hook_test.py

import torch
import torchvision
import numpy as np
model=torchvision.models.vgg19(pretrained=True)
input=torch.rand(1,3,224,224)
def get_features_hook(self, input, output):
    _max = output.data.cpu().numpy().max()
    _min = output.data.cpu().numpy().min()
    np.save(str(self),output.data.cpu().numpy())
    print("self: ",str(self),'max: ',_max,'\nmin: ',_min)
model=torchvision.models.vgg19(pretrained=True)
for i,_ in model.named_children():
    for k in range(len(model._modules.get(i))):
        model._modules.get(i)[k].register_forward_hook(get_features_hook)
model(input)