Merge remote-tracking branch 'upstream/master'

research/astronet/light_curve_util/median_filter_test.py → research/astronet/light_curve/median_filter_test.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 from absl.testing import absltest
 import numpy as np
 
-from light_curve_util import median_filter
+from light_curve import median_filter
 
 
 class MedianFilterTest(absltest.TestCase):

research/astronet/light_curve_util/periodic_event_test.py → research/astronet/light_curve/periodic_event_test.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 
 from absl.testing import absltest
 
-from light_curve_util.periodic_event import Event
+from light_curve.periodic_event import Event
 
 
 class EventTest(absltest.TestCase):

research/astronet/light_curve_util/util_test.py → research/astronet/light_curve/util_test.py

@@ -21,9 +21,9 @@ from __future__ import print_function
 from absl.testing import absltest
 import numpy as np
 
-from light_curve_util import periodic_event
+from light_curve import periodic_event
 
-from light_curve_util import util
+from light_curve import util
 
 
 class LightCurveUtilTest(absltest.TestCase):
@@ -89,13 +89,13 @@ class LightCurveUtilTest(absltest.TestCase):
     ]
     all_flux = [np.ones(25), np.ones(10)]
 
-    self.assertEqual(len(all_time), 2)
-    self.assertEqual(len(all_time[0]), 25)
-    self.assertEqual(len(all_time[1]), 10)
+    self.assertLen(all_time, 2)
+    self.assertLen(all_time[0], 25)
+    self.assertLen(all_time[1], 10)
 
-    self.assertEqual(len(all_flux), 2)
-    self.assertEqual(len(all_flux[0]), 25)
-    self.assertEqual(len(all_flux[1]), 10)
+    self.assertLen(all_flux, 2)
+    self.assertLen(all_flux[0], 25)
+    self.assertLen(all_flux[1], 10)
 
     # Gap width 0.5.
     split_time, split_flux = util.split(all_time, all_flux, gap_width=0.5)
@@ -268,7 +268,7 @@ class LightCurveUtilTest(absltest.TestCase):
         np.array([80, 90]),
     ]
     reshard_xs = util.reshard_arrays(xs, ys)
-    self.assertEqual(5, len(reshard_xs))
+    self.assertLen(reshard_xs, 5)
     np.testing.assert_array_equal([], reshard_xs[0])
     np.testing.assert_array_equal([1, 2], reshard_xs[1])
     np.testing.assert_array_equal([3, 4, 5, 6], reshard_xs[2])

research/astronet/tf_util/BUILD

+package(default_visibility = ["//visibility:public"])
+
+licenses(["notice"])  # Apache 2.0
+
+py_library(
+    name = "configdict",
+    srcs = ["configdict.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+    ],
+)
+
+py_test(
+    name = "configdict_test",
+    size = "small",
+    srcs = ["configdict_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":configdict"],
+)
+
+py_library(
+    name = "config_util",
+    srcs = ["config_util.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_test(
+    name = "config_util_test",
+    size = "small",
+    srcs = ["config_util_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":config_util"],
+)
+
+py_library(
+    name = "estimator_runner",
+    srcs = ["estimator_runner.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "example_util",
+    srcs = ["example_util.py"],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+)
+
+py_test(
+    name = "example_util_test",
+    size = "small",
+    srcs = ["example_util_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":example_util"],
+)

research/astronet/tf_util/__init__.py

+# Copyright 2018 The TensorFlow Authors.
+#
+# 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.
+

research/astronet/astronet/util/config_util_test.py → research/astronet/tf_util/config_util_test.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 
 import tensorflow as tf
 
-from astronet.util import config_util
+from tf_util import config_util
 
 
 class ConfigUtilTest(tf.test.TestCase):

research/astronet/astronet/util/configdict_test.py → research/astronet/tf_util/configdict_test.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 
 from absl.testing import absltest
 
-from astronet.util import configdict
+from tf_util import configdict
 
 
 class ConfigDictTest(absltest.TestCase):

research/astronet/astronet/util/estimator_runner.py → research/astronet/tf_util/estimator_runner.py

