# Copyright (c) OpenMMLab. All rights reserved. import warnings import mmcv import numpy as np import torch from ..builder import BBOX_CODERS from .base_bbox_coder import BaseBBoxCoder @BBOX_CODERS.register_module() class DeltaXYWHBBoxCoder(BaseBBoxCoder): """Delta XYWH BBox coder. Following the practice in `R-CNN `_, this coder encodes bbox (x1, y1, x2, y2) into delta (dx, dy, dw, dh) and decodes delta (dx, dy, dw, dh) back to original bbox (x1, y1, x2, y2). Args: target_means (Sequence[float]): Denormalizing means of target for delta coordinates target_stds (Sequence[float]): Denormalizing standard deviation of target for delta coordinates clip_border (bool, optional): Whether clip the objects outside the border of the image. Defaults to True. add_ctr_clamp (bool): Whether to add center clamp, when added, the predicted box is clamped is its center is too far away from the original anchor's center. Only used by YOLOF. Default False. ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF. Default 32. """ def __init__(self, target_means=(0., 0., 0., 0.), target_stds=(1., 1., 1., 1.), clip_border=True, add_ctr_clamp=False, ctr_clamp=32): super(BaseBBoxCoder, self).__init__() self.means = target_means self.stds = target_stds self.clip_border = clip_border self.add_ctr_clamp = add_ctr_clamp self.ctr_clamp = ctr_clamp def encode(self, bboxes, gt_bboxes): """Get box regression transformation deltas that can be used to transform the ``bboxes`` into the ``gt_bboxes``. 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 = bbox2delta(bboxes, gt_bboxes, self.means, self.stds) return encoded_bboxes def decode(self, bboxes, pred_bboxes, max_shape=None, wh_ratio_clip=16 / 1000): """Apply transformation `pred_bboxes` to `boxes`. Args: bboxes (torch.Tensor): Basic boxes. Shape (B, N, 4) or (N, 4) pred_bboxes (Tensor): Encoded offsets with respect to each roi. Has shape (B, N, num_classes * 4) or (B, N, 4) or (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H when rois is a grid of anchors.Offset encoding follows [1]_. 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. wh_ratio_clip (float, optional): The allowed ratio between width and height. Returns: torch.Tensor: Decoded boxes. """ assert pred_bboxes.size(0) == bboxes.size(0) if pred_bboxes.ndim == 3: assert pred_bboxes.size(1) == bboxes.size(1) if pred_bboxes.ndim == 2 and not torch.onnx.is_in_onnx_export(): # single image decode decoded_bboxes = delta2bbox(bboxes, pred_bboxes, self.means, self.stds, max_shape, wh_ratio_clip, self.clip_border, self.add_ctr_clamp, self.ctr_clamp) else: if pred_bboxes.ndim == 3 and not torch.onnx.is_in_onnx_export(): warnings.warn( 'DeprecationWarning: onnx_delta2bbox is deprecated ' 'in the case of batch decoding and non-ONNX, ' 'please use “delta2bbox” instead. In order to improve ' 'the decoding speed, the batch function will no ' 'longer be supported. ') decoded_bboxes = onnx_delta2bbox(bboxes, pred_bboxes, self.means, self.stds, max_shape, wh_ratio_clip, self.clip_border, self.add_ctr_clamp, self.ctr_clamp) return decoded_bboxes @mmcv.jit(coderize=True) def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.)): """Compute deltas of proposals w.r.t. gt. We usually compute the deltas of x, y, w, h of proposals w.r.t ground truth bboxes to get regression target. This is the inverse function of :func:`delta2bbox`. Args: proposals (Tensor): Boxes to be transformed, shape (N, ..., 4) gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4) means (Sequence[float]): Denormalizing means for delta coordinates stds (Sequence[float]): Denormalizing standard deviation for delta coordinates Returns: Tensor: deltas with shape (N, 4), where columns represent dx, dy, dw, dh. """ assert proposals.size() == gt.size() proposals = proposals.float() gt = gt.float() px = (proposals[..., 0] + proposals[..., 2]) * 0.5 py = (proposals[..., 