Release MobileDet code and model, and require tf_slim installation for OD API. (#8562)

* Merged commit includes the following changes:
311933687  by Sergio Guadarrama:

    Removes spurios use of tf.compat.v2, which results in spurious tf.compat.v1.compat.v2. Adds basic test to nasnet_utils.
    Replaces all remaining import tensorflow as tf with import tensorflow.compat.v1 as tf

--
311766063  by Sergio Guadarrama:

    Removes explicit tf.compat.v1 in all call sites (we already import tf.compat.v1, so this code was  doing tf.compat.v1.compat.v1). The existing code worked in latest version of tensorflow, 2.2, (and 1.15) but not in 1.14 or in 2.0.0a, this CL fixes it.

--
311624958  by Sergio Guadarrama:

    Updates README that doesn't render properly in github documentation

--
310980959  by Sergio Guadarrama:

    Moves research_models/slim off tf.contrib.slim/layers/framework to tf_slim

--
310263156  by Sergio Guadarrama:

    Adds model breakdown for MobilenetV3

--
308640...

research/object_detection/anchor_generators/multiscale_grid_anchor_generator.py

@@ -20,7 +20,7 @@ described in:
 T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.anchor_generators import grid_anchor_generator
 from object_detection.core import anchor_generator

research/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py

@@ -15,7 +15,7 @@
 
 """Tests for anchor_generators.multiscale_grid_anchor_generator_test.py."""
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.anchor_generators import multiscale_grid_anchor_generator as mg
 from object_detection.utils import test_case

research/object_detection/box_coders/faster_rcnn_box_coder.py

@@ -28,7 +28,7 @@ Faster RCNN box coder follows the coding schema described below:
   See http://arxiv.org/abs/1506.01497 for details.
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.core import box_coder
 from object_detection.core import box_list

research/object_detection/box_coders/faster_rcnn_box_coder_test.py

@@ -15,7 +15,7 @@
 
 """Tests for object_detection.box_coder.faster_rcnn_box_coder."""
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.box_coders import faster_rcnn_box_coder
 from object_detection.core import box_list

research/object_detection/box_coders/keypoint_box_coder.py

@@ -35,7 +35,7 @@ to box coordinates):
   anchor-encoded keypoint coordinates.
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.core import box_coder
 from object_detection.core import box_list

research/object_detection/box_coders/keypoint_box_coder_test.py

@@ -15,7 +15,7 @@
 
 """Tests for object_detection.box_coder.keypoint_box_coder."""
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.box_coders import keypoint_box_coder
 from object_detection.core import box_list

research/object_detection/box_coders/mean_stddev_box_coder_test.py

@@ -15,7 +15,7 @@
 
 """Tests for object_detection.box_coder.mean_stddev_boxcoder."""
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.box_coders import mean_stddev_box_coder
 from object_detection.core import box_list

research/object_detection/box_coders/square_box_coder.py

@@ -32,7 +32,7 @@ coder when the objects being detected tend to be square (e.g. faces) and when
 the input images are not distorted via resizing.
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.core import box_coder
 from object_detection.core import box_list

research/object_detection/box_coders/square_box_coder_test.py

@@ -15,7 +15,7 @@
 
 """Tests for object_detection.box_coder.square_box_coder."""
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.box_coders import square_box_coder
 from object_detection.core import box_list

research/object_detection/builders/anchor_generator_builder_test.py

@@ -24,7 +24,7 @@ import math
 
 from six.moves import range
 from six.moves import zip
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from google.protobuf import text_format
 from object_detection.anchor_generators import flexible_grid_anchor_generator

research/object_detection/builders/box_coder_builder_test.py

@@ -15,7 +15,7 @@
 
 """Tests for box_coder_builder."""
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from google.protobuf import text_format
 from object_detection.box_coders import faster_rcnn_box_coder

research/object_detection/builders/box_predictor_builder.py

@@ -16,7 +16,7 @@
 """Function to build box predictor from configuration."""
 
 import collections
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from object_detection.predictors import convolutional_box_predictor
 from object_detection.predictors import convolutional_keras_box_predictor
 from object_detection.predictors import mask_rcnn_box_predictor

research/object_detection/builders/box_predictor_builder_test.py

@@ -17,7 +17,7 @@
 """Tests for box_predictor_builder."""
 
 import mock
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from google.protobuf import text_format
 from object_detection.builders import box_predictor_builder

research/object_detection/builders/calibration_builder.py

@@ -15,7 +15,7 @@
 
 """Tensorflow ops to calibrate class predictions and background class."""
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from object_detection.utils import shape_utils
 
