lcnn/lcnn/models/resnet50_pose.py

import torch
import torch.nn as nn
import torchvision.models as models


class ResNet50Backbone(nn.Module):
    def __init__(self, num_classes=5, num_stacks=1, pretrained=True):
        super(ResNet50Backbone, self).__init__()

        # 加载预训练的ResNet50
        if pretrained:
            resnet = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
        else:
            resnet = models.resnet50(weights=None)

        # 移除最后的全连接层
        self.backbone = nn.Sequential(
            resnet.conv1,#特征图分辨率降低为1/2，通道数从3升为64
            resnet.bn1,
            resnet.relu,
            resnet.maxpool,#特征图分辨率降低为1/4，通道数仍然为64
            resnet.layer1,#stride为1，不改变分辨率，依然为1/4，通道数从64升为64*4=256
            # resnet.layer2,#stride为2，特征图分辨率降低为1/8，通道数从256升为128*4=512
            # resnet.layer3,#stride为2，特征图分辨率降低为1/16，通道数从512升为256*4=1024
            # resnet.layer4,#stride为2，特征图分辨率降低为1/32，通道数从512升为256*4=2048
        )

        # 多任务输出层
        self.score_layers = nn.ModuleList([
            nn.Sequential(
                nn.Conv2d(256, 128, kernel_size=3, padding=1),
                nn.BatchNorm2d(128),
                nn.ReLU(inplace=True),
                nn.Conv2d(128, num_classes, kernel_size=1)
            )
            for _ in range(num_stacks)
        ])

        # 上采样层，确保输出大小为128x128
        self.upsample = nn.Upsample(
            scale_factor=0.25,
            mode='bilinear',
            align_corners=True
        )

    def forward(self, x):
        # 主干网络特征提取
        x = self.backbone(x)

        # # 调整通道数
        # x = self.channel_adjust(x)
        #
        # # 上采样到128x128
        # x = self.upsample(x)

        # 多堆栈输出
        outputs = []
        for score_layer in self.score_layers:
            output = score_layer(x)
            outputs.append(output)

        # 返回第一个输出（如果有多个堆栈）
        return outputs, x


def resnet50(**kwargs):
    model = ResNet50Backbone(
        num_classes=kwargs.get("num_classes", 5),
        num_stacks=kwargs.get("num_stacks", 1),
        pretrained=kwargs.get("pretrained", True)
    )
    return model


__all__ = ["ResNet50Backbone", "resnet50"]


# 测试网络输出
model = resnet50(num_classes=5, num_stacks=1)


# 方法1：直接传入图像张量
x = torch.randn(2, 3, 512, 512)
outputs, feature = model(x)
print("Outputs length:", len(outputs))
print("Output[0] shape:", outputs[0].shape)
print("Feature shape:", feature.shape)