Differences with torch.nn.functional.interpolate

View Source On Gitee

The following mapping relationships can be found in this file.

PyTorch APIs

MindSpore APIs

torch.nn.functional.interpolate

mindspore.ops.interpolate

torch.nn.functional.upsample

mindspore.ops.upsample

torch.nn.Upsample

mindspore.nn.Upsample

torch.nn.functional.interpolate

torch.nn.functional.interpolate(
    input,
    size=None,
    scale_factor=None,
    mode='nearest',
    align_corners=None,
    recompute_scale_factor=None) -> Tensor

For more information, see torch.nn.functional.interpolate.

mindspore.ops.interpolate

mindspore.ops.interpolate(
    x,
    size=None,
    scale_factor=None,
    mode='nearest',
    align_corners=None,
    recompute_scale_factor=None) -> Tensor

For more information, see mindspore.ops.interpolate.

Usage

PyTorch: Data is upsampled or downsampled based on size or scale_factor. The recompute_scale_factor controls whether the scale_factor used for interpolation calculation is re-calculated. If recompute_scale_factor is True, scale_factor must be passed in and the output size is calculated using scale_factor. The calculated output size will be used to infer the new ratio for interpolation. When scale_factor is a floating point number, it may be different from the re-calculated ratio due to rounding and precision issues. If recompute_scale_factor is False, interpolation is performed directly using size or scale_factor. Interpolation can be performed using one of six modes: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area'. The align_corners controls the alignment of the coordinates and is effective for 'linear' | 'bilinear' | 'bicubic' | 'trilinear' modes, with a default value of False.

MindSpore: The functionality is basically the same as PyTorch, but support for some parameters is not complete, such as some modes cannot directly pass in scale_factor, but can be circumvented by setting the recompute_scale_factor parameter to True (when scale_factor is a floating point number, accuracy errors may occur), and the specific differences are as follows.

Categories

Subcategories

PyTorch

MindSpore

Difference

Parameter

Parameter 1

size

size

-

Parameter 2

scale_factor

scale_factor

Function is consistent. Currently only supports 'area' mode directly pass in scale_factor. For unsupported modes, you can bypass by setting recompute_scale_factor parameter to True (when scale_factor is a floating-point number, there may be precision errors)

Parameter 3

mode

mode

Function is consistent

Parameter 4

align_corners

align_corners

Function is consistent, but in 'bicubic' mode align_corners=False, the calculation method is the same as TensorFlow, and the results are different from PyTorch

Parameter 5

recompute_scale_factor

recompute_scale_factor

-

Input

Single input

input

x

Same function, different parameter names

Difference Analysis and Examples

Code Example 1

Using the default 'nearest' mode interpolation, pass size in and the two APIs achieve the same function.

# Pytorch
import torch
import numpy as np

x = torch.tensor(np.array([[[1, 2, 3], [4, 5, 6]]]).astype(np.float32))
output = torch.nn.functional.interpolate(input=x, size=6)
print(output.numpy())
# [[[1. 1. 2. 2. 3. 3.]
#   [4. 4. 5. 5. 6. 6.]]]

# MindSpore
import numpy as np
import mindspore
from mindspore import Tensor
import mindspore.ops as ops

x = Tensor(np.array([[[1, 2, 3], [4, 5, 6]]]).astype(np.float32))
output = ops.interpolate(x, size=6, mode="nearest")
print(output)
# [[[1. 1. 2. 2. 3. 3.]
#   [4. 4. 5. 5. 6. 6.]]]

Code Example 2

Using the 'bilinear' mode, scale with scale_factor. This mode is not directly supported by MindSpore, but the error can be avoided by setting the recompute_scale_factor parameter to True (when scale_factor is a floating point, there may be some inaccuracies).

# Pytorch
import torch
import numpy as np

x = torch.tensor(np.array([[[[1, 2, 3], [4, 5, 6]]]]).astype(np.float32))
output = torch.nn.functional.interpolate(input=x, scale_factor=2.0, mode="bilinear", align_corners=True)
print(output.numpy())
# [[[[1.        1.4000001 1.8       2.2       2.6       3.       ]
#    [2.        2.4       2.8       3.1999998 3.6000001 4.       ]
#    [3.        3.4000003 3.8       4.2000003 4.6       5.       ]
#    [4.        4.4       4.8       5.2       5.6       6.       ]]]]

# MindSpore
import numpy as np
import mindspore
from mindspore import Tensor
import mindspore.ops as ops

x = Tensor(np.array([[[[1, 2, 3], [4, 5, 6]]]]).astype(np.float32))
output = ops.interpolate(x, scale_factor=2.0, recompute_scale_factor=True, mode="bilinear", align_corners=True)
print(output)
# [[[[1.        1.4       1.8       2.2       2.6       3.       ]
#    [2.        2.4       2.8000002 3.2       3.6       4.       ]
#    [3.        3.4       3.8000002 4.2       4.6       5.       ]
#    [4.        4.4       4.8       5.2       5.6       6.       ]]]]