 

research/object_detection/builders/calibration_builder_test.py

@@ -22,7 +22,7 @@ from __future__ import print_function
 import numpy as np
 from scipy import interpolate
 from six.moves import zip
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from object_detection.builders import calibration_builder
 from object_detection.protos import calibration_pb2
 

research/object_detection/builders/dataset_builder.py

@@ -27,7 +27,7 @@ from __future__ import division
 from __future__ import print_function
 
 import functools
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from tensorflow.contrib import data as tf_data
 from object_detection.builders import decoder_builder
@@ -118,7 +118,7 @@ def shard_function_for_context(input_context):
 
 
 def build(input_reader_config, batch_size=None, transform_input_data_fn=None,
-          input_context=None):
+          input_context=None, reduce_to_frame_fn=None):
   """Builds a tf.data.Dataset.
 
   Builds a tf.data.Dataset by applying the `transform_input_data_fn` on all
@@ -132,6 +132,8 @@ def build(input_reader_config, batch_size=None, transform_input_data_fn=None,
     input_context: optional, A tf.distribute.InputContext object used to
       shard filenames and compute per-replica batch_size when this function
       is being called per-replica.
+    reduce_to_frame_fn: Function that extracts frames from tf.SequenceExample
+      type input data.
 
   Returns:
     A tf.data.Dataset based on the input_reader_config.
@@ -151,18 +153,9 @@ def build(input_reader_config, batch_size=None, transform_input_data_fn=None,
     if not config.input_path:
       raise ValueError('At least one input path must be specified in '
                        '`input_reader_config`.')
-
-    def process_fn(value):
-      """Sets up tf graph that decodes, transforms and pads input data."""
-      processed_tensors = decoder.decode(value)
-      if transform_input_data_fn is not None:
-        processed_tensors = transform_input_data_fn(processed_tensors)
-      return processed_tensors
-
     shard_fn = shard_function_for_context(input_context)
     if input_context is not None:
       batch_size = input_context.get_per_replica_batch_size(batch_size)
-
     dataset = read_dataset(
         functools.partial(tf.data.TFRecordDataset, buffer_size=8 * 1000 * 1000),
         config.input_path[:], input_reader_config, filename_shard_fn=shard_fn)
@@ -170,16 +163,12 @@ def build(input_reader_config, batch_size=None, transform_input_data_fn=None,
       dataset = dataset.shard(input_reader_config.sample_1_of_n_examples, 0)
     # TODO(rathodv): make batch size a required argument once the old binaries
     # are deleted.
-    if batch_size:
-      num_parallel_calls = batch_size * input_reader_config.num_parallel_batches
-    else:
-      num_parallel_calls = input_reader_config.num_parallel_map_calls
-    # TODO(b/123952794): Migrate to V2 function.
-    if hasattr(dataset, 'map_with_legacy_function'):
-      data_map_fn = dataset.map_with_legacy_function
-    else:
-      data_map_fn = dataset.map
-    dataset = data_map_fn(process_fn, num_parallel_calls=num_parallel_calls)
+    dataset = dataset.map(decoder.decode, tf.data.experimental.AUTOTUNE)
+    if reduce_to_frame_fn:
+      dataset = reduce_to_frame_fn(dataset)
+    if transform_input_data_fn is not None:
+      dataset = dataset.map(transform_input_data_fn,
+                            tf.data.experimental.AUTOTUNE)
     if batch_size:
       dataset = dataset.apply(
           tf_data.batch_and_drop_remainder(batch_size))

research/object_detection/builders/dataset_builder_test.py

@@ -22,17 +22,17 @@ from __future__ import print_function
 import os
 import numpy as np
 from six.moves import range
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from google.protobuf import text_format
 
 from object_detection.builders import dataset_builder
 from object_detection.core import standard_fields as fields
+from object_detection.dataset_tools import seq_example_util
 from object_detection.protos import input_reader_pb2
 from object_detection.utils import dataset_util
 from object_detection.utils import test_case
 
-
 # pylint: disable=g-import-not-at-top
 try:
   from tensorflow.contrib import lookup as contrib_lookup
@@ -43,15 +43,17 @@ except ImportError:
 
 
 def get_iterator_next_for_testing(dataset, is_tf2):
+  iterator = dataset.make_initializable_iterator()
+  if not is_tf2:
+    tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer)
+  return iterator.get_next()
+
 
