Merge branch 'panoptic-segmentation' into panoptic-deeplab-modeling

official/vision/image_classification/configs/base_configs.py → official/legacy/image_classification/configs/base_configs.py

@@ -16,7 +16,7 @@
 """Definitions for high level configuration groups.."""
 
 import dataclasses
-from typing import Any, List, Mapping, Optional
+from typing import Any, List, Optional
 from official.core import config_definitions
 from official.modeling import hyperparams
 

official/vision/image_classification/configs/configs.py → official/legacy/image_classification/configs/configs.py

@@ -20,10 +20,10 @@ from __future__ import print_function
 
 import dataclasses
 
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification.configs import base_configs
-from official.vision.image_classification.efficientnet import efficientnet_config
-from official.vision.image_classification.resnet import resnet_config
+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
 
 
 @dataclasses.dataclass
@@ -40,10 +40,10 @@ class EfficientNetImageNetConfig(base_configs.ExperimentConfig):
   """
   export: base_configs.ExportConfig = base_configs.ExportConfig()
   runtime: base_configs.RuntimeConfig = base_configs.RuntimeConfig()
-  train_dataset: dataset_factory.DatasetConfig = \
-      dataset_factory.ImageNetConfig(split='train')
-  validation_dataset: dataset_factory.DatasetConfig = \
-      dataset_factory.ImageNetConfig(split='validation')
+  train_dataset: dataset_factory.DatasetConfig = dataset_factory.ImageNetConfig(
+      split='train')
+  validation_dataset: dataset_factory.DatasetConfig = dataset_factory.ImageNetConfig(
+      split='validation')
   train: base_configs.TrainConfig = base_configs.TrainConfig(
       resume_checkpoint=True,
       epochs=500,
@@ -57,8 +57,8 @@ class EfficientNetImageNetConfig(base_configs.ExperimentConfig):
       set_epoch_loop=False)
   evaluation: base_configs.EvalConfig = base_configs.EvalConfig(
       epochs_between_evals=1, steps=None)
-  model: base_configs.ModelConfig = \
-    efficientnet_config.EfficientNetModelConfig()
+  model: base_configs.ModelConfig = efficientnet_config.EfficientNetModelConfig(
+  )
 
 
 @dataclasses.dataclass

official/vision/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b0-gpu.yaml → official/legacy/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b0-gpu.yaml

@@ -13,7 +13,7 @@ train_dataset:
   num_classes: 1000
   num_examples: 1281167
   batch_size: 32
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'float32'
   augmenter:
     name: 'autoaugment'
@@ -25,7 +25,7 @@ validation_dataset:
   num_classes: 1000
   num_examples: 50000
   batch_size: 32
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'float32'
 model:
   model_params:
@@ -46,7 +46,7 @@ model:
   loss:
     label_smoothing: 0.1
 train:
-  resume_checkpoint: True
+  resume_checkpoint: true
   epochs: 500
 evaluation:
   epochs_between_evals: 1

official/vision/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml → official/legacy/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml

@@ -12,7 +12,7 @@ train_dataset:
   num_classes: 1000
   num_examples: 1281167
   batch_size: 128
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'bfloat16'
   augmenter:
     name: 'autoaugment'
@@ -24,7 +24,7 @@ validation_dataset:
   num_classes: 1000
   num_examples: 50000
   batch_size: 128
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'bfloat16'
 model:
   model_params:
@@ -45,8 +45,8 @@ model:
   loss:
     label_smoothing: 0.1
 train:
-  resume_checkpoint: True
+  resume_checkpoint: true
   epochs: 500
-  set_epoch_loop: True
+  set_epoch_loop: true
 evaluation:
   epochs_between_evals: 1

official/vision/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b1-gpu.yaml → official/legacy/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b1-gpu.yaml

@@ -10,7 +10,7 @@ train_dataset:
   num_classes: 1000
   num_examples: 1281167
   batch_size: 32
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'float32'
 validation_dataset:
   name: 'imagenet2012'
@@ -20,7 +20,7 @@ validation_dataset:
   num_classes: 1000
   num_examples: 50000
   batch_size: 32
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'float32'
 model:
   model_params:
@@ -41,7 +41,7 @@ model:
   loss:
     label_smoothing: 0.1
 train:
-  resume_checkpoint: True
+  resume_checkpoint: true
   epochs: 500
 evaluation:
   epochs_between_evals: 1

official/vision/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b1-tpu.yaml → official/legacy/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b1-tpu.yaml

@@ -11,7 +11,7 @@ train_dataset:
   num_classes: 1000
   num_examples: 1281167
   batch_size: 128
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'bfloat16'
   augmenter:
     name: 'autoaugment'
@@ -23,7 +23,7 @@ validation_dataset:
   num_classes: 1000
   num_examples: 50000
   batch_size: 128
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'bfloat16'
 model:
   model_params:
@@ -44,8 +44,8 @@ model:
   loss:
     label_smoothing: 0.1
 train:
-  resume_checkpoint: True
+  resume_checkpoint: true
   epochs: 500
-  set_epoch_loop: True
+  set_epoch_loop: true
 evaluation:
   epochs_between_evals: 1

official/vision/image_classification/configs/examples/resnet/imagenet/gpu.yaml → official/legacy/image_classification/configs/examples/resnet/imagenet/gpu.yaml

