add flag for saving images to summary; strings moved to common.py'

research/object_detection/README.md

@@ -29,6 +29,7 @@ https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.go
 
 * Jonathan Huang, github: [jch1](https://github.com/jch1)
 * Vivek Rathod, github: [tombstone](https://github.com/tombstone)
+* Ronny Votel, github: [ronnyvotel](https://github.com/ronnyvotel)
 * Derek Chow, github: [derekjchow](https://github.com/derekjchow)
 * Chen Sun, github: [jesu9](https://github.com/jesu9)
 * Menglong Zhu, github: [dreamdragon](https://github.com/dreamdragon)
@@ -89,6 +90,16 @@ reporting an issue.
 
 ## Release information
 
+### April 2, 2018
+
+Supercharge your mobile phones with the next generation mobile object detector!
+We are adding support for MobileNet V2 with SSDLite presented in
+[MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381).
+This model is 35% faster than Mobilenet V1 SSD on a Google Pixel phone CPU (200ms vs. 270ms) at the same accuracy.
+Along with the model definition, we are also releasing a model checkpoint trained on the COCO dataset.
+
+<b>Thanks to contributors</b>: Menglong Zhu, Mark Sandler, Zhichao Lu, Vivek Rathod, Jonathan Huang
+
 ### February 9, 2018
 
 We now support instance segmentation!!  In this API update we support a number of instance segmentation models similar to those discussed in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). For further details refer to

research/object_detection/core/box_list_ops_test.py

@@ -17,6 +17,7 @@
 import numpy as np
 import tensorflow as tf
 from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
 
 from object_detection.core import box_list
 from object_detection.core import box_list_ops
@@ -509,9 +510,13 @@ class BoxListOpsTest(tf.test.TestCase):
       with self.assertRaises(ValueError):
         box_list_ops.sort_by_field(boxes, 'misc')
 
-      with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError,
-                                               'Incorrect field size'):
-        sess.run(box_list_ops.sort_by_field(boxes, 'weights').get())
+      if ops._USE_C_API:
+        with self.assertRaises(ValueError):
+          box_list_ops.sort_by_field(boxes, 'weights')
+      else:
+        with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError,
+                                                 'Incorrect field size'):
+          sess.run(box_list_ops.sort_by_field(boxes, 'weights').get())
 
   def test_visualize_boxes_in_image(self):
     image = tf.zeros((6, 4, 3))

research/object_detection/core/preprocessor.py

@@ -2279,7 +2279,11 @@ def resize_image(image,
         return new_masks
 
       def reshape_masks_branch():
-        new_masks = tf.reshape(masks, [0, new_size[0], new_size[1]])
+        # The shape function will be computed for both branches of the
+        # condition, regardless of which branch is actually taken. Make sure
+        # that we don't trigger an assertion in the shape function when trying
+        # to reshape a non empty tensor into an empty one.
+        new_masks = tf.reshape(masks, [-1, new_size[0], new_size[1]])
         return new_masks
 
       masks = tf.cond(num_instances > 0, resize_masks_branch,

research/object_detection/dataset_tools/download_and_preprocess_mscoco.sh

@@ -64,7 +64,7 @@ cd ${SCRATCH_DIR}
 # Download the images.
 BASE_IMAGE_URL="http://images.cocodataset.org/zips"
 
-# TRAIN_IMAGE_FILE="train2017.zip"
+TRAIN_IMAGE_FILE="train2017.zip"
 download_and_unzip ${BASE_IMAGE_URL} ${TRAIN_IMAGE_FILE}
 TRAIN_IMAGE_DIR="${SCRATCH_DIR}/train2017"
 
@@ -91,7 +91,7 @@ download_and_unzip ${BASE_IMAGE_INFO_URL} ${IMAGE_INFO_FILE}
 
 TESTDEV_ANNOTATIONS_FILE="${SCRATCH_DIR}/annotations/image_info_test-dev2017.json"
 
-# # Build TFRecords of the image data.
+# Build TFRecords of the image data.
 cd "${CURRENT_DIR}"
 python object_detection/dataset_tools/create_coco_tf_record.py \
   --logtostderr \