空洞卷积网络实现

代码中涉及的图片实验数据下载地址：https://download.csdn.net/download/m0_37567738/88235543?spm=1001.2014.3001.5501

代码：

import torch.nn as nn
import numpy as np

from matplotlib import pyplot as plt
import time
#from utils import get_accur,load_data,train

import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
import torch
import torch.optim as optim
import numpy as np
def load_data(path, batch_size):
    datasets = torchvision.datasets.ImageFolder(
        root = path,
        transform = transforms.Compose([
            transforms.ToTensor()
        ])
    )

    dataloder = DataLoader(datasets, batch_size=batch_size, shuffle=True)
    return datasets,dataloder

def get_accur(preds, labels):
    preds = preds.argmax(dim=1)
    return torch.sum(preds == labels).item()

def train(model, epochs, learning_rate, dataloader, criterion, testdataloader):
    optimizer = optim.Adam(model.parameters(),lr=learning_rate)

    train_loss_list = []
    test_loss_list = []
    train_accur_list = []
    test_accur_list = []
    train_len = len(dataloader.dataset)
    test_len = len(testdataloader.dataset)

    for i in range(epochs):
        train_loss = 0.0
        train_accur = 0
        test_loss = 0.0
        test_accur = 0
        for batch in dataloader:
            imgs, labels = batch
            preds = model(imgs)
            optimizer.zero_grad()
            loss = criterion(preds, labels)

            loss.backward()
            optimizer.step()
            train_loss += loss.item()
            train_accur += get_accur(preds,labels)

        train_loss_list.append(train_loss)
        train_accur_list.append(train_accur / train_len)

        for batch in testdataloader:
            imgs, labels = batch
            preds = model(imgs)
            loss = criterion(preds, labels)
            test_loss += loss.item()
            test_accur += get_accur(preds,labels)

        test_loss_list.append(test_loss)
        test_accur_list.append(test_accur / test_len)

        print("epoch {} : train_loss : {}; train_accur : {}".format(i + 1, train_loss, train_accur / train_len))

    return np.array(train_accur_list), np.array(train_loss_list), np.array(test_accur_list), np.array(test_loss_list)

class ConvNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=2, padding=0, dilation=1),
            nn.BatchNorm2d(32),
            nn.ReLU()
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=0, dilation=2),
            nn.BatchNorm2d(64),
            nn.ReLU()
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, stride=1, padding=0, dilation=5),
            nn.BatchNorm2d(128),
            nn.ReLU()
        )
        self.fc = nn.Linear(128 * 3 * 3, 3)

    def forward(self, x):
        x = self.layer1(x)

        x = self.layer2(x)

        x = self.layer3(x)

        x = x.view(-1, 128 * 3 * 3)

        out = self.fc(x)

        return out


if __name__ == "__main__":
    train_path = "./cnn/train/"
    test_path = "./cnn/test/"
    train_datasets, train_dataloader = load_data(train_path, 64)
    test_datasets, test_dataloader = load_data(test_path, 64)
    model = ConvNetwork()
    critic = nn.CrossEntropyLoss()
    epoch = 15
    lr = 0.01
    start = time.clock()
    train_accur_list, train_loss_list, test_accur_list, test_loss_list = train(model, epoch, lr, train_dataloader,
                                                                               critic, test_dataloader)
    end = time.clock()
    test_accur = 0
    for batch in test_dataloader:
        imgs, labels = batch
        preds = model(imgs)
        test_accur += get_accur(preds, labels)

    print("Accuracy on test datasets : {}".format(test_accur / len(test_datasets)))
    print("Total time".format(end - start))
    x_axis = np.arange(1, epoch + 1)
    plt.plot(x_axis, train_loss_list, label="train loss")
    plt.plot(x_axis, test_loss_list, label="test loss")
    plt.legend()
    plt.show()

    plt.plot(x_axis, train_accur_list, label="train accur")
    plt.plot(x_axis, test_accur_list, label="test accur")
    plt.legend()
    plt.show()

执行结果：