Merge pull request #9971 from anshkumar:master

PiperOrigin-RevId: 373398932

official/vision/beta/projects/deepmac_maskrcnn/serving/detection.py

+# Copyright 2021 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.
+
+"""Detection input and model functions for serving/inference."""
+
+import tensorflow as tf
+
+from official.vision.beta.projects.deepmac_maskrcnn.configs import deep_mask_head_rcnn as cfg
+from official.vision.beta.projects.deepmac_maskrcnn.tasks import deep_mask_head_rcnn
+from official.vision.beta.serving import detection
+
+
+class DetectionModule(detection.DetectionModule):
+  """Detection Module."""
+
+  def _build_model(self):
+
+    if self._batch_size is None:
+      ValueError("batch_size can't be None for detection models")
+    if not self.params.task.model.detection_generator.use_batched_nms:
+      ValueError('Only batched_nms is supported.')
+    input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] +
+                                            self._input_image_size + [3])
+
+    if isinstance(self.params.task.model, cfg.DeepMaskHeadRCNN):
+      model = deep_mask_head_rcnn.build_maskrcnn(
+          input_specs=input_specs, model_config=self.params.task.model)
+    else:
+      raise ValueError('Detection module not implemented for {} model.'.format(
+          type(self.params.task.model)))
+
+    return model

official/vision/beta/projects/deepmac_maskrcnn/serving/detection_test.py

+# Copyright 2021 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.
+
+# Lint as: python3
+"""Test for image detection export lib."""
+
+import io
+import os
+
+from absl.testing import parameterized
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+from official.core import exp_factory
+from official.vision.beta.projects.deepmac_maskrcnn.serving import detection
+
+
+class DetectionExportTest(tf.test.TestCase, parameterized.TestCase):
+
+  def _get_detection_module(self, experiment_name):
+    params = exp_factory.get_exp_config(experiment_name)
+    params.task.model.backbone.resnet.model_id = 18
+    params.task.model.detection_generator.use_batched_nms = True
+    detection_module = detection.DetectionModule(
+        params, batch_size=1, input_image_size=[640, 640])
+    return detection_module
+
+  def _export_from_module(self, module, input_type, save_directory):
+    signatures = module.get_inference_signatures(
+        {input_type: 'serving_default'})
+    tf.saved_model.save(module, save_directory, signatures=signatures)
+
+  def _get_dummy_input(self, input_type, batch_size, image_size):
+    """Get dummy input for the given input type."""
+    h, w = image_size
+
+    if input_type == 'image_tensor':
+      return tf.zeros((batch_size, h, w, 3), dtype=np.uint8)
+    elif input_type == 'image_bytes':
+      image = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8))
+      byte_io = io.BytesIO()
+      image.save(byte_io, 'PNG')
+      return [byte_io.getvalue() for b in range(batch_size)]
+    elif input_type == 'tf_example':
+      image_tensor = tf.zeros((h, w, 3), dtype=tf.uint8)
+      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).numpy()
+      example = tf.train.Example(
+          features=tf.train.Features(
+              feature={
+                  'image/encoded':
+                      tf.train.Feature(
+                          bytes_list=tf.train.BytesList(value=[encoded_jpeg])),
+              })).SerializeToString()
+      return [example for b in range(batch_size)]
+
+  @parameterized.parameters(
+      ('image_tensor', 'deep_mask_head_rcnn_resnetfpn_coco', [640, 640]),
+      ('image_bytes', 'deep_mask_head_rcnn_resnetfpn_coco', [640, 384]),
+      ('tf_example', 'deep_mask_head_rcnn_resnetfpn_coco', [640, 640]),
+  )
+  def test_export(self, input_type, experiment_name, image_size):
+    tmp_dir = self.get_temp_dir()
+    module = self._get_detection_module(experiment_name)
+
+    self._export_from_module(module, input_type, tmp_dir)
+
+    self.assertTrue(os.path.exists(os.path.join(tmp_dir, 'saved_model.pb')))
+    self.assertTrue(
+        os.path.exists(os.path.join(tmp_dir, 'variables', 'variables.index')))
+    self.assertTrue(
+        os.path.exists(
+            os.path.join(tmp_dir, 'variables',
+                         'variables.data-00000-of-00001')))
+
+    imported = tf.saved_model.load(tmp_dir)
+    detection_fn = imported.signatures['serving_default']
+
+    images = self._get_dummy_input(
+        input_type, batch_size=1, image_size=image_size)
+
+    processed_images, anchor_boxes, image_info = module._build_inputs(
+        tf.zeros((224, 224, 3), dtype=tf.uint8))
+    image_shape = image_info[1, :]
+    image_shape = tf.expand_dims(image_shape, 0)
+    processed_images = tf.expand_dims(processed_images, 0)
+    for l, l_boxes in anchor_boxes.items():
+      anchor_boxes[l] = tf.expand_dims(l_boxes, 0)
+
+    expected_outputs = module.model(
+        images=processed_images,
+        image_shape=image_shape,
+        anchor_boxes=anchor_boxes,
+        training=False)
+    outputs = detection_fn(tf.constant(images))
+
+    self.assertAllClose(outputs['num_detections'].numpy(),
+                        expected_outputs['num_detections'].numpy())
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/vision/beta/projects/deepmac_maskrcnn/serving/export_saved_model.py

