Add support to export savedmodel and tflite for QAT models. Only add...

Add support to export savedmodel and tflite for QAT models. Only add classification and segmentation for now.

PiperOrigin-RevId: 457846906

official/projects/qat/vision/serving/export_module.py

+# Copyright 2022 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.
+
+"""Export modules for QAT model serving/inference."""
+
+import tensorflow as tf
+
+from official.projects.qat.vision.modeling import factory as qat_factory
+from official.vision.serving import image_classification
+from official.vision.serving import semantic_segmentation
+
+
+class ClassificationModule(image_classification.ClassificationModule):
+  """Classification Module."""
+
+  def _build_model(self):
+    model = super()._build_model()
+    input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] +
+                                            self._input_image_size + [3])
+    return qat_factory.build_qat_classification_model(
+        model, self.params.task.quantization, input_specs,
+        self.params.task.model)
+
+
+class SegmentationModule(semantic_segmentation.SegmentationModule):
+  """Segmentation Module."""
+
+  def _build_model(self):
+    model = super()._build_model()
+    input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] +
+                                            self._input_image_size + [3])
+    return qat_factory.build_qat_segmentation_model(
+        model, self.params.task.quantization, input_specs)

official/projects/qat/vision/serving/export_saved_model.py

+# Copyright 2022 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_SIZSE = 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.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_SIZSE.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_SIZSE.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)

official/projects/qat/vision/serving/export_tflite.py

+# Copyright 2022 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.
+
+"""Binary to convert a saved model to TFLite model for the QAT model."""
+
+from absl import app
+
+from official.projects.qat.vision import registry_imports  # pylint: disable=unused-import
+from official.vision.serving import export_tflite
+
+if __name__ == '__main__':
+  app.run(export_tflite.main)