133 lines
6.2 KiB
Python
133 lines
6.2 KiB
Python
import torch
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import torch.nn as nn
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from nets.ConvNext import ConvNeXt_Small, ConvNeXt_Tiny
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from nets.CSPdarknet import C3, Conv, CSPDarknet
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from nets.Swin_transformer import Swin_transformer_Tiny
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#---------------------------------------------------#
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# yolo_body
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#---------------------------------------------------#
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class YoloBody(nn.Module):
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def __init__(self, anchors_mask, num_classes, phi, backbone='cspdarknet', pretrained=False, input_shape=[640, 640]):
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super(YoloBody, self).__init__()
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depth_dict = {'s' : 0.33, 'm' : 0.67, 'l' : 1.00, 'x' : 1.33,}
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width_dict = {'s' : 0.50, 'm' : 0.75, 'l' : 1.00, 'x' : 1.25,}
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dep_mul, wid_mul = depth_dict[phi], width_dict[phi]
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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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# 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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self.backbone_name = backbone
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if backbone == "cspdarknet":
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#---------------------------------------------------#
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# 生成CSPdarknet53的主干模型
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# 获得三个有效特征层，他们的shape分别是：
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# 80,80,256
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# 40,40,512
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# 20,20,1024
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#---------------------------------------------------#
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self.backbone = CSPDarknet(base_channels, base_depth, phi, pretrained)
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else:
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#---------------------------------------------------#
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# 如果输入不为cspdarknet，则调整通道数
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# 使其符合YoloV5的格式
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#---------------------------------------------------#
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self.backbone = {
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'convnext_tiny' : ConvNeXt_Tiny,
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'convnext_small' : ConvNeXt_Small,
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'swin_transfomer_tiny' : Swin_transformer_Tiny,
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}[backbone](pretrained=pretrained, input_shape=input_shape)
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in_channels = {
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'convnext_tiny' : [192, 384, 768],
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'convnext_small' : [192, 384, 768],
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'swin_transfomer_tiny' : [192, 384, 768],
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}[backbone]
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feat1_c, feat2_c, feat3_c = in_channels
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self.conv_1x1_feat1 = Conv(feat1_c, base_channels * 4, 1, 1)
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self.conv_1x1_feat2 = Conv(feat2_c, base_channels * 8, 1, 1)
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self.conv_1x1_feat3 = Conv(feat3_c, base_channels * 16, 1, 1)
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self.upsample = nn.Upsample(scale_factor=2, mode="nearest")
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self.conv_for_feat3 = Conv(base_channels * 16, base_channels * 8, 1, 1)
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self.conv3_for_upsample1 = C3(base_channels * 16, base_channels * 8, base_depth, shortcut=False)
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self.conv_for_feat2 = Conv(base_channels * 8, base_channels * 4, 1, 1)
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self.conv3_for_upsample2 = C3(base_channels * 8, base_channels * 4, base_depth, shortcut=False)
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self.down_sample1 = Conv(base_channels * 4, base_channels * 4, 3, 2)
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self.conv3_for_downsample1 = C3(base_channels * 8, base_channels * 8, base_depth, shortcut=False)
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self.down_sample2 = Conv(base_channels * 8, base_channels * 8, 3, 2)
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self.conv3_for_downsample2 = C3(base_channels * 16, base_channels * 16, base_depth, shortcut=False)
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# 80, 80, 256 => 80, 80, 3 * (5 + num_classes) => 80, 80, 3 * (4 + 1 + num_classes)
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self.yolo_head_P3 = nn.Conv2d(base_channels * 4, len(anchors_mask[2]) * (5 + num_classes), 1)
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# 40, 40, 512 => 40, 40, 3 * (5 + num_classes) => 40, 40, 3 * (4 + 1 + num_classes)
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self.yolo_head_P4 = nn.Conv2d(base_channels * 8, len(anchors_mask[1]) * (5 + num_classes), 1)
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# 20, 20, 1024 => 20, 20, 3 * (5 + num_classes) => 20, 20, 3 * (4 + 1 + num_classes)
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self.yolo_head_P5 = nn.Conv2d(base_channels * 16, len(anchors_mask[0]) * (5 + num_classes), 1)
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def forward(self, x):
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# backbone
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feat1, feat2, feat3 = self.backbone(x)
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if self.backbone_name != "cspdarknet":
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feat1 = self.conv_1x1_feat1(feat1)
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feat2 = self.conv_1x1_feat2(feat2)
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feat3 = self.conv_1x1_feat3(feat3)
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# 20, 20, 1024 -> 20, 20, 512
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P5 = self.conv_for_feat3(feat3)
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# 20, 20, 512 -> 40, 40, 512
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P5_upsample = self.upsample(P5)
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# 40, 40, 512 -> 40, 40, 1024
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P4 = torch.cat([P5_upsample, feat2], 1)
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# 40, 40, 1024 -> 40, 40, 512
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P4 = self.conv3_for_upsample1(P4)
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# 40, 40, 512 -> 40, 40, 256
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P4 = self.conv_for_feat2(P4)
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# 40, 40, 256 -> 80, 80, 256
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P4_upsample = self.upsample(P4)
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# 80, 80, 256 cat 80, 80, 256 -> 80, 80, 512
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P3 = torch.cat([P4_upsample, feat1], 1)
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# 80, 80, 512 -> 80, 80, 256
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P3 = self.conv3_for_upsample2(P3)
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# 80, 80, 256 -> 40, 40, 256
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P3_downsample = self.down_sample1(P3)
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# 40, 40, 256 cat 40, 40, 256 -> 40, 40, 512
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P4 = torch.cat([P3_downsample, P4], 1)
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# 40, 40, 512 -> 40, 40, 512
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P4 = self.conv3_for_downsample1(P4)
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# 40, 40, 512 -> 20, 20, 512
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P4_downsample = self.down_sample2(P4)
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# 20, 20, 512 cat 20, 20, 512 -> 20, 20, 1024
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P5 = torch.cat([P4_downsample, P5], 1)
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# 20, 20, 1024 -> 20, 20, 1024
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P5 = self.conv3_for_downsample2(P5)
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#---------------------------------------------------#
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# 第三个特征层
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# y3=(batch_size,75,80,80)
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#---------------------------------------------------#
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out2 = self.yolo_head_P3(P3)
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#---------------------------------------------------#
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# 第二个特征层
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# y2=(batch_size,75,40,40)
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#---------------------------------------------------#
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out1 = self.yolo_head_P4(P4)
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#---------------------------------------------------#
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# 第一个特征层
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# y1=(batch_size,75,20,20)
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#---------------------------------------------------#
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out0 = self.yolo_head_P5(P5)
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return out0, out1, out2
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