@@ -45,6 +45,8 @@ def evaluate(estimator, eval_args):
   latest_checkpoint = estimator.latest_checkpoint()
   if not latest_checkpoint:
     # This is expected if the training job has not yet saved a checkpoint.
+    tf.logging.info("No checkpoint in %s, skipping evaluation.",
+                    estimator.model_dir)
     return global_step, values
 
   tf.logging.info("Starting evaluation on checkpoint %s", latest_checkpoint)
@@ -54,7 +56,7 @@ def evaluate(estimator, eval_args):
           input_fn, steps=eval_steps, name=eval_name)
       if global_step is None:
         global_step = values[eval_name].get("global_step")
-  except (tf.errors.NotFoundError, ValueError):
+  except tf.errors.NotFoundError:
     # Expected under some conditions, e.g. checkpoint is already deleted by the
     # trainer process. Increasing RunConfig.keep_checkpoint_max may prevent this
     # in some cases.

research/astronet/astronet/util/example_util_test.py → research/astronet/tf_util/example_util_test.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 import numpy as np
 import tensorflow as tf
 
-from astronet.util import example_util
+from tf_util import example_util
 
 
 class ExampleUtilTest(tf.test.TestCase):

research/audioset/README.md

@@ -49,14 +49,15 @@ VGGish depends on the following Python packages:
 * [`resampy`](http://resampy.readthedocs.io/en/latest/)
 * [`tensorflow`](http://www.tensorflow.org/)
 * [`six`](https://pythonhosted.org/six/)
+* [`pysoundfile`](https://pysoundfile.readthedocs.io/)
 
 These are all easily installable via, e.g., `pip install numpy` (as in the
 example command sequence below).
 
 Any reasonably recent version of these packages should work. TensorFlow should
-be at least version 1.0.  We have tested with Python 2.7.6 and 3.4.3 on an
-Ubuntu-like system with NumPy v1.13.1, SciPy v0.19.1, resampy v0.1.5, TensorFlow
-v1.2.1, and Six v1.10.0.
+be at least version 1.0.  We have tested that everything works on Ubuntu and
+Windows 10 with Python 3.6.6, Numpy v1.15.4, SciPy v1.1.0, resampy v0.2.1,
+TensorFlow v1.3.0, Six v1.11.0 and PySoundFile 0.9.0.
 
 VGGish also requires downloading two data files:
 

research/audioset/vggish_input.py

@@ -17,11 +17,12 @@
 
 import numpy as np
 import resampy
-from scipy.io import wavfile
 
 import mel_features
 import vggish_params
 
+import soundfile as sf
+
 
 def waveform_to_examples(data, sample_rate):
   """Converts audio waveform into an array of examples for VGGish.
@@ -80,7 +81,7 @@ def wavfile_to_examples(wav_file):
   Returns:
     See waveform_to_examples.
   """
-  sr, wav_data = wavfile.read(wav_file)
+  wav_data, sr = sf.read(wav_file, dtype='int16')
   assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
   samples = wav_data / 32768.0  # Convert to [-1.0, +1.0]
   return waveform_to_examples(samples, sr)

research/deeplab/export_model.py

@@ -126,9 +126,10 @@ def main(unused_argv):
           eval_scales=FLAGS.inference_scales,
           add_flipped_images=FLAGS.add_flipped_images)
 
+    predictions = tf.cast(predictions[common.OUTPUT_TYPE], tf.float32)
     # Crop the valid regions from the predictions.
     semantic_predictions = tf.slice(
-        predictions[common.OUTPUT_TYPE],
+        predictions,
         [0, 0, 0],
         [1, resized_image_size[0], resized_image_size[1]])
     # Resize back the prediction to the original image size.
@@ -140,7 +141,7 @@ def main(unused_argv):
           label_size,
           method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
           align_corners=True)
-      return tf.squeeze(resized_label, 3)
+      return tf.cast(tf.squeeze(resized_label, 3), tf.int32)
     semantic_predictions = _resize_label(semantic_predictions, image_size)
     semantic_predictions = tf.identity(semantic_predictions, name=_OUTPUT_NAME)