Changed PR folders

official/legacy/image_classification/classifier_trainer.py

@@ -36,6 +36,7 @@ from official.modeling import hyperparams
 from official.modeling import performance
 from official.utils import hyperparams_flags
 from official.utils.misc import keras_utils
+from official.vision.image_classification.vgg16 import vgg_model
 
 
 def get_models() -> Mapping[str, tf.keras.Model]:
@@ -43,6 +44,7 @@ def get_models() -> Mapping[str, tf.keras.Model]:
   return {
       'efficientnet': efficientnet_model.EfficientNet.from_name,
       'resnet': resnet_model.resnet50,
+      'vgg': vgg_model.vgg16,
   }
 
 

official/legacy/image_classification/classifier_trainer_test.py

@@ -53,6 +53,7 @@ def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
       model=[
           'efficientnet',
           'resnet',
+          'vgg',
       ],
       dataset=[
           'imagenet',
@@ -149,6 +150,7 @@ class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
           model=[
               'efficientnet',
               'resnet',
+              'vgg',
           ],
           dataset='imagenet',
           dtype='float16',
@@ -193,6 +195,7 @@ class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
           model=[
               'efficientnet',
               'resnet',
+              'vgg',
           ],
           dataset='imagenet',
           dtype='bfloat16',

official/legacy/image_classification/configs/configs.py

@@ -24,6 +24,7 @@ from official.legacy.image_classification import dataset_factory
 from official.legacy.image_classification.configs import base_configs
 from official.legacy.image_classification.efficientnet import efficientnet_config
 from official.legacy.image_classification.resnet import resnet_config
+from official.vision.image_classification.vgg16 import vgg_config
 
 
 @dataclasses.dataclass
@@ -92,12 +93,44 @@ class ResNetImagenetConfig(base_configs.ExperimentConfig):
   model: base_configs.ModelConfig = resnet_config.ResNetModelConfig()
 
 
+@dataclasses.dataclass
+class VGGImagenetConfig(base_configs.ExperimentConfig):
+  """Base configuration to train vgg-16 on ImageNet."""
+  export: base_configs.ExportConfig = base_configs.ExportConfig()
+  runtime: base_configs.RuntimeConfig = base_configs.RuntimeConfig()
+  train_dataset: dataset_factory.DatasetConfig = \
+      dataset_factory.ImageNetConfig(split='train',
+                                     one_hot=False,
+                                     mean_subtract=True,
+                                     standardize=True)
+  validation_dataset: dataset_factory.DatasetConfig = \
+      dataset_factory.ImageNetConfig(split='validation',
+                                     one_hot=False,
+                                     mean_subtract=True,
+                                     standardize=True)
+  train: base_configs.TrainConfig = base_configs.TrainConfig(
+      resume_checkpoint=True,
+      epochs=90,
+      steps=None,
+      callbacks=base_configs.CallbacksConfig(
+          enable_checkpoint_and_export=True, enable_tensorboard=True),
+      metrics=['accuracy', 'top_5'],
+      time_history=base_configs.TimeHistoryConfig(log_steps=100),
+      tensorboard=base_configs.TensorBoardConfig(
+          track_lr=True, write_model_weights=False),
+      set_epoch_loop=False)
+  evaluation: base_configs.EvalConfig = base_configs.EvalConfig(
+      epochs_between_evals=1, steps=None)
+  model: base_configs.ModelConfig = vgg_config.VGGModelConfig()
+
+
 def get_config(model: str, dataset: str) -> base_configs.ExperimentConfig:
   """Given model and dataset names, return the ExperimentConfig."""
   dataset_model_config_map = {
       'imagenet': {
           'efficientnet': EfficientNetImageNetConfig(),
           'resnet': ResNetImagenetConfig(),
+          'vgg': VGGImagenetConfig(),
       }
   }
   try:

official/vision/image_classification/vgg16/vgg_config.py → official/legacy/image_classification/vgg16/vgg_config.py

@@ -49,5 +49,5 @@ class VGGModelConfig(base_configs.ModelConfig):
           examples_per_epoch=1281167,
           boundaries=[30, 60],
           warmup_epochs=0,
-          scale_by_batch_size=1. / 128.,
+          scale_by_batch_size=1. / 256.,
           multipliers=[0.01 / 256, 0.001 / 256, 0.0001 / 256]))

official/vision/image_classification/vgg16/vgg_model.py → official/legacy/image_classification/vgg16/vgg_model.py

