diff --git a/gcu_mnist_example/README.md b/gcu_mnist_example/README.md index 3c9cb80..ba17ab2 100644 --- a/gcu_mnist_example/README.md +++ b/gcu_mnist_example/README.md @@ -91,7 +91,7 @@ upload_output() | -------- | ----------------------------------------------------------------------------------- | | 代码分支 | 选择仓库代码中要使用的代码分支，默认可选择master分支 | | 镜像 | 镜像选择含有python和torch的镜像 | -| 启动文件 | 启动文件选择代码目录下的启动脚本，在本示例中选择gpu_mnist_example/train.py | +| 启动文件 | 启动文件选择代码目录下的启动脚本，在本示例中选择gcu_mnist_example/train.py | | 数据集 | 数据集选择MnistDataset_torch.zip | | 运行参数 | 选择增加运行参数可以向脚本中其他参数传值，如epoch_size,需要在代码里定义增加的超参数 | | 资源规格 | 规格选择[GCU] | diff --git a/gpgpu_mnist_example/README.md b/gpgpu_mnist_example/README.md index 5713e2f..8ad5016 100644 --- a/gpgpu_mnist_example/README.md +++ b/gpgpu_mnist_example/README.md @@ -81,6 +81,7 @@ upload_output() ##### 1，GPGPU示例代码： +- 训练任务示例请参考示例中[train.py](./train.py)的代码注释 - 推理任务示例请参考示例中[inference.py](./inference.py)的代码注释 ##### 2，创建GPGPU调试任务 @@ -101,7 +102,7 @@ upload_output() 进入对应的代码目录后，可在终端执行python train.py； -##### 3，创建GPGPU推理任务 +##### 3，创建GPGPU训练任务 表2 创建推理作业界面参数说明 @@ -109,7 +110,7 @@ upload_output() | -------- | ----------------------------------------------------------------------------------- | | 代码分支 | 选择仓库代码中要使用的代码分支，默认可选择master分支 | | 镜像 | 镜像选择含有python和torch的镜像 | -| 启动文件 | 启动文件选择代码目录下的启动脚本，在本示例中选择gpu_mnist_example/train.py | +| 启动文件 | 启动文件选择代码目录下的启动脚本，在本示例中选择gpgpu_mnist_example/train.py | | 数据集 | 数据集选择MnistDataset_torch.zip | | 运行参数 | 选择增加运行参数可以向脚本中其他参数传值，如epoch_size,需要在代码里定义增加的超参数 | | 资源规格 | 规格选择[GPGPU] | diff --git a/gpgpu_mnist_example/train.py b/gpgpu_mnist_example/train.py new file mode 100644 index 0000000..e71eaf2 --- /dev/null +++ b/gpgpu_mnist_example/train.py @@ -0,0 +1,119 @@ +#!/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)) + +