def __init__(self, params): super(RetinanetModel, self).__init__(params) # For eval metrics. self._params = params # Architecture generators. self._backbone_fn = factory.backbone_generator(params) self._fpn_fn = factory.multilevel_features_generator(params) self._head_fn = factory.retinanet_head_generator(params.retinanet_head) # Loss function. self._cls_loss_fn = losses.RetinanetClassLoss(params.retinanet_loss) self._box_loss_fn = losses.RetinanetBoxLoss(params.retinanet_loss) self._box_loss_weight = params.retinanet_loss.box_loss_weight self._keras_model = None # Predict function. self._generate_detections_fn = postprocess_ops.MultilevelDetectionGenerator( params.postprocess) self._transpose_input = params.train.transpose_input assert not self._transpose_input, 'Transpose input is not supportted.' # Input layer. input_shape = (params.retinanet_parser.output_size + [params.retinanet_parser.num_channels]) self._input_layer = tf.keras.layers.Input( shape=input_shape, name='', dtype=tf.bfloat16 if self._use_bfloat16 else tf.float32)
def __init__(self, params): super(ShapeMaskModel, self).__init__(params) self._params = params self._keras_model = None # Architecture generators. self._backbone_fn = factory.backbone_generator(params) self._fpn_fn = factory.multilevel_features_generator(params) self._retinanet_head_fn = factory.retinanet_head_generator(params) self._shape_prior_head_fn = factory.shapeprior_head_generator(params) self._coarse_mask_fn = factory.coarsemask_head_generator(params) self._fine_mask_fn = factory.finemask_head_generator(params) # Loss functions. self._cls_loss_fn = losses.RetinanetClassLoss( params.retinanet_loss, params.architecture.num_classes) self._box_loss_fn = losses.RetinanetBoxLoss(params.retinanet_loss) self._box_loss_weight = params.retinanet_loss.box_loss_weight # Mask loss function. self._shapemask_prior_loss_fn = losses.ShapemaskMseLoss() self._shapemask_loss_fn = losses.ShapemaskLoss() self._shape_prior_loss_weight = ( params.shapemask_loss.shape_prior_loss_weight) self._coarse_mask_loss_weight = ( params.shapemask_loss.coarse_mask_loss_weight) self._fine_mask_loss_weight = ( params.shapemask_loss.fine_mask_loss_weight) # Predict function. self._generate_detections_fn = postprocess_ops.MultilevelDetectionGenerator( params.architecture.min_level, params.architecture.max_level, params.postprocess)
def __init__(self, params): super(RetinanetModel, self).__init__(params) # For eval metrics. self._params = params # Architecture generators. self._backbone_fn = factory.backbone_generator(params) self._fpn_fn = factory.multilevel_features_generator(params) self._head_fn = factory.retinanet_head_generator(params) # Loss function. self._cls_loss_fn = losses.RetinanetClassLoss( params.retinanet_loss, params.architecture.num_classes) self._box_loss_fn = losses.RetinanetBoxLoss(params.retinanet_loss) self._box_loss_weight = params.retinanet_loss.box_loss_weight self._keras_model = None # Predict function. self._generate_detections_fn = postprocess_ops.MultilevelDetectionGenerator( params.architecture.min_level, params.architecture.max_level, params.postprocess) self._transpose_input = params.train.transpose_input assert not self._transpose_input, 'Transpose input is not supported.'
def __init__(self, params): super(MaskrcnnModel, self).__init__(params) # For eval metrics. self._params = params self._keras_model = None 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) 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 assert not self._transpose_input, 'Transpose input is not supportted.'
def __init__(self, params): super(OlnMaskModel, self).__init__(params) self._params = params # Different heads and layers. self._include_rpn_class = params.architecture.include_rpn_class self._include_mask = params.architecture.include_mask self._include_frcnn_class = params.architecture.include_frcnn_class self._include_frcnn_box = params.architecture.include_frcnn_box self._include_centerness = params.rpn_head.has_centerness self._include_box_score = (params.frcnn_head.has_scoring and params.architecture.include_frcnn_box) self._include_mask_score = (params.mrcnn_head.has_scoring and 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) if self._include_centerness: self._rpn_head_fn = factory.oln_rpn_head_generator(params) else: self._rpn_head_fn = factory.rpn_head_generator(params) self._generate_rois_fn = roi_ops.OlnROIGenerator(params.roi_proposal) self._sample_rois_fn = target_ops.ROIScoreSampler(params.roi_sampling) self._sample_masks_fn = target_ops.MaskSampler( params.architecture.mask_target_size, params.mask_sampling.num_mask_samples_per_image) if self._include_box_score: self._frcnn_head_fn = factory.oln_box_score_head_generator(params) else: self._frcnn_head_fn = factory.fast_rcnn_head_generator(params) if self._include_mask: if self._include_mask_score: self._mrcnn_head_fn = factory.oln_mask_score_head_generator( params) else: 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) if self._include_centerness: self._rpn_iou_loss_fn = losses.OlnRpnIoULoss() self._rpn_center_loss_fn = losses.OlnRpnCenterLoss() self._frcnn_class_loss_fn = losses.FastrcnnClassLoss() self._frcnn_box_loss_fn = losses.FastrcnnBoxLoss(params.frcnn_box_loss) if self._include_box_score: self._frcnn_box_score_loss_fn = losses.OlnBoxScoreLoss( params.frcnn_box_score_loss) if self._include_mask: self._mask_loss_fn = losses.MaskrcnnLoss() self._generate_detections_fn = postprocess_ops.OlnDetectionGenerator( params.postprocess) self._transpose_input = params.train.transpose_input assert not self._transpose_input, 'Transpose input is not supportted.'