Moved code from TF1 to TF2 for DELF logging, testing directories and feature...

Moved code from TF1 to TF2 for DELF logging, testing directories and feature extraction scripts (#8591)

* Merged commit includes the following changes:

FolderOrigin-RevId: /google/src/cloud/bfusinska/delf

* Added the import for utils
Co-authored-by: Andre Araujo <andrearaujo@google.com>

research/delf/delf/__init__.py

@@ -30,6 +30,7 @@ from delf.python import feature_aggregation_extractor
 from delf.python import feature_aggregation_similarity
 from delf.python import feature_extractor
 from delf.python import feature_io
+from delf.python import utils
 from delf.python.examples import detector
 from delf.python.examples import extractor
 from delf.python import detect_to_retrieve

research/delf/delf/python/delf_v1.py

@@ -67,7 +67,7 @@ class DelfV1(object):
   """
 
   def __init__(self, target_layer_type=_SUPPORTED_TARGET_LAYER[0]):
-    tf.compat.v1.logging.info('Creating model %s ', target_layer_type)
+    print('Creating model %s ' % target_layer_type)
 
     self._target_layer_type = target_layer_type
     if self._target_layer_type not in _SUPPORTED_TARGET_LAYER:

research/delf/delf/python/delg/extract_features.py

@@ -33,14 +33,13 @@ import time
 from absl import app
 from absl import flags
 import numpy as np
-from PIL import Image
-from PIL import ImageFile
 import tensorflow as tf
 
 from google.protobuf import text_format
 from delf import delf_config_pb2
 from delf import datum_io
 from delf import feature_io
+from delf import utils
 from delf.python.detect_to_retrieve import dataset
 from delf import extractor
 
@@ -71,27 +70,10 @@ _DELG_GLOBAL_EXTENSION = '.delg_global'
 _DELG_LOCAL_EXTENSION = '.delg_local'
 _IMAGE_EXTENSION = '.jpg'
 
-# To avoid PIL crashing for truncated (corrupted) images.
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
 # Pace to report extraction log.
 _STATUS_CHECK_ITERATIONS = 50
 
 
-def _PilLoader(path):
-  """Helper function to read image with PIL.
-
-  Args:
-    path: Path to image to be loaded.
-
-  Returns:
-    PIL image in RGB format.
-  """
-  with tf.io.gfile.GFile(path, 'rb') as f:
-    img = Image.open(f)
-    return img.convert('RGB')
-
-
 def main(argv):
   if len(argv) > 1:
     raise RuntimeError('Too many command-line arguments.')
@@ -155,7 +137,7 @@ def main(argv):
           print('Skipping %s' % image_name)
           continue
 
-        pil_im = _PilLoader(input_image_filename)
+        pil_im = utils.RgbLoader(input_image_filename)
         resize_factor = 1.0
         if FLAGS.image_set == 'query':
           # Crop query image according to bounding box.

research/delf/delf/python/detect_to_retrieve/boxes_and_features_extraction.py

@@ -24,14 +24,13 @@ import os
 import time
 
 import numpy as np
-from PIL import Image
-from PIL import ImageFile
 import tensorflow as tf
 
 from google.protobuf import text_format
 from delf import delf_config_pb2
 from delf import box_io
 from delf import feature_io
+from delf import utils
 from delf import detector
 from delf import extractor
 
@@ -42,23 +41,6 @@ _DELF_EXTENSION = '.delf'
 # Pace to report extraction log.
 _STATUS_CHECK_ITERATIONS = 100
 
-# To avoid crashing for truncated (corrupted) images.
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-
-def _PilLoader(path):
-  """Helper function to read image with PIL.
-
-  Args:
-    path: Path to image to be loaded.
-
-  Returns:
-    PIL image in RGB format.
-  """
-  with tf.io.gfile.GFile(path, 'rb') as f:
-    img = Image.open(f)
-    return img.convert('RGB')
-
 
 def _WriteMappingBasenameToIds(index_names_ids_and_boxes, output_path):
   """Helper function to write CSV mapping from DELF file name to IDs.
@@ -157,7 +139,7 @@ def ExtractBoxesAndFeaturesToFiles(image_names, image_paths, delf_config_path,
         output_box_filename = os.path.join(output_boxes_dir,
                                            image_name + _BOX_EXTENSION)
 
