Add in instructions for TFDS for classifier_trainer.

PiperOrigin-RevId: 303828114

official/vision/image_classification/README.md

@@ -19,11 +19,25 @@ installed and
 
 ### ImageNet preparation
 
+#### Using TFDS
+`classifier_trainer.py` supports ImageNet with
+[TensorFlow Datasets (TFDS)](https://www.tensorflow.org/datasets/overview).
+
+Please see the following [example snippet](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/scripts/download_and_prepare.py)
+for more information on how to use TFDS to download and prepare datasets, and
+specifically the [TFDS ImageNet readme](https://github.com/tensorflow/datasets/blob/master/docs/catalog/imagenet2012.md)
+for manual download instructions.
+
+#### Legacy TFRecords
 Download the ImageNet dataset and convert it to TFRecord format.
 The following [script](https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py)
 and [README](https://github.com/tensorflow/tpu/tree/master/tools/datasets#imagenet_to_gcspy)
 provide a few options.
 
+Note that the legacy ResNet runners, e.g. [resnet/resnet_ctl_imagenet_main.py](resnet/resnet_ctl_imagenet_main.py)
+require TFRecords whereas `classifier_trainer.py` can use both by setting the
+builder to 'records' or 'tfds' in the configurations.
+
 ### Running on Cloud TPUs
 
 Note: These models will **not** work with TPUs on Colab.
@@ -114,7 +128,7 @@ python3 classifier_trainer.py \
   --tpu=$TPU_NAME \
   --model_dir=$MODEL_DIR \
   --data_dir=$DATA_DIR \
-  --config_file=config/examples/resnet/imagenet/tpu.yaml
+  --config_file=configs/examples/resnet/imagenet/tpu.yaml
 ```
 
 ### EfficientNet
@@ -141,7 +155,7 @@ python3 classifier_trainer.py \
   --tpu=$TPU_NAME \
   --model_dir=$MODEL_DIR \
   --data_dir=$DATA_DIR \
-  --config_file=config/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml
+  --config_file=configs/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml
 ```
 
 Note that the number of GPU devices can be overridden in the command line using

official/vision/image_classification/classifier_trainer.py

@@ -99,7 +99,7 @@ def _get_dataset_builders(params: base_configs.ExperimentConfig,
   image_size = get_image_size_from_model(params)
 
   dataset_configs = [
-      params.train_dataset, params.validation_dataset, params.test_dataset
+      params.train_dataset, params.validation_dataset
   ]
   builders = []
 
@@ -154,9 +154,6 @@ def _get_params_from_flags(flags_obj: flags.FlagValues):
       'validation_dataset': {
           'data_dir': flags_obj.data_dir,
       },
-      'test_dataset': {
-          'data_dir': flags_obj.data_dir,
-      },
   }
 
   overriding_configs = (flags_obj.config_file,
@@ -300,8 +297,8 @@ def train_and_eval(
   datasets = [builder.build() if builder else None for builder in builders]
 
   # Unpack datasets and builders based on train/val/test splits
-  train_builder, validation_builder, test_builder = builders  # pylint: disable=unbalanced-tuple-unpacking
-  train_dataset, validation_dataset, test_dataset = datasets
+  train_builder, validation_builder = builders  # pylint: disable=unbalanced-tuple-unpacking
+  train_dataset, validation_dataset = datasets
 
   train_epochs = params.train.epochs
   train_steps = params.train.steps or train_builder.num_steps

official/vision/image_classification/classifier_trainer_test.py

@@ -82,12 +82,6 @@ def basic_params_override() -> MutableMapping[str, Any]:
           'use_per_replica_batch_size': True,
           'image_size': 224,
       },
-      'test_dataset': {
-          'builder': 'synthetic',
-          'batch_size': 1,
-          'use_per_replica_batch_size': True,
-          'image_size': 224,
-      },
       'train': {
           'steps': 1,
           'epochs': 1,

official/vision/image_classification/configs/base_configs.py

@@ -216,7 +216,6 @@ class ExperimentConfig(base_config.Config):
   runtime: RuntimeConfig = None
   train_dataset: Any = None
   validation_dataset: Any = None
-  test_dataset: Any = None
   train: TrainConfig = None
   evaluation: EvalConfig = None
   model: ModelConfig = None

official/vision/image_classification/configs/configs.py

@@ -45,8 +45,6 @@ class EfficientNetImageNetConfig(base_configs.ExperimentConfig):
       dataset_factory.ImageNetConfig(split='train')
   validation_dataset: dataset_factory.DatasetConfig = \
       dataset_factory.ImageNetConfig(split='validation')
-  test_dataset: dataset_factory.DatasetConfig = \
-      dataset_factory.ImageNetConfig(split='validation')
   train: base_configs.TrainConfig = base_configs.TrainConfig(
       resume_checkpoint=True,
       epochs=500,
@@ -78,11 +76,6 @@ class ResNetImagenetConfig(base_configs.ExperimentConfig):
                                      one_hot=False,
                                      mean_subtract=True,
                                      standardize=True)
-  test_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,

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

 # Training configuration for ResNet trained on ImageNet on GPUs.
-# Takes ~3 minutes, 15 seconds per epoch for 8 V100s.
-# Reaches ~76.1% within 90 epochs.
+# Reaches > 76.1% within 90 epochs.
 # Note: This configuration uses a scaled per-replica batch size based on the number of devices.
 runtime:
   model_dir: null
@@ -10,7 +9,7 @@ runtime:
 train_dataset:
   name: 'imagenet2012'
   data_dir: null
-  builder: 'records'
+  builder: 'tfds'
   split: 'train'
   image_size: 224
   num_classes: 1000
@@ -23,7 +22,7 @@ train_dataset:
 validation_dataset:
   name: 'imagenet2012'
   data_dir: null
-  builder: 'records'
+  builder: 'tfds'
   split: 'validation'
   image_size: 224
   num_classes: 1000

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

 # Training configuration for ResNet trained on ImageNet on TPUs.
-# Takes ~2 minutes, 43 seconds per epoch for a v3-32.
-# Reaches ~76.1% within 90 epochs.
+# Takes ~4 minutes, 30 seconds seconds per epoch for a v3-32.
+# Reaches > 76.1% within 90 epochs.
 # Note: This configuration uses a scaled per-replica batch size based on the number of devices.
 runtime:
   model_dir: null
@@ -9,7 +9,7 @@ runtime:
 train_dataset:
   name: 'imagenet2012'
   data_dir: null
-  builder: 'records'
+  builder: 'tfds'
   split: 'train'
   one_hot: False
   image_size: 224
@@ -23,7 +23,7 @@ train_dataset:
 validation_dataset:
   name: 'imagenet2012'
   data_dir: null
-  builder: 'records'
+  builder: 'tfds'
   split: 'validation'
   one_hot: False
   image_size: 224