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