-  # In TF2, lookup tables are not supported in one shot iterators, but
-  # initialization is implicit.
-  if is_tf2:
-    return dataset.make_initializable_iterator().get_next()
-  # In TF1, we use one shot iterator because it does not require running
-  # a separate init op.
-  else:
-    return dataset.make_one_shot_iterator().get_next()
+def _get_labelmap_path():
+  """Returns an absolute path to label map file."""
+  parent_path = os.path.dirname(tf.resource_loader.get_data_files_path())
+  return os.path.join(parent_path, 'data',
+                      'pet_label_map.pbtxt')
 
 
 class DatasetBuilderTest(test_case.TestCase):
@@ -111,6 +113,57 @@ class DatasetBuilderTest(test_case.TestCase):
 
     return os.path.join(self.get_temp_dir(), '?????.tfrecord')
 
+  def _make_random_serialized_jpeg_images(self, num_frames, image_height,
+                                          image_width):
+    def graph_fn():
+      images = tf.cast(tf.random.uniform(
+          [num_frames, image_height, image_width, 3],
+          maxval=256,
+          dtype=tf.int32), dtype=tf.uint8)
+      images_list = tf.unstack(images, axis=0)
+      encoded_images_list = [tf.io.encode_jpeg(image) for image in images_list]
+      return encoded_images_list
+
+    encoded_images = self.execute(graph_fn, [])
+    return encoded_images
+
+  def create_tf_record_sequence_example(self):
+    path = os.path.join(self.get_temp_dir(), 'seq_tfrecord')
+    writer = tf.python_io.TFRecordWriter(path)
+
+    num_frames = 4
+    image_height = 4
+    image_width = 5
+    image_source_ids = [str(i) for i in range(num_frames)]
+    with self.test_session():
+      encoded_images = self._make_random_serialized_jpeg_images(
+          num_frames, image_height, image_width)
+      sequence_example_serialized = seq_example_util.make_sequence_example(
+          dataset_name='video_dataset',
+          video_id='video',
+          encoded_images=encoded_images,
+          image_height=image_height,
+          image_width=image_width,
+          image_source_ids=image_source_ids,
+          image_format='JPEG',
+          is_annotated=[[1], [1], [1], [1]],
+          bboxes=[
+              [[]],  # Frame 0.
+              [[0., 0., 1., 1.]],  # Frame 1.
+              [[0., 0., 1., 1.],
+               [0.1, 0.1, 0.2, 0.2]],  # Frame 2.
+              [[]],  # Frame 3.
+          ],
+          label_strings=[
+              [],  # Frame 0.
+              ['Abyssinian'],  # Frame 1.
+              ['Abyssinian', 'american_bulldog'],  # Frame 2.
+              [],  # Frame 3
+          ]).SerializeToString()
+      writer.write(sequence_example_serialized)
+      writer.close()
+    return path
+
   def test_build_tf_record_input_reader(self):
     tf_record_path = self.create_tf_record()
 
@@ -143,6 +196,71 @@ class DatasetBuilderTest(test_case.TestCase):
         [0.0, 0.0, 1.0, 1.0],
         output_dict[fields.InputDataFields.groundtruth_boxes][0][0])
 
+  def get_mock_reduce_to_frame_fn(self):
+    def mock_reduce_to_frame_fn(dataset):
+      def get_frame(tensor_dict):
+        out_tensor_dict = {}
+        out_tensor_dict[fields.InputDataFields.source_id] = (
+            tensor_dict[fields.InputDataFields.source_id][0])
+        return out_tensor_dict
+      return dataset.map(get_frame, tf.data.experimental.AUTOTUNE)
+    return mock_reduce_to_frame_fn
+
+  def test_build_tf_record_input_reader_sequence_example_train(self):
+    tf_record_path = self.create_tf_record_sequence_example()
+    label_map_path = _get_labelmap_path()
+    input_type = 'TF_SEQUENCE_EXAMPLE'
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      input_type: {1}
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path, input_type)
+    input_reader_proto = input_reader_pb2.InputReader()
+    input_reader_proto.label_map_path = label_map_path
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    reduce_to_frame_fn = self.get_mock_reduce_to_frame_fn()
+
+    def graph_fn():
+      return get_iterator_next_for_testing(
+          dataset_builder.build(input_reader_proto, batch_size=1,
+                                reduce_to_frame_fn=reduce_to_frame_fn),
+          self.is_tf2())
+
+    output_dict = self.execute(graph_fn, [])
+
+    self.assertEqual((1,),
+                     output_dict[fields.InputDataFields.source_id].shape)
+
+  def test_build_tf_record_input_reader_sequence_example_test(self):
+    tf_record_path = self.create_tf_record_sequence_example()
+    input_type = 'TF_SEQUENCE_EXAMPLE'
+    label_map_path = _get_labelmap_path()
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      input_type: {1}
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path, input_type)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+    input_reader_proto.label_map_path = label_map_path
+    reduce_to_frame_fn = self.get_mock_reduce_to_frame_fn()
+    def graph_fn():
+      return get_iterator_next_for_testing(
+          dataset_builder.build(input_reader_proto, batch_size=1,
+                                reduce_to_frame_fn=reduce_to_frame_fn),
+          self.is_tf2())
+
+    output_dict = self.execute(graph_fn, [])
+
+    self.assertEqual((1,),
+                     output_dict[fields.InputDataFields.source_id].shape)
+
   def test_build_tf_record_input_reader_and_load_instance_masks(self):
     tf_record_path = self.create_tf_record()
 

