Internal change

PiperOrigin-RevId: 425740068

official/vision/modeling/backbones/spinenet_mobile_test.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.
-
-# Lint as: python3
-# Copyright 2020 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.
-# ==============================================================================
-"""Tests for SpineNet."""
-# Import libraries
-from absl.testing import parameterized
-import tensorflow as tf
-
-from official.vision.modeling.backbones import spinenet_mobile
-
-
-class SpineNetMobileTest(parameterized.TestCase, tf.test.TestCase):
-
-  @parameterized.parameters(
-      (128, 0.6, 1, 0.0, 24),
-      (128, 0.65, 1, 0.2, 40),
-      (256, 1.0, 1, 0.2, 48),
-  )
-  def test_network_creation(self, input_size, filter_size_scale, block_repeats,
-                            se_ratio, endpoints_num_filters):
-    """Test creation of SpineNet models."""
-    min_level = 3
-    max_level = 7
-
-    tf.keras.backend.set_image_data_format('channels_last')
-
-    input_specs = tf.keras.layers.InputSpec(
-        shape=[None, input_size, input_size, 3])
-    model = spinenet_mobile.SpineNetMobile(
-        input_specs=input_specs,
-        min_level=min_level,
-        max_level=max_level,
-        endpoints_num_filters=endpoints_num_filters,
-        resample_alpha=se_ratio,
-        block_repeats=block_repeats,
-        filter_size_scale=filter_size_scale,
-        init_stochastic_depth_rate=0.2,
-    )
-
-    inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1)
-    endpoints = model(inputs)
-
-    for l in range(min_level, max_level + 1):
-      self.assertIn(str(l), endpoints.keys())
-      self.assertAllEqual(
-          [1, input_size / 2**l, input_size / 2**l, endpoints_num_filters],
-          endpoints[str(l)].shape.as_list())
-
-  def test_serialize_deserialize(self):
-    # Create a network object that sets all of its config options.
-    kwargs = dict(
-        min_level=3,
-        max_level=7,
-        endpoints_num_filters=256,
-        se_ratio=0.2,
-        expand_ratio=6,
-        block_repeats=1,
-        filter_size_scale=1.0,
-        init_stochastic_depth_rate=0.2,
-        use_sync_bn=False,
-        activation='relu',
-        norm_momentum=0.99,
-        norm_epsilon=0.001,
-        kernel_initializer='VarianceScaling',
-        kernel_regularizer=None,
-        bias_regularizer=None,
-        use_keras_upsampling_2d=False,
-    )
-    network = spinenet_mobile.SpineNetMobile(**kwargs)
-
-    expected_config = dict(kwargs)
-    self.assertEqual(network.get_config(), expected_config)
-
-    # Create another network object from the first object's config.
-    new_network = spinenet_mobile.SpineNetMobile.from_config(
-        network.get_config())
-
-    # Validate that the config can be forced to JSON.
-    _ = new_network.to_json()
-
-    # If the serialization was successful, the new config should match the old.
-    self.assertAllEqual(network.get_config(), new_network.get_config())
-
-
-if __name__ == '__main__':
-  tf.test.main()

