Source code for torchvision.ops.ps_roi_align

import torch
from torch import nn, Tensor
from torch.nn.modules.utils import _pair
from torchvision.extension import _assert_has_ops

from ..utils import _log_api_usage_once
from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape

[docs]def ps_roi_align( input: Tensor, boxes: Tensor, output_size: int, spatial_scale: float = 1.0, sampling_ratio: int = -1, ) -> Tensor: """ Performs Position-Sensitive Region of Interest (RoI) Align operator mentioned in Light-Head R-CNN. Args: input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element contains ``C`` feature maps of dimensions ``H x W``. boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2) format where the regions will be taken from. The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. If a single Tensor is passed, then the first column should contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``. If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i in the batch. output_size (int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling is performed, as (height, width). spatial_scale (float): a scaling factor that maps the box coordinates to the input coordinates. For example, if your boxes are defined on the scale of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of the original image), you'll want to set this to 0.5. Default: 1.0 sampling_ratio (int): number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If <= 0, then an adaptive number of grid points are used (computed as ``ceil(roi_width / output_width)``, and likewise for height). Default: -1 Returns: Tensor[K, C / (output_size[0] * output_size[1]), output_size[0], output_size[1]]: The pooled RoIs """ if not torch.jit.is_scripting() and not torch.jit.is_tracing(): _log_api_usage_once(ps_roi_align) _assert_has_ops() check_roi_boxes_shape(boxes) rois = boxes output_size = _pair(output_size) if not isinstance(rois, torch.Tensor): rois = convert_boxes_to_roi_format(rois) output, _ = torch.ops.torchvision.ps_roi_align( input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio ) return output
class PSRoIAlign(nn.Module): """ See :func:`ps_roi_align`. """ def __init__( self, output_size: int, spatial_scale: float, sampling_ratio: int, ): super().__init__() _log_api_usage_once(self) self.output_size = output_size self.spatial_scale = spatial_scale self.sampling_ratio = sampling_ratio def forward(self, input: Tensor, rois: Tensor) -> Tensor: return ps_roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio) def __repr__(self) -> str: s = ( f"{self.__class__.__name__}(" f"output_size={self.output_size}" f", spatial_scale={self.spatial_scale}" f", sampling_ratio={self.sampling_ratio}" f")" ) return s


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