def test_builder(self, backbone_type, input_size): num_classes = 2 input_specs = tf.keras.layers.InputSpec( shape=[None, input_size[0], input_size[1], 3]) model_config = maskrcnn_cfg.MaskRCNN( num_classes=num_classes, backbone=backbones.Backbone(type=backbone_type)) l2_regularizer = tf.keras.regularizers.l2(5e-5) _ = factory.build_maskrcnn(input_specs=input_specs, model_config=model_config, l2_regularizer=l2_regularizer)
def build_model(self): """Build Mask R-CNN model.""" input_specs = tf.keras.layers.InputSpec( shape=[None] + self.task_config.model.input_size) l2_weight_decay = self.task_config.losses.l2_weight_decay # Divide weight decay by 2.0 to match the implementation of tf.nn.l2_loss. # (https://www.tensorflow.org/api_docs/python/tf/keras/regularizers/l2) # (https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss) l2_regularizer = (tf.keras.regularizers.l2(l2_weight_decay / 2.0) if l2_weight_decay else None) model = factory.build_maskrcnn(input_specs=input_specs, model_config=self.task_config.model, l2_regularizer=l2_regularizer) return model
def _build_model(self): if self._batch_size is None: raise ValueError('batch_size cannot be None for detection models.') input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] + self._input_image_size + [3]) if isinstance(self.params.task.model, configs.maskrcnn.MaskRCNN): model = factory.build_maskrcnn( input_specs=input_specs, model_config=self.params.task.model) elif isinstance(self.params.task.model, configs.retinanet.RetinaNet): model = factory.build_retinanet( input_specs=input_specs, model_config=self.params.task.model) else: raise ValueError('Detection module not implemented for {} model.'.format( type(self.params.task.model))) return model
def build_model(self): if self._batch_size is None: ValueError("batch_size can't be None for detection models") if not self._params.task.model.detection_generator.use_batched_nms: ValueError('Only batched_nms is supported.') input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] + self._input_image_size + [3]) if isinstance(self._params.task.model, configs.maskrcnn.MaskRCNN): self._model = factory.build_maskrcnn( input_specs=input_specs, model_config=self._params.task.model) elif isinstance(self._params.task.model, configs.retinanet.RetinaNet): self._model = factory.build_retinanet( input_specs=input_specs, model_config=self._params.task.model) else: raise ValueError( 'Detection module not implemented for {} model.'.format( type(self._params.task.model))) return self._model
def build_panoptic_maskrcnn( input_specs: tf.keras.layers.InputSpec, model_config: panoptic_maskrcnn_cfg.PanopticMaskRCNN, l2_regularizer: tf.keras.regularizers.Regularizer = None ) -> tf.keras.Model: # pytype: disable=annotation-type-mismatch # typed-keras """Builds Panoptic Mask R-CNN model. This factory function builds the mask rcnn first, builds the non-shared semantic segmentation layers, and finally combines the two models to form the panoptic segmentation model. Args: input_specs: `tf.keras.layers.InputSpec` specs of the input tensor. model_config: Config instance for the panoptic maskrcnn model. l2_regularizer: Optional `tf.keras.regularizers.Regularizer`, if specified, the model is built with the provided regularization layer. Returns: tf.keras.Model for the panoptic segmentation model. """ norm_activation_config = model_config.norm_activation segmentation_config = model_config.segmentation_model # Builds the maskrcnn model. maskrcnn_model = models_factory.build_maskrcnn( input_specs=input_specs, model_config=model_config, l2_regularizer=l2_regularizer) # Builds the semantic segmentation branch. if not model_config.shared_backbone: segmentation_backbone = backbones.factory.build_backbone( input_specs=input_specs, backbone_config=segmentation_config.backbone, norm_activation_config=norm_activation_config, l2_regularizer=l2_regularizer) segmentation_decoder_input_specs = segmentation_backbone.output_specs else: segmentation_backbone = None segmentation_decoder_input_specs = maskrcnn_model.backbone.output_specs if not model_config.shared_decoder: segmentation_decoder = decoder_factory.build_decoder( input_specs=segmentation_decoder_input_specs, model_config=segmentation_config, l2_regularizer=l2_regularizer) else: segmentation_decoder = None segmentation_head_config = segmentation_config.head detection_head_config = model_config.detection_head postprocessing_config = model_config.panoptic_segmentation_generator segmentation_head = segmentation_heads.SegmentationHead( num_classes=segmentation_config.num_classes, level=segmentation_head_config.level, num_convs=segmentation_head_config.num_convs, prediction_kernel_size=segmentation_head_config.prediction_kernel_size, num_filters=segmentation_head_config.num_filters, upsample_factor=segmentation_head_config.upsample_factor, feature_fusion=segmentation_head_config.feature_fusion, low_level=segmentation_head_config.low_level, low_level_num_filters=segmentation_head_config.low_level_num_filters, activation=norm_activation_config.activation, use_sync_bn=norm_activation_config.use_sync_bn, norm_momentum=norm_activation_config.norm_momentum, norm_epsilon=norm_activation_config.norm_epsilon, kernel_regularizer=l2_regularizer) if model_config.generate_panoptic_masks: max_num_detections = model_config.detection_generator.max_num_detections mask_binarize_threshold = postprocessing_config.mask_binarize_threshold panoptic_segmentation_generator_obj = panoptic_segmentation_generator.PanopticSegmentationGenerator( output_size=postprocessing_config.output_size, max_num_detections=max_num_detections, stuff_classes_offset=model_config.stuff_classes_offset, mask_binarize_threshold=mask_binarize_threshold, score_threshold=postprocessing_config.score_threshold, things_class_label=postprocessing_config.things_class_label, void_class_label=postprocessing_config.void_class_label, void_instance_id=postprocessing_config.void_instance_id) else: panoptic_segmentation_generator_obj = None # Combines maskrcnn, and segmentation models to build panoptic segmentation # model. model = panoptic_maskrcnn_model.PanopticMaskRCNNModel( backbone=maskrcnn_model.backbone, decoder=maskrcnn_model.decoder, rpn_head=maskrcnn_model.rpn_head, detection_head=maskrcnn_model.detection_head, roi_generator=maskrcnn_model.roi_generator, roi_sampler=maskrcnn_model.roi_sampler, roi_aligner=maskrcnn_model.roi_aligner, detection_generator=maskrcnn_model.detection_generator, panoptic_segmentation_generator=panoptic_segmentation_generator_obj, mask_head=maskrcnn_model.mask_head, mask_sampler=maskrcnn_model.mask_sampler, mask_roi_aligner=maskrcnn_model.mask_roi_aligner, segmentation_backbone=segmentation_backbone, segmentation_decoder=segmentation_decoder, segmentation_head=segmentation_head, class_agnostic_bbox_pred=detection_head_config. class_agnostic_bbox_pred, cascade_class_ensemble=detection_head_config.cascade_class_ensemble, min_level=model_config.min_level, max_level=model_config.max_level, num_scales=model_config.anchor.num_scales, aspect_ratios=model_config.anchor.aspect_ratios, anchor_size=model_config.anchor.anchor_size) return model