目录

一、知识点

二、代码

三、查看卷积层的feature map

1. 查看每层信息

​2. show_featureMap.py

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背景：LeNet-5是一个经典的CNN，由Yann LeCun在1998年提出，旨在解决手写数字识别问题。

一、知识点

1. iter()+next()

iter()：返回迭代器

next()：使用next()来获取下一条数据

data = [1, 2, 3]
data_iter = iter(data)
print(next(data_iter))  # 1
print(next(data_iter))  # 2
print(next(data_iter))  # 3

2. enumerate

enumerate(sequence,[start=0]) 函数用于将一个可遍历的数据对象组合为一个索引序列，同时列出数据和数据下标，一般用在 for 循环当中。

start--下标起始位置的值。 

data = ['zs', 'ls', 'ww']
print(list(enumerate(data)))
# [(0, 'zs'), (1, 'ls'), (2, 'ww')]

3. torch.no_grad()

在该模块下，所有计算得出的tensor的requires_grad都自动设置为False。

当requires_grad设置为False时，在反向传播时就不会自动求导了，可以节约存储空间。

4. torch.max(input,dim)

input -- tensor类型

dim=0 -- 行比较

dim=1 -- 列比较

import torchdata = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
x = torch.max(data, dim=0)
print(x)
# values=tensor([7., 8., 9.]),
# indices=tensor([2, 2, 2])
x = torch.max(data, dim=1)
print(x)
# values=tensor([3., 6., 9.]),
# indices=tensor([2, 2, 2])

5. torch.eq：对两个张量Tensor进行逐个元素的比较，若相同位置的两个元素相同，则返回True；若不同，返回False。

注意：item返回一个数。

import torchdata1 = torch.tensor([1, 2, 3, 4, 5])
data2 = torch.tensor([2, 3, 3, 9, 5])
x = torch.eq(data1, data2)
print(x)  # tensor([False, False,  True, False,  True])
sum = torch.eq(data1, data2).sum()
print(sum)  # tensor(2)
sum_item = torch.eq(data1, data2).sum().item()
print(sum_item)  # 2

6. squeeze(input,dim)函数

squeeze(0)：若第一维度值为1，则去除第一维度

squeeze(1)：若第二维度值为2，则去除第二维度

squeeze(-1)：去除最后维度值为1的维度

7. unsqueeze(input,dim)

增加大小为1的维度，即返回一个新的张量，对输入的指定位置插入维度 1且必须指明维度。

二、代码

model.py

import torch.nn as nn
import torch.nn.functional as Fclass LeNet(nn.Module):def __init__(self):super(LeNet, self).__init__()self.conv1 = nn.Conv2d(3, 16, 5)  # output(16,28,28)self.pool1 = nn.MaxPool2d(2, 2)  # output(16,14,14)self.conv2 = nn.Conv2d(16, 32, 5)  # output(32,10,10)self.pool2 = nn.MaxPool2d(2, 2)  # output(32,5,5)self.fc1 = nn.Linear(32 * 5 * 5, 120)  # output:120self.fc2 = nn.Linear(120, 84)  # output:84self.fc3 = nn.Linear(84, 10)  # output:10def forward(self, x):x = F.relu(self.conv1(x))x = self.pool1(x)x = F.relu(self.conv2(x))x = self.pool2(x)x = x.view(-1, 32 * 5 * 5)x = F.relu(self.fc1(x))x = F.relu(self.fc2(x))x = self.fc3(x)return x

