Open source MnasFPN and minor fixes to OD API (#8484)

310447280  by lzc:

    Internal change

310420845  by Zhichao Lu:

    Open source the internal Context RCNN code.

--
310362339  by Zhichao Lu:

    Internal change

310259448  by lzc:

    Update required TF version for OD API.

--
310252159  by Zhichao Lu:

    Port patch_ops_test to TF1/TF2 as TPUs.

--
310247180  by Zhichao Lu:

    Ignore keypoint heatmap loss in the regions/bounding boxes with target keypoint
    class but no valid keypoint annotations.

--
310178294  by Zhichao Lu:

    Opensource MnasFPN
    https://arxiv.org/abs/1912.01106

--
310094222  by lzc:

    Internal changes.

--
310085250  by lzc:

    Internal Change.

--
310016447  by huizhongc:

    Remove unrecognized classes from labeled_classes.

--
310009470  by rathodv:

    Mark batcher.py as TF1 only.

--
310001984  by rathodv:

    Update core/preprocessor.py to be compatible with TF1/TF2..

--
309455035  by Zhichao Lu:

    Makes the freezable_batch_norm_test run w/ v2 behavior.

    The main change is in v2 updates will happen right away when running batchnorm in training mode. So, we need to restore the weights between batchnorm calls to make sure the numerical checks all start from the same place.

--
309425881  by Zhichao Lu:

    Make TF1/TF2 optimizer builder tests explicit.

--
309408646  by Zhichao Lu:

    Make dataset builder tests TF1 and TF2 compatible.

--
309246305  by Zhichao Lu:

    Added the functionality of combining the person keypoints and object detection
    annotations in the binary that converts the COCO raw data to TfRecord.

--
309125076  by Zhichao Lu:

    Convert target_assigner_utils to TF1/TF2.

--
308966359  by huizhongc:

    Support SSD training with partially labeled groundtruth.

--
308937159  by rathodv:

    Update core/target_assigner.py to be compatible with TF1/TF2.

--
308774302  by Zhichao Lu:

    Internal

--
308732860  by rathodv:

    Make core/prefetcher.py  compatible with TF1 only.

--
308726984  by rathodv:

    Update core/multiclass_nms_test.py to be TF1/TF2 compatible.

--
308714718  by rathodv:

    Update core/region_similarity_calculator_test.py to be TF1/TF2 compatible.

--
308707960  by rathodv:

    Update core/minibatch_sampler_test.py to be TF1/TF2 compatible.

--
308700595  by rathodv:

    Update core/losses_test.py to be TF1/TF2 compatible and remove losses_test_v2.py

--
308361472  by rathodv:

    Update core/matcher_test.py to be TF1/TF2 compatible.

--
308335846  by Zhichao Lu:

    Updated the COCO evaluation logics and populated the groundturth area
    information through. This change matches the groundtruth format expected by the
    COCO keypoint evaluation.

--
308256924  by rathodv:

    Update core/keypoints_ops_test.py to be TF1/TF2 compatible.

--
308256826  by rathodv:

    Update class_agnostic_nms_test.py to be TF1/TF2 compatible.

--
308256112  by rathodv:

    Update box_list_ops_test.py to be TF1/TF2 compatible.

--
308159360  by Zhichao Lu:

    Internal change

308145008  by Zhichao Lu:

    Added 'image/class/confidence' field in the TFExample decoder.

--
307651875  by rathodv:

    Refactor core/box_list.py to support TF1/TF2.

--
307651798  by rathodv:

    Modify box_coder.py base class to work with with TF1/TF2

--
307651652  by rathodv:

    Refactor core/balanced_positive_negative_sampler.py to support TF1/TF2.

--
307651571  by rathodv:

    Modify BoxCoders tests to use test_case:execute method to allow testing with TF1.X and TF2.X

--
307651480  by rathodv:

    Modify Matcher tests to use test_case:execute method to allow testing with TF1.X and TF2.X

--
307651409  by rathodv:

    Modify AnchorGenerator tests to use test_case:execute method to allow testing with TF1.X and TF2.X

--
307651314  by rathodv:

    Refactor model_builder to support TF1 or TF2 models based on TensorFlow version.

--
307092053  by Zhichao Lu:

    Use manager to save checkpoint.