-        pil_im = _PilLoader(image_paths[i])
+        pil_im = utils.RgbLoader(image_paths[i])
         width, height = pil_im.size
 
         # Extract and save boxes.

research/delf/delf/python/detect_to_retrieve/dataset_test.py

@@ -20,11 +20,14 @@ from __future__ import print_function
 
 import os
 
+from absl import flags
 import numpy as np
 import tensorflow as tf
 
 from delf.python.detect_to_retrieve import dataset
 
+FLAGS = flags.FLAGS
+
 
 class DatasetTest(tf.test.TestCase):
 
@@ -206,7 +209,7 @@ class DatasetTest(tf.test.TestCase):
         'medium': np.array([0.5, 1.0])
     }
     pr_ranks = [1, 5]
-    output_path = os.path.join(tf.compat.v1.test.get_temp_dir(), 'metrics.txt')
+    output_path = os.path.join(FLAGS.test_tmpdir, 'metrics.txt')
 
     # Run tested function.
     dataset.SaveMetricsFile(mean_average_precision, mean_precisions,
@@ -240,7 +243,7 @@ class DatasetTest(tf.test.TestCase):
         'medium': np.array([0.5, 1.0])
     }
     pr_ranks = [1, 5]
-    output_path = os.path.join(tf.compat.v1.test.get_temp_dir(), 'metrics.txt')
+    output_path = os.path.join(FLAGS.test_tmpdir, 'metrics.txt')
 
     # Run tested functions.
     dataset.SaveMetricsFile(mean_average_precision, mean_precisions,
@@ -261,7 +264,7 @@ class DatasetTest(tf.test.TestCase):
 
   def testReadMetricsWithRepeatedProtocolFails(self):
     # Define inputs.
-    input_path = os.path.join(tf.compat.v1.test.get_temp_dir(), 'metrics.txt')
+    input_path = os.path.join(FLAGS.test_tmpdir, 'metrics.txt')
     with tf.io.gfile.GFile(input_path, 'w') as f:
       f.write('hard\n'
               '  mAP=70.0\n'

research/delf/delf/python/detect_to_retrieve/extract_query_features.py

@@ -31,14 +31,13 @@ import sys
 import time
 
 import numpy as np
-from PIL import Image
-from PIL import ImageFile
 import tensorflow as tf
 
 from google.protobuf import text_format
 from tensorflow.python.platform import app
 from delf import delf_config_pb2
 from delf import feature_io
+from delf import utils
 from delf.python.detect_to_retrieve import dataset
 from delf import extractor
 
@@ -48,37 +47,17 @@ cmd_args = None
 _DELF_EXTENSION = '.delf'
 _IMAGE_EXTENSION = '.jpg'
 
-# To avoid PIL crashing for truncated (corrupted) images.
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-
-def _PilLoader(path):
-  """Helper function to read image with PIL.
-
-  Args:
-    path: Path to image to be loaded.
-
-  Returns:
-    PIL image in RGB format.
-  """
-  with tf.io.gfile.GFile(path, 'rb') as f:
-    img = Image.open(f)
-    return img.convert('RGB')
-
 
 def main(argv):
   if len(argv) > 1:
     raise RuntimeError('Too many command-line arguments.')
 
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
-
   # Read list of query images from dataset file.
-  tf.compat.v1.logging.info(
-      'Reading list of query images and boxes from dataset file...')
+  print('Reading list of query images and boxes from dataset file...')
   query_list, _, ground_truth = dataset.ReadDatasetFile(
       cmd_args.dataset_file_path)
   num_images = len(query_list)
-  tf.compat.v1.logging.info('done! Found %d images', num_images)
+  print(f'done! Found {num_images} images')
 