official/vision/modeling/backbones/spinenet_test.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.
-
-# Lint as: python3
-"""Tests for SpineNet."""
-# Import libraries
-from absl.testing import parameterized
-import tensorflow as tf
-
-from official.vision.modeling.backbones import spinenet
-
-
-class SpineNetTest(parameterized.TestCase, tf.test.TestCase):
-
-  @parameterized.parameters(
-      (128, 0.65, 1, 0.5, 128, 4, 6),
-      (256, 1.0, 1, 0.5, 256, 3, 6),
-      (384, 1.0, 2, 0.5, 256, 4, 7),
-      (512, 1.0, 3, 1.0, 256, 3, 7),
-      (640, 1.3, 4, 1.0, 384, 3, 7),
-  )
-  def test_network_creation(self, input_size, filter_size_scale, block_repeats,
-                            resample_alpha, endpoints_num_filters, min_level,
-                            max_level):
-    """Test creation of SpineNet models."""
-
-    tf.keras.backend.set_image_data_format('channels_last')
-
-    input_specs = tf.keras.layers.InputSpec(
-        shape=[None, input_size, input_size, 3])
-    model = spinenet.SpineNet(
-        input_specs=input_specs,
-        min_level=min_level,
-        max_level=max_level,
-        endpoints_num_filters=endpoints_num_filters,
-        resample_alpha=resample_alpha,
-        block_repeats=block_repeats,
-        filter_size_scale=filter_size_scale,
-        init_stochastic_depth_rate=0.2,
-    )
-
-    inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1)
-    endpoints = model(inputs)
-
-    for l in range(min_level, max_level + 1):
-      self.assertIn(str(l), endpoints.keys())
-      self.assertAllEqual(
-          [1, input_size / 2**l, input_size / 2**l, endpoints_num_filters],
-          endpoints[str(l)].shape.as_list())
-
-  @parameterized.parameters(
-      ((128, 128), (128, 128)),
-      ((128, 128), (256, 256)),
-      ((640, 640), (896, 1664)),
-  )
-  def test_load_from_different_input_specs(self, input_size_1, input_size_2):
-    """Test loading checkpoints with different input size."""
-
-    def build_spinenet(input_size):
-      tf.keras.backend.set_image_data_format('channels_last')
-      input_specs = tf.keras.layers.InputSpec(
-          shape=[None, input_size[0], input_size[1], 3])
-      model = spinenet.SpineNet(
-          input_specs=input_specs,
-          min_level=3,
-          max_level=7,
-          endpoints_num_filters=384,
-          resample_alpha=1.0,
-          block_repeats=2,
-          filter_size_scale=0.5)
-      return model
-
-    model_1 = build_spinenet(input_size_1)
-    model_2 = build_spinenet(input_size_2)
-
-    ckpt_1 = tf.train.Checkpoint(backbone=model_1)
-    ckpt_2 = tf.train.Checkpoint(backbone=model_2)
-
-    ckpt_path = self.get_temp_dir() + '/ckpt'
-    ckpt_1.write(ckpt_path)
-    ckpt_2.restore(ckpt_path).expect_partial()
-
-  def test_serialize_deserialize(self):
-    # Create a network object that sets all of its config options.
-    kwargs = dict(
-        min_level=3,
-        max_level=7,
-        endpoints_num_filters=256,
-        resample_alpha=0.5,
-        block_repeats=1,
-        filter_size_scale=1.0,
-        init_stochastic_depth_rate=0.2,
-        use_sync_bn=False,
-        activation='relu',
-        norm_momentum=0.99,
-        norm_epsilon=0.001,
-        kernel_initializer='VarianceScaling',
-        kernel_regularizer=None,
-        bias_regularizer=None,
-    )
-    network = spinenet.SpineNet(**kwargs)
-
-    expected_config = dict(kwargs)
-    self.assertEqual(network.get_config(), expected_config)
-
-    # Create another network object from the first object's config.
-    new_network = spinenet.SpineNet.from_config(network.get_config())
-
-    # Validate that the config can be forced to JSON.
-    _ = new_network.to_json()
-
-    # If the serialization was successful, the new config should match the old.
-    self.assertAllEqual(network.get_config(), new_network.get_config())
-
-
-if __name__ == '__main__':
-  tf.test.main()

official/vision/modeling/decoders/__init__.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.
-
-# Lint as: python3
-"""Decoders package definition."""
-
-from official.vision.modeling.decoders.aspp import ASPP
-from official.vision.modeling.decoders.fpn import FPN
-from official.vision.modeling.decoders.nasfpn import NASFPN

official/vision/modeling/heads/__init__.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.
-
-# Lint as: python3
-"""Heads package definition."""
-
-from official.vision.modeling.heads.dense_prediction_heads import RetinaNetHead
-from official.vision.modeling.heads.dense_prediction_heads import RPNHead
-from official.vision.modeling.heads.instance_heads import DetectionHead
-from official.vision.modeling.heads.instance_heads import MaskHead
-from official.vision.modeling.heads.segmentation_heads import SegmentationHead