keras_imagenet_main.py

# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Runs a ResNet model on the ImageNet dataset."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl import app as absl_app
from absl import flags
import tensorflow as tf  # pylint: disable=g-bad-import-order

from official.resnet import imagenet_main
from official.resnet.keras import keras_common
from official.resnet.keras import resnet_model
from official.resnet.keras import trivial_model
from official.utils.flags import core as flags_core
from official.utils.logs import logger
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
from official.utils.misc import model_helpers


LR_SCHEDULE = [    # (multiplier, epoch to start) tuples
    (1.0, 5), (0.1, 30), (0.01, 60), (0.001, 80)
]


def learning_rate_schedule(current_epoch,
                           current_batch,
                           batches_per_epoch,
                           batch_size):
  """Handles linear scaling rule, gradual warmup, and LR decay.

  Scale learning rate at epoch boundaries provided in LR_SCHEDULE by the
  provided scaling factor.

  Args:
    current_epoch: integer, current epoch indexed from 0.
    current_batch: integer, current batch in the current epoch, indexed from 0.
    batches_per_epoch: integer, number of steps in an epoch.
    batch_size: integer, total batch sized.

  Returns:
    Adjusted learning rate.
  """
  initial_lr = keras_common.BASE_LEARNING_RATE * batch_size / 256
  epoch = current_epoch + float(current_batch) / batches_per_epoch
  warmup_lr_multiplier, warmup_end_epoch = LR_SCHEDULE[0]
  if epoch < warmup_end_epoch:
    # Learning rate increases linearly per step.
    return initial_lr * warmup_lr_multiplier * epoch / warmup_end_epoch
  for mult, start_epoch in LR_SCHEDULE:
    if epoch >= start_epoch:
      learning_rate = initial_lr * mult
    else:
      break
  return learning_rate


def parse_record_keras(raw_record, is_training, dtype):
  """Adjust the shape of label."""
  image, label = imagenet_main.parse_record(raw_record, is_training, dtype)

  # Subtract one so that labels are in [0, 1000), and cast to float32 for
  # Keras model.
  label = tf.cast(tf.cast(tf.reshape(label, shape=[1]), dtype=tf.int32) - 1,
                  dtype=tf.float32)
  return image, label


def run(flags_obj):
  """Run ResNet ImageNet training and eval loop using native Keras APIs.

  Args:
    flags_obj: An object containing parsed flag values.

  Raises:
    ValueError: If fp16 is passed as it is not currently supported.

  Returns:
    Dictionary of training and eval stats.
  """
  keras_utils.set_session_config(
      enable_eager=flags_obj.enable_eager,
      enable_xla=flags_obj.enable_xla,
      enable_grappler_layout_optimizer=
      flags_obj.enable_grappler_layout_optimizer)

  # Execute flag override logic for better model performance
  if flags_obj.tf_gpu_thread_mode:
    keras_common.set_gpu_thread_mode_and_count(flags_obj)
  if flags_obj.data_delay_prefetch:
    keras_common.data_delay_prefetch()
  keras_common.set_cudnn_batchnorm_mode()

  dtype = flags_core.get_tf_dtype(flags_obj)
  if dtype == 'float16':
    policy = tf.keras.mixed_precision.experimental.Policy('infer_float32_vars')
    tf.keras.mixed_precision.experimental.set_policy(policy)

  data_format = flags_obj.data_format
  if data_format is None:
    data_format = ('channels_first'
                   if tf.test.is_built_with_cuda() else 'channels_last')
  tf.keras.backend.set_image_data_format(data_format)

  strategy = distribution_utils.get_distribution_strategy(
      distribution_strategy=flags_obj.distribution_strategy,
      num_gpus=flags_obj.num_gpus,
      num_workers=distribution_utils.configure_cluster(),
      all_reduce_alg=flags_obj.all_reduce_alg,
      num_packs=flags_obj.num_packs)

  if strategy:
    # flags_obj.enable_get_next_as_optional controls whether enabling
    # get_next_as_optional behavior in DistributedIterator. If true, last
    # partial batch can be supported.
    strategy.extended.experimental_enable_get_next_as_optional = (
        flags_obj.enable_get_next_as_optional
    )

  strategy_scope = distribution_utils.get_strategy_scope(strategy)

  # pylint: disable=protected-access
  if flags_obj.use_synthetic_data:
    distribution_utils.set_up_synthetic_data()
    input_fn = keras_common.get_synth_input_fn(
        height=imagenet_main.DEFAULT_IMAGE_SIZE,
        width=imagenet_main.DEFAULT_IMAGE_SIZE,
        num_channels=imagenet_main.NUM_CHANNELS,
        num_classes=imagenet_main.NUM_CLASSES,
        dtype=dtype,
        drop_remainder=True)
  else:
    distribution_utils.undo_set_up_synthetic_data()
    input_fn = imagenet_main.input_fn

