train.py

import os
import sys
import torchvision
from model import *
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm

# dir configs
save_dir = "mini-vision"
if not os.path.exists(save_dir):
    os.mkdir(save_dir)

# visualization
writer = SummaryWriter("mini-vision-logs")

# training config
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
batchsize = 256
learning_rate = 7e-3

# dataset preprocessing
train_transforms = torchvision.transforms.Compose([torchvision.transforms.RandomCrop(32, 4),
                                                   torchvision.transforms.RandomHorizontalFlip(),
                                                   torchvision.transforms.ToTensor()])

# dataset
train_data = torchvision.datasets.CIFAR10("CIFAR10", True, train_transforms,
                                          download=True)
test_data = torchvision.datasets.CIFAR10("CIFAR10", False, torchvision.transforms.ToTensor(),
                                          download=True)

# dataset length
train_data_size = len(train_data)
test_data_size = len(test_data)
print(train_data_size)
print(test_data_size)

# load dataset
train_dataloader = DataLoader(train_data, batchsize, True)
test_dataloader = DataLoader(test_data, batchsize, False)

# create model
mynetwork = MyNetwork().to(device)

# loss function
loss_fn = nn.CrossEntropyLoss().to(device)

# optimizer
optimizer = torch.optim.SGD(mynetwork.parameters(), learning_rate, 0.9)
schedular = torch.optim.lr_scheduler.StepLR(optimizer, 5, 0.5)

# training records
# record train step
total_train_step = 0
# record test step
total_test_step = 0
# training epochs
epoch = 100



for i in range(epoch):
    print(f"---------------Epoch {i + 1} start, LR：{optimizer.param_groups[0]["lr"]}---------------")
    # start training
    mynetwork.train()
    total_train_loss = 0
    print("Training Progress: ", flush=True)
    for data in tqdm(train_dataloader, file=sys.stdout):
        imgs, targets = data
        imgs = imgs.to(device)
        targets = targets.to(device)

        output = mynetwork(imgs)
        loss = loss_fn(output, targets)

        # optim model
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_train_loss += loss.item()
        total_train_step += 1
        writer.add_scalar("train_loss", loss.item(), total_train_step + 1)
    train_loss_num = train_data_size / batchsize
    total_train_loss /= train_loss_num
    print(f"Total avg loss on train data: {total_train_loss:.2f}", flush=True)

    # start testing
    mynetwork.eval()
    total_test_loss = 0
    total_accuracy = 0
    with torch.no_grad():
        print("Testing Progress", flush=True)
        for data in tqdm(test_dataloader, file=sys.stdout):
            imgs, targets = data
            imgs = imgs.to(device)
            targets = targets.to(device)

            output = mynetwork(imgs)
            loss = loss_fn(output, targets)
            total_test_loss += loss.item()
            accuracy = (output.argmax(1) == targets).sum()
            total_accuracy += accuracy

        accuracy_percentage = round(float(total_accuracy / test_data_size * 100), 2)
        test_loss_num = test_data_size / batchsize
        total_test_loss /= test_loss_num
        print(f"Total avg loss on test data: {total_test_loss:.2f}", flush=True)
        print(f"Accuracy on test data: {accuracy_percentage}%", flush=True)
        writer.add_scalar("test_loss", total_test_loss, total_test_step + 1)
        writer.add_scalar("test_accuracy", accuracy_percentage, total_test_step + 1)
        total_test_step += 1

    schedular.step()
    torch.save(mynetwork, f"{save_dir}/Mini-Vision-V1{i + 1}.pth")     # save every epoch
    print("Model saved", flush=True)

writer.close()