   # Parse DelfConfig proto.
   config = delf_config_pb2.DelfConfig()
@@ -104,12 +83,12 @@ def main(argv):
         output_feature_filename = os.path.join(
             cmd_args.output_features_dir, query_image_name + _DELF_EXTENSION)
         if tf.io.gfile.exists(output_feature_filename):
-          tf.compat.v1.logging.info('Skipping %s', query_image_name)
+          print(f'Skipping {query_image_name}')
           continue
 
         # Crop query image according to bounding box.
         bbox = [int(round(b)) for b in ground_truth[i]['bbx']]
-        im = np.array(_PilLoader(input_image_filename).crop(bbox))
+        im = np.array(utils.RgbLoader(input_image_filename).crop(bbox))
 
         # Extract and save features.
         extracted_features = extractor_fn(im)

research/delf/delf/python/examples/extract_boxes.py

@@ -34,6 +34,7 @@ import tensorflow as tf
 
 from tensorflow.python.platform import app
 from delf import box_io
+from delf import utils
 from delf import detector
 
 cmd_args = None
@@ -130,13 +131,11 @@ def main(argv):
   if len(argv) > 1:
     raise RuntimeError('Too many command-line arguments.')
 
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
-
   # Read list of images.
-  tf.compat.v1.logging.info('Reading list of images...')
+  print('Reading list of images...')
   image_paths = _ReadImageList(cmd_args.list_images_path)
   num_images = len(image_paths)
-  tf.compat.v1.logging.info('done! Found %d images', num_images)
+  print(f'done! Found {num_images} images')
 
   # Create output directories if necessary.
   if not tf.io.gfile.exists(cmd_args.output_dir):
@@ -147,48 +146,36 @@ def main(argv):
 
   # Tell TensorFlow that the model will be built into the default Graph.
   with tf.Graph().as_default():
-    # Reading list of images.
-    filename_queue = tf.compat.v1.train.string_input_producer(
-        image_paths, shuffle=False)
-    reader = tf.compat.v1.WholeFileReader()
-    _, value = reader.read(filename_queue)
-    image_tf = tf.io.decode_jpeg(value, channels=3)
-    image_tf = tf.expand_dims(image_tf, 0)
-
     with tf.compat.v1.Session() as sess:
       init_op = tf.compat.v1.global_variables_initializer()
       sess.run(init_op)
 
       detector_fn = detector.MakeDetector(sess, cmd_args.detector_path)
 
-      # Start input enqueue threads.
-      coord = tf.train.Coordinator()
-      threads = tf.compat.v1.train.start_queue_runners(sess=sess, coord=coord)
       start = time.clock()
       for i, image_path in enumerate(image_paths):
         # Write to log-info once in a while.
         if i == 0:
-          tf.compat.v1.logging.info('Starting to detect objects in images...')
+          print('Starting to detect objects in images...')
         elif i % _STATUS_CHECK_ITERATIONS == 0:
           elapsed = (time.clock() - start)
-          tf.compat.v1.logging.info(
-              'Processing image %d out of %d, last %d '
-              'images took %f seconds', i, num_images, _STATUS_CHECK_ITERATIONS,
-              elapsed)
+          print(
+              f'Processing image {i} out of {num_images}, last '
+              f'{_STATUS_CHECK_ITERATIONS} images took {elapsed} seconds'
+              )
           start = time.clock()
 
-        # # Get next image.
-        im = sess.run(image_tf)
-
         # If descriptor already exists, skip its computation.
         base_boxes_filename, _ = os.path.splitext(os.path.basename(image_path))
         out_boxes_filename = base_boxes_filename + _BOX_EXT
         out_boxes_fullpath = os.path.join(cmd_args.output_dir,
                                           out_boxes_filename)
         if tf.io.gfile.exists(out_boxes_fullpath):
-          tf.compat.v1.logging.info('Skipping %s', image_path)
+          print(f'Skipping {image_path}')
           continue
 
+        im = np.expand_dims(np.array(utils.RgbLoader(image_paths[i])), 0)
+
         # Extract and save boxes.
         (boxes_out, scores_out, class_indices_out) = detector_fn(im)
         (selected_boxes, selected_scores,
@@ -205,10 +192,6 @@ def main(argv):
                                           out_viz_filename)
           _PlotBoxesAndSaveImage(im[0], selected_boxes, out_viz_fullpath)
 
