# Copyright (c) OpenMMLab. All rights reserved. import warnings import numpy as np import torch from mmdet.models.task_modules.coders.delta_xywh_bbox_coder import \ DeltaXYWHBBoxCoder from mmdet.registry import TASK_UTILS from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor @TASK_UTILS.register_module() class YXYXDeltaXYWHBBoxCoder(DeltaXYWHBBoxCoder): 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 or :obj:`BaseBoxes`): Source boxes, e.g., object proposals. gt_bboxes (torch.Tensor or :obj:`BaseBoxes`): Target of the transformation, e.g., ground-truth boxes. Returns: torch.Tensor: Box transformation deltas """ bboxes = get_box_tensor(bboxes) gt_bboxes = get_box_tensor(gt_bboxes) assert bboxes.size(0) == gt_bboxes.size(0) assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 encoded_bboxes = YXbbox2delta(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 or :obj:`BaseBoxes`): 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: Union[torch.Tensor, :obj:`BaseBoxes`]: Decoded boxes. """ bboxes = get_box_tensor(bboxes) 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 = YXdelta2bbox(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 = YXonnx_delta2bbox(bboxes, pred_bboxes, self.means, self.stds, max_shape, wh_ratio_clip, self.clip_border, self.add_ctr_clamp, self.ctr_clamp) if self.use_box_type: assert decoded_bboxes.size(-1) == 4, \ ('Cannot warp decoded boxes with box type when decoded boxes' 'have shape of (N, num_classes * 4)') decoded_bboxes = HorizontalBoxes(decoded_bboxes) return decoded_bboxes def YXdelta2bbox(rois, deltas, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.), max_shape=None, hw_ratio_clip=1000 / 16, 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 dyx = denorm_deltas[:, :2] dhw = denorm_deltas[:, 2:] # Compute width/height of each roi rois_ = rois.repeat(1, num_classes).reshape(-1, 4) pyx = ((rois_[:, :2] + rois_[:, 2:]) * 0.5) phw = (rois_[:, 2:] - rois_[:, :2]) dyx_hw = phw * dyx max_ratio = np.abs(np.log(hw_ratio_clip)) if add_ctr_clamp: dyx_hw = torch.clamp(dyx_hw, max=ctr_clamp, min=-ctr_clamp) dhw = torch.clamp(dhw, max=max_ratio) else: dhw = dhw.clamp(min=-max_ratio, max=max_ratio) gyx = pyx + dyx_hw ghw = phw * dhw.exp() y1x1 = gyx - (ghw * 0.5) y2x2 = gyx + (ghw * 0.5) ymin, xmin = y1x1[:, 0].reshape(-1, 1), y1x1[:, 1].reshape(-1, 1) ymax, xmax = y2x2[:, 0].reshape(-1, 1), y2x2[:, 1].reshape(-1, 1) bboxes = torch.cat([xmin, ymin, xmax, ymax], 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 YXbbox2delta(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() py = (proposals[..., 0] + proposals[..., 2]) * 0.5 px = (proposals[..., 1] + proposals[..., 3]) * 0.5 ph = proposals[..., 2] - proposals[..., 0] pw = 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([dy, dx, dh, dw], dim=-1) means = deltas.new_tensor(means).unsqueeze(0) stds = deltas.new_tensor(stds).unsqueeze(0) deltas = deltas.sub_(means).div_(stds) return deltas def YXonnx_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 dy = denorm_deltas[..., 0::4] dx = denorm_deltas[..., 1::4] dh = denorm_deltas[..., 2::4] dw = denorm_deltas[..., 3::4] y1, x1 = rois[..., 0], rois[..., 1] y2, x2 = 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