pytorch 实现在测试的时候启用dropout

def apply_dropout(m):
    if type(m) == nn.Dropout:
        m.train()

# coding: utf-8
import torch
import torch.nn as nn
import numpy as np
class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc = nn.Linear(8, 8)
        self.dropout = nn.Dropout(0.5)
    def forward(self, x):
        x = self.fc(x)
        x = self.dropout(x)
        return x
net = SimpleNet()
x = torch.FloatTensor([1]*8)
net.train()
y = net(x)
print('train mode result: ', y)
net.eval()
y = net(x)
print('eval mode result: ', y)
net.eval()
y = net(x)
print('eval2 mode result: ', y)
def apply_dropout(m):
    if type(m) == nn.Dropout:
        m.train()
net.eval()
net.apply(apply_dropout)
y = net(x)
print('apply eval result:', y)