It seems that NOT ONLY THE DROPOUT.
Run this following code and you can find that features[0] is different from features[1] !
import torch
from torch import nn
from torchvision import models

features = []

def hook(module, inputs, outputs):
    features.append(outputs.clone())

x = torch.randn(10, 3, 224, 224).float()
glnet = models.googlenet(pretrained=True)
glnet.eval()

glnet.conv1.register_forward_hook(hook)

with torch.no_grad():
    model_output = glnet(x.clone())
    print(model_output)

    out = x.clone()
    for name, layer in glnet.named_children():
        if name == 'fc':
            out = nn.Flatten()(out)
        out = layer(out)
    print(out)

for feature in features:
    print("feature", feature[0, 0, 0, :10])

It’s so strange…I don’t know why…