Use backbone factory

official/projects/detr/configs/detr.py

@@ -62,6 +62,7 @@ class Losses(hyperparams.Config):
   lambda_box: float = 5.0
   lambda_giou: float = 2.0
   background_cls_weight: float = 0.1
+  l2_weight_decay: float = 1e-4
 
 @dataclasses.dataclass
 class Detr(hyperparams.Config):
@@ -73,7 +74,7 @@ class Detr(hyperparams.Config):
   input_size: List[int] = dataclasses.field(default_factory=list)
   backbone: backbones.Backbone = backbones.Backbone(
       type='resnet', resnet=backbones.ResNet(
-          model_id=101,
+          model_id=50,
           bn_trainable=False))
   norm_activation: common.NormActivation = common.NormActivation()
 
@@ -105,7 +106,7 @@ def detr_coco() -> cfg.ExperimentConfig:
   decay_at = train_steps - 100 * steps_per_epoch  # 400 epochs
   config = cfg.ExperimentConfig(
       task=DetrTask(
-          init_checkpoint='gs://ghpark-imagenet-tfrecord/ckpt/resnet101_imagenet',
+          init_checkpoint='gs://ghpark-imagenet-tfrecord/ckpt/resnet50_imagenet',
           init_checkpoint_modules='backbone',
           annotation_file=os.path.join(COCO_INPUT_PATH_BASE,
                                        'instances_val2017.json'),

official/projects/detr/modeling/detr.py

@@ -24,7 +24,7 @@ import tensorflow as tf
 
 from official.modeling import tf_utils
 from official.projects.detr.modeling import transformer
-#from official.vision.modeling.backbones import resnet
+from official.vision.modeling.backbones import resnet
 
 
 def position_embedding_sine(attention_mask,
@@ -116,7 +116,7 @@ class DETR(tf.keras.Model):
       raise ValueError("hidden_size must be a multiple of 2.")
     # TODO(frederickliu): Consider using the backbone factory.
     # TODO(frederickliu): Add to factory once we get skeleton code in.
-    #self._backbone = resnet.ResNet(50, bn_trainable=False)
+    #self._backbone = resnet.ResNet(101, bn_trainable=False)
     # (gunho) use backbone factory
     self._backbone = backbone
 

official/projects/detr/tasks/detection.py

@@ -48,12 +48,17 @@ class DectectionTask(base_task.Task):
     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)
 
     backbone = backbones.factory.build_backbone(
         input_specs=input_specs,
         backbone_config=self._task_config.model.backbone,
         norm_activation_config=self._task_config.model.norm_activation)
-
     model = detr.DETR(
         backbone,
         self._task_config.model.num_queries,