1] + proposals[..., 3]) * 0.5 pw = proposals[..., 2] - proposals[..., 0] ph = proposals[..., 3] - proposals[..., 1] gx = (gt[..., 0] + gt[..., 2]) * 0.5 gy = (gt[..., 1] + gt[..., 3]) * 0.5 gw = gt[..., 2] - gt[..., 0] gh = gt[..., 3] - gt[..., 1] dx = (gx - px) / pw dy = (gy - py) / ph dw = torch.log(gw / pw) dh = torch.log(gh / ph) deltas = torch.stack([dx, dy, dw, dh], dim=-1) means = deltas.new_tensor(means).unsqueeze(0) stds = deltas.new_tensor(stds).unsqueeze(0) deltas = deltas.sub_(means).div_(stds) return deltas @mmcv.jit(coderize=True) def delta2bbox(rois, deltas, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.), max_shape=None, wh_ratio_clip=16 / 1000, clip_border=True, add_ctr_clamp=False, ctr_clamp=32): """Apply deltas to shift/scale base boxes. Typically the rois are anchor or proposed bounding boxes and the deltas are network outputs used to shift/scale those boxes. This is the inverse function of :func:`bbox2delta`. Args: rois (Tensor): Boxes to be transformed. Has shape (N, 4). deltas (Tensor): Encoded offsets relative to each roi. Has shape (N, num_classes * 4) or (N, 4). Note N = num_base_anchors * W * H, when rois is a grid of anchors. Offset encoding follows [1]_. means (Sequence[float]): Denormalizing means for delta coordinates. Default (0., 0., 0., 0.). stds (Sequence[float]): Denormalizing standard deviation for delta coordinates. Default (1., 1., 1., 1.). max_shape (tuple[int, int]): Maximum bounds for boxes, specifies (H, W). Default None. wh_ratio_clip (float): Maximum aspect ratio for boxes. Default 16 / 1000. clip_border (bool, optional): Whether clip the objects outside the border of the image. Default True. add_ctr_clamp (bool): Whether to add center clamp. When set to True, the center of the prediction bounding box will be clamped to avoid being too far away from the center of the anchor. Only used by YOLOF. Default False. ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF. Default 32. Returns: Tensor: Boxes with shape (N, num_classes * 4) or (N, 4), where 4 represent tl_x, tl_y, br_x, br_y. References: .. [1] https://arxiv.org/abs/1311.2524 Example: >>> rois = torch.Tensor([[ 0., 0., 1., 1.], >>> [ 0., 0., 1., 1.], >>> [ 0., 0., 1., 1.], >>> [ 5., 5., 5., 5.]]) >>> deltas = torch.Tensor([[ 0., 0., 0., 0.], >>> [ 1., 1., 1., 1.], >>> [ 0., 0., 2., -1.], >>> [ 0.7, -1.9, -0.5, 0.3]]) >>> delta2bbox(rois, deltas, max_shape=(32, 32, 3)) tensor([[0.0000, 0.0000, 1.0000, 1.0000], [0.1409, 0.1409, 2.8591, 2.8591], [0.0000, 0.3161, 4.1945, 0.6839], [5.0000, 5.0000, 5.0000, 5.0000]]) """ num_bboxes, num_classes = deltas.size(0), deltas.size(1) // 4 if num_bboxes == 0: return deltas deltas = deltas.reshape(-1, 4) means = deltas.new_tensor(means).view(1, -1) stds = deltas.new_tensor(stds).view(1, -1) denorm_deltas = deltas * stds + means dxy = denorm_deltas[:, :2] dwh = denorm_deltas[:, 2:] # Compute width/height of each roi rois_ = rois.repeat(1, num_classes).reshape(-1, 4) pxy = ((rois_[:, :2] + rois_[:, 2:]) * 0.5) pwh = (rois_[:, 2:] - rois_[:, :2]) dxy_wh = pwh * dxy max_ratio = np.abs(np.log(wh_ratio_clip)) if add_ctr_clamp: dxy_wh = torch.clamp(dxy_wh, max=ctr_clamp, min=-ctr_clamp) dwh = torch.clamp(dwh, max=max_ratio) else: dwh = dwh.clamp(min=-max_ratio, max=max_ratio) gxy = pxy + dxy_wh gwh = pwh * dwh.exp() x1y1 = gxy - (gwh * 0.5) x2y2 = gxy + (gwh * 0.5) bboxes = torch.cat([x1y1, x2y2], dim=-1) if clip_border and max_shape is not None: bboxes[..., 0::2].clamp_(min=0, max=max_shape[1]) bboxes[..., 1::2].clamp_(min=0, max=max_shape[0]) bboxes = bboxes.reshape(num_bboxes, -1) return bboxes def onnx_delta2bbox(rois, deltas, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.), max_shape=None, wh_ratio_clip=16 / 1000, clip_border=True, add_ctr_clamp=False, ctr_clamp=32): """Apply deltas to shift/scale base boxes. Typically the rois are anchor or proposed bounding