Function Differences with torch.flatten

torch.flatten

torch.flatten(
    input,
    start_dim=0,
    end_dim=-1
)

For more information, see torch.flatten.

mindspore.ops.Flatten

class mindspore.ops.Flatten(*args, **kwargs)(input_x)

For more information, see mindspore.ops.Flatten.

Differences

PyTorch: Supports the flatten of elements by specified dimensions.

MindSpore：Only the 0th dimension element is reserved and the elements of the remaining dimensions are flattened.

Code Example

import mindspore
from mindspore import Tensor
import mindspore.ops as ops
import torch
import numpy as np

# In MindSpore, only the 0th dimension will be reserved and the rest will be flattened.
input_tensor = Tensor(np.ones(shape=[1, 2, 3, 4]), mindspore.float32)
flatten = ops.Flatten()
output = flatten(input_tensor)
print(output.shape)
# Out：
# (1, 24)

# In torch, the dimension to reserve will be specified and the rest will be flattened.
input_tensor = torch.Tensor(np.ones(shape=[1, 2, 3, 4]))
output1 = torch.flatten(input=input_tensor, start_dim=1)
print(output1.shape)
# Out：
# torch.Size([1, 24])

input_tensor = torch.Tensor(np.ones(shape=[1, 2, 3, 4]))
output2 = torch.flatten(input=input_tensor, start_dim=2)
print(output2.shape)
# Out：
# torch.Size([1, 2, 12])