import msadapter
 import torch
 from torch import nn
 from torch.utils.data import DataLoader
 from torchvision import datasets
 from torchvision.transforms import ToTensor

 # 1.Working with data
 # Download training data from open datasets.
 training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
 # Download test data from open datasets.
 test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())

 # 2.Creating Models
 class NeuralNetwork(nn.Module):
     def __init__(self):
         super().__init__()
         self.flatten = nn.Flatten()
         self.linear_relu_stack = nn.Sequential(
             nn.Linear(28*28, 512),
             nn.ReLU(),
             nn.Linear(512, 512),
             nn.ReLU(),
             nn.Linear(512, 10)
         )

     def forward(self, x):
         x = self.flatten(x)
         logits = self.linear_relu_stack(x)
         return logits

 if __name__ == '__main__':
     train_dataloader = DataLoader(training_data, batch_size=64)
     test_dataloader = DataLoader(test_data, batch_size=64)

     # 3.create Models
     model = NeuralNetwork()

     classes = [
         "T-shirt/top",
         "Trouser",
         "Pullover",
         "Dress",
         "Coat",
         "Sandal",
         "Shirt",
         "Sneaker",
         "Bag",
         "Ankle boot",
     ]
     # 4.Predict
     model.eval()
     x, y = test_data[0][0], test_data[0][1]
     with torch.no_grad():
         pred = model(x)
         predicted, actual = classes[pred[0].argmax(0)], classes[y]
         print(f'Predicted: "{predicted}", Actual: "{actual}"')

示例代码：

  from torch.utils.data import DataLoader
  from torchvision import datasets
  from torchvision.transforms import ToTensor

  training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
  train_dataloader = DataLoader(training_data, batch_size=64, pin_memory=True)
  for batch, (X, y) in enumerate(train_dataloader):
      X, y = X.cuda(), y.cuda()

报错信息如下：

 Traceback (most recent call last):
     File "/path/to/your/torch/utils/data/_utils/pin_memory.py", line 98, in pin_memory
         clone[i] = pin_memory(item, device)
     File "/path/to/your/torch/utils/data/_utils/pin_memory.py", line 64, in pin_memory
         return data.pin_memory(device)
 TypeError: pin_memory() takes 1 positional argument but 2 were given

示例代码：

  from torch.utils.data import DataLoader
  from torchvision import datasets
  from torchvision.transforms import ToTensor

  training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
  train_dataloader = DataLoader(training_data, batch_size=64, pin_memory=True)
  for batch, (X, y) in enumerate(train_dataloader):
      X, y = X.cuda(), y.cuda()

报错信息如下：

 Traceback (most recent call last):
     File "/path/to/your/torch/utils/data/_utils/pin_memory.py", line 98, in pin_memory
         clone[i] = pin_memory(item, device)
     File "/path/to/your/torch/utils/data/_utils/pin_memory.py", line 64, in pin_memory
         return data.pin_memory(device)
 TypeError: pin_memory() takes 1 positional argument but 2 were given

示例代码：

 import torch
 from torch import nn
 import mindspore as ms

 class NeuralNetwork(nn.Module):
     def __init__(self):
         super().__init__()
         self.linear = nn.Linear(28*28, 512)

     def forward(self, x):
         logits = self.linear(x)
         return logits

 class myNN(ms.nn.Cell):
     def __init__(self):
         super().__init__()
         self.linear = nn.Linear(28*28, 512)

     def construct(self, x):
         logits = self.linear(x)
         return logits

 model = myNN()
 ms.save_checkpoint(model, "./net.ckpt")
 model2 = NeuralNetwork()
 model.load_state_dict(torch.load("./net.ckpt"))

报错信息如下：

 Traceback (most recent call last):
     File "/path/to/your/demo.py", line 99, in <module>
         model.load_state_dict(torch.load("./mynn.ckpt"))
     File "/path/to/your/torch/serialization.py", line 1020, in load
         return _legacy_load(opened_file, pickle_module, **pickle_load_args)
     File "/path/to/your/torch/serialization.py", line 1118, in _legacy_load
         magic_number = pickle_module.load(f, **pickle_load_args)
 EOFError: Ran out of input

示例代码：

 from mindspore import Tensor

 a = Tensor([2, 2])
 print(f'before import torch: a.shape={a.shape}')

 import torch
 print(f'after import torch: a.shape={a.shape}')

执行结果如下，可以看到，import torch后，原本的mindspore.Tensor.shape行为发生了改变。

 before import torch: a.shape=(2,)
 after import torch: a.shape=torch.Size([2])

不支持混跑的MindSpore接口详见下表: