比较与torchvision.ops.roi_align的差异

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torchvision.ops.roi_align

torchvision.ops.roi_align(input: torch.Tensor, boxes: torch.Tensor, output_size: None, spatial_scale: float = 1.0, sampling_ratio: int = -1, aligned: bool = False)

更多内容详见torchvision.ops.roi_align

mindspore.ops.ROIAlign

class mindspore.ops.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num=2, roi_end_mode=1)(features, rois)

更多内容详见mindspore.ops.ROIAlign

差异对比

PyTorch:感兴趣区域对齐(RoI Align)。

MindSpore:感兴趣区域对齐(RoI Align),参数输入形式不同,对齐模式实现不同。

分类

子类

PyTorch

MindSpore

差异

参数

参数1

input

-

输入特征,此参数位于MindSpore算子的输入参数列表中

参数2

boxes

-

边界框坐标,此参数位于MindSpore算子的输入参数列表中

参数3

output_size

[pooled_height, pooled_width]

特征尺寸,MindSopre分别用2个参数表示

参数4

spatial_scale

spatial_scale

坐标缩放系数

参数5

sampling_ratio

sample_num

插值采样数

参数6

aligned

roi_end_mode

对齐的模式。False时实现相同,True时实现不同

输入

输入1

-

features

输入特征

输入2

-

rois

roi坐标

输出

输出1

Tensor

Tensor

对齐后的特征

代码示例

# PyTorch
import numpy as np
import torch
import torchvision as tv

pooled_height, pooled_width, spatial_scale, sample_num, roi_end_mode = 3, 3, 0.25, 2, 1

features = np.array([[[[1., 2.], [3., 4.]]]]).astype(np.float32)
rois = np.array([[0, 0.2, 0.3, 0.2, 0.3]]).astype(np.float32)

features_t = torch.from_numpy(features)
rois_t = torch.from_numpy(rois)

output = tv.ops.roi_align(features_t, rois_t, (pooled_height, pooled_width), spatial_scale, sample_num, 0)
print(output)
# Out: tensor([[[[1.7000, 2.0333, 2.3667],
#                [2.3667, 2.7000, 3.0333],
#                [3.0333, 3.3667, 3.7000]]]])

# MindSpore
import mindspore as ms
from mindspore import ops

features = ms.Tensor(np.array([[[[1., 2.], [3., 4.]]]]), ms.float32)
rois = ms.Tensor(np.array([[0, 0.2, 0.3, 0.2, 0.3]]), ms.float32)

roi_align = ops.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num, 0)

output = roi_align(features, rois)
print(output)
# Out: [[[[1.7       2.0333333 2.3666668]
#         [2.3666668 2.7       3.0333335]
#         [3.0333333 3.3666668 3.7      ]]]]