178 lines
7.4 KiB
Python
178 lines
7.4 KiB
Python
import torch
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import torch.nn as 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 autopad(k, p=None):
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if p is None:
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p = k // 2 if isinstance(k, int) else [x // 2 for x in k]
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return p
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class Focus(nn.Module):
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super(Focus, self).__init__()
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self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
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def forward(self, x):
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# 320, 320, 12 => 320, 320, 64
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return self.conv(
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# 640, 640, 3 => 320, 320, 12
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torch.cat(
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[
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x[..., ::2, ::2],
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x[..., 1::2, ::2],
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x[..., ::2, 1::2],
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x[..., 1::2, 1::2]
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], 1
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)
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)
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class Conv(nn.Module):
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
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super(Conv, self).__init__()
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
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self.bn = nn.BatchNorm2d(c2, eps=0.001, momentum=0.03)
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self.act = SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
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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 Bottleneck(nn.Module):
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# Standard bottleneck
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def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
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super(Bottleneck, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_, c2, 3, 1, g=g)
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self.add = shortcut and c1 == c2
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def forward(self, x):
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return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
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class C3(nn.Module):
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# CSP Bottleneck with 3 convolutions
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(C3, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c1, c_, 1, 1)
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self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
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self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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# self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
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def forward(self, x):
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return self.cv3(torch.cat(
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(
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self.m(self.cv1(x)),
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self.cv2(x)
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)
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, dim=1))
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class SPP(nn.Module):
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# Spatial pyramid pooling layer used in YOLOv3-SPP
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def __init__(self, c1, c2, k=(5, 9, 13)):
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super(SPP, self).__init__()
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c_ = c1 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
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self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
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def forward(self, x):
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x = self.cv1(x)
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return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
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class CSPDarknet(nn.Module):
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def __init__(self, base_channels, base_depth, phi, pretrained):
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super().__init__()
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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base_channels是64
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#-----------------------------------------------#
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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, k=3)
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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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# 320, 320, 64 -> 160, 160, 128
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Conv(base_channels, base_channels * 2, 3, 2),
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# 160, 160, 128 -> 160, 160, 128
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C3(base_channels * 2, base_channels * 2, base_depth),
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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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# 在这里引出有效特征层80, 80, 256
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# 进行加强特征提取网络FPN的构建
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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),
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C3(base_channels * 4, base_channels * 4, base_depth * 3),
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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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# 在这里引出有效特征层40, 40, 512
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# 进行加强特征提取网络FPN的构建
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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),
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C3(base_channels * 8, base_channels * 8, base_depth * 3),
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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),
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SPP(base_channels * 16, base_channels * 16),
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C3(base_channels * 16, base_channels * 16, base_depth, shortcut=False),
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)
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if pretrained:
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url = {
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's' : 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_s_backbone.pth',
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'm' : 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_m_backbone.pth',
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'l' : 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_l_backbone.pth',
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'x' : 'https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/cspdarknet_x_backbone.pth',
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}[phi]
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checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", model_dir="./model_data")
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self.load_state_dict(checkpoint, strict=False)
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print("Load weights from ", url.split('/')[-1])
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def forward(self, x):
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x = self.stem(x)
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x = self.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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feat1 = 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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feat2 = 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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feat3 = x
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return feat1, feat2, feat3
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