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