一些测试结果

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers import *
from data import voc, coco
import torchvision
import os
# ssd.py
cfg = [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256]
flag = False
layers=[]
in_channels=1024
for k, v in enumerate(cfg):
    print(k,v)
    if in_channels != 'S':
        if v == 'S':
            layers += [nn.Conv2d(in_channels=in_channels, out_channels=cfg[k+1],
                                    kernel_size=(1, 3)[flag], stride=2, padding=1)]
        else:
            layers += [nn.Conv2d(in_channels=in_channels, out_channels=v,
                                    kernel_size=(1, 3)[flag])]
        flag = not flag
        in_channels = v
        print(v)
        print(layers)

这样基本明白这个模块的作用。但是不知道为什么到第三个卷积层就中断循环？