transforms.zip

# -*- coding: utf-8 -*- """ # @file name : train_lenet.py # @author : tingsongyu # @date : 2019-09-07 10:08:00 # @brief : 人民币分类模型训练 """ 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 matplotlib import pyplot as plt from model.lenet import LeNet from tools.my_dataset import RMBDataset def set_seed(seed=1): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) set_seed() # 设置随机种子 rmb_label = {"1": 0, "100": 1} # 参数设置 MAX_EPOCH = 10 BATCH_SIZE = 16 LR = 0.01 log_interval = 10 val_interval = 1 # ============================ step 1/5 数据 ============================ split_dir = os.path.join("..", "..", "rmb_split") train_dir = os.path.join(split_dir, "train") valid_dir = os.path.join(split_dir, "valid") 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.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=10, gamma=0.1) # 设置学习率下降策略 # ============================ step 5/5 训练 ============================ train_curve = list() valid_curve = list() for epoch in range(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 # 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_val/valid_loader.__len__()) 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, correct_val / total_val)) 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() # ============================ inference ============================ BASE_DIR = os.path.dirname(os.path.abspath(__file__)) test_dir = os.path.join(BASE_DIR, "test_data") test_data = RMBDataset(data_dir=test_dir, transform=valid_transform) valid_loader = DataLoader(dataset=test_data, batch_size=1) for i, data in enumerate(valid_loader): # forward inputs, labels = data outputs = net(inputs) _, predicted = torch.max(outputs.data, 1) rmb = 1 if predicted.numpy()[0] == 0 else 100 print("模型获得{}元".format(rmb))