# Copyright (c) 2020 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. import paddle from ppdet.core.workspace import register, serializable from .target import rpn_anchor_target, generate_proposal_target, generate_mask_target @register @serializable class RPNTargetAssign(object): def __init__(self, batch_size_per_im=256, fg_fraction=0.5, positive_overlap=0.7, negative_overlap=0.3, use_random=True): super(RPNTargetAssign, self).__init__() self.batch_size_per_im = batch_size_per_im self.fg_fraction = fg_fraction self.positive_overlap = positive_overlap self.negative_overlap = negative_overlap self.use_random = use_random def __call__(self, inputs, anchors): """ inputs: ground-truth instances. anchor_box (Tensor): [num_anchors, 4], num_anchors are all anchors in all feature maps. """ gt_boxes = inputs['gt_bbox'] batch_size = gt_boxes.shape[0] tgt_labels, tgt_bboxes, tgt_deltas = rpn_anchor_target( anchors, gt_boxes, self.batch_size_per_im, self.positive_overlap, self.negative_overlap, self.fg_fraction, self.use_random, batch_size) norm = self.batch_size_per_im * batch_size return tgt_labels, tgt_bboxes, tgt_deltas, norm @register class BBoxAssigner(object): __shared__ = ['num_classes'] def __init__(self, batch_size_per_im=512, fg_fraction=.25, fg_thresh=[.5, ], bg_thresh=[.5, ], use_random=True, is_cls_agnostic=False, num_classes=80): super(BBoxAssigner, self).__init__() self.batch_size_per_im = batch_size_per_im self.fg_fraction = fg_fraction self.fg_thresh = fg_thresh self.bg_thresh = bg_thresh self.use_random = use_random self.is_cls_agnostic = is_cls_agnostic self.num_classes = num_classes def __call__(self, rpn_rois, rpn_rois_num, inputs, stage=0, max_overlap=None): is_cascade = True if stage > 0 else False gt_classes = inputs['gt_class'] gt_boxes = inputs['gt_bbox'] # rois, tgt_labels, tgt_bboxes, tgt_gt_inds # new_rois_num, sampled_max_overlaps outs = generate_proposal_target( rpn_rois, gt_classes, gt_boxes, self.batch_size_per_im, self.fg_fraction, self.fg_thresh[stage], self.bg_thresh[stage], self.num_classes, self.use_random, is_cascade, max_overlap) rois = outs[0] rois_num = outs[-2] max_overlaps = outs[-1] # tgt_labels, tgt_bboxes, tgt_gt_inds targets = outs[1:4] return rois, rois_num, max_overlaps, targets @register @serializable class MaskAssigner(object): __shared__ = ['num_classes', 'mask_resolution'] def __init__(self, num_classes=80, mask_resolution=14): super(MaskAssigner, self).__init__() self.num_classes = num_classes self.mask_resolution = mask_resolution def __call__(self, rois, tgt_labels, tgt_gt_inds, inputs): gt_segms = inputs['gt_poly'] outs = generate_mask_target(gt_segms, rois, tgt_labels, tgt_gt_inds, self.num_classes, self.mask_resolution) # mask_rois, mask_rois_num, tgt_classes, tgt_masks, mask_index, tgt_weights return outs