-      # Finalize enqueue threads.
-      coord.request_stop()
-      coord.join(threads)
-
 
 if __name__ == '__main__':
   parser = argparse.ArgumentParser()

research/delf/delf/python/examples/extract_features.py

@@ -27,6 +27,7 @@ import os
 import sys
 import time
 
+import numpy as np
 from six.moves import range
 import tensorflow as tf
 
@@ -34,6 +35,7 @@ from google.protobuf import text_format
 from tensorflow.python.platform import app
 from delf import delf_config_pb2
 from delf import feature_io
+from delf import utils
 from delf import extractor
 
 cmd_args = None
@@ -61,13 +63,11 @@ def _ReadImageList(list_path):
 
 
 def main(unused_argv):
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
-
   # Read list of images.
-  tf.compat.v1.logging.info('Reading list of images...')
+  print('Reading list of images...')
   image_paths = _ReadImageList(cmd_args.list_images_path)
   num_images = len(image_paths)
-  tf.compat.v1.logging.info('done! Found %d images', num_images)
+  print(f'done! Found {num_images} images')
 
   # Parse DelfConfig proto.
   config = delf_config_pb2.DelfConfig()
@@ -80,47 +80,35 @@ def main(unused_argv):
 
   # Tell TensorFlow that the model will be built into the default Graph.
   with tf.Graph().as_default():
-    # Reading list of images.
-    filename_queue = tf.compat.v1.train.string_input_producer(
-        image_paths, shuffle=False)
-    reader = tf.compat.v1.WholeFileReader()
-    _, value = reader.read(filename_queue)
-    image_tf = tf.io.decode_jpeg(value, channels=3)
-
     with tf.compat.v1.Session() as sess:
       init_op = tf.compat.v1.global_variables_initializer()
       sess.run(init_op)
 
       extractor_fn = extractor.MakeExtractor(sess, config)
 
-      # Start input enqueue threads.
-      coord = tf.train.Coordinator()
-      threads = tf.compat.v1.train.start_queue_runners(sess=sess, coord=coord)
       start = time.clock()
       for i in range(num_images):
         # Write to log-info once in a while.
         if i == 0:
-          tf.compat.v1.logging.info(
-              'Starting to extract DELF features from images...')
+          print('Starting to extract DELF features from images...')
         elif i % _STATUS_CHECK_ITERATIONS == 0:
           elapsed = (time.clock() - start)
-          tf.compat.v1.logging.info(
-              'Processing image %d out of %d, last %d '
-              'images took %f seconds', i, num_images, _STATUS_CHECK_ITERATIONS,
-              elapsed)
+          print(
+              f'Processing image {i} out of {num_images}, last '
+              f'{_STATUS_CHECK_ITERATIONS} images took {elapsed} seconds'
+              )
           start = time.clock()
 
-        # # Get next image.
-        im = sess.run(image_tf)
-
         # If descriptor already exists, skip its computation.
         out_desc_filename = os.path.splitext(os.path.basename(
             image_paths[i]))[0] + _DELF_EXT
         out_desc_fullpath = os.path.join(cmd_args.output_dir, out_desc_filename)
         if tf.io.gfile.exists(out_desc_fullpath):
-          tf.compat.v1.logging.info('Skipping %s', image_paths[i])
+          print(f'Skipping {image_paths[i]}')
           continue
 
+        im = np.array(utils.RgbLoader(image_paths[i]))
+
         # Extract and save features.
         extracted_features = extractor_fn(im)
         locations_out = extracted_features['local_features']['locations']
@@ -132,10 +120,6 @@ def main(unused_argv):
                                feature_scales_out, descriptors_out,
                                attention_out)
 
