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| import torch | |
| import torch.nn as nn | |
| from nets.CSPdarknet53_tiny import darknet53_tiny | |
| from nets.attention import cbam_block, eca_block, se_block, CA_Block | |
| attention_block = [se_block, cbam_block, eca_block, CA_Block] | |
| #-------------------------------------------------# | |
| # 卷积块 -> 卷积 + 标准化 + 激活函数 | |
| # Conv2d + BatchNormalization + LeakyReLU | |
| #-------------------------------------------------# | |
| class BasicConv(nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size, stride=1): | |
| super(BasicConv, self).__init__() | |
| self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, kernel_size//2, bias=False) | |
| self.bn = nn.BatchNorm2d(out_channels) | |
| self.activation = nn.LeakyReLU(0.1) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| x = self.activation(x) | |
| return x | |
| #---------------------------------------------------# | |
| # 卷积 + 上采样 | |
| #---------------------------------------------------# | |
| class Upsample(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(Upsample, self).__init__() | |
| self.upsample = nn.Sequential( | |
| BasicConv(in_channels, out_channels, 1), | |
| nn.Upsample(scale_factor=2, mode='nearest') | |
| ) | |
| def forward(self, x,): | |
| x = self.upsample(x) | |
| return x | |
| #---------------------------------------------------# | |
| # 最后获得yolov4的输出 | |
| #---------------------------------------------------# | |
| def yolo_head(filters_list, in_filters): | |
| m = nn.Sequential( | |
| BasicConv(in_filters, filters_list[0], 3), | |
| nn.Conv2d(filters_list[0], filters_list[1], 1), | |
| ) | |
| return m | |
| #---------------------------------------------------# | |
| # yolo_body | |
| #---------------------------------------------------# | |
| class YoloBodytiny(nn.Module): | |
| def __init__(self, anchors_mask, num_classes, phi=0, pretrained=False): | |
| super(YoloBodytiny, self).__init__() | |
| self.phi = phi | |
| self.backbone = darknet53_tiny(pretrained) | |
| self.conv_for_P5 = BasicConv(512,256,1) | |
| self.yolo_headP5 = yolo_head([512, len(anchors_mask[0]) * (5 + num_classes)],256) | |
| self.upsample = Upsample(256,128) | |
| self.yolo_headP4 = yolo_head([256, len(anchors_mask[1]) * (5 + num_classes)],384) | |
| if 1 <= self.phi and self.phi <= 4: | |
| self.feat1_att = attention_block[self.phi - 1](256) | |
| self.feat2_att = attention_block[self.phi - 1](512) | |
| self.upsample_att = attention_block[self.phi - 1](128) | |
| def forward(self, x): | |
| #---------------------------------------------------# | |
| # 生成CSPdarknet53_tiny的主干模型 | |
| # feat1的shape为26,26,256 | |
| # feat2的shape为13,13,512 | |
| #---------------------------------------------------# | |
| feat1, feat2 = self.backbone(x) | |
| if 1 <= self.phi and self.phi <= 4: | |
| feat1 = self.feat1_att(feat1) | |
| feat2 = self.feat2_att(feat2) | |
| # 13,13,512 -> 13,13,256 | |
| P5 = self.conv_for_P5(feat2) | |
| # 13,13,256 -> 13,13,512 -> 13,13,255 | |
| out0 = self.yolo_headP5(P5) | |
| # 13,13,256 -> 13,13,128 -> 26,26,128 | |
| P5_Upsample = self.upsample(P5) | |
| # 26,26,256 + 26,26,128 -> 26,26,384 | |
| if 1 <= self.phi and self.phi <= 4: | |
| P5_Upsample = self.upsample_att(P5_Upsample) | |
| P4 = torch.cat([P5_Upsample,feat1],axis=1) | |
| # 26,26,384 -> 26,26,256 -> 26,26,255 | |
| out1 = self.yolo_headP4(P4) | |
| return out0, out1 | |