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| """ | |
| This file contains specific functions for computing losses on the RetinaNet | |
| file | |
| """ | |
| import torch | |
| from torch.nn import functional as F | |
| from ..utils import concat_box_prediction_layers | |
| from maskrcnn_benchmark.layers import smooth_l1_loss | |
| from maskrcnn_benchmark.layers import SigmoidFocalLoss | |
| from maskrcnn_benchmark.modeling.matcher import Matcher | |
| from maskrcnn_benchmark.modeling.utils import cat | |
| from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou | |
| from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist | |
| from maskrcnn_benchmark.modeling.rpn.loss import RPNLossComputation | |
| class RetinaNetLossComputation(RPNLossComputation): | |
| """ | |
| This class computes the RetinaNet loss. | |
| """ | |
| def __init__(self, proposal_matcher, box_coder, | |
| generate_labels_func, | |
| sigmoid_focal_loss, | |
| bbox_reg_beta=0.11, | |
| regress_norm=1.0): | |
| """ | |
| Arguments: | |
| proposal_matcher (Matcher) | |
| box_coder (BoxCoder) | |
| """ | |
| self.proposal_matcher = proposal_matcher | |
| self.box_coder = box_coder | |
| self.box_cls_loss_func = sigmoid_focal_loss | |
| self.bbox_reg_beta = bbox_reg_beta | |
| self.copied_fields = ['labels'] | |
| self.generate_labels_func = generate_labels_func | |
| self.discard_cases = ['between_thresholds'] | |
| self.regress_norm = regress_norm | |
| def __call__(self, anchors, box_cls, box_regression, targets): | |
| """ | |
| Arguments: | |
| anchors (list[BoxList]) | |
| box_cls (list[Tensor]) | |
| box_regression (list[Tensor]) | |
| targets (list[BoxList]) | |
| Returns: | |
| retinanet_cls_loss (Tensor) | |
| retinanet_regression_loss (Tensor | |
| """ | |
| anchors = [cat_boxlist(anchors_per_image) for anchors_per_image in anchors] | |
| labels, regression_targets = self.prepare_targets(anchors, targets) | |
| N = len(labels) | |
| box_cls, box_regression = \ | |
| concat_box_prediction_layers(box_cls, box_regression) | |
| labels = torch.cat(labels, dim=0) | |
| regression_targets = torch.cat(regression_targets, dim=0) | |
| pos_inds = torch.nonzero(labels > 0).squeeze(1) | |
| retinanet_regression_loss = smooth_l1_loss( | |
| box_regression[pos_inds], | |
| regression_targets[pos_inds], | |
| beta=self.bbox_reg_beta, | |
| size_average=False, | |
| ) / (max(1, pos_inds.numel() * self.regress_norm)) | |
| labels = labels.int() | |
| retinanet_cls_loss = self.box_cls_loss_func( | |
| box_cls, | |
| labels | |
| ) / (pos_inds.numel() + N) | |
| return retinanet_cls_loss, retinanet_regression_loss | |
| def generate_retinanet_labels(matched_targets): | |
| labels_per_image = matched_targets.get_field("labels") | |
| return labels_per_image | |
| def make_retinanet_loss_evaluator(cfg, box_coder): | |
| matcher = Matcher( | |
| cfg.MODEL.RETINANET.FG_IOU_THRESHOLD, | |
| cfg.MODEL.RETINANET.BG_IOU_THRESHOLD, | |
| allow_low_quality_matches=True, | |
| ) | |
| sigmoid_focal_loss = SigmoidFocalLoss( | |
| cfg.MODEL.RETINANET.LOSS_GAMMA, | |
| cfg.MODEL.RETINANET.LOSS_ALPHA | |
| ) | |
| loss_evaluator = RetinaNetLossComputation( | |
| matcher, | |
| box_coder, | |
| generate_retinanet_labels, | |
| sigmoid_focal_loss, | |
| bbox_reg_beta = cfg.MODEL.RETINANET.BBOX_REG_BETA, | |
| regress_norm = cfg.MODEL.RETINANET.BBOX_REG_WEIGHT, | |
| ) | |
| return loss_evaluator | |