神经网络框架——从加载数据到展示结果

Data Process

from torch.utils.data import Dataset
from PIL import Image
from torchvision import transforms
import torch
import numpy as np


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

to_tensor = transforms.ToTensor()


class MyDataset(Dataset):
    def __init__(self, root_path, resize):

        self.img_dir = root_path + "JPEGImages/"    # path for image
        self.label_dir = "./SegmentationClass/"  # path for label
        self.resize = resize

        # transformer
        self.transform_img = transforms.Compose([
            transforms.Resize((resize[0], resize[1])),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ])


    def __getitem__(self, index):

        name = # image name list
        img_path = self.img_dir + name + '.jpg'
        label_path = self.label_dir + name + '.png'

        # load image&label and process
        img = Image.open(img_path).convert('RGB')
        img = self.transform_img(img)

        label = Image.open(label_path)
        label = label.resize((self.resize[0], self.resize[1]), Image.NEAREST)
        label = np.array(label)

        return {'image': img, 'label': torch.from_numpy(label).type(torch.uint8)}

    def __len__(self):
        return len(self.img_names)

Define Network

class MyNet(nn.Module):
    def __init__(self, args, num_classes):
        super(MyNet, self).__init__()
        self.args = args
        self.backbone = args.backbone
        self.myblock = MyBlock()
    
        self.conv1 = nn.Conv2d(256, 256, 3, padding=1, bias=False)
        self.conv2 = nn.Conv2d(256, num_classes, 1)
        self.bn = nn.BatchNorm2d(256)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.backbone(x)
        x = self.myblock(x)
        
        x = self.relu(self.bn(self.conv1(x)))
        x = self.conv2(x)
        x = F.interpolate(x, input_shape[2:], mode='bilinear', align_corners=True)

        return x


class MyBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(MyBlock, self).__init__()
        self.mylayer = MyLayer()
        ... ...

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


class MyLayer(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, padding, dilation):
        super(MyLayer, self).__init__()
        ... ...

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

Define Loss Function

class MyLoss(nn.Module):
    def __init__(self):
        super(MyLoss, self).__init__()

    def forward(self, y, C):
        
        loss = torch.zeros(1, requires_grad=True)
        loss = ... + ... + ...
        
        return loss # 注意最后只能返回Tensor值，且带梯度，即 loss.requires_grad == True

Trainning

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def train(args, model, criterion, optimizer, train_loader, test_loader, starting=0):
    train_loss_list = []
    train_acc_list = []
    test_acc_list = []
    best_test = 1.0

    for epoch in range(starting, args.epoch_num):
        train_loss = 0
        train_acc = 0
        adding_time = 0

        # train
        model.train()

        for batch_index, batch in enumerate(train_loader):
            batch_img = batch['image'].to(device)
            batch_label = batch['label'].to(device)

            if len(batch_img) == 1:    # 网络用到了BN
                break

            out = model(batch_img)      # out(n,21,H,W)
            loss = criterion(out, batch_label.long())

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            train_loss += loss.item()
            pred = torch.max(out, 1)[1]
            train_correct = (pred == batch_label).sum()
            train_acc += train_correct.item()
            adding_time += batch_label.shape[0] * batch_label.shape[1] * batch_label.shape[2]
            
            if batch_index % 100 == 0 and batch_index != 0:
                print('epoch:{}, iter:{}, loss:{:.5f}'.format(epoch, batch_index, loss.item()))

        epoch_train_loss = train_loss / len(train_loader)
        epoch_train_acc = train_acc / adding_time
        train_loss_list.append(epoch_train_loss)
        train_acc_list.append(epoch_train_acc)
        print('Epoch: {} : Train Loss: {:.6f}, Acc: {:.6f}'.format(epoch, epoch_train_loss, epoch_train_acc))

        # evaluate the model and save the best
        if epoch % 10 == 0:
            epoch_test_acc = evaluate(args, model, test_loader)
            
            print('testing accuracy: {:.3f}, %'.format(epoch_test_acc))
            test_acc_list.append(epoch_test_acc)
            
            # save the last checkpoint
            torch.save({
                'epoch': epoch,
                'model_state_dict': model.state_dict(),
                'optimizer_state_dict': optimizer.state_dict(),
            }, './checkpoints/{}_epoch_checkpoint.pth'.format(args.exp_id))

            print('saved at epoch: {}'.format(epoch))
            print('-------------------------------------------------')

