比较与torch.nn.Flatten的功能差异

torch.nn.Flatten

class torch.nn.Flatten(
    start_dim=1,
    end_dim=-1
)

更多内容详见torch.nn.Flatten。

mindspore.nn.Flatten

class mindspore.nn.Flatten()(input)

更多内容详见mindspore.nn.Flatten。

使用方式

PyTorch：支持指定维度对元素进行展开，默认保留第0维，对其余维度的元素进行展开；需要同torch.nn.Sequential一起使用。

MindSpore：仅支持保留第0维元素，对其余维度的元素进行展开。

代码示例

import mindspore as ms
from mindspore import nn
import torch
import numpy as np

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

# In torch, the dimension to reserve can be specified and the rest will be flattened.
# Different from torch.flatten, you should pass it as parameter into torch.nn.Sequential.
input_tensor = torch.Tensor(np.ones(shape=[1, 2, 3, 4]))
flatten1 = torch.nn.Sequential(torch.nn.Flatten(start_dim=1))
output1 = flatten1(input_tensor)
print(output1.shape)
# Out：
# torch.Size([1, 24])

input_tensor = torch.Tensor(np.ones(shape=[1, 2, 3, 4]))
flatten2 = torch.nn.Sequential(torch.nn.Flatten(start_dim=2))
output2 = flatten2(input_tensor)
print(output2.shape)
# Out：
# torch.Size([1, 2, 12])