Function Differences with torch.Tensor.float

torch.Tensor.float(memory_format=torch.preserve_format)

class mindspore.ops.Cast(*args, **kwargs)(
    input_x,
    type
)

import mindspore as ms
import mindspore.ops as ops
import torch
import numpy as np

# In MindSpore, you can specify the data type to be transformed into.
input_x = ms.Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
cast = ops.Cast()
output = cast(input_x, ms.int32)
print(output.dtype)
print(output.shape)
# Out：
# Int32
# (2, 3, 4, 5)

# In torch, the input will be transformed into float.
input_x = torch.Tensor(np.random.randn(2, 3, 4, 5).astype(np.int32))
output = input_x.float()
print(output.dtype)
print(output.shape)
# Out：
# torch.float32
# torch.Size([2, 3, 4, 5])