--
307071352  by ronnyvotel:

    Fixing keypoint visibilities. Now by default, the visibility is marked True if the keypoint is labeled (regardless of whether it is visible or not).
    Also, if visibilities are not present in the dataset, they will be created based on whether the keypoint coordinates are finite (vis = True) or NaN (vis = False).

--
307069557  by Zhichao Lu:

    Internal change to add few fields related to postprocessing parameters in
    center_net.proto and populate those parameters to the keypoint postprocessing
    functions.

--
307012091  by Zhichao Lu:

    Make Adam Optimizer's epsilon proto configurable.

    Potential issue: tf.compat.v1's AdamOptimizer has a default epsilon on 1e-08 ([doc-link](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/AdamOptimizer))  whereas tf.keras's AdamOptimizer has default epsilon 1e-07 ([doc-link](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam))

--
306858598  by Zhichao Lu:

    Internal changes to update the CenterNet model:
    1) Modified eval job loss computation to avoid averaging over batches with zero loss.
    2) Updated CenterNet keypoint heatmap target assigner to apply box size to heatmap Guassian standard deviation.
    3) Updated the CenterNet meta arch keypoint losses computation to apply weights outside of loss function.

--
306731223  by jonathanhuang:

    Internal change.

--
306549183  by rathodv:

    Internal Update.

--
306542930  by rathodv:

    Internal Update

--
306322697  by rathodv:

    Internal.

--
305345036  by Zhichao Lu:

    Adding COCO Camera Traps Json to tf.Example beam code

--
304104869  by lzc:

    Internal changes.

--
304068971  by jonathanhuang:

    Internal change.

--
304050469  by Zhichao Lu:

    Internal change.

--
303880642  by huizhongc:

    Support parsing partially labeled groundtruth.

--
303841743  by Zhichao Lu:

    Deprecate nms_on_host in SSDMetaArch.

--
303803204  by rathodv:

    Internal change.

--
303793895  by jonathanhuang:

    Internal change.

--
303467631  by rathodv:

    Py3 update for detection inference test.

--
303444542  by rathodv:

    Py3 update to metrics module

--
303421960  by rathodv:

    Update json_utils to python3.

--
302787583  by ronnyvotel:

    Coco results generator for submission to the coco test server.

--
302719091  by Zhichao Lu:

    Internal change to add the ResNet50 image feature extractor for CenterNet model.

--
302116230  by Zhichao Lu:

    Added the functions to overlay the heatmaps with images in visualization util
    library.

--
301888316  by Zhichao Lu:

    Fix checkpoint_filepath not defined error.

--
301840312  by ronnyvotel:

    Adding keypoint_scores to visualizations.

--
301683475  by ronnyvotel:

    Introducing the ability to preprocess `keypoint_visibilities`.

    Some data augmentation ops such as random crop can filter instances and keypoints. It's important to also filter keypoint visibilities, so that the groundtruth tensors are always in alignment.

--
301532344  by Zhichao Lu:

    Don't use tf.divide since "Quantization not yet supported for op: DIV"

--
301480348  by ronnyvotel:

    Introducing keypoint evaluation into model lib v2.
    Also, making some fixes to coco keypoint evaluation.

--
301454018  by Zhichao Lu:

    Added the image summary to visualize the train/eval input images and eval's
    prediction/groundtruth side-by-side image.

--
301317527  by Zhichao Lu:

    Updated the random_absolute_pad_image function in the preprocessor library to
    support the keypoints argument.

--
301300324  by Zhichao Lu:

    Apply name change(experimental_run_v2 -> run) for all callers in Tensorflow.

--
301297115  by ronnyvotel:

    Utility function for setting keypoint visibilities based on keypoint coordinates.

--
301248885  by Zhichao Lu:

    Allow MultiworkerMirroredStrategy(MWMS) use by adding checkpoint handling with temporary directories in model_lib_v2. Added missing WeakKeyDictionary cfer_fn_cache field in CollectiveAllReduceStrategyExtended.

--
301224559  by Zhichao Lu:

    ...1) Fixes model_lib to also use keypoints while preparing model groundtruth.
    ...2) Tests model_lib with newly added keypoint metrics config.

--
300836556  by Zhichao Lu:

    Internal changes to add keypoint estimation parameters in CenterNet proto.

--
300795208  by Zhichao Lu:

    Updated the eval_util library to populate the keypoint groundtruth to
    eval_dict.

