|
- '''
- 在训练环境中，代码会自动放在/tmp/code目录下，上传的数据集会自动放在/tmp/dataset目录下，模型下载路径默认在/tmp/output下，请将模型输出位置指定到/tmp/model，
- 启智平台界面会提供/tmp/output目录下的文件下载。
- '''
-
-
- 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
-
- # Training settings
- parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
- #数据集位置放在/tmp/dataset下
- parser.add_argument('--traindata', default="/tmp/dataset/train" ,help='path to train dataset')
- parser.add_argument('--testdata', default="/tmp/dataset/test" ,help='path to test dataset')
- parser.add_argument('--epoch_size', type=int, default=1, help='how much epoch to train')
- parser.add_argument('--batch_size', type=int, default=256, help='how much batch_size in epoch')
-
- if __name__ == '__main__':
- args = parser.parse_args()
- #日志输出
- 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
- train_dataset = mnist.MNIST(root=args.traindata, train=True, transform=ToTensor(),download=False)
- test_dataset = mnist.MNIST(root=args.testdata, train=False, transform=ToTensor(),download=False)
- train_loader = DataLoader(train_dataset, batch_size=batch_size)
- test_loader = DataLoader(test_dataset, batch_size=batch_size)
- model = Model().to(device)
- sgd = SGD(model.parameters(), lr=1e-1)
- cost = CrossEntropyLoss()
- epoch = args.epoch_size
- #日志输出
- print('epoch_size is:{}'.format(epoch))
- for _epoch in range(epoch):
- print('the {} epoch_size begin'.format(_epoch + 1))
- model.train()
- for idx, (train_x, train_label) in enumerate(train_loader):
- train_x = train_x.to(device)
- train_label = train_label.to(device)
- label_np = np.zeros((train_label.shape[0], 10))
- sgd.zero_grad()
- predict_y = model(train_x.float())
- loss = cost(predict_y, train_label.long())
- if idx % 10 == 0:
- print('idx: {}, loss: {}'.format(idx, loss.sum().item()))
- loss.backward()
- sgd.step()
-
- correct = 0
- _sum = 0
- model.eval()
- for idx, (test_x, test_label) in enumerate(test_loader):
- test_x = test_x
- test_label = test_label
- predict_y = model(test_x.to(device).float()).detach()
- predict_ys = np.argmax(predict_y.cpu(), axis=-1)
- label_np = test_label.numpy()
- _ = predict_ys == test_label
- correct += np.sum(_.numpy(), axis=-1)
- _sum += _.shape[0]
- #日志输出
- print('accuracy: {:.2f}'.format(correct / _sum))
- #模型输出位置放在/tmp/output下
- torch.save(model, '/tmp/output/mnist_epoch{}_{:.2f}.pkl'.format(_epoch+1, correct / _sum))
|
