tensorflow/models

# Copyright 2024 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.

r"""Vision models export binary for serving/inference.

To export a trained checkpoint in saved_model format (shell script):

EXPERIMENT_TYPE = XX
CHECKPOINT_PATH = XX
EXPORT_DIR_PATH = XX
export_saved_model --experiment=${EXPERIMENT_TYPE} \
                   --export_dir=${EXPORT_DIR_PATH}/ \
                   --checkpoint_path=${CHECKPOINT_PATH} \
                   --batch_size=2 \
                   --input_image_size=224,224

To serve (python):

export_dir_path = XX
input_type = XX
input_images = XX
imported = tf.saved_model.load(export_dir_path)
model_fn = imported.signatures['serving_default']
output = model_fn(input_images)
"""
from absl import app
from absl import flags

from official.core import exp_factory
from official.modeling import hyperparams
from official.projects.qat.vision import registry_imports  # pylint: disable=unused-import
from official.projects.qat.vision.serving import export_module
from official.vision import configs
from official.vision.serving import export_saved_model_lib

FLAGS = flags.FLAGS

_EXPERIMENT = flags.DEFINE_string(
    'experiment', None, 'experiment type, e.g. retinanet_resnetfpn_coco')
_EXPORT_DIR = flags.DEFINE_string('export_dir', None, 'The export directory.')
_CHECKPOINT_PATH = flags.DEFINE_string('checkpoint_path', None,
                                       'Checkpoint path.')
_CONFIG_FILE = flags.DEFINE_multi_string(
    'config_file',
    default=None,
    help='YAML/JSON files which specifies overrides. The override order '
    'follows the order of args. Note that each file '
    'can be used as an override template to override the default parameters '
    'specified in Python. If the same parameter is specified in both '
    '`--config_file` and `--params_override`, `config_file` will be used '
    'first, followed by params_override.')
_PARAMS_OVERRIDE = flags.DEFINE_string(
    'params_override', '',
    'The JSON/YAML file or string which specifies the parameter to be overriden'
    ' on top of `config_file` template.')
_BATCH_SIZE = flags.DEFINE_integer('batch_size', None, 'The batch size.')
_IMAGE_TYPE = flags.DEFINE_string(
    'input_type', 'image_tensor',
    'One of `image_tensor`, `image_bytes`, `tf_example` and `tflite`.')
_INPUT_IMAGE_SIZE = flags.DEFINE_string(
    'input_image_size', '224,224',
    'The comma-separated string of two integers representing the height,width '
    'of the input to the model.')
_EXPORT_CHECKPOINT_SUBDIR = flags.DEFINE_string(
    'export_checkpoint_subdir', 'checkpoint',
    'The subdirectory for checkpoints.')
_EXPORT_SAVED_MODEL_SUBDIR = flags.DEFINE_string(
    'export_saved_model_subdir', 'saved_model',
    'The subdirectory for saved model.')
_LOG_MODEL_FLOPS_AND_PARAMS = flags.DEFINE_bool(
    'log_model_flops_and_params', False,
    'If true, logs model flops and parameters.')
_INPUT_NAME = flags.DEFINE_string(
    'input_name', None,
    'Input tensor name in signature def. Default at None which'
    'produces input tensor name `inputs`.')


def main(_):

  params = exp_factory.get_exp_config(_EXPERIMENT.value)
  for config_file in _CONFIG_FILE.value or []:
    params = hyperparams.override_params_dict(
        params, config_file, is_strict=True)
  if _PARAMS_OVERRIDE.value:
    params = hyperparams.override_params_dict(
        params, _PARAMS_OVERRIDE.value, is_strict=True)

  params.validate()
  params.lock()

  input_image_size = [int(x) for x in _INPUT_IMAGE_SIZE.value.split(',')]

  if isinstance(params.task,
                configs.image_classification.ImageClassificationTask):
    export_module_cls = export_module.ClassificationModule
  elif isinstance(params.task, configs.retinanet.RetinaNetTask):
    export_module_cls = export_module.DetectionModule
  elif isinstance(params.task,
                  configs.semantic_segmentation.SemanticSegmentationTask):
    export_module_cls = export_module.SegmentationModule
  else:
    raise TypeError(f'Export module for {type(params.task)} is not supported.')

  module = export_module_cls(
      params=params,
      batch_size=_BATCH_SIZE.value,
      input_image_size=input_image_size,
      input_type=_IMAGE_TYPE.value,
      num_channels=3)

  export_saved_model_lib.export_inference_graph(
      input_type=_IMAGE_TYPE.value,
      batch_size=_BATCH_SIZE.value,
      input_image_size=input_image_size,
      params=params,
      checkpoint_path=_CHECKPOINT_PATH.value,
      export_dir=_EXPORT_DIR.value,
      export_checkpoint_subdir=_EXPORT_CHECKPOINT_SUBDIR.value,
      export_saved_model_subdir=_EXPORT_SAVED_MODEL_SUBDIR.value,
      export_module=module,
      log_model_flops_and_params=_LOG_MODEL_FLOPS_AND_PARAMS.value,
      input_name=_INPUT_NAME.value)


if __name__ == '__main__':
  app.run(main)