rm lrge fntune

official/vision/beta/projects/yolo/configs/experiments/scaled-yolo/detection-finetune/yolo_l_p5_896_tpu.yaml

@@ -74,3 +74,8 @@ task:
       area_thresh: 0.1
   validation_data:
     input_path: '/readahead/200M/placer/prod/home/tensorflow-performance-data/datasets/coco/val*'
+  trainer:
+    train_steps: 831600 # epoch 300 to 450 
+    learning_rate:
+      cosine:
+        decay_steps: 831600 # epoch 300 to 450 
\ No newline at end of file

official/vision/beta/projects/yolo/configs/experiments/scaled-yolo/detection-finetune/yolo_l_p6_1280_tpu.yaml

@@ -76,3 +76,8 @@ task:
       area_thresh: 0.1
   validation_data:
     input_path: '/readahead/200M/placer/prod/home/tensorflow-performance-data/datasets/coco/val*'
+  trainer:
+    train_steps: 831600 # epoch 300 to 450 
+    learning_rate:
+      cosine:
+        decay_steps: 831600 # epoch 300 to 450 
\ No newline at end of file

official/vision/beta/projects/yolo/configs/experiments/scaled-yolo/detection-finetune/yolo_l_p7_1536_tpu.yaml

@@ -78,3 +78,8 @@ task:
       area_thresh: 0.1
   validation_data:
     input_path: '/readahead/200M/placer/prod/home/tensorflow-performance-data/datasets/coco/val*'
+  trainer:
+    train_steps: 831600 # epoch 300 to 450 
+    learning_rate:
+      cosine:
+        decay_steps: 831600 # epoch 300 to 450 
\ No newline at end of file

official/vision/beta/projects/yolo/configs/yolo.py

@@ -619,115 +619,4 @@ def large_yolo() -> cfg.ExperimentConfig:
           'task.validation_data.is_training != None'
       ])
 
-  return config
-
-@exp_factory.register_config_factory('large_yolo_finetune')
-def large_yolo_finetune() -> cfg.ExperimentConfig:
-  """COCO object detection with YOLOv4-csp and v4."""
-  train_batch_size = 64
-  eval_batch_size = 8
-  train_epochs = 300
-  fine_tune_epochs = 450
-  warmup_epochs = 3
-
-  validation_interval = 5
-  steps_per_epoch = COCO_TRAIN_EXAMPLES // train_batch_size
-
-  max_num_instances = 300
-
-  config = cfg.ExperimentConfig(
-      runtime=cfg.RuntimeConfig(mixed_precision_dtype='bfloat16'),
-      task=YoloTask(
-          smart_bias_lr=0.1,
-          init_checkpoint_modules='',
-          annotation_file=None,
-          weight_decay=0.0,
-          model=Yolo(
-              darknet_based_model=False,
-              norm_activation=common.NormActivation(
-                  activation='mish',
-                  use_sync_bn=True,
-                  norm_epsilon=0.0001,
-                  norm_momentum=0.97),
-              head=YoloHead(smart_bias=True),
-              loss=YoloLoss(use_scaled_loss=True)),
-          train_data=DataConfig(
-              input_path=os.path.join(COCO_INPUT_PATH_BASE, 'train*'),
-              is_training=True,
-              global_batch_size=train_batch_size,
-              dtype='float32',
-              parser=Parser(
-                  aug_rand_saturation=0.7,
-                  aug_rand_brightness=0.4,
-                  aug_rand_hue=0.015,
-                  letter_box=True,
-                  use_tie_breaker=True,
-                  best_match_only=True,
-                  anchor_thresh=4.0,
-                  random_pad=False,
-                  area_thresh=0.1,
-                  max_num_instances=max_num_instances,
-                  mosaic=Mosaic(
-                      mosaic_crop_mode='scale',
-                      mosaic_frequency=1.0,
-                      mixup_frequency=0.2,
-                  ))),
-          validation_data=DataConfig(
-              input_path=os.path.join(COCO_INPUT_PATH_BASE, 'val*'),
-              is_training=False,
-              global_batch_size=eval_batch_size,
-              drop_remainder=True,
-              dtype='float32',
-              parser=Parser(
-                  letter_box=True,
-                  use_tie_breaker=True,
-                  best_match_only=True,
-                  anchor_thresh=4.0,
-                  area_thresh=0.1,
-                  max_num_instances=max_num_instances,
-              ))),
-      trainer=cfg.TrainerConfig(
-          train_steps=fine_tune_epochs * steps_per_epoch,
-          validation_steps=COCO_VAL_EXAMPLES // eval_batch_size,
-          validation_interval=validation_interval * steps_per_epoch,
-          steps_per_loop=steps_per_epoch,
-          summary_interval=steps_per_epoch,
-          checkpoint_interval=steps_per_epoch,
-          optimizer_config=optimization.OptimizationConfig({
-              'ema': {
-                  'average_decay': 0.9999,
-                  'trainable_weights_only': False,
-                  'dynamic_decay': True,
-              },
-              'optimizer': {
-                  'type': 'sgd_torch',
-                  'sgd_torch': {
-                      'momentum': 0.937,
-                      'momentum_start': 0.9,
-                      'nesterov': True,
-                      'warmup_steps': steps_per_epoch * warmup_epochs,
-                      'weight_decay': 0.0005 * train_batch_size / 64.0,
-                  }
-              },
-              'learning_rate': {
-                  'type': 'cosine',
-                  'cosine': {
-                      'initial_learning_rate': 0.01,
-                      'alpha': 0.2,
-                      'decay_steps': fine_tune_epochs * steps_per_epoch,
-                  }
-              },
-              'warmup': {
-                  'type': 'linear',
-                  'linear': {
-                      'warmup_steps': steps_per_epoch * warmup_epochs,
-                      'warmup_learning_rate': 0
-                  }
-              }
-          })),
-      restrictions=[
-          'task.train_data.is_training != None',
-          'task.validation_data.is_training != None'
-      ])
-
   return config
\ No newline at end of file