From 93ccd57b2cb015272f61cbba595055b35bcf3c9f Mon Sep 17 00:00:00 2001 From: xqh5201314 <106581654+xqh5201314@users.noreply.github.com> Date: Wed, 21 Jun 2023 10:31:49 +0800 Subject: [PATCH] Add files via upload --- SPPF-imporve/SPPF-imporve.md | 35 +++++++ SPPF-imporve/SPPF_imporve.py | 111 +++++++++++++++++++++ SPPF-imporve/YOLOv5_sppf_imporve.yaml.yaml | 52 ++++++++++ 3 files changed, 198 insertions(+) create mode 100644 SPPF-imporve/SPPF-imporve.md create mode 100644 SPPF-imporve/SPPF_imporve.py create mode 100644 SPPF-imporve/YOLOv5_sppf_imporve.yaml.yaml diff --git a/SPPF-imporve/SPPF-imporve.md b/SPPF-imporve/SPPF-imporve.md new file mode 100644 index 0000000..8b43891 --- /dev/null +++ b/SPPF-imporve/SPPF-imporve.md @@ -0,0 +1,35 @@ +这是关于SPPF的系列改进包括SimSPPF，SPPCSPC,SimCSPSPPF + +SimSPPF为YOLOv6提出的，论文地址:[YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications | PDF (arxiv.org)](https://arxiv.org/pdf/2209.02976v1.pdf)； + +SPPCSPC为YOLOv7提出的,论文地址:[YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors | PDF (arxiv.org)](https://arxiv.org/pdf/2207.02696v1.pdf)； + +SimCSPSPPF为YOLOv6 3.0提出的,论文地址：https://arxiv.org/pdf/2301.05586v1.pdf。 + +具体改进详情见论文。 + +使用方法: + +1.将SPPF_imporve.py复制到models文件夹下 + +2.在models/yolo.py导入所需模块 + +from SPPF_imporve import SimSPPF, SPPCSPC, SimCSPSPPF + +``` +elif m is [SimSPPF，SPPCSPC,SimCSPSPPF]: + c1, c2 = ch[f], args[0] + if c2 != no: # if not output + c2 = make_divisible(c2 * gw, 8) + args = [c1, c2, *args[1:]] + if m in [SimSPPF，SPPCSPC,SimCSPSPPF]: + args.insert(2, n) # number of repeats + n = 1 +``` + +3.进行训练 + +```python +python train.py --cfg YOLOv5_sppf_imporve.yaml +``` + diff --git a/SPPF-imporve/SPPF_imporve.py b/SPPF-imporve/SPPF_imporve.py new file mode 100644 index 0000000..de7db46 --- /dev/null +++ b/SPPF-imporve/SPPF_imporve.py @@ -0,0 +1,111 @@ +import time +import torch +import torch.nn as nn + +def autopad(k, p=None): # kernel, padding + # Pad to 'same' + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad + return p + +class Conv(nn.Module): + # Standard convolution + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super(Conv, self).__init__() + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + def forward_fuse(self, x): + return self.act(self.conv(x)) + +class SimConv(nn.Module): + '''Normal Conv with ReLU activation''' + + def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False): + super().__init__() + padding = kernel_size // 2 + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + bias=bias, + ) + self.bn = nn.BatchNorm2d(out_channels) + self.act = nn.ReLU() + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + def forward_fuse(self, x): + return self.act(self.conv(x)) + +class SimSPPF(nn.Module): + '''Simplified SPPF with ReLU activation''' + + def __init__(self, in_channels, out_channels, kernel_size=5): + super().__init__() + c_ = in_channels // 2 # hidden channels + self.cv1 = SimConv(in_channels, c_, 1, 1) + self.cv2 = SimConv(c_ * 4, out_channels, 1, 1) + self.m = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=kernel_size // 2) + + def forward(self, x): + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) + +class SPPCSPC(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): + super(SPPCSPC, self).__init__() + c_ = int(2 * c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(c_, c_, 3, 1) + self.cv4 = Conv(c_, c_, 1, 1) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) + self.cv5 = Conv(4 * c_, c_, 1, 1) + self.cv6 = Conv(c_, c_, 3, 1) + self.cv7 = Conv(2 * c_, c2, 1, 1) + + def forward(self, x): + x1 = self.cv4(self.cv3(self.cv1(x))) + y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1))) + y2 = self.cv2(x) + return self.cv7(torch.cat((y1, y2), dim=1)) + + +class SimCSPSPPF(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, in_channels, out_channels, kernel_size=5, e=0.5): + super(SimCSPSPPF, self).__init__() + c_ = int(out_channels * e) # hidden channels + self.cv1 = SimConv(in_channels, c_, 1, 1) + self.cv2 = SimConv(in_channels, c_, 1, 1) + self.cv3 = SimConv(c_, c_, 3, 1) + self.cv4 = SimConv(c_, c_, 1, 1) + + self.m = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=kernel_size // 2) + self.cv5 = SimConv(4 * c_, c_, 1, 1) + self.cv6 = SimConv(c_, c_, 3, 1) + self.cv7 = SimConv(2 * c_, out_channels, 1, 1) + + def forward(self, x): + x1 = self.cv4(self.cv3(self.cv1(x))) + y0 = self.cv2(x) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + y1 = self.m(x1) + y2 = self.m(y1) + y3 = self.cv6(self.cv5(torch.cat([x1, y1, y2, self.m(y2)], 1))) + return self.cv7(torch.cat((y0, y3), dim=1)) diff --git a/SPPF-imporve/YOLOv5_sppf_imporve.yaml.yaml b/SPPF-imporve/YOLOv5_sppf_imporve.yaml.yaml new file mode 100644 index 0000000..7fa1eff --- /dev/null +++ b/SPPF-imporve/YOLOv5_sppf_imporve.yaml.yaml @@ -0,0 +1,52 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + # [-1, 1, SimSPPF, [1024, 5]], + # [-1, 1, SPPCSPC, [1024]], + # [-1, 1, SimCSPSPPF, [1024, 5]], + ] + + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] \ No newline at end of file