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mmdet.core.bbox.coder.tblr_bbox_coder 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch

from ..builder import BBOX_CODERS
from .base_bbox_coder import BaseBBoxCoder


[文档]@BBOX_CODERS.register_module() class TBLRBBoxCoder(BaseBBoxCoder): """TBLR BBox coder. Following the practice in `FSAF <https://arxiv.org/abs/1903.00621>`_, this coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left, right) and decode it back to the original. Args: normalizer (list | float): Normalization factor to be divided with when coding the coordinates. If it is a list, it should have length of 4 indicating normalization factor in tblr dims. Otherwise it is a unified float factor for all dims. Default: 4.0 clip_border (bool, optional): Whether clip the objects outside the border of the image. Defaults to True. """ def __init__(self, normalizer=4.0, clip_border=True): super(BaseBBoxCoder, self).__init__() self.normalizer = normalizer self.clip_border = clip_border
[文档] def encode(self, bboxes, gt_bboxes): """Get box regression transformation deltas that can be used to transform the ``bboxes`` into the ``gt_bboxes`` in the (top, left, bottom, right) order. Args: bboxes (torch.Tensor): source boxes, e.g., object proposals. gt_bboxes (torch.Tensor): target of the transformation, e.g., ground truth boxes. Returns: torch.Tensor: Box transformation deltas """ assert bboxes.size(0) == gt_bboxes.size(0) assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 encoded_bboxes = bboxes2tblr( bboxes, gt_bboxes, normalizer=self.normalizer) return encoded_bboxes
[文档] def decode(self, bboxes, pred_bboxes, max_shape=None): """Apply transformation `pred_bboxes` to `boxes`. Args: bboxes (torch.Tensor): Basic boxes.Shape (B, N, 4) or (N, 4) pred_bboxes (torch.Tensor): Encoded boxes with shape (B, N, 4) or (N, 4) max_shape (Sequence[int] or torch.Tensor or Sequence[ Sequence[int]],optional): Maximum bounds for boxes, specifies (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then the max_shape should be a Sequence[Sequence[int]] and the length of max_shape should also be B. Returns: torch.Tensor: Decoded boxes. """ decoded_bboxes = tblr2bboxes( bboxes, pred_bboxes, normalizer=self.normalizer, max_shape=max_shape, clip_border=self.clip_border) return decoded_bboxes
@mmcv.jit(coderize=True) def bboxes2tblr(priors, gts, normalizer=4.0, normalize_by_wh=True): """Encode ground truth boxes to tblr coordinate. It first convert the gt coordinate to tblr format, (top, bottom, left, right), relative to prior box centers. The tblr coordinate may be normalized by the side length of prior bboxes if `normalize_by_wh` is specified as True, and it is then normalized by the `normalizer` factor. Args: priors (Tensor): Prior boxes in point form Shape: (num_proposals,4). gts (Tensor): Coords of ground truth for each prior in point-form Shape: (num_proposals, 4). normalizer (Sequence[float] | float): normalization parameter of encoded boxes. If it is a list, it has to have length = 4. Default: 4.0 normalize_by_wh (bool): Whether to normalize tblr coordinate by the side length (wh) of prior bboxes. Return: encoded boxes (Tensor), Shape: (num_proposals, 4) """ # dist b/t match center and prior's center if not isinstance(normalizer, float): normalizer = torch.tensor(normalizer, device=priors.device) assert len(normalizer) == 4, 'Normalizer must have length = 4' assert priors.size(0) == gts.size(0) prior_centers = (priors[:, 0:2] + priors[:, 2:4]) / 2 xmin, ymin, xmax, ymax = gts.split(1, dim=1) top = prior_centers[:, 1].unsqueeze(1) - ymin bottom = ymax - prior_centers[:, 1].unsqueeze(1) left = prior_centers[:, 0].unsqueeze(1) - xmin right = xmax - prior_centers[:, 0].unsqueeze(1) loc = torch.cat((top, bottom, left, right), dim=1) if normalize_by_wh: # Normalize tblr by anchor width and height wh = priors[:, 2:4] - priors[:, 0:2] w, h = torch.split(wh, 1, dim=1) loc[:, :2] /= h # tb is normalized by h loc[:, 2:] /= w # lr is normalized by w # Normalize tblr by the given normalization factor return loc / normalizer @mmcv.jit(coderize=True) def tblr2bboxes(priors, tblr, normalizer=4.0, normalize_by_wh=True, max_shape=None, clip_border=True): """Decode tblr outputs to prediction boxes. The process includes 3 steps: 1) De-normalize tblr coordinates by multiplying it with `normalizer`; 2) De-normalize tblr coordinates by the prior bbox width and height if `normalize_by_wh` is `True`; 3) Convert tblr (top, bottom, left, right) pair relative to the center of priors back to (xmin, ymin, xmax, ymax) coordinate. Args: priors (Tensor): Prior boxes in point form (x0, y0, x1, y1) Shape: (N,4) or (B, N, 4). tblr (Tensor): Coords of network output in tblr form Shape: (N, 4) or (B, N, 4). normalizer (Sequence[float] | float): Normalization parameter of encoded boxes. By list, it represents the normalization factors at tblr dims. By float, it is the unified normalization factor at all dims. Default: 4.0 normalize_by_wh (bool): Whether the tblr coordinates have been normalized by the side length (wh) of prior bboxes. max_shape (Sequence[int] or torch.Tensor or Sequence[ Sequence[int]],optional): Maximum bounds for boxes, specifies (H, W, C) or (H, W). If priors shape is (B, N, 4), then the max_shape should be a Sequence[Sequence[int]] and the length of max_shape should also be B. clip_border (bool, optional): Whether clip the objects outside the border of the image. Defaults to True. Return: encoded boxes (Tensor): Boxes with shape (N, 4) or (B, N, 4) """ if not isinstance(normalizer, float): normalizer = torch.tensor(normalizer, device=priors.device) assert len(normalizer) == 4, 'Normalizer must have length = 4' assert priors.size(0) == tblr.size(0) if priors.ndim == 3: assert priors.size(1) == tblr.size(1) loc_decode = tblr * normalizer prior_centers = (priors[..., 0:2] + priors[..., 2:4]) / 2 if normalize_by_wh: wh = priors[..., 2:4] - priors[..., 0:2] w, h = torch.split(wh, 1, dim=-1) # Inplace operation with slice would failed for exporting to ONNX th = h * loc_decode[..., :2] # tb tw = w * loc_decode[..., 2:] # lr loc_decode = torch.cat([th, tw], dim=-1) # Cannot be exported using onnx when loc_decode.split(1, dim=-1) top, bottom, left, right = loc_decode.split((1, 1, 1, 1), dim=-1) xmin = prior_centers[..., 0].unsqueeze(-1) - left xmax = prior_centers[..., 0].unsqueeze(-1) + right ymin = prior_centers[..., 1].unsqueeze(-1) - top ymax = prior_centers[..., 1].unsqueeze(-1) + bottom bboxes = torch.cat((xmin, ymin, xmax, ymax), dim=-1) if clip_border and max_shape is not None: # clip bboxes with dynamic `min` and `max` for onnx if torch.onnx.is_in_onnx_export(): from mmdet.core.export import dynamic_clip_for_onnx xmin, ymin, xmax, ymax = dynamic_clip_for_onnx( xmin, ymin, xmax, ymax, max_shape) bboxes = torch.cat([xmin, ymin, xmax, ymax], dim=-1) return bboxes if not isinstance(max_shape, torch.Tensor): max_shape = priors.new_tensor(max_shape) max_shape = max_shape[..., :2].type_as(priors) if max_shape.ndim == 2: assert bboxes.ndim == 3 assert max_shape.size(0) == bboxes.size(0) min_xy = priors.new_tensor(0) max_xy = torch.cat([max_shape, max_shape], dim=-1).flip(-1).unsqueeze(-2) bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) return bboxes
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