module): def __init__(self, in_channels, out_channels, ksize, stride=1, act="silu"): super().__init__() self.dconv = BaseConv(in_channels, in_channels, ksize=ksize, stride=stride, groups=in_channels, act=act,) self.pconv = BaseConv(in_channels, out_channels, ksize=1, stride=1, groups=1, act=act) def forward(self, x): x = self.dconv(x) return self.pconv(x) class SPPBottleneck(nn.Module): def __init__(self, in_channels, out_channels, kernel_sizes=(5, 9, 13), activation="silu"): super().__init__() hidden_channels = in_channels // 2 self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=activation) self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2) for ks in kernel_sizes]) conv2_channels = hidden_channels * (len(kernel_sizes) + 1) self.conv2 = BaseConv(conv2_channels, out_channels, 1, stride=1, act=activation) def forward(self, x): x = self.conv1(x) x = torch.cat([x] + [m(x) for m in self.m], dim=1) x = self.conv2(x) return x #--------------------------------------------------# # 残差结构的构建，小的残差结构 #--------------------------------------------------# class Bottleneck(nn.Module): # Standard bottleneck def __init__(self, in_channels, out_channels, shortcut=True, expansion=0.5, depthwise=False, act="silu",): super().__init__() hidden_channels = int(out_channels * expansion) Conv = DWConv if depthwise else BaseConv #--------------------------------------------------# # 利用1x1卷积进行通道数的缩减。缩减率一般是50% #--------------------------------------------------# self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act) #--------------------------------------------------# # 利用3x3卷积进行通道数的拓张。并且完成特征提取 #--------------------------------------------------# self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act) self.use_add = shortcut and in_channels == out_channels def forward(self, x): y = self.conv2(self.conv1(x)) if self.use_add: y = y + x return y class CSPLayer(nn.Module): def __init__(self, in_channels, out_channels, n=1, shortcut=True, expansion=0.5, depthwise=False, act="silu",): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__() hidden_channels = int(out_channels * expansion) #--------------------------------------------------# # 主干部分的初次卷积 #--------------------------------------------------# self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act) #--------------------------------------------------# # 大的残差边部分的初次卷积 #--------------------------------------------------# self.conv2 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act) #-----------------------------------------------# # 对堆叠的结果进行卷积的处理 #-----------------------------------------------# self.conv3 = BaseConv(2 * hidden_channels, out_channels, 1, stride=1, act=act) #--------------------------------------------------# # 根据循环的次数构建上述Bottleneck残差结构 #--------------------------------------------------# module_list = [Bottleneck(hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act) for _ in range(n)] self.m = nn.Sequential(*module_list) def forward(self, x): #-------------------------------# # x_1是主干部分 #-------------------------------# x_1 = self.conv1(x) #-------------------------------# # x_2是大的残差边部分 #-------------------------------# x_2 = self.conv2(x) #-----------------------------------------------# # 主干部分利用残差结构堆叠继续进行特征提取 #-----------------------------------------------# x_1 = self.m(x_1) #-----------------------------------------------# # 主干部分和大的残差边部分进行堆叠 #-----------------------------------------------# x = torch.cat((x_1, x_2), dim=1) #-----------------------------------------------# # 对堆叠的结果进行卷积的处理 #-----------------------------------------------# return self.conv3(x) class CSPDarknet(nn.Module): def __init__(self, dep_mul, wid_mul, out_features=("dark3", "dark4", "dark5"), depthwise=False, act="silu",): super().__init__() assert out_features, "please provide output features of Darknet" self.out_features = out_features Conv = DWConv if depthwise else BaseConv #-----------------------------------------------# # 输入图片是640, 640, 3 # 初始的基本通道是64 #-----------------------------------------------# base_channels = int(wid_mul * 64) # 64 base_depth = max(round(dep_mul * 3), 1) # 3 #-----------------------------------------------# # 利用focus网络结构进行特征提取 # 640, 640, 3 -> 320, 320, 12 -> 320, 320, 64 #-----------------------------------------------# self.stem = Focus(3, base_channels, ksize=3, act=act) #-----------------------------------------------# # 完成卷积之后，320, 320, 64 -> 160, 160, 128 # 完成CSPlayer之后，160, 160, 128 -> 160, 160, 128 #-----------------------------------------------# self.dark2 = nn.Sequential( Conv(base_channels, base_channels * 2, 3, 2, act=act), CSPLayer(base_channels * 2, base_channels * 2, n=base_depth, depthwise=depthwise, act=act), ) #-----------------------------------------------# # 完成卷积之后，160, 160, 128 -> 80, 80, 256 # 完成CSPlayer之后，80, 80, 256 -> 80, 80, 256 #-----------------------------------------------# self.dark3 = nn.Sequential( Conv(base_channels * 2, base_channels * 4, 3, 2, act=act), CSPLayer(base_channels * 4, base_channels * 4, n=base_depth * 3, depthwise=depthwise, act=act), ) #-----------------------------------------------# # 完成卷积之后，80, 80, 256 -> 40, 40, 512 # 完成CSPlayer之后，40, 40, 512 -> 40, 40, 512 #-----------------------------------------------# self.dark4 = nn.Sequential( Conv(base_channels * 4, base_channels * 8, 3, 2, act=act), CSPLayer(base_channels * 8, base_channels * 8, n=base_depth * 3, depthwise=depthwise, act=act), ) #-----------------------------------------------# # 完成卷积之后，40, 40, 512 -> 20, 20, 1024 # 完成SPP之后，20, 20, 1024 -> 20, 20, 1024 # 完成CSPlayer之后，20, 20, 1024 -> 20, 20, 1024 #-----------------------------------------------# self.dark5 = nn.Sequential( Conv(base_channels * 8, base_channels * 16, 3, 2, act=act), SPPBottleneck(base_channels * 16, base_channels * 16, activation=act), CSPLayer(base_channels * 16, base_channels * 16, n=base_depth, shortcut=False, depthwise=depthwise, act=act), ) def forward(self, x): outputs = {} x = self.stem(x) outputs["stem"] = x x = self.dark2(x) outputs["dark2"] = x #-----------------------------------------------# # dark3的输出为80, 80, 256，是一个有效特征层 #-----------------------------------------------# x = self.dark3(x) outputs["dark3"] = x #-----------------------------------------------# # dark4的输出为40, 40, 512，是一个有效特征层 #-----------------------------------------------# x = self.dark4(x) outputs["dark4"] = x #-----------------------------------------------# # dark5的输出为20, 20, 1024，是一个有效特征层 #-----------------------------------------------# x = self.dark5(x) outputs["dark5"] = x return {k: v for k, v in outputs.items() if k in self.out_features} if __name__ == '__main__': print(CSPDarknet(1, 1))