from audioop import bias
import torch
import torch.nn as nn

class SSD(nn.Module):
    def __init__(self):
        super(SSD, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=5, kernel_size=3, stride=2, padding=1, bias=False)

    def forward(self, x):
        x = self.conv1(x)
        return x

model = SSD()
model.train()
input = torch.ones([1,3,256,256])
output = model(input)
print(model.training)
print(output.shape)

print("*"*20)

with torch.no_grad():
    model.eval()
    input = torch.ones([1, 3, 256, 256])
    output = model(input)
    print(model.training)
    print(output.shape)

在model.eval()打开之后，model.training=False

在model内部的self.training=False，直接执行下一块result=self.postprocess(locs, confs)