Question

我试图以高效的方式将Tensorflow ssd_mobilenet_v1_coco模型转换为PyTorch模型，因此我获得了所有的Tensorflow层，并将它们映射到预定义MobileNetV1_SSD类的层中。

该类摘自https://github.com/qfgaohao/pytorch-ssd，并引用了官方论文中定义的标准MobileNetV1 SSD架构。问题是，当我将图层从TF映射到PyTorch时，出现了一些图层尺寸不匹配的情况，例如与初始体系结构相比是否有一些更改。根据下载的模型调整班级，我得到了这个：

extras = ModuleList([
    Sequential(
        Conv2d(in_channels=1024, out_channels=256, kernel_size=1),
        ReLU(),
        Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1),
        ReLU()
    ),
    Sequential(
        Conv2d(in_channels=512, out_channels=128,kernel_size=1),
        ReLU(),
        Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1),
        ReLU()
    ),
    Sequential(
        Conv2d(in_channels=256, out_channels=128, kernel_size=1),
        ReLU(),
        Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1),
        ReLU()
    ),
    Sequential(
        Conv2d(in_channels=256, out_channels=64, kernel_size=1),
        ReLU(),
        Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1),
        ReLU()
    )
])

regression_headers = ModuleList([
    Conv2d(in_channels=512, out_channels=3 * 4, kernel_size=1, padding=1),
    Conv2d(in_channels=1024, out_channels=6 * 4, kernel_size=1, padding=1),
    Conv2d(in_channels=512, out_channels=6 * 4, kernel_size=1, padding=1),
    Conv2d(in_channels=256, out_channels=6 * 4, kernel_size=1, padding=1),
    Conv2d(in_channels=256, out_channels=6 * 4, kernel_size=1, padding=1),
    Conv2d(in_channels=128, out_channels=6 * 4, kernel_size=1, padding=1), 
])

classification_headers = ModuleList([
    Conv2d(in_channels=512, out_channels=3 * num_classes, kernel_size=1, padding=1),
    Conv2d(in_channels=1024, out_channels=6 * num_classes, kernel_size=1, padding=1),
    Conv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=1, padding=1),
    Conv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=1, padding=1),
    Conv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=1, padding=1),
    Conv2d(in_channels=128, out_channels=6 * num_classes, kernel_size=1, padding=1),
])

确定：模型之间的翻译没有错误

失败：我不得不评估PyTorch模型时遇到一个问题，因为内核大小以及Extras / Classification / Regression中的某些in_channel和out_channels与初始体系结构规范不匹配

RuntimeError：在非单维度1时，张量a（2781）的大小必须与张量b（3000）的大小匹配

问题是否有人对Tensorflow开发人员如何实现此模型有所了解？那么当选择锚点时，他们如何管理第一层的输出减少（从6 * num_classes减少到3 * num_classes）？

Tensorflow MobileNetV1 SSD Coco实施如何工作

0 个答案: