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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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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.detr.configs import detr as exp_cfg  # pylint: disable=unused-import
from official.projects.detr.serving import export_module
from official.vision.serving import export_saved_model_lib

FLAGS = flags.FLAGS

_EXPERIMENT = flags.DEFINE_string('experiment', None,
                                  'experiment type, e.g. detr_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.')


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=False)
  if _PARAMS_OVERRIDE.value:
    params = hyperparams.override_params_dict(
        params, _PARAMS_OVERRIDE.value, is_strict=False)

  params.validate()
  params.lock()

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


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