Differences with torch.flatten
torch.flatten
torch.flatten(
input,
start_dim=0,
end_dim=-1
)
For more information, see torch.flatten.
mindspore.ops.flatten
mindspore.ops.flatten(input, order='C', *, start_dim=1, end_dim=-1)
For more information, see mindspore.ops.flatten.
Differences
PyTorch: Supports the flatten operation of elements by specified dimensions, where start_dim
defaults to 0 and end_dim
defaults to -1.
MindSpore:Supports the flatten operation of elements by specified dimensions, where start_dim
defaults to 1 and end_dim
defaults to -1. Prioritizes row or column flatten by order
to "C" or "F".
Categories |
Subcategories |
PyTorch |
MindSpore |
Differences |
---|---|---|---|---|
Parameter |
Parameter 1 |
input |
input |
Same function |
Parameter 2 |
- |
order |
Flatten order, PyTorch does not have this Parameter |
|
Parameter 3 |
start_dim |
start_dim |
Same function |
|
Parameter 4 |
end_dim |
end_dim |
Same function |
Code Example
import mindspore as ms
import mindspore.ops as ops
import torch
import numpy as np
# MindSpore
input_tensor = ms.Tensor(np.ones(shape=[1, 2, 3, 4]), ms.float32)
output = ops.flatten(input_tensor)
print(output.shape)
# Out:
# (1, 24)
input_tensor = ms.Tensor(np.ones(shape=[1, 2, 3, 4]), ms.float32)
output = ops.flatten(input_tensor, start_dim=2)
print(output.shape)
# Out:
# (1, 2, 12)
# PyTorch
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])