Differences with torchvision.ops.roi_align

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)

For more information, see 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)

For more information, see mindspore.ops.ROIAlign.

Differences

PyTorch: Computes the Region of Interest (RoI) Align operator.

MindSpore: Computes the Region of Interest (RoI) Align operator. The input list is different and align mode is different.

| Categories | Subcategories |PyTorch | MindSpore | Difference || — | — | — | — |— | |Parameter | Parameter1 | input | - | The input features, defined in the input list of MindSpore | | | Parameter2 | boxes | - | Box coordinates, defined in the input list of MindSpore | | | Parameter3 | output_size | [pooled_height, pooled_width] | The size of output features, defined in two parameters in MindSpore | | | Parameter4 | spatial_scale | spatial_scale | Scaling factor for box coordinates | | | Parameter5 | sampling_ratio | sample_num | Number of sampling points in the interpolation | | | Parameter6 | aligned | roi_end_mode | Align mode. The implementation is the same for False, but different for True | |Input | Input1 | - | features | The input features | | | Input2 | - | rois | The input box coordinates | |Output | Output1 | Tensor | Tensor | Aligned rois |

Code Example

# 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      ]]]]