train.py

import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transformsfrom model import LeNetdef main():# preprocess datatransform = transforms.Compose([# Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]transforms.ToTensor(),# (mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])# 训练集 如果数据集已经下载了，则download=Falsetrain_data = torchvision.datasets.CIFAR10('./data', train=True, transform=transform, download=False)train_loader = torch.utils.data.DataLoader(train_data, batch_size=36, shuffle=True, num_workers=0)# 验证集val_data = torchvision.datasets.CIFAR10('./data', train=False, download=False, transform=transform)val_loader = torch.utils.data.DataLoader(val_data, batch_size=10000, shuffle=False, num_workers=0)# 返回迭代器val_data_iter = iter(val_loader)val_image, val_label = next(val_data_iter)net = LeNet()loss_function = nn.CrossEntropyLoss()optimizer = optim.Adam(net.parameters(), lr=0.001)# loop over the dataset multiple timesfor epoch in range(5):epoch_loss = 0for step, data in enumerate(train_loader, start=0):# get the inputs from train_loader;data is a list of[inputs,labels]inputs, labels = data# 在处理每一个batch时并不需要与其他batch的梯度混合起来累积计算，因此需要对每个batch调用一遍zero_grad()将参数梯度设置为0optimizer.zero_grad()# 1.forwardoutputs = net(inputs)# 2.lossloss = loss_function(outputs, labels)# 3.backpropagationloss.backward()# 4.update x by optimizeroptimizer.step()# print statistics# 使用item()取出的元素值的精度更高epoch_loss += loss.item()# print every 500 mini-batchesif step % 500 == 499:with torch.no_grad():outputs = net(val_image)predict_y = torch.max(outputs, dim=1)[1]  # [0]取每行最大值，[1]取每行最大值的索引val_accuracy = torch.eq(predict_y, val_label).sum().item() / val_label.size(0)print('[epoch：%d step：%5d] train_loss:%.3f test_accuracy:%.3f' % (epoch + 1, step + 1, epoch_loss / 500, val_accuracy))epoch_loss = 0print('Train finished!')sava_path = './model/LeNet.pth'torch.save(net.state_dict(), sava_path)if __name__ == '__main__':main()

predict.py

import torch
import torchvision.transforms as transforms
from PIL import Image
from model import LeNetdef main():transform = transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),  # CHW格式transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']net = LeNet()net.load_state_dict(torch.load('./model/LeNet.pth'))image = Image.open('./predict/2.png')  # HWC格式image = transform(image)image = torch.unsqueeze(image, dim=0)  # 在第0维加一个维度 #[N,C,H,W] N:Batch批处理大小with torch.no_grad():outputs = net(image)predict = torch.max(outputs, dim=1)[1]print(classes[predict])if __name__ == '__main__':main()

2.png

 

三、查看卷积层的feature map

1. 查看每层信息

    for i in net.children():print(i)

2. show_featureMap.py

import torch
import torch.nn as nn
from model import LeNet
import torchvision
import torchvision.transforms as transforms
from PIL import Image
import matplotlib.pyplot as pltdef main():transform = transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),  # CHW格式transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])image = Image.open('./predict/2.png')  # HWC格式image = transform(image)image = torch.unsqueeze(image, dim=0)  # 在第0维加一个维度 #[N,C,H,W] N:Batch批处理大小net = LeNet()net.load_state_dict(torch.load('./model/LeNet.pth'))conv_weights = []  # 模型权重conv_layers = []  # 模型卷积层counter = 0  # 模型里有多少个卷积层# 1.将卷积层以及对应权重放入列表中model_children = list(net.children())for i in range(len(model_children)):if type(model_children[i]) == nn.Conv2d:counter += 1conv_weights.append(model_children[i].weight)conv_layers.append(model_children[i])outputs = []names = []for layer in conv_layers[0:]:# 2.每个卷积层对image进行计算image = layer(image)outputs.append(image)names.append(str(layer))# 3.进行维度转换print(outputs[0].shape)  # torch.Size([1, 16, 28, 28]) 1-batch 16-channel 28-H 28-Wprint(outputs[0].squeeze(0).shape)  # torch.Size([16, 28, 28]) 去除第0维# 将16颜色通道的feature map加起来，变为一张28×28的feature map，sum将所有灰度图映射到一张print(torch.sum(outputs[0].squeeze(0), 0).shape)  # torch.Size([28, 28])processed_data = []for feature_map in outputs:feature_map = feature_map.squeeze(0)  # torch.Size([16, 28, 28])gray_scale = torch.sum(feature_map, 0)  # torch.Size([28, 28])# 取所有灰度图的平均值gray_scale = gray_scale / feature_map.shape[0]processed_data.append(gray_scale.data.numpy())# 4.可视化特征图figure = plt.figure()for i in range(len(processed_data)):x = figure.add_subplot(1, 2, i + 1)x.imshow(processed_data[i])x.set_title(names[i].split('(')[0])plt.show()if __name__ == '__main__':main()