def test_fpn_decoder_creation(self, num_filters, use_separable_conv): """Test creation of FPN decoder.""" min_level = 3 max_level = 7 input_specs = {} for level in range(min_level, max_level): input_specs[str(level)] = tf.TensorShape( [1, 128 // (2**level), 128 // (2**level), 3]) network = decoders.FPN(input_specs=input_specs, num_filters=num_filters, use_separable_conv=use_separable_conv, use_sync_bn=True) model_config = configs.retinanet.RetinaNet() model_config.min_level = min_level model_config.max_level = max_level model_config.num_classes = 10 model_config.input_size = [None, None, 3] model_config.decoder = decoders_cfg.Decoder( type='fpn', fpn=decoders_cfg.FPN(num_filters=num_filters, use_separable_conv=use_separable_conv)) factory_network = factory.build_decoder(input_specs=input_specs, model_config=model_config) network_config = network.get_config() factory_network_config = factory_network.get_config() self.assertEqual(network_config, factory_network_config)
class RetinaNet(hyperparams.Config): num_classes: int = 0 input_size: List[int] = dataclasses.field(default_factory=list) min_level: int = 3 max_level: int = 7 anchor: Anchor = Anchor() backbone: backbones.Backbone = backbones.Backbone( type='resnet', resnet=backbones.ResNet()) decoder: decoders.Decoder = decoders.Decoder(type='fpn', fpn=decoders.FPN()) head: RetinaNetHead = RetinaNetHead() detection_generator: DetectionGenerator = DetectionGenerator() norm_activation: common.NormActivation = common.NormActivation()
class MaskRCNN(hyperparams.Config): num_classes: int = 0 input_size: List[int] = dataclasses.field(default_factory=list) min_level: int = 2 max_level: int = 6 anchor: Anchor = Anchor() include_mask: bool = True backbone: backbones.Backbone = backbones.Backbone( type='resnet', resnet=backbones.ResNet()) decoder: decoders.Decoder = decoders.Decoder(type='fpn', fpn=decoders.FPN()) rpn_head: RPNHead = RPNHead() detection_head: DetectionHead = DetectionHead() roi_generator: ROIGenerator = ROIGenerator() roi_sampler: ROISampler = ROISampler() roi_aligner: ROIAligner = ROIAligner() detection_generator: DetectionGenerator = DetectionGenerator() mask_head: Optional[MaskHead] = MaskHead() mask_sampler: Optional[MaskSampler] = MaskSampler() mask_roi_aligner: Optional[MaskROIAligner] = MaskROIAligner() norm_activation: common.NormActivation = common.NormActivation( norm_momentum=0.997, norm_epsilon=0.0001, use_sync_bn=True)
def seg_resnetfpn_pascal() -> cfg.ExperimentConfig: """Image segmentation on imagenet with resnet-fpn.""" train_batch_size = 256 eval_batch_size = 32 steps_per_epoch = PASCAL_TRAIN_EXAMPLES // train_batch_size config = cfg.ExperimentConfig( task=SemanticSegmentationTask( model=SemanticSegmentationModel( num_classes=21, input_size=[512, 512, 3], min_level=3, max_level=7, backbone=backbones.Backbone( type='resnet', resnet=backbones.ResNet(model_id=50)), decoder=decoders.Decoder(type='fpn', fpn=decoders.FPN()), head=SegmentationHead(level=3, num_convs=3), norm_activation=common.NormActivation(activation='swish', use_sync_bn=True)), losses=Losses(l2_weight_decay=1e-4), train_data=DataConfig(input_path=os.path.join( PASCAL_INPUT_PATH_BASE, 'train_aug*'), is_training=True, global_batch_size=train_batch_size, aug_scale_min=0.2, aug_scale_max=1.5), validation_data=DataConfig(input_path=os.path.join( PASCAL_INPUT_PATH_BASE, 'val*'), is_training=False, global_batch_size=eval_batch_size, resize_eval_groundtruth=False, groundtruth_padded_size=[512, 512], drop_remainder=False), ), trainer=cfg.TrainerConfig( steps_per_loop=steps_per_epoch, summary_interval=steps_per_epoch, checkpoint_interval=steps_per_epoch, train_steps=450 * steps_per_epoch, validation_steps=PASCAL_VAL_EXAMPLES // eval_batch_size, validation_interval=steps_per_epoch, optimizer_config=optimization.OptimizationConfig({ 'optimizer': { 'type': 'sgd', 'sgd': { 'momentum': 0.9 } }, 'learning_rate': { 'type': 'polynomial', 'polynomial': { 'initial_learning_rate': 0.007, 'decay_steps': 450 * steps_per_epoch, 'end_learning_rate': 0.0, 'power': 0.9 } }, 'warmup': { 'type': 'linear', 'linear': { 'warmup_steps': 5 * steps_per_epoch, 'warmup_learning_rate': 0 } } })), restrictions=[ 'task.train_data.is_training != None', 'task.validation_data.is_training != None' ]) return config