添加 'gpu_mnist_example/train_gcu.py'

gpu_mnist_example/train_gcu.py

@@ -0,0 +1,167 @@
+#!/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   
+
+示例选用的数据集是MnistDataset_torch.zip
+数据集结构是：
+ MnistDataset_torch.zip
+  ├── test
+  │   ├── MNIST/processed/test.pt
+  │   └── MNIST/processed/training.pt
+  │   ├── MNIST/raw/train-images-idx3-ubyte
+  │   └── MNIST/raw/train-labels-idx1-ubyte
+  │   ├── MNIST/raw/t10k-images-idx3-ubyte
+  │   └── MNIST/raw/t10k-labels-idx1-ubyte
+  ├── train
+  │   ├── MNIST/processed/test.pt
+  │   └── MNIST/processed/training.pt
+  │   ├── MNIST/raw/train-images-idx3-ubyte
+  │   └── MNIST/raw/train-labels-idx1-ubyte
+  │   ├── MNIST/raw/t10k-images-idx3-ubyte
+  │   └── MNIST/raw/t10k-labels-idx1-ubyte
+
+
+
+示例选用的预训练模型文件为：mnist_epoch1_0.86.pkl           
+
+
+代码会自动放置在/tmp/code目录下。
+数据集在界面选择后，会自动放置在/tmp/dataset目录下。
+预训练模型文件在界面选择后，会自动放置在/tmp/pretrainmodel目录下。
+输出的模型文件也需要放置在/tmp/output目录下，平台会自动下载/tmp/output目录下的文件。
+如果选用了多数据集，则应在/tmp/dataset后带上数据集名称，比如/tmp/dataset/MnistDataset_torch/train
+'''
+
+import torch
+from model import Model
+import numpy as np
+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
+
+import importlib.util
+
+def is_torch_dtu_available():
+    if importlib.util.find_spec("torch_dtu") is None:
+        return False
+    if importlib.util.find_spec("torch_dtu.core") is None:
+        return False
+    return importlib.util.find_spec("torch_dtu.core.dtu_model") is not None
+
+# Training settings
+parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+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__':
+    #获取参数并忽略超参数报错
+    args, unknown = parser.parse_known_args()
+    
+    #初始化导入数据集和预训练模型到容器内
+    openi_context = prepare()
+
+    #获取数据集路径，预训练模型路径，输出路径
+    dataset_path = openi_context.dataset_path
+    pretrain_model_path = openi_context.pretrain_model_path
+    output_path = openi_context.output_path
+
+    dataset_path_A = dataset_path + "/MnistDataset_torch"
+    pretrain_model_path_A = pretrain_model_path + "/MNIST_PytorchExample_GPU_test34_model_7f9j"
+
+    print("dataset_path:")
+    print(os.listdir(dataset_path))
+    os.listdir(dataset_path)
+    print("pretrain_model_path:")
+    print(os.listdir(pretrain_model_path))
+    os.listdir(pretrain_model_path)
+
+    print("output_path:")
+    print(os.listdir(output_path))
+    os.listdir(output_path)
+    # load DPU envs-xx.sh
+    DTU_FLAG = True
+    if is_torch_dtu_available():
+        import torch_dtu
+        import torch_dtu.distributed as dist
+        import torch_dtu.core.dtu_model as dm
+        from torch_dtu.nn.parallel import DistributedDataParallel as torchDDP
+        print('dtu is available: True') 
+        device = dm.dtu_device()
+        DTU_FLAG = True
+    else:
+        print('dtu is available: False')
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        DTU_FLAG = False
+        
+    
+    # 参数声明
+    model = Model().to(device)
+    optimizer = SGD(model.parameters(), lr=1e-1)
+    #log output
+    batch_size = args.batch_size
+    train_dataset = mnist.MNIST(root=dataset_path_A + "/train", train=True, transform=ToTensor(),download=False)
+    test_dataset = mnist.MNIST(root=dataset_path_A + "/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)
+    model = Model().to(device)
+    sgd = SGD(model.parameters(), lr=1e-1)
+    cost = CrossEntropyLoss()
+    epochs = args.epoch_size
+    print('epoch_size is:{}'.format(epochs))
+
+    # 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(pretrain_model_path_A+"/mnist_epoch1_0.70.pkl"):
+        checkpoint = torch.load(pretrain_model_path_A+"/mnist_epoch1_0.70.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, epochs):
+        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()
+            if DTU_FLAG:
+                dm.optimizer_step(sgd, barrier=True)
+            else:
+                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]
+        print('accuracy: {:.2f}'.format(correct / _sum))
+        #The model output location is placed under /tmp/output
+        state = {'model':model.state_dict(), 'optimizer':optimizer.state_dict(), 'epoch':_epoch+1}
+        torch.save(state, '/tmp/output/mnist_epoch{}_{:.2f}.pkl'.format(_epoch+1, correct / _sum))
+        print('test:')
+        print(os.listdir("/tmp/output"))