--
299474766  by Zhichao Lu:

    ...Modifies eval_util to create Keypoint Evaluator objects when configured in eval config.

--
299453920  by Zhichao Lu:

    Add swish activation as a hyperperams option.

--
299240093  by ronnyvotel:

    Keypoint postprocessing for CenterNetMetaArch.

--
299176395  by Zhichao Lu:

    Internal change.

--
299135608  by Zhichao Lu:

    Internal changes to refactor the CenterNet model in preparation for keypoint estimation tasks.

--
298915482  by Zhichao Lu:

    Make dataset_builder aware of input_context for distributed training.

--
298713595  by Zhichao Lu:

    Handling data with negative size boxes.

--
298695964  by Zhichao Lu:

    Expose change_coordinate_frame as a config parameter; fix multiclass_scores optional field.

--
298492150  by Zhichao Lu:

    Rename optimizer_builder_test_v2.py -> optimizer_builder_v2_test.py

--
298476471  by Zhichao Lu:

    Internal changes to support CenterNet keypoint estimation.

--
298365851  by ronnyvotel:

    Fixing a bug where groundtruth_keypoint_weights were being padded with a dynamic dimension.

--
297843700  by Zhichao Lu:

    Internal change.

--
297706988  by lzc:

    Internal change.

--
297705287  by ronnyvotel:

    Creating the "snapping" behavior in CenterNet, where regressed keypoints are refined with updated candidate keypoints from a heatmap.

--
297700447  by Zhichao Lu:

    Improve checkpoint checking logic with TF2 loop.

--
297686094  by Zhichao Lu:

    Convert "import tensorflow as tf" to "import tensorflow.compat.v1".

--
297670468  by lzc:

    Internal change.

--
297241327  by Zhichao Lu:

    Convert "import tensorflow as tf" to "import tensorflow.compat.v1".

--
297205959  by Zhichao Lu:

    Internal changes to support refactored the centernet object detection target assigner into a separate library.

--
297143806  by Zhichao Lu:

    Convert "import tensorflow as tf" to "import tensorflow.compat.v1".

--
297129625  by Zhichao Lu:

    Explicitly replace "import tensorflow" with "tensorflow.compat.v1" for TF2.x migration

--
297117070  by Zhichao Lu:

    Explicitly replace "import tensorflow" with "tensorflow.compat.v1" for TF2.x migration

--
297030190  by Zhichao Lu:

    Add configuration options for visualizing keypoint edges

--
296359649  by Zhichao Lu:

    Support DepthwiseConv2dNative (of separable conv) in weight equalization loss.

--
296290582  by Zhichao Lu:

    Internal change.

--
296093857  by Zhichao Lu:

    Internal changes to add general target assigner utilities.

--
295975116  by Zhichao Lu:

    Fix visualize_boxes_and_labels_on_image_array to show max_boxes_to_draw correctly.

--
295819711  by Zhichao Lu:

    Adds a flag to visualize_boxes_and_labels_on_image_array to skip the drawing of axis aligned bounding boxes.

--
295811929  by Zhichao Lu:

    Keypoint support in random_square_crop_by_scale.

--
295788458  by rathodv:

    Remove unused checkpoint to reduce repo size on github

--
295787184  by Zhichao Lu:

    Enable visualization of edges between keypoints

--
295763508  by Zhichao Lu:

    [Context RCNN] Add an option to enable / disable cropping feature in the post
    process step in the meta archtecture.

--
295605344  by Zhichao Lu:

    internal change.

--
294926050  by ronnyvotel:

    Adding per-keypoint groundtruth weights. These weights are intended to be used as multipliers in a keypoint loss function.

    Groundtruth keypoint weights are constructed as follows:
    - Initialize the weight for each keypoint type based on user-specified weights in the input_reader proto
    - Mask out (i.e. make zero) all keypoint weights that are not visible.

--
294829061  by lzc:

    Internal change.

--
294566503  by Zhichao Lu:

    Changed internal CenterNet Model configuration.

--
294346662  by ronnyvotel:

    Using NaN values in keypoint coordinates that are not visible.

--
294333339  by Zhichao Lu:

    Change experimetna_distribute_dataset -> experimental_distribute_dataset_from_function

--
293928752  by Zhichao Lu:

    Internal change

--
293909384  by Zhichao Lu:

    Add capabilities to train 1024x1024 CenterNet models.

