|
- #!/usr/bin/python
- #coding=utf-8
- '''
- If there are Chinese comments in the code,please add at the beginning:
- #!/usr/bin/python
- #coding=utf-8
-
- 数据集结构是:
- MnistDataset_torch.zip
- ├── test
- └── train
-
- 预训练模型文件夹结构是:
- Torch_MNIST_Example_Model
- ├── mnist_epoch1_0.76.pkl
-
- '''
-
-
- from model import Model
- import numpy as np
- import torch
- from torchvision.datasets import mnist
- from torch.nn import CrossEntropyLoss
- from torch.optim import SGD
- from torch.utils.data import DataLoader
- from torchvision.transforms import ToTensor
- import argparse
- import os
- #导入c2net包
- from c2net.context import prepare
-
- # Training settings
- parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
- parser.add_argument('--epoch_size', type=int, default=10, help='how much epoch to train')
- parser.add_argument('--batch_size', type=int, default=256, help='how much batch_size in epoch')
-
- # 参数声明
- WORKERS = 0 # dataloder线程数
- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
- model = Model().to(device)
- optimizer = SGD(model.parameters(), lr=1e-1)
- cost = CrossEntropyLoss()
-
- # 模型训练
- def train(model, train_loader, epoch):
- model.train()
- train_loss = 0
- for i, data in enumerate(train_loader, 0):
- x, y = data
- x = x.to(device)
- y = y.to(device)
- optimizer.zero_grad()
- y_hat = model(x)
- loss = cost(y_hat, y)
- loss.backward()
- optimizer.step()
- train_loss += loss
- loss_mean = train_loss / (i+1)
- print('Train Epoch: {}\t Loss: {:.6f}'.format(epoch, loss_mean.item()))
-
- # 模型测试
- def test(model, test_loader, test_data):
- model.eval()
- test_loss = 0
- correct = 0
- with torch.no_grad():
- for i, data in enumerate(test_loader, 0):
- x, y = data
- x = x.to(device)
- y = y.to(device)
- optimizer.zero_grad()
- y_hat = model(x)
- test_loss += cost(y_hat, y).item()
- pred = y_hat.max(1, keepdim=True)[1]
- correct += pred.eq(y.view_as(pred)).sum().item()
- test_loss /= (i+1)
- print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
- test_loss, correct, len(test_data), 100. * correct / len(test_data)))
-
- if __name__ == '__main__':
- args, unknown = parser.parse_known_args()
-
- #初始化导入数据集和预训练模型到容器内
- c2net_context = prepare()
- #获取数据集路径
- MnistDataset_torch_path = c2net_context.dataset_path+"/"+"MnistDataset_torch"
- #获取预训练模型路径
- Torch_MNIST_Example_Model_path = c2net_context.pretrain_model_path+"/"+"Torch_MNIST_Example_Model"
-
- #log output
- print('cuda is available:{}'.format(torch.cuda.is_available()))
- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
- batch_size = args.batch_size
- epochs = args.epoch_size
- train_dataset = mnist.MNIST(root=os.path.join(MnistDataset_torch_path, "train"), train=True, transform=ToTensor(),download=False)
- test_dataset = mnist.MNIST(root=os.path.join(MnistDataset_torch_path, "test"), train=False, transform=ToTensor(),download=False)
- train_loader = DataLoader(train_dataset, batch_size=batch_size)
- test_loader = DataLoader(test_dataset, batch_size=batch_size)
-
- #如果有保存的模型,则加载模型,并在其基础上继续训练
- if os.path.exists(os.path.join(Torch_MNIST_Example_Model_path, "mnist_epoch1_0.76.pkl")):
- checkpoint = torch.load(os.path.join(Torch_MNIST_Example_Model_path, "mnist_epoch1_0.76.pkl"))
- model.load_state_dict(checkpoint['model'])
- optimizer.load_state_dict(checkpoint['optimizer'])
- start_epoch = checkpoint['epoch']
- print('加载 epoch {} 权重成功!'.format(start_epoch))
- else:
- start_epoch = 0
- print('无保存模型,将从头开始训练!')
-
- for epoch in range(start_epoch+1, epochs+1):
- train(model, train_loader, epoch)
- test(model, test_loader, test_dataset)
- # 将模型保存到c2net_context.output_path
- state = {'model':model.state_dict(), 'optimizer':optimizer.state_dict(), 'epoch':epoch}
- torch.save(state, '{}/mnist_epoch{}.pkl'.format(c2net_context.output_path, epoch))
-
|