boxes and the deltas are network outputs used to shift/scale those boxes. This is the inverse function of :func:`bbox2delta`. Args: rois (Tensor): Boxes to be transformed. Has shape (N, 4) or (B, N, 4) deltas (Tensor): Encoded offsets with respect to each roi. Has shape (B, N, num_classes * 4) or (B, N, 4) or (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H when rois is a grid of anchors.Offset encoding follows [1]_. means (Sequence[float]): Denormalizing means for delta coordinates. Default (0., 0., 0., 0.). stds (Sequence[float]): Denormalizing standard deviation for delta coordinates. Default (1., 1., 1., 1.). max_shape (Sequence[int] or torch.Tensor or Sequence[ Sequence[int]],optional): Maximum bounds for boxes, specifies (H, W, C) or (H, W). If rois 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. Default None. wh_ratio_clip (float): Maximum aspect ratio for boxes. Default 16 / 1000. clip_border (bool, optional): Whether clip the objects outside the border of the image. Default True. add_ctr_clamp (bool): Whether to add center clamp, when added, the predicted box is clamped is its center is too far away from the original anchor's center. Only used by YOLOF. Default False. ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF. Default 32. Returns: Tensor: Boxes with shape (B, N, num_classes * 4) or (B, N, 4) or (N, num_classes * 4) or (N, 4), where 4 represent tl_x, tl_y, br_x, br_y. References: .. [1] https://arxiv.org/abs/1311.2524 Example: >>> rois = torch.Tensor([[ 0., 0., 1., 1.], >>> [ 0., 0., 1., 1.], >>> [ 0., 0., 1., 1.], >>> [ 5., 5., 5., 5.]]) >>> deltas = torch.Tensor([[ 0., 0., 0., 0.], >>> [ 1., 1., 1., 1.], >>> [ 0., 0., 2., -1.], >>> [ 0.7, -1.9, -0.5, 0.3]]) >>> delta2bbox(rois, deltas, max_shape=(32, 32, 3)) tensor([[0.0000, 0.0000, 1.0000, 1.0000], [0.1409, 0.1409, 2.8591, 2.8591], [0.0000, 0.3161, 4.1945, 0.6839], [5.0000, 5.0000, 5.0000, 5.0000]]) """ means = deltas.new_tensor(means).view(1, -1).repeat(1, deltas.size(-1) // 4) stds = deltas.new_tensor(stds).view(1, -1).repeat(1, deltas.size(-1) // 4) denorm_deltas = deltas * stds + means dx = denorm_deltas[..., 0::4] dy = denorm_deltas[..., 1::4] dw = denorm_deltas[..., 2::4] dh = denorm_deltas[..., 3::4] x1, y1 = rois[..., 0], rois[..., 1] x2, y2 = rois[..., 2], rois[..., 3] # Compute center of each roi px = ((x1 + x2) * 0.5).unsqueeze(-1).expand_as(dx) py = ((y1 + y2) * 0.5).unsqueeze(-1).expand_as(dy) # Compute width/height of each roi pw = (x2 - x1).unsqueeze(-1).expand_as(dw) ph = (y2 - y1).unsqueeze(-1).expand_as(dh) dx_width = pw * dx dy_height = ph * dy max_ratio = np.abs(np.log(wh_ratio_clip)) if add_ctr_clamp: dx_width = torch.clamp(dx_width, max=ctr_clamp, min=-ctr_clamp) dy_height = torch.clamp(dy_height, max=ctr_clamp, min=-ctr_clamp) dw = torch.clamp(dw, max=max_ratio) dh = torch.clamp(dh, max=max_ratio) else: dw = dw.clamp(min=-max_ratio, max=max_ratio) dh = dh.clamp(min=-max_ratio, max=max_ratio) # Use exp(network energy) to enlarge/shrink each roi gw = pw * dw.exp() gh = ph * dh.exp() # Use network energy to shift the center of each roi gx = px + dx_width gy = py + dy_height # Convert center-xy/width/height to top-left, bottom-right x1 = gx - gw * 0.5 y1 = gy - gh * 0.5 x2 = gx + gw * 0.5 y2 = gy + gh * 0.5 bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size()) 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 x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape) bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size()) return bboxes if not isinstance(max_shape, torch.Tensor): max_shape = x1.new_tensor(max_shape) max_shape = max_shape[..., :2].type_as(x1) if max_shape.ndim == 2: assert bboxes.ndim == 3 assert max_shape.size(0) == bboxes.size(0) min_xy = x1.new_tensor(0) max_xy = torch.cat( [max_shape] * (deltas.size(-1) // 2), 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