checkpoint_resume.py

# -*- coding: utf-8 -*-
"""
# @file name  : save_checkpoint.py
# @author     :  TingsongYu https://github.com/TingsongYu
# @date       : 2019-11-04
# @brief      : 模拟训练意外停止
"""
import os
import random
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
import torch.optim as optim
from PIL import Image
from matplotlib import pyplot as plt
import sys
hello_pytorch_DIR = os.path.abspath(os.path.dirname(__file__)+os.path.sep+".."+os.path.sep+"..")
sys.path.append(hello_pytorch_DIR)
from model.lenet import LeNet
from tools.my_dataset import RMBDataset
from tools.common_tools import set_seed
import torchvision


set_seed(1)  # 设置随机种子
rmb_label = {"1": 0, "100": 1}

# 参数设置
checkpoint_interval = 5
MAX_EPOCH = 10
BATCH_SIZE = 16
LR = 0.01
log_interval = 10
val_interval = 1


# ============================ step 1/5 数据 ============================

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
split_dir = os.path.abspath(os.path.join(BASE_DIR, "..", "..", "data", "rmb_split"))
train_dir = os.path.join(split_dir, "train")
valid_dir = os.path.join(split_dir, "valid")

if not os.path.exists(split_dir):
    raise Exception(r"数据 {} 不存在, 回到lesson-06\1_split_dataset.py生成数据".format(split_dir))

norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]

train_transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.RandomCrop(32, padding=4),
    transforms.RandomGrayscale(p=0.8),
    transforms.ToTensor(),
    transforms.Normalize(norm_mean, norm_std),
])

valid_transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize(norm_mean, norm_std),
])

# 构建MyDataset实例
train_data = RMBDataset(data_dir=train_dir, transform=train_transform)
valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform)

# 构建DataLoder
train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)

# ============================ step 2/5 模型 ============================

net = LeNet(classes=2)
net.initialize_weights()

# ============================ step 3/5 损失函数 ============================
criterion = nn.CrossEntropyLoss()                                                   # 选择损失函数

# ============================ step 4/5 优化器 ============================
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9)                        # 选择优化器
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=6, gamma=0.1)     # 设置学习率下降策略


# ============================ step 5+/5 断点恢复 ============================

path_checkpoint = "./checkpoint_4_epoch.pkl"
checkpoint = torch.load(path_checkpoint)

net.load_state_dict(checkpoint['model_state_dict'])

optimizer.load_state_dict(checkpoint['optimizer_state_dict'])

start_epoch = checkpoint['epoch']

scheduler.last_epoch = start_epoch

# ============================ step 5/5 训练 ============================
train_curve = list()
valid_curve = list()

for epoch in range(start_epoch + 1, MAX_EPOCH):

    loss_mean = 0.
    correct = 0.
    total = 0.

    net.train()
    for i, data in enumerate(train_loader):

        # forward
        inputs, labels = data
        outputs = net(inputs)

        # backward
        optimizer.zero_grad()
        loss = criterion(outputs, labels)
        loss.backward()

        # update weights
        optimizer.step()

        # 统计分类情况
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).squeeze().sum().numpy()

        # 打印训练信息
        loss_mean += loss.item()
        train_curve.append(loss.item())
        if (i+1) % log_interval == 0:
            loss_mean = loss_mean / log_interval
            print("Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format(
                epoch, MAX_EPOCH, i+1, len(train_loader), loss_mean, correct / total))
            loss_mean = 0.

    scheduler.step()  # 更新学习率

    if (epoch+1) % checkpoint_interval == 0:

        checkpoint = {"model_state_dict": net.state_dict(),
                      "optimizer_state_dic": optimizer.state_dict(),
                      "loss": loss,
                      "epoch": epoch}
        path_checkpoint = "./checkpint_{}_epoch.pkl".format(epoch)
        torch.save(checkpoint, path_checkpoint)

    # if epoch > 5:
    #     print("训练意外中断...")
    #     break

    # validate the model
    if (epoch+1) % val_interval == 0:

        correct_val = 0.
        total_val = 0.
        loss_val = 0.
        net.eval()
        with torch.no_grad():
            for j, data in enumerate(valid_loader):
                inputs, labels = data
                outputs = net(inputs)
                loss = criterion(outputs, labels)

                _, predicted = torch.max(outputs.data, 1)
                total_val += labels.size(0)
                correct_val += (predicted == labels).squeeze().sum().numpy()

                loss_val += loss.item()

            valid_curve.append(loss.item())
            print("Valid:\t Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format(
                epoch, MAX_EPOCH, j+1, len(valid_loader), loss_val/len(valid_loader), correct / total))


train_x = range(len(train_curve))
train_y = train_curve

train_iters = len(train_loader)
valid_x = np.arange(1, len(valid_curve)+1) * train_iters*val_interval # 由于valid中记录的是epochloss，需要对记录点进行转换到iterations
valid_y = valid_curve

plt.plot(train_x, train_y, label='Train')
plt.plot(valid_x, valid_y, label='Valid')

plt.legend(loc='upper right')
plt.ylabel('loss value')
plt.xlabel('Iteration')
plt.show()