Internal change

PiperOrigin-RevId: 341913626

official/vision/beta/configs/experiments/retinanet/coco_spinenet143_tpu.yaml

@@ -4,7 +4,6 @@ runtime:
   mixed_precision_dtype: 'bfloat16'
 task:
   annotation_file: '/readahead/200M/placer/prod/home/snaggletooth/test/data/coco/instances_val2017.json'
-  gradient_clip_norm: 10.0
   losses:
     l2_weight_decay: 4.0e-05
   model:

official/vision/beta/configs/experiments/retinanet/coco_spinenet190_tpu.yaml

@@ -3,7 +3,6 @@ runtime:
   mixed_precision_dtype: 'bfloat16'
 task:
   annotation_file: '/readahead/200M/placer/prod/home/snaggletooth/test/data/coco/instances_val2017.json'
-  gradient_clip_norm: 0.0
   losses:
     l2_weight_decay: 4.0e-05
   model:

official/vision/beta/configs/experiments/retinanet/coco_spinenet49_tpu.yaml

@@ -4,7 +4,6 @@ runtime:
   mixed_precision_dtype: 'bfloat16'
 task:
   annotation_file: '/readahead/200M/placer/prod/home/snaggletooth/test/data/coco/instances_val2017.json'
-  gradient_clip_norm: 0.0
   losses:
     l2_weight_decay: 4.0e-05
   model:

official/vision/beta/configs/experiments/retinanet/coco_spinenet96_tpu.yaml

@@ -4,7 +4,6 @@ runtime:
   mixed_precision_dtype: 'bfloat16'
 task:
   annotation_file: '/readahead/200M/placer/prod/home/snaggletooth/test/data/coco/instances_val2017.json'
-  gradient_clip_norm: 0.0
   losses:
     l2_weight_decay: 4.0e-05
   model:

official/vision/beta/configs/image_classification.py

@@ -70,7 +70,6 @@ class ImageClassificationTask(cfg.TaskConfig):
   validation_data: DataConfig = DataConfig(is_training=False)
   losses: Losses = Losses()
   evaluation: Evaluation = Evaluation()
-  gradient_clip_norm: float = 0.0
   init_checkpoint: Optional[str] = None
   init_checkpoint_modules: str = 'all'  # all or backbone
 

official/vision/beta/configs/maskrcnn.py

@@ -207,7 +207,6 @@ class MaskRCNNTask(cfg.TaskConfig):
   init_checkpoint: Optional[str] = None
   init_checkpoint_modules: str = 'all'  # all or backbone
   annotation_file: Optional[str] = None
-  gradient_clip_norm: float = 0.0
   per_category_metrics: bool = False
   # If set, we only use masks for the specified class IDs.
   allowed_mask_class_ids: Optional[List[int]] = None

official/vision/beta/configs/retinanet.py

@@ -128,7 +128,6 @@ class RetinaNetTask(cfg.TaskConfig):
   init_checkpoint: Optional[str] = None
   init_checkpoint_modules: str = 'all'  # all or backbone
   annotation_file: Optional[str] = None
-  gradient_clip_norm: float = 0.0
   per_category_metrics: bool = False
 
 

official/vision/beta/configs/semantic_segmentation.py

@@ -87,7 +87,6 @@ class SemanticSegmentationTask(cfg.TaskConfig):
   train_data: DataConfig = DataConfig(is_training=True)
   validation_data: DataConfig = DataConfig(is_training=False)
   losses: Losses = Losses()
-  gradient_clip_norm: float = 0.0
   init_checkpoint: Optional[str] = None
   init_checkpoint_modules: Union[
       str, List[str]] = 'all'  # all, backbone, and/or decoder

official/vision/beta/configs/video_classification.py

@@ -97,7 +97,6 @@ class VideoClassificationTask(cfg.TaskConfig):
   train_data: DataConfig = DataConfig(is_training=True)
   validation_data: DataConfig = DataConfig(is_training=False)
   losses: Losses = Losses()
-  gradient_clip_norm: float = -1.0
 
 
 def add_trainer(experiment: cfg.ExperimentConfig,

official/vision/beta/tasks/image_classification.py

@@ -180,11 +180,6 @@ class ImageClassificationTask(base_task.Task):
     if isinstance(
         optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
       grads = optimizer.get_unscaled_gradients(grads)
-
-    # Apply gradient clipping.
-    if self.task_config.gradient_clip_norm > 0:
-      grads, _ = tf.clip_by_global_norm(
-          grads, self.task_config.gradient_clip_norm)
     optimizer.apply_gradients(list(zip(grads, tvars)))
 
     logs = {self.loss: loss}

official/vision/beta/tasks/maskrcnn.py

@@ -280,11 +280,6 @@ class MaskRCNNTask(base_task.Task):
     # Scales back gradient when LossScaleOptimizer is used.
     if isinstance(optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
       grads = optimizer.get_unscaled_gradients(grads)
-
-    # Apply gradient clipping.
-    if self.task_config.gradient_clip_norm > 0:
-      grads, _ = tf.clip_by_global_norm(
-          grads, self.task_config.gradient_clip_norm)
     optimizer.apply_gradients(list(zip(grads, tvars)))
 
     logs = {self.loss: losses['total_loss']}

official/vision/beta/tasks/retinanet.py

@@ -218,11 +218,6 @@ class RetinaNetTask(base_task.Task):
     # Scales back gradient when LossScaleOptimizer is used.
     if isinstance(optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
       grads = optimizer.get_unscaled_gradients(grads)
-
-    # Apply gradient clipping.
-    if self.task_config.gradient_clip_norm > 0:
-      grads, _ = tf.clip_by_global_norm(
-          grads, self.task_config.gradient_clip_norm)
     optimizer.apply_gradients(list(zip(grads, tvars)))
 
     logs = {self.loss: loss}

official/vision/beta/tasks/semantic_segmentation.py

@@ -188,11 +188,6 @@ class SemanticSegmentationTask(base_task.Task):
     # used.
     if isinstance(optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
       grads = optimizer.get_unscaled_gradients(grads)
-
-    # Apply gradient clipping.
-    if self.task_config.gradient_clip_norm > 0:
-      grads, _ = tf.clip_by_global_norm(
-          grads, self.task_config.gradient_clip_norm)
     optimizer.apply_gradients(list(zip(grads, tvars)))
 
     logs = {self.loss: loss}

official/vision/beta/tasks/video_classification.py

@@ -160,11 +160,6 @@ class VideoClassificationTask(base_task.Task):
     # used.
     if isinstance(optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
       grads = optimizer.get_unscaled_gradients(grads)
-
-    # Apply gradient clipping.
-    if self.task_config.gradient_clip_norm > 0:
-      grads, _ = tf.clip_by_global_norm(
-          grads, self.task_config.gradient_clip_norm)
     optimizer.apply_gradients(list(zip(grads, tvars)))
 
     logs = {self.loss: loss}