--
293637554  by ronnyvotel:

    Adding keypoint visibilities to TfExampleDecoder.

--
293501558  by lzc:

    Internal change.

--
293252851  by Zhichao Lu:

    Change tf.gfile.GFile to tf.io.gfile.GFile.

--
292730217  by Zhichao Lu:

    Internal change.

--
292456563  by lzc:

    Internal changes.

--
292355612  by Zhichao Lu:

    Use tf.gather and tf.scatter_nd instead of matrix ops.

--
292245265  by rathodv:

    Internal

--
291989323  by richardmunoz:

    Refactor out building a DataDecoder from building a tf.data.Dataset.

--
291950147  by Zhichao Lu:

    Flip bounding boxes in arbitrary shaped tensors.

--
291401052  by huizhongc:

    Fix multiscale grid anchor generator to allow fully convolutional inference. When exporting model with identity_resizer as image_resizer, there is an incorrect box offset on the detection results. We add the anchor offset to address this problem.

--
291298871  by Zhichao Lu:

    Py3 compatibility changes.

--
290957957  by Zhichao Lu:

    Hourglass feature extractor for CenterNet.

--
290564372  by Zhichao Lu:

    Internal change.

--
290155278  by rathodv:

    Remove Dataset Explorer.

--
290155153  by Zhichao Lu:

    Internal change

--
290122054  by Zhichao Lu:

    Unify the format in the faster_rcnn.proto

--
290116084  by Zhichao Lu:

    Deprecate tensorflow.contrib.

--
290100672  by Zhichao Lu:

    Update MobilenetV3 SSD candidates

--
289926392  by Zhichao Lu:

    Internal change

--
289553440  by Zhichao Lu:

    [Object Detection API] Fix the comments about the dimension of the rpn_box_encodings from 4-D to 3-D.

--
288994128  by lzc:

    Internal changes.

--
288942194  by lzc:

    Internal change.

--
288746124  by Zhichao Lu:

    Configurable channel mean/std. dev in CenterNet feature extractors.

--
288552509  by rathodv:

    Internal.

--
288541285  by rathodv:

    Internal update.

--
288396396  by Zhichao Lu:

    Make object detection import contrib explicitly

--
288255791  by rathodv:

    Internal

--
288078600  by Zhichao Lu:

    Fix model_lib_v2 test

--
287952244  by rathodv:

    Internal

--
287921774  by Zhichao Lu:

    internal change

--
287906173  by Zhichao Lu:

    internal change

--
287889407  by jonathanhuang:

    PY3 compatibility

--
287889042  by rathodv:

    Internal

--
287876178  by Zhichao Lu:

    Internal change.

--
287770490  by Zhichao Lu:

    Add CenterNet proto and builder

--
287694213  by Zhichao Lu:

    Support for running multiple steps per tf.function call.

--
287377183  by jonathanhuang:

    PY3 compatibility

--
287371344  by rathodv:

    Support loading keypoint labels and ids.

--
287368213  by rathodv:

    Add protos supporting keypoint evaluation.

--
286673200  by rathodv:

    dataset_tools PY3 migration

--
286635106  by Zhichao Lu:

    Update code for upcoming tf.contrib removal

--
286479439  by Zhichao Lu:

    Internal change

--
286311711  by Zhichao Lu:

    Skeleton of context model within TFODAPI

--
286005546  by Zhichao Lu:

    Fix Faster-RCNN training when using keep_aspect_ratio_resizer with pad_to_max_dimension

--
285906400  by derekjchow:

    Internal change

--
285822795  by Zhichao Lu:

    Add CenterNet meta arch target assigners.

--
285447238  by Zhichao Lu:

    Internal changes.

--
285016927  by Zhichao Lu:

    Make _dummy_computation a tf.function. This fixes breakage caused by
    cl/284256438

--
284827274  by Zhichao Lu:

    Convert to python 3.

--
284645593  by rathodv:

    Internal change

--
284639893  by rathodv:

    Add missing documentation for keypoints in eval_util.py.

--
284323712  by Zhichao Lu:

    Internal changes.

--
284295290  by Zhichao Lu:

    Updating input config proto and dataset builder to include context fields

    Updating standard_fields and tf_example_decoder to include context features

--
284226821  by derekjchow:

    Update exporter.

--
284211030  by Zhichao Lu:

    API changes in CenterNet informed by the experiments with hourlgass network.

