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本篇内容主要讲解“pytorch的hook函数怎么使用”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“pytorch的hook函数怎么使用”吧!
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"""
@brief : pytorch的hook函数
"""
import torch
import torch.nn as nn
from tools.common_tools2 import set_seed
set_seed(1)
# ----------------------------------- 1 tensor hook 1
flag = 0
# flag = 1
if flag:
w = torch.tensor([1.], requires_grad=True)
x = torch.tensor([2.], requires_grad=True)
a = torch.add(w, x)
b = torch.add(w, 1)
y = torch.mul(a, b)
a_grad = list()
def grad_hook(grad):
a_grad.append(grad)
handle = a.register_hook(grad_hook)
y.backward()
# 查看梯度
print("gradient:", w.grad, x.grad, a.grad, b.grad, y.grad)
print("a_grad[0]:", a_grad[0])
handle.remove()
# ----------------------------------- 2 tensor hook 2
flag = 0
# flag = 1
if flag:
w = torch.tensor([1.], requires_grad=True)
x = torch.tensor([2.], requires_grad=True)
a = torch.add(w, x)
b = torch.add(w, 1)
y = torch.mul(a, b)
a_grad = list()
def grad_hook(grad):
grad *= 2
return grad * 3
handle = w.register_hook(grad_hook)
y.backward()
print("w.grad:", w.grad)
handle.remove()
# --------------------------- 3 Module.register_forward_hook and pre hook
# flag = 0
flag = 1
if flag:
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 2, 3)
self.pool1 = nn.MaxPool2d(2, 2)
def forward(self, x):
x = self.conv1(x)
x = self.pool1(x)
return x
def forward_hook(module, data_input, data_output):
fmap_block.append(data_output)
input_block.append(data_input)
def forward_pre_hook(module, data_input):
print("forward_pre_hook input:{}".format(data_input))
def backward_hook(module, grad_input, grad_output):
print("backward hook input:{}".format(grad_input))
print("backward hook output:{}".format(grad_output))
# 初始化网络
net = Net()
net.conv1.weight[0].detach().fill_(1)
net.conv1.weight[1].detach().fill_(2)
net.conv1.bias.data.detach().zero_()
# 注册hook
fmap_block = list()
input_block = list()
net.conv1.register_forward_hook(forward_hook)
net.conv1.register_forward_pre_hook(forward_pre_hook)
net.conv1.register_backward_hook(backward_hook)
# inference
fake_img = torch.ones((1, 1, 4, 4)) # batch size * channel * H * W
output = net(fake_img) # 前向传播
loss_fnc = nn.L1Loss()
target = torch.randn_like(output)
loss = loss_fnc(target, output)
loss.backward()
# 观察
print("output shape: {}\noutput value: {}\n".format(output.shape, output))
print("feature maps shape: {}\noutput value: {}\n".format(fmap_block[0].shape, fmap_block[0]))
print("input shape: {}\ninput value: {}".format(input_block[0][0].shape, input_block[0]))1. 采用torch.nn.Module.register_forward_hook机制实现AlexNet第一个卷积层输出特征图的可视化，并将/torchvision/models/alexnet.py中第28行改为：nn.ReLU(inplace=False)，观察
inplace=True与inplace=False的差异
# -*- coding:utf-8 -*-
"""
@brief : 采用hook函数可视化特征图
"""
import torch.nn as nn
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
import torchvision.utils as vutils
from torch.utils.tensorboard import SummaryWriter
from tools.common_tools2 import set_seed
import torchvision.models as models
set_seed(1) # 设置随机种子
# ----------------------------------- feature map visualization
# flag = 0
flag = 1
if flag:
writer = SummaryWriter(comment='test_your_comment', filename_suffix="_test_your_filename_suffix")
# 数据
path_img = "./lena.png" # your path to image
normMean = [0.49139968, 0.48215827, 0.44653124]
normStd = [0.24703233, 0.24348505, 0.26158768]
norm_transform = transforms.Normalize(normMean, normStd)
img_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
norm_transform
])
img_pil = Image.open(path_img).convert('RGB')
if img_transforms is not None:
img_tensor = img_transforms(img_pil)
img_tensor.unsqueeze_(0) # chw --> bchw
# 模型
alexnet = models.alexnet(pretrained=True)
# 注册hook
fmap_dict = dict()
for name, sub_module in alexnet.named_modules():
if isinstance(sub_module, nn.Conv2d):
key_name = str(sub_module.weight.shape)
fmap_dict.setdefault(key_name, list())
n1, n2 = name.split(".")
def hook_func(m, i, o):
key_name = str(m.weight.shape)
fmap_dict[key_name].append(o)
alexnet._modules[n1]._modules[n2].register_forward_hook(hook_func)
# forward
output = alexnet(img_tensor)
# add image
for layer_name, fmap_list in fmap_dict.items():
fmap = fmap_list[0]
fmap.transpose_(0, 1)
nrow = int(np.sqrt(fmap.shape[0]))
fmap_grid = vutils.make_grid(fmap, normalize=True, scale_each=True, nrow=nrow)
writer.add_image('feature map in {}'.format(layer_name), fmap_grid, global_step=322)到此，相信大家对“pytorch的hook函数怎么使用”有了更深的了解，不妨来实际操作一番吧！这里是创新互联网站，更多相关内容可以进入相关频道进行查询，关注我们，继续学习！