            # save the best checkpoint
            if epoch_test_acc > best_test:
                best_test = epoch_test_acc
                torch.save({
                    'epoch': epoch,
                    'model_state_dict': model.state_dict(),
                    'optimizer_state_dict': optimizer.state_dict(),
                }, './checkpoints/{}_best_checkpoint.pth'.format(args.exp_id))
                print('new best model saved at epoch: {}'.format(epoch))
                print('-------------------------------------------------')

    print('-------------------------------------------------')
    print('best testing achieved: {:.3f}'.format(best_test))
    print("train_loss: ", train_loss_list)
    print("train_acc: ", train_acc_list)
    print("test_acc: ", test_acc_list)


def evaluate(args, model, test_loader):
    test_acc = 0.0
    adding_time = 0
    hist = np.zeros((21, 21))       # class_number=21
    with torch.no_grad():
        for batch_index, batch in enumerate(test_loader):

            batch_x = batch['image'].to(device)
            batch_y = batch['label'].to(device)

            if len(batch_x) == 1:
                break

            out = model(batch_x)
            pred = torch.max(out, 1)[1]

            # confusion matrix
            pred_np = pred.data.cpu().numpy()
            target_np = batch_y.cpu().numpy()

            hist = hist + generate_matrix(target_np, pred_np, 21)

            test_correct = (pred == batch_y).sum()
            test_acc += test_correct.item()
            adding_time += batch_y.shape[0] * batch_y.shape[1] * batch_y.shape[2]

        # mIoU for one epoch
        mIoU = Mean_Intersection_over_Union(hist)

        epoch_val_acc = test_acc / adding_time

    return epoch_val_acc, mIoU


def resume(args, model, optimizer):
    checkpoint_path = './checkpoints/' + args.resume_path
    assert os.path.exists(checkpoint_path), ('checkpoint do not exits for %s' % checkpoint_path)

    checkpoint_saved = torch.load(checkpoint_path)

    epoch = checkpoint_saved['epoch']
    model.load_state_dict(checkpoint_saved['model_state_dict'])
    optimizer.load_state_dict(checkpoint_saved['optimizer_state_dict'])

    print('Resume completed for the modeln')

    return model, optimizer, epoch

主函数

import data_loader
from torch.utils.data import DataLoader
import argparse


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def parse_args():
    parser = argparse.ArgumentParser()

    parser.add_argument('--exp_id', type=str, default='exp_test')
    parser.add_argument('--resume_path', type=str, default='', help='resume path')
    parser.add_argument('--resume', type=int, default=0, help='resume the trained model')
    parser.add_argument('--test', type=int, default=0, help='test with trained model')
    parser.add_argument('--seed', type=int, default=1, help='random seed')

    parser.add_argument('--root_path', type=str, default='./VOC2012/', help='VOC2012 data path')
    parser.add_argument('--batch_size', type=int, default=16)
    parser.add_argument('--epoch_num', type=int, default=10, help='number of training epochs')

    # parameters that we are focus on
    parser.add_argument('--resize', type=int, default=320, help='resize')
    parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
    parser.add_argument('--backbone', type=str, default='MobileNet', help='[MobileNet, ResNet]')

    args = parser.parse_args()

    return args


if __name__ == '__main__':
    args = parse_args()

    torch.manual_seed(args.seed)
    torch.cuda.manual_seed(args.seed)

    data_train = data_loader.MyDataset(root_path=args.root_path, resize=None)
    data_test = data_loader.MyDataset(root_path=args.root_path, resize=None)
    train_loader = DataLoader(data_train, batch_size=args.batch_size, num_workers=2)
    test_loader = DataLoader(data_test, batch_size=args.batch_size, num_workers=2)

    # network
    model = MyNet(args, 21).to(device)

    # loss
    criterion = MyLoss()

    # optimizer
    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999))

    # resume the trained model or train again
    if args.resume:
        model, optimizer, starting = train.resume(args, model, optimizer)
        starting += 1
    else:
        starting = 0
    if args.test == 1:  # test mode, resume the trained model and test
        testing_accuracy = train.evaluate(args, model, test_loader)
        print('testing finished, accuracy: {:.3f}'.format(testing_accuracy))
    else:               # train mode, train the network from scratch
        train.train(args, model, criterion, optimizer, train_loader, test_loader, starting)
        print('training finished')

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