From 66ef58ee74683921aee41e111e6bb4e84cc77cdd Mon Sep 17 00:00:00 2001 From: Bubbliiiing <47347516+bubbliiiing@users.noreply.github.com> Date: Tue, 21 Sep 2021 19:55:15 +0800 Subject: [PATCH] Delete yolo4.py --- nets/yolo4.py | 188 -------------------------------------------------- 1 file changed, 188 deletions(-) delete mode 100644 nets/yolo4.py diff --git a/nets/yolo4.py b/nets/yolo4.py deleted file mode 100644 index dc4b1a6..0000000 --- a/nets/yolo4.py +++ /dev/null @@ -1,188 +0,0 @@ -from collections import OrderedDict - -import torch -import torch.nn as nn - -from nets.CSPdarknet import darknet53 - - -def conv2d(filter_in, filter_out, kernel_size, stride=1): - pad = (kernel_size - 1) // 2 if kernel_size else 0 - return nn.Sequential(OrderedDict([ - ("conv", nn.Conv2d(filter_in, filter_out, kernel_size=kernel_size, stride=stride, padding=pad, bias=False)), - ("bn", nn.BatchNorm2d(filter_out)), - ("relu", nn.LeakyReLU(0.1)), - ])) - -#---------------------------------------------------# -# SPP结构，利用不同大小的池化核进行池化 -# 池化后堆叠 -#---------------------------------------------------# -class SpatialPyramidPooling(nn.Module): - def __init__(self, pool_sizes=[5, 9, 13]): - super(SpatialPyramidPooling, self).__init__() - - self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size//2) for pool_size in pool_sizes]) - - def forward(self, x): - features = [maxpool(x) for maxpool in self.maxpools[::-1]] - features = torch.cat(features + [x], dim=1) - - return features - -#---------------------------------------------------# -# 卷积 + 上采样 -#---------------------------------------------------# -class Upsample(nn.Module): - def __init__(self, in_channels, out_channels): - super(Upsample, self).__init__() - - self.upsample = nn.Sequential( - conv2d(in_channels, out_channels, 1), - nn.Upsample(scale_factor=2, mode='nearest') - ) - - def forward(self, x,): - x = self.upsample(x) - return x - -#---------------------------------------------------# -# 三次卷积块 -#---------------------------------------------------# -def make_three_conv(filters_list, in_filters): - m = nn.Sequential( - conv2d(in_filters, filters_list[0], 1), - conv2d(filters_list[0], filters_list[1], 3), - conv2d(filters_list[1], filters_list[0], 1), - ) - return m - -#---------------------------------------------------# -# 五次卷积块 -#---------------------------------------------------# -def make_five_conv(filters_list, in_filters): - m = nn.Sequential( - conv2d(in_filters, filters_list[0], 1), - conv2d(filters_list[0], filters_list[1], 3), - conv2d(filters_list[1], filters_list[0], 1), - conv2d(filters_list[0], filters_list[1], 3), - conv2d(filters_list[1], filters_list[0], 1), - ) - return m - -#---------------------------------------------------# -# 最后获得yolov4的输出 -#---------------------------------------------------# -def yolo_head(filters_list, in_filters): - m = nn.Sequential( - conv2d(in_filters, filters_list[0], 3), - nn.Conv2d(filters_list[0], filters_list[1], 1), - ) - return m - -#---------------------------------------------------# -# yolo_body -#---------------------------------------------------# -class YoloBody(nn.Module): - def __init__(self, num_anchors, num_classes): - super(YoloBody, self).__init__() - #---------------------------------------------------# - # 生成CSPdarknet53的主干模型 - # 获得三个有效特征层，他们的shape分别是： - # 52,52,256 - # 26,26,512 - # 13,13,1024 - #---------------------------------------------------# - self.backbone = darknet53(None) - - self.conv1 = make_three_conv([512,1024],1024) - self.SPP = SpatialPyramidPooling() - self.conv2 = make_three_conv([512,1024],2048) - - self.upsample1 = Upsample(512,256) - self.conv_for_P4 = conv2d(512,256,1) - self.make_five_conv1 = make_five_conv([256, 512],512) - - self.upsample2 = Upsample(256,128) - self.conv_for_P3 = conv2d(256,128,1) - self.make_five_conv2 = make_five_conv([128, 256],256) - - # 3*(5+num_classes) = 3*(5+20) = 3*(4+1+20)=75 - final_out_filter2 = num_anchors * (5 + num_classes) - self.yolo_head3 = yolo_head([256, final_out_filter2],128) - - self.down_sample1 = conv2d(128,256,3,stride=2) - self.make_five_conv3 = make_five_conv([256, 512],512) - - # 3*(5+num_classes) = 3*(5+20) = 3*(4+1+20)=75 - final_out_filter1 = num_anchors * (5 + num_classes) - self.yolo_head2 = yolo_head([512, final_out_filter1],256) - - self.down_sample2 = conv2d(256,512,3,stride=2) - self.make_five_conv4 = make_five_conv([512, 1024],1024) - - # 3*(5+num_classes)=3*(5+20)=3*(4+1+20)=75 - final_out_filter0 = num_anchors * (5 + num_classes) - self.yolo_head1 = yolo_head([1024, final_out_filter0],512) - - - def forward(self, x): - # backbone - x2, x1, x0 = self.backbone(x) - - # 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,2048 - P5 = self.conv1(x0) - P5 = self.SPP(P5) - # 13,13,2048 -> 13,13,512 -> 13,13,1024 -> 13,13,512 - P5 = self.conv2(P5) - - # 13,13,512 -> 13,13,256 -> 26,26,256 - P5_upsample = self.upsample1(P5) - # 26,26,512 -> 26,26,256 - P4 = self.conv_for_P4(x1) - # 26,26,256 + 26,26,256 -> 26,26,512 - P4 = torch.cat([P4,P5_upsample],axis=1) - # 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 - P4 = self.make_five_conv1(P4) - - # 26,26,256 -> 26,26,128 -> 52,52,128 - P4_upsample = self.upsample2(P4) - # 52,52,256 -> 52,52,128 - P3 = self.conv_for_P3(x2) - # 52,52,128 + 52,52,128 -> 52,52,256 - P3 = torch.cat([P3,P4_upsample],axis=1) - # 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128 - P3 = self.make_five_conv2(P3) - - # 52,52,128 -> 26,26,256 - P3_downsample = self.down_sample1(P3) - # 26,26,256 + 26,26,256 -> 26,26,512 - P4 = torch.cat([P3_downsample,P4],axis=1) - # 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256 - P4 = self.make_five_conv3(P4) - - # 26,26,256 -> 13,13,512 - P4_downsample = self.down_sample2(P4) - # 13,13,512 + 13,13,512 -> 13,13,1024 - P5 = torch.cat([P4_downsample,P5],axis=1) - # 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 - P5 = self.make_five_conv4(P5) - - #---------------------------------------------------# - # 第三个特征层 - # y3=(batch_size,75,52,52) - #---------------------------------------------------# - out2 = self.yolo_head3(P3) - #---------------------------------------------------# - # 第二个特征层 - # y2=(batch_size,75,26,26) - #---------------------------------------------------# - out1 = self.yolo_head2(P4) - #---------------------------------------------------# - # 第一个特征层 - # y1=(batch_size,75,13,13) - #---------------------------------------------------# - out0 = self.yolo_head1(P5) - - return out0, out1, out2 - -- GitLab