--
284190451  by Zhichao Lu:

    Add support for CenterNet losses in protos and builders.

--
284093961  by lzc:

    Internal changes.

--
284028174  by Zhichao Lu:

    Internal change

--
284014719  by derekjchow:

    Do not pad top_down feature maps unnecessarily.

--
284005765  by Zhichao Lu:

    Add new pad_to_multiple_resizer

--
283858233  by Zhichao Lu:

    Make target assigner work when under tf.function.

--
283836611  by Zhichao Lu:

    Make config getters more general.

--
283808990  by Zhichao Lu:

    Internal change

--
283754588  by Zhichao Lu:

    Internal changes.

--
282460301  by Zhichao Lu:

    Add ability to restore v2 style checkpoints.

--
281605842  by lzc:

    Add option to disable loss computation in OD API eval job.

--
280298212  by Zhichao Lu:

    Add backwards compatible change

--
280237857  by Zhichao Lu:

    internal change

--

PiperOrigin-RevId: 310447280

research/object_detection/README.md

-![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow Requirement: 1.15](https://img.shields.io/badge/TensorFlow%20Requirement-1.15-brightgreen)
 ![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
 
 # Tensorflow Object Detection API
@@ -31,7 +31,7 @@ https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.go
 
 | Name | GitHub |
 | --- | --- |
-| Jonathan Huang | [jch1](https://github.com/jch1) | 
+| Jonathan Huang | [jch1](https://github.com/jch1) |
 | Vivek Rathod | [tombstone](https://github.com/tombstone) |
 | Ronny Votel | [ronnyvotel](https://github.com/ronnyvotel) |
 | Derek Chow | [derekjchow](https://github.com/derekjchow) |
@@ -40,7 +40,6 @@ https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.go
 | Alireza Fathi | [afathi3](https://github.com/afathi3) |
 | Zhichao Lu | [pkulzc](https://github.com/pkulzc) |
 
-
 ## Table of contents
 
 Setup:
@@ -105,6 +104,25 @@ reporting an issue.
 
 ## Release information
 
+### May 7th, 2020
+We have released a mobile model with the
+[MnasFPN head](https://arxiv.org/abs/1912.01106).
+
+
+* MnasFPN with MobileNet-V2 backbone is the most accurate (26.6 mAP at 183ms on
+Pixel 1) mobile detection model we have released to date. With depth-multiplier,
+MnasFPN with MobileNet-V2 backbone is 1.8 mAP higher than MobileNet-V3-Large
+with SSDLite (23.8 mAP vs 22.0 mAP) at similar latency (120ms) on Pixel 1.
+
+We have released model definition, model checkpoints trained on
+the COCO14 dataset and a converted TFLite model.
+
+<b>Thanks to contributors</b>: Bo Chen, Golnaz Ghiasi, Hanxiao Liu,
+Tsung-Yi Lin, Dmitry Kalenichenko, Hartwig Adam, Quoc Le, Zhichao Lu,
+Jonathan Huang.
+
+
+
 ### Nov 13th, 2019
 We have released MobileNetEdgeTPU SSDLite model.
 

research/object_detection/anchor_generators/flexible_grid_anchor_generator_test.py

@@ -24,52 +24,51 @@ from object_detection.utils import test_case
 class FlexibleGridAnchorGeneratorTest(test_case.TestCase):
 
   def test_construct_single_anchor(self):
-    anchor_strides = [(32, 32),]
-    anchor_offsets = [(16, 16),]
-    base_sizes = [(128.0,)]
-    aspect_ratios = [(1.0,)]
-    im_height = 64
-    im_width = 64
-    feature_map_shape_list = [(2, 2)]
+    def graph_fn():
+      anchor_strides = [(32, 32),]
+      anchor_offsets = [(16, 16),]
+      base_sizes = [(128.0,)]
+      aspect_ratios = [(1.0,)]
+      im_height = 64
+      im_width = 64
+      feature_map_shape_list = [(2, 2)]
+      anchor_generator = fg.FlexibleGridAnchorGenerator(
+          base_sizes, aspect_ratios, anchor_strides, anchor_offsets,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list, im_height=im_height, im_width=im_width)
+      anchor_corners = anchors_list[0].get()
+      return anchor_corners
+    anchor_corners_out = self.execute(graph_fn, [])
     exp_anchor_corners = [[-48, -48, 80, 80],
                           [-48, -16, 80, 112],
                           [-16, -48, 112, 80],
                           [-16, -16, 112, 112]]
-    anchor_generator = fg.FlexibleGridAnchorGenerator(
-        base_sizes, aspect_ratios, anchor_strides, anchor_offsets,
-        normalize_coordinates=False)
-    anchors_list = anchor_generator.generate(
-        feature_map_shape_list, im_height=im_height, im_width=im_width)
-    anchor_corners = anchors_list[0].get()
-
-    with self.test_session():
-      anchor_corners_out = anchor_corners.eval()
-      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
 