research/object_detection/builders/decoder_builder.py

@@ -23,6 +23,7 @@ from __future__ import division
 from __future__ import print_function
 
 from object_detection.data_decoders import tf_example_decoder
+from object_detection.data_decoders import tf_sequence_example_decoder
 from object_detection.protos import input_reader_pb2
 
 
@@ -46,16 +47,24 @@ def build(input_reader_config):
     label_map_proto_file = None
     if input_reader_config.HasField('label_map_path'):
       label_map_proto_file = input_reader_config.label_map_path
-    decoder = tf_example_decoder.TfExampleDecoder(
-        load_instance_masks=input_reader_config.load_instance_masks,
-        load_multiclass_scores=input_reader_config.load_multiclass_scores,
-        load_context_features=input_reader_config.load_context_features,
-        instance_mask_type=input_reader_config.mask_type,
-        label_map_proto_file=label_map_proto_file,
-        use_display_name=input_reader_config.use_display_name,
-        num_additional_channels=input_reader_config.num_additional_channels,
-        num_keypoints=input_reader_config.num_keypoints)
-
-    return decoder
+    input_type = input_reader_config.input_type
+    if input_type == input_reader_pb2.InputType.TF_EXAMPLE:
+      decoder = tf_example_decoder.TfExampleDecoder(
+          load_instance_masks=input_reader_config.load_instance_masks,
+          load_multiclass_scores=input_reader_config.load_multiclass_scores,
+          load_context_features=input_reader_config.load_context_features,
+          instance_mask_type=input_reader_config.mask_type,
+          label_map_proto_file=label_map_proto_file,
+          use_display_name=input_reader_config.use_display_name,
+          num_additional_channels=input_reader_config.num_additional_channels,
+          num_keypoints=input_reader_config.num_keypoints,
+          expand_hierarchy_labels=input_reader_config.expand_labels_hierarchy)
+      return decoder
+    elif input_type == input_reader_pb2.InputType.TF_SEQUENCE_EXAMPLE:
+      decoder = tf_sequence_example_decoder.TfSequenceExampleDecoder(
+          label_map_proto_file=label_map_proto_file,
+          load_context_features=input_reader_config.load_context_features)
+      return decoder
+    raise ValueError('Unsupported input_type in config.')
 
   raise ValueError('Unsupported input_reader_config.')

research/object_detection/builders/decoder_builder_test.py

@@ -19,16 +19,25 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from google.protobuf import text_format
 from object_detection.builders import decoder_builder
 from object_detection.core import standard_fields as fields
+from object_detection.dataset_tools import seq_example_util
 from object_detection.protos import input_reader_pb2
 from object_detection.utils import dataset_util
 
 
+def _get_labelmap_path():
+  """Returns an absolute path to label map file."""
+  parent_path = os.path.dirname(tf.resource_loader.get_data_files_path())
+  return os.path.join(parent_path, 'data',
+                      'pet_label_map.pbtxt')
+
+
 class DecoderBuilderTest(tf.test.TestCase):
 
   def _make_serialized_tf_example(self, has_additional_channels=False):
@@ -60,6 +69,50 @@ class DecoderBuilderTest(tf.test.TestCase):
     example = tf.train.Example(features=tf.train.Features(feature=features))
     return example.SerializeToString()
 
