Update code to v2.8.0

official/benchmark/models/resnet_imagenet_test_tpu.py

+# Copyright 2017 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.
+# ==============================================================================
+"""Test the keras ResNet model with ImageNet data on TPU."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl.testing import parameterized
+import tensorflow as tf
+from official.benchmark.models import resnet_imagenet_main
+from official.legacy.image_classification.resnet import imagenet_preprocessing
+from official.utils.testing import integration
+
+
+class KerasImagenetTest(tf.test.TestCase, parameterized.TestCase):
+  """Unit tests for Keras Models with ImageNet."""
+
+  _extra_flags_dict = {
+      "resnet": [
+          "-batch_size",
+          "4",
+          "-train_steps",
+          "1",
+          "-use_synthetic_data",
+          "true"
+          "-model",
+          "resnet50_v1.5",
+          "-optimizer",
+          "resnet50_default",
+      ],
+      "resnet_polynomial_decay": [
+          "-batch_size",
+          "4",
+          "-train_steps",
+          "1",
+          "-use_synthetic_data",
+          "true",
+          "-model",
+          "resnet50_v1.5",
+          "-optimizer",
+          "resnet50_default",
+          "-pruning_method",
+          "polynomial_decay",
+      ],
+  }
+  _tempdir = None
+
+  @classmethod
+  def setUpClass(cls):  # pylint: disable=invalid-name
+    super(KerasImagenetTest, cls).setUpClass()
+    resnet_imagenet_main.define_imagenet_keras_flags()
+
+  def setUp(self):
+    super(KerasImagenetTest, self).setUp()
+    imagenet_preprocessing.NUM_IMAGES["validation"] = 4
+    self.policy = tf.keras.mixed_precision.global_policy()
+
+  def tearDown(self):
+    super(KerasImagenetTest, self).tearDown()
+    tf.io.gfile.rmtree(self.get_temp_dir())
+    tf.keras.mixed_precision.set_global_policy(self.policy)
+
+  @parameterized.parameters([
+      "resnet",
+      # "resnet_polynomial_decay"  b/151854314
+  ])
+  def test_end_to_end_tpu(self, flags_key):
+    """Test Keras model with TPU distribution strategy."""
+
+    extra_flags = [
+        "-distribution_strategy",
+        "tpu",
+        "-data_format",
+        "channels_last",
+        "-enable_checkpoint_and_export",
+        "1",
+    ]
+    extra_flags = extra_flags + self._extra_flags_dict[flags_key]
+
+    integration.run_synthetic(
+        main=resnet_imagenet_main.run,
+        tmp_root=self.get_temp_dir(),
+        extra_flags=extra_flags)
+
+  @parameterized.parameters(["resnet"])
+  def test_end_to_end_tpu_bf16(self, flags_key):
+    """Test Keras model with TPU and bfloat16 activation."""
+
+    extra_flags = [
+        "-distribution_strategy",
+        "tpu",
+        "-data_format",
+        "channels_last",
+        "-dtype",
+        "bf16",
+    ]
+    extra_flags = extra_flags + self._extra_flags_dict[flags_key]
+
+    integration.run_synthetic(
+        main=resnet_imagenet_main.run,
+        tmp_root=self.get_temp_dir(),
+        extra_flags=extra_flags)
+
+
+if __name__ == "__main__":
+  tf.test.main()

official/benchmark/models/shakespeare/README.md

+# Shakespeare character LSTM model
+
+This is an implemention of a simple character LSTM used to generate text.
+
+## Instructions
+
+First download the source data:
+
+```
+wget https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt
+```
+
+Note that files other than shakepeare.txt can also be used to train the model to generater other text.
+
+Then train the model:
+
+```python
+python3 shakespeare_main.py --training_data shakespeare.txt \
+    --model_dir /tmp/shakespeare
+```
+
+This will place model checkpoints in `/tmp/shakespeare`, so that we can use them to make predictions.
+
+Then generate predictions:
+
+```python
+python3 shakespeare_main.py --training_data shakespeare.txt \
+    --model_dir /tmp/shakespeare --notrain --predict_context=ROMEO:
+```
+
+Change `--predict_context` and `--predict_length` to suit your needs.

official/benchmark/models/shakespeare/__init__.py

+