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| #!/usr/bin/env python3 | |
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | |
| import torch | |
| from .inference import make_roi_box_post_processor | |
| from .loss import make_roi_box_loss_evaluator | |
| from .roi_box_feature_extractors import make_roi_box_feature_extractor | |
| from .roi_box_predictors import make_roi_box_predictor | |
| class ROIBoxHead(torch.nn.Module): | |
| """ | |
| Generic Box Head class. | |
| """ | |
| def __init__(self, cfg): | |
| super(ROIBoxHead, self).__init__() | |
| self.cfg = cfg | |
| self.feature_extractor = make_roi_box_feature_extractor(cfg) | |
| self.predictor = make_roi_box_predictor(cfg) | |
| self.post_processor = make_roi_box_post_processor(cfg) | |
| self.loss_evaluator = make_roi_box_loss_evaluator(cfg) | |
| def forward(self, features, proposals, targets=None): | |
| """ | |
| Arguments: | |
| features (list[Tensor]): feature-maps from possibly several levels | |
| proposals (list[BoxList]): proposal boxes | |
| targets (list[BoxList], optional): the ground-truth targets. | |
| Returns: | |
| x (Tensor): the result of the feature extractor | |
| proposals (list[BoxList]): during training, the subsampled proposals | |
| are returned. During testing, the predicted boxlists are returned | |
| losses (dict[Tensor]): During training, returns the losses for the | |
| head. During testing, returns an empty dict. | |
| """ | |
| if self.training: | |
| # Faster R-CNN subsamples during training the proposals with a fixed | |
| # positive / negative ratio | |
| with torch.no_grad(): | |
| proposals = self.loss_evaluator.subsample(proposals, targets) | |
| # extract features that will be fed to the final classifier. The | |
| # feature_extractor generally corresponds to the pooler + heads | |
| x = self.feature_extractor(features, proposals) | |
| # final classifier that converts the features into predictions | |
| class_logits, box_regression = self.predictor(x) | |
| if not self.training: | |
| if self.cfg.MODEL.ROI_BOX_HEAD.INFERENCE_USE_BOX: | |
| result = self.post_processor((class_logits, box_regression), proposals) | |
| # print(result[0].get_field('masks')) | |
| return x, result, {} | |
| else: | |
| return x, proposals, {} | |
| loss_classifier, loss_box_reg = self.loss_evaluator( | |
| [class_logits], [box_regression] | |
| ) | |
| if self.cfg.MODEL.ROI_BOX_HEAD.USE_REGRESSION: | |
| return ( | |
| x, | |
| proposals, | |
| dict(loss_classifier=loss_classifier, loss_box_reg=loss_box_reg), | |
| ) | |
| else: | |
| return ( | |
| x, | |
| proposals, | |
| dict(loss_classifier=loss_classifier), | |
| ) | |
| def build_roi_box_head(cfg): | |
| """ | |
| Constructs a new box head. | |
| By default, uses ROIBoxHead, | |
| but if it turns out not to be enough, just register a new class | |
| and make it a parameter in the config | |
| """ | |
| return ROIBoxHead(cfg) | |