@@ -174,17 +174,23 @@ def vgg16(num_classes,
     
     x = layers.Flatten(name='flatten')(x)
     x = layers.Dense(4096,
+                     #kernel_initializer=tf.initializers.random_normal(stddev=0.01),
                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+                     #bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
                      name='fc1')(x)
     x = layers.Activation('relu')(x)
     x = layers.Dropout(0.5)(x)
     x = layers.Dense(4096,
+                     #kernel_initializer=tf.initializers.random_normal(stddev=0.01),
                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+                     #bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
                      name='fc2')(x)
     x = layers.Activation('relu')(x)
     x = layers.Dropout(0.5)(x)
     x = layers.Dense(num_classes,
+                     #kernel_initializer=tf.initializers.random_normal(stddev=0.01),
                      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
+                     #bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),                     
                      name='fc1000')(x)
     
     # A softmax that is followed by the model loss must be done cannot be done

official/vision/image_classification/classifier_trainer.py

@@ -36,7 +36,6 @@ from official.vision.image_classification.configs import configs
 from official.vision.image_classification.efficientnet import efficientnet_model
 from official.vision.image_classification.resnet import common
 from official.vision.image_classification.resnet import resnet_model
-from official.vision.image_classification.vgg16 import vgg_model
 
 
 def get_models() -> Mapping[str, tf.keras.Model]:
@@ -44,7 +43,6 @@ def get_models() -> Mapping[str, tf.keras.Model]:
   return {
       'efficientnet': efficientnet_model.EfficientNet.from_name,
       'resnet': resnet_model.resnet50,
-      'vgg': vgg_model.vgg16,
   }
 
 

official/vision/image_classification/classifier_trainer_test.py

@@ -53,7 +53,6 @@ def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
       model=[
           'efficientnet',
           'resnet',
-          'vgg',
       ],
       dataset=[
           'imagenet',
@@ -150,7 +149,6 @@ class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
           model=[
               'efficientnet',
               'resnet',
-              'vgg',
           ],
           dataset='imagenet',
           dtype='float16',
@@ -195,7 +193,6 @@ class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
           model=[
               'efficientnet',
               'resnet',
-              'vgg',
           ],
           dataset='imagenet',
           dtype='bfloat16',

official/vision/image_classification/configs/configs.py

@@ -91,36 +91,6 @@ class ResNetImagenetConfig(base_configs.ExperimentConfig):
       epochs_between_evals=1, steps=None)
   model: base_configs.ModelConfig = resnet_config.ResNetModelConfig()
 
-@dataclasses.dataclass
-class VGGImagenetConfig(base_configs.ExperimentConfig):
-  """Base configuration to train vgg-16 on ImageNet."""
-  export: base_configs.ExportConfig = base_configs.ExportConfig()
-  runtime: base_configs.RuntimeConfig = base_configs.RuntimeConfig()
-  train_dataset: dataset_factory.DatasetConfig = \
-      dataset_factory.ImageNetConfig(split='train',
-                                     one_hot=False,
-                                     mean_subtract=True,
-                                     standardize=True)
-  validation_dataset: dataset_factory.DatasetConfig = \
-      dataset_factory.ImageNetConfig(split='validation',
-                                     one_hot=False,
-                                     mean_subtract=True,
-                                     standardize=True)
-  train: base_configs.TrainConfig = base_configs.TrainConfig(
-      resume_checkpoint=True,
-      epochs=90,
-      steps=None,
-      callbacks=base_configs.CallbacksConfig(
-          enable_checkpoint_and_export=True, enable_tensorboard=True),
-      metrics=['accuracy', 'top_5'],
-      time_history=base_configs.TimeHistoryConfig(log_steps=100),
-      tensorboard=base_configs.TensorBoardConfig(
-          track_lr=True, write_model_weights=False),
-      set_epoch_loop=False)
-  evaluation: base_configs.EvalConfig = base_configs.EvalConfig(
-      epochs_between_evals=1, steps=None)
-  model: base_configs.ModelConfig = vgg_config.VGGModelConfig()
-  
 
 def get_config(model: str, dataset: str) -> base_configs.ExperimentConfig:
   """Given model and dataset names, return the ExperimentConfig."""
@@ -128,7 +98,6 @@ def get_config(model: str, dataset: str) -> base_configs.ExperimentConfig:
       'imagenet': {
           'efficientnet': EfficientNetImageNetConfig(),
           'resnet': ResNetImagenetConfig(),
-          'vgg': VGGImagenetConfig()
       }
   }
   try: