Rename TFRUtil to TFRecorder.

Change-Id: I1c3f66fc7564428790aa7db1532e7816d442a2f8

Author: cfezequiel

.pylintrc

@@ -16,7 +16,7 @@ persistent=no
 # usually to register additional checkers.
 load-plugins=
 # Use multiple processes to speed up Pylint.
-jobs=4
+jobs=1
 # Allow loading of arbitrary C extensions. Extensions are imported into the
 # active Python interpreter and may run arbitrary code.
 unsafe-load-any-extension=no

Makefile

@@ -2,9 +2,9 @@ init:
 	pip install -r requirements.txt
 
 test:
-	nosetests --with-coverage --nocapture -v --cover-package=tfrutil
+	nosetests --with-coverage --nocapture -v --cover-package=tfrecorder
 
 pylint:
-	pylint tfrutil
+	pylint tfrecorder
 
-.PHONY: init glint coverage test
+.PHONY: init glint coverage test

README.md

@@ -1,10 +1,10 @@
-# TFRecord Utilities (TFRUtil)
+# TFRecorder
 
-TFRUtil makes it easy to create TFRecords from images and labels in 
+TFRecorder makes it easy to create TFRecords from images and labels in 
 Pandas DataFrames or CSV files.
-Today, TFRUtil supports data stored in 'image csv format' similar to 
+Today, TFRecorder supports data stored in 'image csv format' similar to 
 GCP AutoML Vision. 
-In the future TFRUtil will support converting any Pandas DataFrame or CSV 
+In the future TFRecorder will support converting any Pandas DataFrame or CSV 
 file into TFRecords. 
 
 ## Installation
@@ -23,7 +23,7 @@ pip install .
 
 ```bash
 import pandas as pd
-import tfrutil
+import tfrecorder
 df = pd.read_csv(...)
 df.tensorflow.to_tfrecord(output_dir="gs://my/bucket")
 ```
@@ -41,12 +41,14 @@ df.tensorflow.to_tfrecord(
 ### Command-line interface
 
 ```bash
-tfrutil create-tfrecords --output_dir="gs://my/bucket" data.csv
+tfrecorder create-tfrecords --output_dir="gs://my/bucket" data.csv
 ```
 
 ## Input format
 
-TFRUtil currently expects data to be in the same format as [AutoML Vision](https://cloud.google.com/vision/automl/docs/prepare).  This format looks like a pandas dataframe or CSV formatted as:
+TFRecorder currently expects data to be in the same format as 
+[AutoML Vision](https://cloud.google.com/vision/automl/docs/prepare).  
+This format looks like a pandas dataframe or CSV formatted as:
 
 | split | image_uri                 | label |
 |-------|---------------------------|-------|

kokoro/gcp_ubuntu/kokoro_build.sh

@@ -65,6 +65,6 @@ echo $(python -V)  # should be Python 3
 # Code under repo is checked out to ${KOKORO_ARTIFACTS_DIR}/git.
 # The final directory name in this path is determined by the scm name specified
 # in the job configuration.
-cd ${KOKORO_ARTIFACTS_DIR}/git/tfrutil
+cd ${KOKORO_ARTIFACTS_DIR}/git/tfrecorder
 
 ./build.sh

setup.py

@@ -38,13 +38,13 @@
 
 
 setup(
-    name='tfrutil',
+    name='tfrecorder',
     version='0.1',
     install_requires=REQUIRED_PACKAGES,
     packages=find_packages(),
     include_package_data=True,
-    description='TFRUtil creates TensorFlow Records easily.',
+    description='TFRecorder creates TensorFlow Records easily.',
     entry_points = {
-        'console_scripts': ['tfrutil=tfrutil.cli:main'],
+        'console_scripts': ['tfrecorder=tfrecorder.cli:main'],
     },
 )

tfrutil/__init__.py renamed to tfrecorder/__init__.py

@@ -15,5 +15,5 @@
 # limitations under the License.
 
 """Imports."""
-from tfrutil import accessor
-from tfrutil import client
+from tfrecorder import accessor
+from tfrecorder import client

tfrutil/accessor.py renamed to tfrecorder/accessor.py

@@ -14,23 +14,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Creates a pandas DataFrame accessor for TFRUtil.
+"""Creates a pandas DataFrame accessor for TFRecorder.
 
-accessor.py contains TFRUtilAccessor which provides a pandas DataFrame
+accessor.py contains TFRecorderAccessor which provides a pandas DataFrame
 accessor.  This accessor allows us to inject the to_tfr() function into
 pandas DataFrames.
 """
+
 from typing import Any, Dict, Optional, Union
 import pandas as pd
 from IPython.core import display
 
-from tfrutil import client
-from tfrutil import constants
+from tfrecorder import client
+from tfrecorder import constants
 
 
 @pd.api.extensions.register_dataframe_accessor('tensorflow')
-class TFRUtilAccessor:
-  """DataFrame Accessor class for TFRUtil."""
+class TFRecorderAccessor:
+  """DataFrame Accessor class for TFRecorder."""
 
   def __init__(self, pandas_obj):
     self._df = pandas_obj
@@ -46,15 +47,15 @@ def to_tfr(
       job_label: str = 'to-tfr',
       compression: Optional[str] = 'gzip',
       num_shards: int = 0) -> Dict[str, Any]:
-    """TFRUtil Pandas Accessor.
+    """TFRecorder Pandas Accessor.
 
-    TFRUtil provides an easy interface to create image-based tensorflow records
-    from a dataframe containing GCS locations of the images and labels.
+    TFRecorder provides an easy interface to create image-based tensorflow
+    records from a dataframe containing GCS locations of the images and labels.
 
     Usage:
-      import tfrutil
+      import tfrecorder
 
-      df.tfrutil.to_tfr(
+      df.tfrecorder.to_tfr(
           output_dir='gcs://foo/bar/train',
           runner='DirectRunner',
           compression='gzip',

tfrutil/accessor_test.py renamed to tfrecorder/accessor_test.py

@@ -14,15 +14,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Tests for pandas accessor."""
+"""Tests for Pandas accessor."""
 
 import os
 import unittest
 
 # pylint: disable=unused-import
+import tfrecorder
 
-import tfrutil
-from tfrutil import test_utils
+from tfrecorder import test_utils
 
 
 class DataFrameAccessor(unittest.TestCase):
@@ -40,5 +40,6 @@ def test_accessor(self):
         runner='DirectRunner', output_dir=self.output_dir)
     self.assertTrue('metrics' in r)
 
+
 if __name__ == '__main__':
   unittest.main()

tfrutil/beam_image.py renamed to tfrecorder/beam_image.py

@@ -26,9 +26,9 @@
 from PIL import Image
 import tensorflow_transform as tft
 
-from tfrutil import beam_image
-from tfrutil import constants
-from tfrutil import test_utils
+from tfrecorder import beam_image
+from tfrecorder import constants
+from tfrecorder import test_utils
 
 
 class BeamImageTests(unittest.TestCase):
@@ -37,7 +37,7 @@ class BeamImageTests(unittest.TestCase):
   def setUp(self):
     self.pipeline = test_utils.get_test_pipeline()
     self.df = test_utils.get_test_df()
-    self.image_file = 'tfrutil/test_data/images/cat/cat-640x853-1.jpg'
+    self.image_file = 'tfrecorder/test_data/images/cat/cat-640x853-1.jpg'
 
   def test_load(self):
     """Tests the image loading function."""
@@ -47,7 +47,7 @@ def test_load(self):
   def test_file_not_found_load(self):
     """Test loading an image that doesn't exist."""
     with self.assertRaises(OSError):
-      _ = beam_image.load('tfrutil/test_data/images/cat/food.jpg')
+      _ = beam_image.load('tfrecorder/test_data/images/cat/food.jpg')
 
   def test_mode_to_channel(self):
     """Tests `mode_to_channel`."""