Internal change

PiperOrigin-RevId: 358194199

official/vision/beta/modeling/classification_model.py

@@ -36,6 +36,7 @@ class ClassificationModel(tf.keras.Model):
                use_sync_bn: bool = False,
                norm_momentum: float = 0.99,
                norm_epsilon: float = 0.001,
+               skip_logits_layer: bool = False,
                **kwargs):
     """Classification initialization function.
 
@@ -55,6 +56,7 @@ class ClassificationModel(tf.keras.Model):
       norm_momentum: `float` normalization momentum for the moving average.
       norm_epsilon: `float` small float added to variance to avoid dividing by
         zero.
+      skip_logits_layer: `bool`, whether to skip the prediction layer.
       **kwargs: keyword arguments to be passed.
     """
     self._self_setattr_tracking = False
@@ -88,12 +90,13 @@ class ClassificationModel(tf.keras.Model):
     if add_head_batch_norm:
       x = self._norm(axis=axis, momentum=norm_momentum, epsilon=norm_epsilon)(x)
     x = tf.keras.layers.GlobalAveragePooling2D()(x)
-    x = tf.keras.layers.Dropout(dropout_rate)(x)
-    x = tf.keras.layers.Dense(
-        num_classes, kernel_initializer=kernel_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer)(
-            x)
+    if not skip_logits_layer:
+      x = tf.keras.layers.Dropout(dropout_rate)(x)
+      x = tf.keras.layers.Dense(
+          num_classes, kernel_initializer=kernel_initializer,
+          kernel_regularizer=self._kernel_regularizer,
+          bias_regularizer=self._bias_regularizer)(
+              x)
 
     super(ClassificationModel, self).__init__(
         inputs=inputs, outputs=x, **kwargs)

official/vision/beta/modeling/factory.py

@@ -41,7 +41,8 @@ from official.vision.beta.modeling.layers import roi_sampler
 def build_classification_model(
     input_specs: tf.keras.layers.InputSpec,
     model_config: classification_cfg.ImageClassificationModel,
-    l2_regularizer: tf.keras.regularizers.Regularizer = None):
+    l2_regularizer: tf.keras.regularizers.Regularizer = None,
+    skip_logits_layer: bool = False):
   """Builds the classification model."""
   backbone = backbones.factory.build_backbone(
       input_specs=input_specs,
@@ -58,7 +59,8 @@ def build_classification_model(
       add_head_batch_norm=model_config.add_head_batch_norm,
       use_sync_bn=norm_activation_config.use_sync_bn,
       norm_momentum=norm_activation_config.norm_momentum,
-      norm_epsilon=norm_activation_config.norm_epsilon)
+      norm_epsilon=norm_activation_config.norm_epsilon,
+      skip_logits_layer=skip_logits_layer)
   return model
 
 

official/vision/beta/serving/export_base.py

@@ -53,7 +53,7 @@ class ExportModule(tf.Module, metaclass=abc.ABCMeta):
     self._model = model
 
   @abc.abstractmethod
-  def build_model(self):
+  def build_model(self, **kwargs):
     """Builds model and sets self._model."""
 
   @abc.abstractmethod

official/vision/beta/serving/export_tfhub.py

+# Lint as: python3
+# 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.
+# ==============================================================================
+"""A script to export the image classification as a TF-Hub SavedModel."""
+
+# Import libraries
+from absl import app
+from absl import flags
+
+import tensorflow as tf
+
+from official.common import registry_imports  # pylint: disable=unused-import
+from official.core import exp_factory
+from official.modeling import hyperparams
+from official.vision.beta.serving import image_classification
+
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string(
+    'experiment', None, 'experiment type, e.g. resnet_imagenet')
+flags.DEFINE_string(
+    'checkpoint_path', None, 'Checkpoint path.')
+flags.DEFINE_string(
+    'export_path', None, 'The export directory.')
+flags.DEFINE_multi_string(
+    'config_file',
+    None,
+    'A 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_image_size',
+    '224,224',
+    'The comma-separated string of two integers representing the height,width '
+    'of the input to the model.')
+flags.DEFINE_boolean(
+    'skip_logits_layer',
+    False,
+    'Whether to skip the prediction layer and only output the feature vector.')
+
+
+def export_model_to_tfhub(params,
+                          batch_size,
+                          input_image_size,
+                          skip_logits_layer,
+                          checkpoint_path,
+                          export_path):
+  """Export an image classification model to TF-Hub."""
+  export_module = image_classification.ClassificationModule(
+      params=params, batch_size=batch_size, input_image_size=input_image_size)
+
+  model = export_module.build_model(skip_logits_layer=skip_logits_layer)
+  checkpoint = tf.train.Checkpoint(model=model)
+  checkpoint.restore(checkpoint_path).assert_existing_objects_matched()
+  model.save(export_path, include_optimizer=False, save_format='tf')
+
+
+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_model_to_tfhub(
+      params=params,
+      batch_size=FLAGS.batch_size,
+      input_image_size=[int(x) for x in FLAGS.input_image_size.split(',')],
+      skip_logits_layer=FLAGS.skip_logits_layer,
+      checkpoint_path=FLAGS.checkpoint_path,
+      export_path=FLAGS.export_path)
+
+
+if __name__ == '__main__':
+  app.run(main)

official/vision/beta/serving/image_classification.py

@@ -29,14 +29,15 @@ STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
 class ClassificationModule(export_base.ExportModule):
   """classification Module."""
 
-  def build_model(self):
+  def build_model(self, skip_logits_layer=False):
     input_specs = tf.keras.layers.InputSpec(
         shape=[self._batch_size] + self._input_image_size + [3])
 
     self._model = factory.build_classification_model(
         input_specs=input_specs,
         model_config=self._params.task.model,
-        l2_regularizer=None)
+        l2_regularizer=None,
+        skip_logits_layer=skip_logits_layer)
 
     return self._model