Differences with torch.ger

torch.Tensor.to

torch.Tensor.to(dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor
torch.Tensor.to(device=None, dtype=None, non_blocking=False, copy=False, memory_format=torch.preserve_format) -> Tensor
torch.Tensor.to(other, non_blocking=False, copy=False) -> Tensor

For more information, see torch.Tensor.to.

mindspore.Tensor.to

mindspore.Tensor.to(dtype)

For more information, see mindspore.Tensor.to.

Differences

API function of MindSpore is not consistent with that of PyTorch.

PyTorch: Three interface usages are supported.

When only the dtype parameter is supplied, the interface returns Tensor of the specified data type, and the usage is the same as MindSpore.
When the device parameter is provided, the Tensor returned by the interface specifies the device, which MindSpore does not support.
When other is supplied, the interface returns a Tensor of the same data type and device as other, which is not supported by MindSpore.

MindSpore：Only the dtype parameter is supported, which returns Tensor of the specified data type.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	dtype	dtype	Use the data types under the corresponding framework.
	Parameter 2	device	-	PyTorch specifies the device, which MindSpore does not support.
	Parameter 3	other	-	PyTorch specifies the Tensor used, which MindSpore does not support.
	Parameter 4	non_blocking	-	PyTorch uses this for asynchronous copying between the CPU and GPU, which MindSpore does not support.
	Parameter 5	copy	-	PyTorch uses this to force the creation of new Tensors, which MindSpore does not support.
	Parameter 6	memory_format	-	For details, see General Difference Parameter Table

Code Example 1

Specify only the dtype parameter.

# PyTorch
import torch
import numpy as np
input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_x = torch.tensor(input_np)
dtype = torch.int32
output = input_x.to(dtype)
print(output.dtype)
# torch.int32

# MindSpore
import mindspore
from mindspore import Tensor
import numpy as np
input_x = Tensor(input_np)
dtype = mindspore.int32
output = input_x.to(dtype)
print(output.dtype)
# Int32

Code Example 2

Specify the device parameter.

# PyTorch
import torch
import numpy as np
input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_x = torch.tensor(input_np)
device = torch.device('cpu')
output = input_x.to(device)
print(output.device)
# cpu

# MindSpore doesn't support this feature currently.

Code Example 3

Specify another Tensor.

# PyTorch
import torch
import numpy as np
input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_x = torch.tensor(input_np)
input_y = torch.tensor(input_np).type(torch.int64)
output = input_x.to(input_y)
print(output.dtype)
# torch.int64

# MindSpore doesn't support this feature currently.