Add resnet_config.py.

Remove callback_test.py as it uses private TF symbol callback_test

PiperOrigin-RevId: 302990143

official/vision/image_classification/callbacks_test.py

-# Copyright 2019 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 callbacks."""
-
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import collections
-import functools
-import os
-
-from absl.testing import parameterized
-
-import numpy as np
-import tensorflow as tf
-
-from tensorflow.python.keras import callbacks_test
-from tensorflow.python.keras import keras_parameterized
-from official.vision.image_classification import callbacks
-
-_ObservedSummary = collections.namedtuple('_ObservedSummary', ('logdir', 'tag'))
-
-
-def _trivial_function(a):
-  return a
-
-
-class UtilFunctionTests(tf.test.TestCase, parameterized.TestCase):
-  """Tests to check utility functions provided in callbacks.py."""
-
-  @parameterized.named_parameters(
-      ('integer', 1),
-      ('float', 1.),
-      ('lambda', lambda: 1),
-      ('partial', functools.partial(_trivial_function, 1)))
-  def test_scalar_from_tensors(self, t):
-    t = tf.Variable(t)
-    value = callbacks.get_scalar_from_tensor(t)
-    print (value)
-    self.assertTrue(np.isscalar(value))
-
-
-@keras_parameterized.run_with_all_model_types
-@keras_parameterized.run_all_keras_modes(always_skip_v1=True)
-class CustomTensorBoardTest(callbacks_test.TestTensorBoardV2):
-
-  def test_custom_tb_learning_rate(self):
-    os.chdir(self.get_temp_dir())
-    model = self._get_model()
-    x, y = np.ones((10, 10, 10, 1)), np.ones((10, 1))
-    tb_cbk = callbacks.CustomTensorBoard(log_dir=self.logdir,
-                                         track_lr=True)
-
-    model.fit(
-        x,
-        y,
-        batch_size=2,
-        epochs=2,
-        validation_data=(x, y),
-        callbacks=[tb_cbk])
-
-    summary_file = callbacks_test.list_summaries(logdir=self.logdir)
-    self.assertEqual(
-        summary_file.scalars, {
-            _ObservedSummary(logdir=self.train_dir, tag='epoch_loss'),
-            _ObservedSummary(logdir=self.train_dir, tag='epoch_learning_rate'),
-            _ObservedSummary(logdir=self.validation_dir, tag='epoch_loss'),
-        })
-
-
-if __name__ == '__main__':
-  tf.test.main()

official/vision/image_classification/resnet/resnet_config.py

+# Lint as: python3
+# Copyright 2019 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.
+# ==============================================================================
+"""Configuration definitions for ResNet losses, learning rates, and optimizers."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from typing import Any, Mapping
+
+import dataclasses
+
+from official.vision.image_classification.configs import base_configs
+
+
+_RESNET_LR_SCHEDULE = [    # (multiplier, epoch to start) tuples
+    (1.0, 5), (0.1, 30), (0.01, 60), (0.001, 80)
+]
+_RESNET_LR_BOUNDARIES = list(p[1] for p in _RESNET_LR_SCHEDULE[1:])
+_RESNET_LR_MULTIPLIERS = list(p[0] for p in _RESNET_LR_SCHEDULE)
+_RESNET_LR_WARMUP_EPOCHS = _RESNET_LR_SCHEDULE[0][1]
+
+
+@dataclasses.dataclass
+class ResNetModelConfig(base_configs.ModelConfig):
+  """Configuration for the ResNet model."""
+  name: str = 'ResNet'
+  num_classes: int = 1000
+  model_params: Mapping[str, Any] = dataclasses.field(default_factory=lambda: {
+      'num_classes': 1000,
+      'batch_size': None,
+      'use_l2_regularizer': True,
+      'rescale_inputs': False,
+  })
+  loss: base_configs.LossConfig = base_configs.LossConfig(
+      name='sparse_categorical_crossentropy')
+  optimizer: base_configs.OptimizerConfig = base_configs.OptimizerConfig(
+      name='momentum',
+      decay=0.9,
+      epsilon=0.001,
+      momentum=0.9,
+      moving_average_decay=None)
+  learning_rate: base_configs.LearningRateConfig = (
+      base_configs.LearningRateConfig(
+          name='piecewise_constant_with_warmup',
+          examples_per_epoch=1281167,
+          warmup_epochs=_RESNET_LR_WARMUP_EPOCHS,
+          boundaries=_RESNET_LR_BOUNDARIES,
+          multipliers=_RESNET_LR_MULTIPLIERS))