-      # Finalize enqueue threads.
-      coord.request_stop()
-      coord.join(threads)
-
 
 if __name__ == '__main__':
   parser = argparse.ArgumentParser()

research/delf/delf/python/examples/match_images.py

@@ -37,7 +37,6 @@ from scipy import spatial
 from skimage import feature
 from skimage import measure
 from skimage import transform
-import tensorflow as tf
 
 from tensorflow.python.platform import app
 from delf import feature_io
@@ -48,17 +47,15 @@ _DISTANCE_THRESHOLD = 0.8
 
 
 def main(unused_argv):
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
-
   # Read features.
   locations_1, _, descriptors_1, _, _ = feature_io.ReadFromFile(
       cmd_args.features_1_path)
   num_features_1 = locations_1.shape[0]
-  tf.compat.v1.logging.info("Loaded image 1's %d features" % num_features_1)
+  print(f"Loaded image 1's {num_features_1} features")
   locations_2, _, descriptors_2, _, _ = feature_io.ReadFromFile(
       cmd_args.features_2_path)
   num_features_2 = locations_2.shape[0]
-  tf.compat.v1.logging.info("Loaded image 2's %d features" % num_features_2)
+  print(f"Loaded image 2's {num_features_2} features")
 
   # Find nearest-neighbor matches using a KD tree.
   d1_tree = spatial.cKDTree(descriptors_1)
@@ -84,7 +81,7 @@ def main(unused_argv):
                               residual_threshold=20,
                               max_trials=1000)
 
-  tf.compat.v1.logging.info('Found %d inliers' % sum(inliers))
+  print(f'Found {sum(inliers)} inliers')
 
   # Visualize correspondences, and save to file.
   _, ax = plt.subplots()

research/delf/delf/python/google_landmarks_dataset/dataset_file_io_test.py

@@ -20,16 +20,19 @@ from __future__ import print_function
 
 import os
 
+from absl import flags
 import tensorflow as tf
 
 from delf.python.google_landmarks_dataset import dataset_file_io
 
+FLAGS = flags.FLAGS
+
 
 class DatasetFileIoTest(tf.test.TestCase):
 
   def testReadRecognitionSolutionWorks(self):
     # Define inputs.
-    file_path = os.path.join(tf.compat.v1.test.get_temp_dir(),
+    file_path = os.path.join(FLAGS.test_tmpdir,
                              'recognition_solution.csv')
     with tf.io.gfile.GFile(file_path, 'w') as f:
       f.write('id,landmarks,Usage\n')
@@ -61,7 +64,7 @@ class DatasetFileIoTest(tf.test.TestCase):
 
   def testReadRetrievalSolutionWorks(self):
     # Define inputs.
-    file_path = os.path.join(tf.compat.v1.test.get_temp_dir(),
+    file_path = os.path.join(FLAGS.test_tmpdir,
                              'retrieval_solution.csv')
     with tf.io.gfile.GFile(file_path, 'w') as f:
       f.write('id,images,Usage\n')
@@ -93,7 +96,7 @@ class DatasetFileIoTest(tf.test.TestCase):
 
   def testReadRecognitionPredictionsWorks(self):
     # Define inputs.
-    file_path = os.path.join(tf.compat.v1.test.get_temp_dir(),
+    file_path = os.path.join(FLAGS.test_tmpdir,
                              'recognition_predictions.csv')
     with tf.io.gfile.GFile(file_path, 'w') as f:
       f.write('id,landmarks\n')
@@ -131,7 +134,7 @@ class DatasetFileIoTest(tf.test.TestCase):
 
   def testReadRetrievalPredictionsWorks(self):
     # Define inputs.
-    file_path = os.path.join(tf.compat.v1.test.get_temp_dir(),
+    file_path = os.path.join(FLAGS.test_tmpdir,
                              'retrieval_predictions.csv')
     with tf.io.gfile.GFile(file_path, 'w') as f:
       f.write('id,images\n')

research/delf/delf/python/utils.py

+# 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.
+# ==============================================================================
+"""Helper functions for DELF."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from PIL import Image
+from PIL import ImageFile
+import tensorflow as tf
+
+# To avoid PIL crashing for truncated (corrupted) images.
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+def RgbLoader(path):
+  """Helper function to read image with PIL.
+
+  Args:
+    path: Path to image to be loaded.
+
+  Returns:
+    PIL image in RGB format.
+  """
+  with tf.io.gfile.GFile(path, 'rb') as f:
+    img = Image.open(f)
+    return img.convert('RGB')
+