@@ -4,7 +4,7 @@
 runtime:
   distribution_strategy: 'mirrored'
   num_gpus: 1
-  batchnorm_spatial_persistent: True
+  batchnorm_spatial_persistent: true
 train_dataset:
   name: 'imagenet2012'
   data_dir: null
@@ -14,10 +14,10 @@ train_dataset:
   num_classes: 1000
   num_examples: 1281167
   batch_size: 256
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'float16'
-  mean_subtract: True
-  standardize: True
+  mean_subtract: true
+  standardize: true
 validation_dataset:
   name: 'imagenet2012'
   data_dir: null
@@ -27,14 +27,14 @@ validation_dataset:
   num_classes: 1000
   num_examples: 50000
   batch_size: 256
-  use_per_replica_batch_size: True
+  use_per_replica_batch_size: true
   dtype: 'float16'
-  mean_subtract: True
-  standardize: True
+  mean_subtract: true
+  standardize: true
 model:
   name: 'resnet'
   model_params:
-    rescale_inputs: False
+    rescale_inputs: false
   optimizer:
     name: 'momentum'
     momentum: 0.9
@@ -43,7 +43,7 @@ model:
   loss:
     label_smoothing: 0.1
 train:
-  resume_checkpoint: True
+  resume_checkpoint: true
   epochs: 90
 evaluation:
   epochs_between_evals: 1

official/vision/image_classification/configs/examples/resnet/imagenet/tpu.yaml → official/legacy/image_classification/configs/examples/resnet/imagenet/tpu.yaml

@@ -9,47 +9,47 @@ train_dataset:
   data_dir: null
   builder: 'tfds'
   split: 'train'
-  one_hot: False
+  one_hot: false
   image_size: 224
   num_classes: 1000
   num_examples: 1281167
   batch_size: 128
-  use_per_replica_batch_size: True
-  mean_subtract: False
-  standardize: False
+  use_per_replica_batch_size: true
+  mean_subtract: false
+  standardize: false
   dtype: 'bfloat16'
 validation_dataset:
   name: 'imagenet2012'
   data_dir: null
   builder: 'tfds'
   split: 'validation'
-  one_hot: False
+  one_hot: false
   image_size: 224
   num_classes: 1000
   num_examples: 50000
   batch_size: 128
-  use_per_replica_batch_size: True
-  mean_subtract: False
-  standardize: False
+  use_per_replica_batch_size: true
+  mean_subtract: false
+  standardize: false
   dtype: 'bfloat16'
 model:
   name: 'resnet'
   model_params:
-    rescale_inputs: True
+    rescale_inputs: true
   optimizer:
     name: 'momentum'
     momentum: 0.9
     decay: 0.9
     epsilon: 0.001
     moving_average_decay: 0.
-    lookahead: False
+    lookahead: false
   loss:
     label_smoothing: 0.1
 train:
   callbacks:
-    enable_checkpoint_and_export: True
-  resume_checkpoint: True
+    enable_checkpoint_and_export: true
+  resume_checkpoint: true
   epochs: 90
-  set_epoch_loop: True
+  set_epoch_loop: true
 evaluation:
   epochs_between_evals: 1

official/vision/image_classification/dataset_factory.py → official/legacy/image_classification/dataset_factory.py

@@ -17,18 +17,16 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-
+import dataclasses
 import os
-from typing import Any, List, Optional, Tuple, Mapping, Union
+from typing import Any, List, Mapping, Optional, Tuple, Union
 
 from absl import logging
-from dataclasses import dataclass
 import tensorflow as tf
 import tensorflow_datasets as tfds
-
+from official.legacy.image_classification import augment
+from official.legacy.image_classification import preprocessing
 from official.modeling.hyperparams import base_config
-from official.vision.image_classification import augment
-from official.vision.image_classification import preprocessing
 
 AUGMENTERS = {
     'autoaugment': augment.AutoAugment,
@@ -36,14 +34,14 @@ AUGMENTERS = {
 }
 
 
-@dataclass
+@dataclasses.dataclass
 class AugmentConfig(base_config.Config):
   """Configuration for image augmenters.
 
   Attributes:
     name: The name of the image augmentation to use. Possible options are None
       (default), 'autoaugment', or 'randaugment'.
-    params: Any paramaters used to initialize the augmenter.
+    params: Any parameters used to initialize the augmenter.
   """
   name: Optional[str] = None
   params: Optional[Mapping[str, Any]] = None
@@ -55,7 +53,7 @@ class AugmentConfig(base_config.Config):
     return augmenter(**params) if augmenter is not None else None
 
 
-@dataclass
+@dataclasses.dataclass
 class DatasetConfig(base_config.Config):
   """The base configuration for building datasets.
 