+  def _make_random_serialized_jpeg_images(self, num_frames, image_height,
+                                          image_width):
+    images = tf.cast(tf.random.uniform(
+        [num_frames, image_height, image_width, 3],
+        maxval=256,
+        dtype=tf.int32), dtype=tf.uint8)
+    images_list = tf.unstack(images, axis=0)
+    encoded_images_list = [tf.io.encode_jpeg(image) for image in images_list]
+    with tf.Session() as sess:
+      encoded_images = sess.run(encoded_images_list)
+    return encoded_images
+
+  def _make_serialized_tf_sequence_example(self):
+    num_frames = 4
+    image_height = 20
+    image_width = 30
+    image_source_ids = [str(i) for i in range(num_frames)]
+    with self.test_session():
+      encoded_images = self._make_random_serialized_jpeg_images(
+          num_frames, image_height, image_width)
+      sequence_example_serialized = seq_example_util.make_sequence_example(
+          dataset_name='video_dataset',
+          video_id='video',
+          encoded_images=encoded_images,
+          image_height=image_height,
+          image_width=image_width,
+          image_source_ids=image_source_ids,
+          image_format='JPEG',
+          is_annotated=[[1], [1], [1], [1]],
+          bboxes=[
+              [[]],  # Frame 0.
+              [[0., 0., 1., 1.]],  # Frame 1.
+              [[0., 0., 1., 1.],
+               [0.1, 0.1, 0.2, 0.2]],  # Frame 2.
+              [[]],  # Frame 3.
+          ],
+          label_strings=[
+              [],  # Frame 0.
+              ['Abyssinian'],  # Frame 1.
+              ['Abyssinian', 'american_bulldog'],  # Frame 2.
+              [],  # Frame 3
+          ]).SerializeToString()
+    return sequence_example_serialized
+
   def test_build_tf_record_input_reader(self):
     input_reader_text_proto = 'tf_record_input_reader {}'
     input_reader_proto = input_reader_pb2.InputReader()
@@ -82,6 +135,43 @@ class DecoderBuilderTest(tf.test.TestCase):
         [0.0, 0.0, 1.0, 1.0],
         output_dict[fields.InputDataFields.groundtruth_boxes][0])
 
+  def test_build_tf_record_input_reader_sequence_example(self):
+    label_map_path = _get_labelmap_path()
+    input_reader_text_proto = """
+      input_type: TF_SEQUENCE_EXAMPLE
+      tf_record_input_reader {}
+    """
+    input_reader_proto = input_reader_pb2.InputReader()
+    input_reader_proto.label_map_path = label_map_path
+    text_format.Parse(input_reader_text_proto, input_reader_proto)
+
+    decoder = decoder_builder.build(input_reader_proto)
+    tensor_dict = decoder.decode(self._make_serialized_tf_sequence_example())
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+
+    expected_groundtruth_classes = [[-1, -1], [1, -1], [1, 2], [-1, -1]]
+    expected_groundtruth_boxes = [[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
+                                  [[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]],
+                                  [[0.0, 0.0, 1.0, 1.0], [0.1, 0.1, 0.2, 0.2]],
+                                  [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]
+    expected_num_groundtruth_boxes = [0, 1, 2, 0]
+
+    self.assertNotIn(
+        fields.InputDataFields.groundtruth_instance_masks, output_dict)
+    # Sequence example images are encoded.
+    self.assertEqual((4,), output_dict[fields.InputDataFields.image].shape)
+    self.assertAllEqual(expected_groundtruth_classes,
+                        output_dict[fields.InputDataFields.groundtruth_classes])
+    self.assertEqual(
+        (4, 2, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
+    self.assertAllClose(expected_groundtruth_boxes,
+                        output_dict[fields.InputDataFields.groundtruth_boxes])
+    self.assertAllClose(
+        expected_num_groundtruth_boxes,
+        output_dict[fields.InputDataFields.num_groundtruth_boxes])
+
   def test_build_tf_record_input_reader_and_load_instance_masks(self):
     input_reader_text_proto = """
       load_instance_masks: true

research/object_detection/builders/graph_rewriter_builder.py

@@ -14,11 +14,10 @@
 # ==============================================================================
 """Functions for quantized training and evaluation."""
 
-import tensorflow as tf
-
+import tensorflow.compat.v1 as tf
+import tf_slim as slim
 # pylint: disable=g-import-not-at-top
 try:
-  from tensorflow.contrib import layers as contrib_layers
   from tensorflow.contrib import quantize as contrib_quantize
 except ImportError:
   # TF 2.0 doesn't ship with contrib.
@@ -49,7 +48,6 @@ def build(graph_rewriter_config, is_training):
       contrib_quantize.experimental_create_eval_graph(
           input_graph=tf.get_default_graph()
       )
-
-    contrib_layers.summarize_collection('quant_vars')
+    slim.summarize_collection('quant_vars')
 
   return graph_rewrite_fn