+# Copyright 2021 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"""Deepmac model export binary for serving/inference.
+
+To export a trained checkpoint in saved_model format (shell script):
+
+CHECKPOINT_PATH = XX
+EXPORT_DIR_PATH = XX
+CONFIG_FILE_PATH = XX
+export_saved_model --export_dir=${EXPORT_DIR_PATH}/ \
+                   --checkpoint_path=${CHECKPOINT_PATH} \
+                   --config_file=${CONFIG_FILE_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.vision.beta.projects.deepmac_maskrcnn.serving import detection
+from official.vision.beta.projects.deepmac_maskrcnn.tasks import deep_mask_head_rcnn  # pylint: disable=unused-import
+from official.vision.beta.serving import export_saved_model_lib
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('experiment', 'deep_mask_head_rcnn_resnetfpn_coco',
+                    'experiment type, e.g. retinanet_resnetfpn_coco')
+flags.DEFINE_string('export_dir', None, 'The export directory.')
+flags.DEFINE_string('checkpoint_path', None, 'Checkpoint path.')
+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.')
+flags.DEFINE_string(
+    'params_override', '',
+    'The JSON/YAML file or string which specifies the parameter to be overriden'
+    ' on top of `config_file` template.')
+flags.DEFINE_integer('batch_size', None, 'The batch size.')
+flags.DEFINE_string('input_type', 'image_tensor',
+                    'One of `image_tensor`, `image_bytes`, `tf_example`.')
+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(FLAGS.experiment)
+  for config_file in FLAGS.config_file or []:
+    params = hyperparams.override_params_dict(
+        params, config_file, is_strict=True)
+  if FLAGS.params_override:
+    params = hyperparams.override_params_dict(
+        params, FLAGS.params_override, is_strict=True)
+
+  params.validate()
+  params.lock()
+
+  export_module = detection.DetectionModule(
+      params=params,
+      batch_size=FLAGS.batch_size,
+      input_image_size=[int(x) for x in FLAGS.input_image_size.split(',')],
+      num_channels=3)
+
+  export_saved_model_lib.export_inference_graph(
+      input_type=FLAGS.input_type,
+      batch_size=FLAGS.batch_size,
+      input_image_size=[int(x) for x in FLAGS.input_image_size.split(',')],
+      params=params,
+      checkpoint_path=FLAGS.checkpoint_path,
+      export_dir=FLAGS.export_dir,
+      export_module=export_module,
+      export_checkpoint_subdir='checkpoint',
+      export_saved_model_subdir='saved_model')
+
+
+if __name__ == '__main__':
+  app.run(main)