Differences with torch.nn.functional.grid_sample

torch.nn.functional.grid_sample

torch.nn.functional.grid_sample(input, grid, mode='bilinear', padding_mode='zero', align_corners=None) -> Tensor

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

mindspore.ops.grid_sample

mindspore.ops.grid_sample(input, grid, mode='bilinear', padding_mode='zeros', align_corners=False)

For more information, see mindspore.ops.grid_sample.

Differences

PyTorch: Given an input and a flow-field grid, computes the output using input values and pixel locations from grid. Only 4-D (GridSampler2D) and 5-D (GridSampler3D) inputs are supported.

MindSpore: MindSpore API implements functions basically same as PyTorch, but the mode of "bicubic" is not supported yet in MindSpore.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	input	input	Same function
	Parameter 2	grid	grid	Same function
	Parameter 3	mode	mode	Same function, MindSpore does not support "bicubic" mode yet
	Parameter 4	padding_mode	padding_mode	Same function
	Parameter 5	align_corners	align_corners	Same function

Code Example 1

# PyTorch
import torch
from torch import tensor
import numpy as np
input_x = tensor(np.arange(16).reshape((2, 2, 2, 2)).astype(np.float32))
grid = tensor(np.arange(0.2, 1, 0.1).reshape((2, 2, 1, 2)).astype(np.float32))
output = torch.nn.functional.grid_sample(input_x, grid)
print(output)
#tensor([[[[ 2.3000],
#          [ 2.9000]],
#
#         [[ 6.3000],
#          [ 6.9000]]],
#
#
#        [[[ 7.9200],
#          [ 4.6200]],
#
#         [[10.8000],
#          [ 6.3000]]]])

# MindSpore
from mindspore import Tensor
import mindspore.ops as ops
import numpy as np
input_x = Tensor(np.arange(16).reshape((2, 2, 2, 2)).astype(np.float32))
grid = Tensor(np.arange(0.2, 1, 0.1).reshape((2, 2, 1, 2)).astype(np.float32))
output = ops.grid_sample(input_x, grid, mode='bilinear', padding_mode='zeros', align_corners=False)
print(output)
#[[[[ 2.3      ]
#   [ 2.8999999]]
#
#  [[ 6.3      ]
#   [ 6.8999996]]]
#
#
# [[[ 7.919999 ]
#   [ 4.6200004]]
#
#  [[10.799998 ]
#   [ 6.3000007]]]]