@@ -135,7 +133,7 @@ class DatasetConfig(base_config.Config):
     return self.name or self.data_dir or self.filenames
 
 
-@dataclass
+@dataclasses.dataclass
 class ImageNetConfig(DatasetConfig):
   """The base ImageNet dataset config."""
   name: str = 'imagenet2012'
@@ -148,7 +146,7 @@ class ImageNetConfig(DatasetConfig):
   batch_size: int = 128
 
 
-@dataclass
+@dataclasses.dataclass
 class Cifar10Config(DatasetConfig):
   """The base CIFAR-10 dataset config."""
   name: str = 'cifar10'
@@ -338,7 +336,6 @@ class DatasetBuilder:
     """Return a dataset loading files from TFDS."""
 
     logging.info('Using TFDS to load data.')
-
     builder = tfds.builder(self.config.name, data_dir=self.config.data_dir)
 
     if self.config.download:

official/vision/image_classification/efficientnet/common_modules.py → official/legacy/image_classification/efficientnet/common_modules.py

@@ -16,12 +16,10 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-
+from typing import Optional, Text
 import numpy as np
 import tensorflow as tf
 import tensorflow.compat.v1 as tf1
-from typing import Text, Optional
-
 from tensorflow.python.tpu import tpu_function
 
 

official/vision/image_classification/efficientnet/efficientnet_config.py → official/legacy/image_classification/efficientnet/efficientnet_config.py

@@ -17,13 +17,9 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-
-from typing import Any, Mapping
-
 import dataclasses
-
+from official.legacy.image_classification.configs import base_configs
 from official.modeling.hyperparams import base_config
-from official.vision.image_classification.configs import base_configs
 
 
 @dataclasses.dataclass

official/vision/image_classification/efficientnet/efficientnet_model.py → official/legacy/image_classification/efficientnet/efficientnet_model.py

@@ -23,22 +23,19 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-
+import dataclasses
 import math
-import os
 from typing import Any, Dict, Optional, Text, Tuple
 
 from absl import logging
-from dataclasses import dataclass
 import tensorflow as tf
-
+from official.legacy.image_classification import preprocessing
+from official.legacy.image_classification.efficientnet import common_modules
 from official.modeling import tf_utils
 from official.modeling.hyperparams import base_config
-from official.vision.image_classification import preprocessing
-from official.vision.image_classification.efficientnet import common_modules
 
 
-@dataclass
+@dataclasses.dataclass
 class BlockConfig(base_config.Config):
   """Config for a single MB Conv Block."""
   input_filters: int = 0
@@ -53,7 +50,7 @@ class BlockConfig(base_config.Config):
   conv_type: str = 'depthwise'
 
 
-@dataclass
+@dataclasses.dataclass
 class ModelConfig(base_config.Config):
   """Default Config for Efficientnet-B0."""
   width_coefficient: float = 1.0

official/vision/image_classification/efficientnet/tfhub_export.py → official/legacy/image_classification/efficientnet/tfhub_export.py

@@ -25,7 +25,7 @@ from absl import flags
 
 import tensorflow as tf
 
-from official.vision.image_classification.efficientnet import efficientnet_model
+from official.legacy.image_classification.efficientnet import efficientnet_model
 
 FLAGS = flags.FLAGS
 

official/vision/image_classification/learning_rate_test.py → official/legacy/image_classification/learning_rate_test.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 
 import tensorflow as tf
 
-from official.vision.image_classification import learning_rate
+from official.legacy.image_classification import learning_rate
 
 
 class LearningRateTests(tf.test.TestCase):

official/vision/image_classification/mnist_main.py → official/legacy/image_classification/mnist_main.py

@@ -26,9 +26,9 @@ from absl import logging
 import tensorflow as tf
 import tensorflow_datasets as tfds
 from official.common import distribute_utils
+from official.legacy.image_classification.resnet import common
 from official.utils.flags import core as flags_core
 from official.utils.misc import model_helpers
-from official.vision.image_classification.resnet import common
 
 FLAGS = flags.FLAGS
 

official/vision/image_classification/mnist_test.py → official/legacy/image_classification/mnist_test.py

@@ -25,8 +25,8 @@ import tensorflow as tf
 
 from tensorflow.python.distribute import combinations
 from tensorflow.python.distribute import strategy_combinations
+from official.legacy.image_classification import mnist_main
 from official.utils.testing import integration
-from official.vision.image_classification import mnist_main
 
 
 mnist_main.define_mnist_flags()

official/vision/image_classification/optimizer_factory.py → official/legacy/image_classification/optimizer_factory.py

@@ -22,10 +22,9 @@ from typing import Any, Dict, Optional, Text
 from absl import logging
 import tensorflow as tf
 import tensorflow_addons as tfa
-
+from official.legacy.image_classification import learning_rate
+from official.legacy.image_classification.configs import base_configs
 from official.modeling import optimization
-from official.vision.image_classification import learning_rate
-from official.vision.image_classification.configs import base_configs
 
 # pylint: disable=protected-access