def __init__(self, params): super(MaskrcnnModel, self).__init__(params) self._anchor_params = params.anchor self._include_mask = params.architecture.include_mask # Architecture generators. self._backbone_fn = factory.backbone_generator(params) self._fpn_fn = factory.multilevel_features_generator(params) self._rpn_head_fn = factory.rpn_head_generator(params.rpn_head) self._generate_rois_fn = roi_ops.ROIGenerator(params.roi_proposal) self._sample_rois_fn = sampling_ops.ROISampler(params.roi_sampling) self._sample_masks_fn = sampling_ops.MaskSampler(params.mask_sampling) self._frcnn_head_fn = factory.fast_rcnn_head_generator( params.frcnn_head) if self._include_mask: self._mrcnn_head_fn = factory.mask_rcnn_head_generator( params.mrcnn_head) # Loss function. self._rpn_score_loss_fn = losses.RpnScoreLoss(params.rpn_score_loss) self._rpn_box_loss_fn = losses.RpnBoxLoss(params.rpn_box_loss) self._frcnn_class_loss_fn = losses.FastrcnnClassLoss() self._frcnn_box_loss_fn = losses.FastrcnnBoxLoss(params.frcnn_box_loss) if self._include_mask: self._mask_loss_fn = losses.MaskrcnnLoss() self._generate_detections_fn = postprocess_ops.GenericDetectionGenerator( params.postprocess) self._transpose_input = params.train.transpose_input
def __init__(self, params): super(AttributeMaskrcnnModel, self).__init__(params) self._params = params self._include_mask = params.architecture.include_mask # Architecture generators. self._backbone_fn = factory.backbone_generator(params) self._fpn_fn = factory.multilevel_features_generator(params) self._rpn_head_fn = factory.rpn_head_generator(params) self._generate_rois_fn = roi_ops.ROIGenerator(params.roi_proposal) self._sample_rois_fn = roi_sampler.ROISampler(params.roi_sampling) self._sample_masks_fn = target_ops.MaskSampler( params.architecture.mask_target_size, params.mask_sampling.num_mask_samples_per_image) self._frcnn_head_fn = factory.fast_rcnn_head_generator(params) if self._include_mask: self._mrcnn_head_fn = factory.mask_rcnn_head_generator(params) # Loss function. self._rpn_score_loss_fn = losses.RpnScoreLoss(params.rpn_score_loss) self._rpn_box_loss_fn = losses.RpnBoxLoss(params.rpn_box_loss) self._frcnn_class_loss_fn = losses.FastrcnnClassLoss() self._frcnn_attribute_loss_fn = attribute_loss.FastrcnnAttributeLoss() self._frcnn_box_loss_fn = losses.FastrcnnBoxLoss(params.frcnn_box_loss) if self._include_mask: self._mask_loss_fn = losses.MaskrcnnLoss() self._generate_detections_fn = postprocess_ops.GenericDetectionGenerator( params.postprocess)
def __init__(self, params): super(ViLDModel, self).__init__(params) self._params = params self._include_mask = params.architecture.include_mask self._losses = params.train.losses # feature distill self._feat_distill = params.architecture.visual_feature_distill if self._feat_distill == 'None': self._feat_distill = None self._feat_distill_dim = params.architecture.visual_feature_dim self._max_distill_rois = params.architecture.max_num_rois self._feat_distill_weight = params.architecture.feat_distill_weight self._normalize_feat_during_training = params.architecture.normalize_feat_during_training # Architecture generators. self._backbone_fn = factory.backbone_generator(params) self._fpn_fn = factory.multilevel_features_generator(params) self._rpn_head_fn = factory.rpn_head_generator(params) self._generate_rois_fn = roi_ops.ROIGenerator(params.roi_proposal) self._sample_rois_fn = target_ops.ROISampler(params.roi_sampling) self._sample_masks_fn = target_ops.MaskSampler( params.architecture.mask_target_size, params.mask_sampling.num_mask_samples_per_image) self._frcnn_head_fn = factory.vild_fast_rcnn_head_generator(params) if self._include_mask: self._mrcnn_head_fn = factory.mask_rcnn_head_generator(params) # Loss function. self._rpn_score_loss_fn = losses.RpnScoreLoss(params.rpn_score_loss) self._rpn_box_loss_fn = losses.RpnBoxLoss(params.rpn_box_loss) self._frcnn_class_loss_fn = vild_losses.FastrcnnClassLoss( params.frcnn_class_loss) self._frcnn_box_loss_fn = losses.FastrcnnBoxLoss( params.frcnn_box_loss, class_agnostic_bbox_pred=params.frcnn_head.class_agnostic_bbox_pred ) if self._include_mask: self._mask_loss_fn = losses.MaskrcnnLoss() self._generate_detections_fn = postprocess_ops.GenericDetectionGenerator( params.postprocess, discard_background=params.postprocess.discard_background, visual_feature_distill=self._feat_distill)
def __init__(self, params): super(CascadeMaskrcnnModel, self).__init__(params) self._params = params self._include_mask = params.architecture.include_mask # Architecture generators. self._backbone_fn = factory.backbone_generator(params) self._fpn_fn = factory.multilevel_features_generator(params) self._rpn_head_fn = factory.rpn_head_generator(params) self._generate_rois_fn = roi_ops.ROIGenerator(params.roi_proposal) self._sample_rois_fn = target_ops.ROISampler(params.roi_sampling) self._sample_masks_fn = target_ops.MaskSampler( params.architecture.mask_target_size, params.mask_sampling.num_mask_samples_per_image) self._frcnn_head_fn = factory.fast_rcnn_head_generator(params) if self._include_mask: self._mrcnn_head_fn = factory.mask_rcnn_head_generator(params) # Loss function. self._rpn_score_loss_fn = losses.RpnScoreLoss(params.rpn_score_loss) self._rpn_box_loss_fn = losses.RpnBoxLoss(params.rpn_box_loss) self._frcnn_class_loss_fn = losses.FastrcnnClassLoss() self._frcnn_box_loss_fn = losses.FastrcnnBoxLoss( params.frcnn_box_loss, params.frcnn_head.class_agnostic_bbox_pred) if self._include_mask: self._mask_loss_fn = losses.MaskrcnnLoss() # IoU thresholds for additional FRCNN heads in Cascade mode. 'fg_iou_thresh' # is the first threshold. self._cascade_iou_thresholds = params.roi_sampling.cascade_iou_thresholds self._num_roi_samples = params.roi_sampling.num_samples_per_image # Weights for the regression losses for each FRCNN layer. # TODO(golnazg): makes this param configurable. self._cascade_layer_to_weights = [ [10.0, 10.0, 5.0, 5.0], [20.0, 20.0, 10.0, 10.0], [30.0, 30.0, 15.0, 15.0], ] self._class_agnostic_bbox_pred = params.frcnn_head.class_agnostic_bbox_pred self._cascade_class_ensemble = params.frcnn_head.cascade_class_ensemble self._generate_detections_fn = postprocess_ops.GenericDetectionGenerator( params.postprocess)