  # When `enable_xla` is True, we always drop the remainder of the batches
  # in the dataset, as XLA-GPU doesn't support dynamic shapes.
  drop_remainder = flags_obj.enable_xla

  train_input_dataset = input_fn(
      is_training=True,
      data_dir=flags_obj.data_dir,
      batch_size=flags_obj.batch_size,
      num_epochs=flags_obj.train_epochs,
      parse_record_fn=parse_record_keras,
      datasets_num_private_threads=flags_obj.datasets_num_private_threads,
      dtype=dtype,
      drop_remainder=drop_remainder,
      tf_data_experimental_slack=flags_obj.tf_data_experimental_slack,
  )

  eval_input_dataset = None
  if not flags_obj.skip_eval:
    eval_input_dataset = input_fn(
        is_training=False,
        data_dir=flags_obj.data_dir,
        batch_size=flags_obj.batch_size,
        num_epochs=flags_obj.train_epochs,
        parse_record_fn=parse_record_keras,
        dtype=dtype,
        drop_remainder=drop_remainder)

  lr_schedule = 0.1
  if flags_obj.use_tensor_lr:
    lr_schedule = keras_common.PiecewiseConstantDecayWithWarmup(
        batch_size=flags_obj.batch_size,
        epoch_size=imagenet_main.NUM_IMAGES['train'],
        warmup_epochs=LR_SCHEDULE[0][1],
        boundaries=list(p[1] for p in LR_SCHEDULE[1:]),
        multipliers=list(p[0] for p in LR_SCHEDULE),
        compute_lr_on_cpu=True)

  with strategy_scope:
    optimizer = keras_common.get_optimizer(lr_schedule)
    if dtype == 'float16':
      # TODO(reedwm): Remove manually wrapping optimizer once mixed precision
      # can be enabled with a single line of code.
      optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
          optimizer, loss_scale=flags_core.get_loss_scale(flags_obj,
                                                          default_for_fp16=128))

    if flags_obj.use_trivial_model:
      model = trivial_model.trivial_model(imagenet_main.NUM_CLASSES, dtype)
    else:
      model = resnet_model.resnet50(
          num_classes=imagenet_main.NUM_CLASSES,
          dtype=dtype)

    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=(['sparse_categorical_accuracy']
                           if flags_obj.report_accuracy_metrics else None),
                  run_eagerly=flags_obj.run_eagerly,
                  cloning=flags_obj.clone_model_in_keras_dist_strat)

  callbacks = keras_common.get_callbacks(
      learning_rate_schedule, imagenet_main.NUM_IMAGES['train'])

  train_steps = imagenet_main.NUM_IMAGES['train'] // flags_obj.batch_size
  train_epochs = flags_obj.train_epochs

  if flags_obj.train_steps:
    train_steps = min(flags_obj.train_steps, train_steps)
    train_epochs = 1

  num_eval_steps = (imagenet_main.NUM_IMAGES['validation'] //
                    flags_obj.batch_size)

  validation_data = eval_input_dataset
  if flags_obj.skip_eval:
    # Only build the training graph. This reduces memory usage introduced by
    # control flow ops in layers that have different implementations for
    # training and inference (e.g., batch norm).
    tf.keras.backend.set_learning_phase(1)
    num_eval_steps = None
    validation_data = None

  history = model.fit(train_input_dataset,
                      epochs=train_epochs,
                      steps_per_epoch=train_steps,
                      callbacks=callbacks,
                      validation_steps=num_eval_steps,
                      validation_data=validation_data,
                      validation_freq=flags_obj.epochs_between_evals,
                      verbose=2)

  eval_output = None
  if not flags_obj.skip_eval:
    eval_output = model.evaluate(eval_input_dataset,
                                 steps=num_eval_steps,
                                 verbose=2)
  stats = keras_common.build_stats(history, eval_output, callbacks)
  return stats


def define_imagenet_keras_flags():
  imagenet_main.define_imagenet_flags(dynamic_loss_scale=True, enable_xla=True)
  keras_common.define_keras_flags()


def main(_):
  model_helpers.apply_clean(flags.FLAGS)
  with logger.benchmark_context(flags.FLAGS):
    return run(flags.FLAGS)


if __name__ == '__main__':
  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
  define_imagenet_keras_flags()
  absl_app.run(main)