# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle import paddle.nn.functional as F __all__ = [ 'pad_gt', 'gather_topk_anchors', 'check_points_inside_bboxes', 'compute_max_iou_anchor', 'compute_max_iou_gt', 'generate_anchors_for_grid_cell' ] def pad_gt(gt_labels, gt_bboxes, gt_scores=None): r""" Pad 0 in gt_labels and gt_bboxes. Args: gt_labels (Tensor|List[Tensor], int64): Label of gt_bboxes, shape is [B, n, 1] or [[n_1, 1], [n_2, 1], ...], here n = sum(n_i) gt_bboxes (Tensor|List[Tensor], float32): Ground truth bboxes, shape is [B, n, 4] or [[n_1, 4], [n_2, 4], ...], here n = sum(n_i) gt_scores (Tensor|List[Tensor]|None, float32): Score of gt_bboxes, shape is [B, n, 1] or [[n_1, 4], [n_2, 4], ...], here n = sum(n_i) Returns: pad_gt_labels (Tensor, int64): shape[B, n, 1] pad_gt_bboxes (Tensor, float32): shape[B, n, 4] pad_gt_scores (Tensor, float32): shape[B, n, 1] pad_gt_mask (Tensor, float32): shape[B, n, 1], 1 means bbox, 0 means no bbox """ if isinstance(gt_labels, paddle.Tensor) and isinstance(gt_bboxes, paddle.Tensor): assert gt_labels.ndim == gt_bboxes.ndim and \ gt_bboxes.ndim == 3 pad_gt_mask = ( gt_bboxes.sum(axis=-1, keepdim=True) > 0).astype(gt_bboxes.dtype) if gt_scores is None: gt_scores = pad_gt_mask.clone() assert gt_labels.ndim == gt_scores.ndim return gt_labels, gt_bboxes, gt_scores, pad_gt_mask elif isinstance(gt_labels, list) and isinstance(gt_bboxes, list): assert len(gt_labels) == len(gt_bboxes), \ 'The number of `gt_labels` and `gt_bboxes` is not equal. ' num_max_boxes = max([len(a) for a in gt_bboxes]) batch_size = len(gt_bboxes) # pad label and bbox pad_gt_labels = paddle.zeros( [batch_size, num_max_boxes, 1], dtype=gt_labels[0].dtype) pad_gt_bboxes = paddle.zeros( [batch_size, num_max_boxes, 4], dtype=gt_bboxes[0].dtype) pad_gt_scores = paddle.zeros( [batch_size, num_max_boxes, 1], dtype=gt_bboxes[0].dtype) pad_gt_mask = paddle.zeros( [batch_size, num_max_boxes, 1], dtype=gt_bboxes[0].dtype) for i, (label, bbox) in enumerate(zip(gt_labels, gt_bboxes)): if len(label) > 0 and len(bbox) > 0: pad_gt_labels[i, :len(label)] = label pad_gt_bboxes[i, :len(bbox)] = bbox pad_gt_mask[i, :len(bbox)] = 1. if gt_scores is not None: pad_gt_scores[i, :len(gt_scores[i])] = gt_scores[i] if gt_scores is None: pad_gt_scores = pad_gt_mask.clone() return pad_gt_labels, pad_gt_bboxes, pad_gt_scores, pad_gt_mask else: raise ValueError('The input `gt_labels` or `gt_bboxes` is invalid! ') def gather_topk_anchors(metrics, topk, largest=True, topk_mask=None, eps=1e-9): r""" Args: metrics (Tensor, float32): shape[B, n, L], n: num_gts, L: num_anchors topk (int): The number of top elements to look for along the axis. largest (bool) : largest is a flag, if set to true, algorithm will sort by descending order, otherwise sort by ascending order. Default: True topk_mask (Tensor, bool|None): shape[B, n, topk], ignore bbox mask, Default: None eps (float): Default: 1e-9 Returns: is_in_topk (Tensor, float32): shape[B, n, L], value=1. means selected """ num_anchors = metrics.shape[-1] topk_metrics, topk_idxs = paddle.topk( metrics, topk, axis=-1, largest=largest) if topk_mask is None: topk_mask = (topk_metrics.max(axis=-1, keepdim=True) > eps).tile( [1, 1, topk]) topk_idxs = paddle.where(topk_mask, topk_idxs, paddle.zeros_like(topk_idxs)) is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(axis=-2) is_in_topk = paddle.where(is_in_topk > 1, paddle.zeros_like(is_in_topk), is_in_topk) return is_in_topk.astype(metrics.dtype) def check_points_inside_bboxes(points, bboxes, center_radius_tensor=None, eps=1e-9): r""" Args: points (Tensor, float32): shape[L, 2], "xy" format, L: num_anchors bboxes (Tensor, float32): shape[B, n, 4], "xmin, ymin, xmax, ymax" format center_radius_tensor (Tensor, float32): shape [L, 1] Default: None. eps (float): Default: 1e-9 Returns: is_in_bboxes (Tensor, float32): shape[B, n, L], value=1. means selected """ points = points.unsqueeze([0, 1]) x, y = points.chunk(2, axis=-1) xmin, ymin, xmax, ymax = bboxes.unsqueeze(2).chunk(4, axis=-1) if center_radius_tensor is not None: center_radius_tensor = center_radius_tensor.unsqueeze([0, 1]) bboxes_cx = (xmin + xmax) / 2. bboxes_cy = (ymin + ymax) / 2. xmin_sampling = bboxes_cx - center_radius_tensor ymin_sampling = bboxes_cy - center_radius_tensor xmax_sampling = bboxes_cx + center_radius_tensor ymax_sampling = bboxes_cy + center_radius_tensor xmin = paddle.maximum(xmin, xmin_sampling) ymin = paddle.maximum(ymin, ymin_sampling) xmax = paddle.minimum(xmax, xmax_sampling) ymax = paddle.minimum(ymax, ymax_sampling) l = x - xmin t = y - ymin r = xmax - x b = ymax - y bbox_ltrb = paddle.concat([l, t, r, b], axis=-1) return (bbox_ltrb.min(axis=-1) > eps).astype(bboxes.dtype) def compute_max_iou_anchor(ious): r""" For each anchor, find the GT with the largest IOU. Args: ious (Tensor, float32): shape[B, n, L], n: num_gts, L: num_anchors Returns: is_max_iou (Tensor, float32): shape[B, n, L], value=1. means selected """ num_max_boxes = ious.shape[-2] max_iou_index = ious.argmax(axis=-2) is_max_iou = F.one_hot(max_iou_index, num_max_boxes).transpose([0, 2, 1]) return is_max_iou.astype(ious.dtype) def compute_max_iou_gt(ious): r""" For each GT, find the anchor with the largest IOU. Args: ious (Tensor, float32): shape[B, n, L], n: num_gts, L: num_anchors Returns: is_max_iou (Tensor, float32): shape[B, n, L], value=1. means selected """ num_anchors = ious.shape[-1] max_iou_index = ious.argmax(axis=-1) is_max_iou = F.one_hot(max_iou_index, num_anchors) return is_max_iou.astype(ious.dtype) def generate_anchors_for_grid_cell(feats, fpn_strides, grid_cell_size=5.0, grid_cell_offset=0.5): r""" Like ATSS, generate anchors based on grid size. Args: feats (List[Tensor]): shape[s, (b, c, h, w)] fpn_strides (tuple|list): shape[s], stride for each scale feature grid_cell_size (float): anchor size grid_cell_offset (float): The range is between 0 and 1. Returns: anchors (Tensor): shape[l, 4], "xmin, ymin, xmax, ymax" format. anchor_points (Tensor): shape[l, 2], "x, y" format. num_anchors_list (List[int]): shape[s], contains [s_1, s_2, ...]. stride_tensor (Tensor): shape[l, 1], contains the stride for each scale. """ assert len(feats) == len(fpn_strides) anchors = [] anchor_points = [] num_anchors_list = [] stride_tensor = [] for feat, stride in zip(feats, fpn_strides): _, _, h, w = feat.shape cell_half_size = grid_cell_size * stride * 0.5 shift_x = (paddle.arange(end=w) + grid_cell_offset) * stride shift_y = (paddle.arange(end=h) + grid_cell_offset) * stride shift_y, shift_x = paddle.meshgrid(shift_y, shift_x) anchor = paddle.stack( [ shift_x - cell_half_size, shift_y - cell_half_size, shift_x + cell_half_size, shift_y + cell_half_size ], axis=-1).astype(feat.dtype) anchor_point = paddle.stack( [shift_x, shift_y], axis=-1).astype(feat.dtype) anchors.append(anchor.reshape([-1, 4])) anchor_points.append(anchor_point.reshape([-1, 2])) num_anchors_list.append(len(anchors[-1])) stride_tensor.append( paddle.full( [num_anchors_list[-1], 1], stride, dtype=feat.dtype)) anchors = paddle.concat(anchors) anchors.stop_gradient = True anchor_points = paddle.concat(anchor_points) anchor_points.stop_gradient = True stride_tensor = paddle.concat(stride_tensor) stride_tensor.stop_gradient = True return anchors, anchor_points, num_anchors_list, stride_tensor