   def test_construct_single_anchor_unit_dimensions(self):
-    anchor_strides = [(32, 32),]
-    anchor_offsets = [(16, 16),]
-    base_sizes = [(32.0,)]
-    aspect_ratios = [(1.0,)]
-    im_height = 1
-    im_width = 1
-    feature_map_shape_list = [(2, 2)]
+    def graph_fn():
+      anchor_strides = [(32, 32),]
+      anchor_offsets = [(16, 16),]
+      base_sizes = [(32.0,)]
+      aspect_ratios = [(1.0,)]
+      im_height = 1
+      im_width = 1
+      feature_map_shape_list = [(2, 2)]
+      anchor_generator = fg.FlexibleGridAnchorGenerator(
+          base_sizes, aspect_ratios, anchor_strides, anchor_offsets,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list, im_height=im_height, im_width=im_width)
+      anchor_corners = anchors_list[0].get()
+      return anchor_corners
     # Positive offsets are produced.
     exp_anchor_corners = [[0, 0, 32, 32],
                           [0, 32, 32, 64],
                           [32, 0, 64, 32],
                           [32, 32, 64, 64]]
-
-    anchor_generator = fg.FlexibleGridAnchorGenerator(
-        base_sizes, aspect_ratios, anchor_strides, anchor_offsets,
-        normalize_coordinates=False)
-    anchors_list = anchor_generator.generate(
-        feature_map_shape_list, im_height=im_height, im_width=im_width)
-    anchor_corners = anchors_list[0].get()
-
-    with self.test_session():
-      anchor_corners_out = anchor_corners.eval()
-      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
 
   def test_construct_normalized_anchors_fails_with_unit_dimensions(self):
     anchor_generator = fg.FlexibleGridAnchorGenerator(
@@ -80,27 +79,27 @@ class FlexibleGridAnchorGeneratorTest(test_case.TestCase):
           feature_map_shape_list=[(2, 2)], im_height=1, im_width=1)
 
   def test_construct_single_anchor_in_normalized_coordinates(self):
-    anchor_strides = [(32, 32),]
-    anchor_offsets = [(16, 16),]
-    base_sizes = [(128.0,)]
-    aspect_ratios = [(1.0,)]
-    im_height = 64
-    im_width = 128
-    feature_map_shape_list = [(2, 2)]
+    def graph_fn():
+      anchor_strides = [(32, 32),]
+      anchor_offsets = [(16, 16),]
+      base_sizes = [(128.0,)]
+      aspect_ratios = [(1.0,)]
+      im_height = 64
+      im_width = 128
+      feature_map_shape_list = [(2, 2)]
+      anchor_generator = fg.FlexibleGridAnchorGenerator(
+          base_sizes, aspect_ratios, anchor_strides, anchor_offsets,
+          normalize_coordinates=True)
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list, im_height=im_height, im_width=im_width)
+      anchor_corners = anchors_list[0].get()
+      return anchor_corners
     exp_anchor_corners = [[-48./64, -48./128, 80./64, 80./128],
                           [-48./64, -16./128, 80./64, 112./128],
                           [-16./64, -48./128, 112./64, 80./128],
                           [-16./64, -16./128, 112./64, 112./128]]
-    anchor_generator = fg.FlexibleGridAnchorGenerator(
-        base_sizes, aspect_ratios, anchor_strides, anchor_offsets,
-        normalize_coordinates=True)
-    anchors_list = anchor_generator.generate(
-        feature_map_shape_list, im_height=im_height, im_width=im_width)
-    anchor_corners = anchors_list[0].get()
-
-    with self.test_session():
-      anchor_corners_out = anchor_corners.eval()
-      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
 
   def test_num_anchors_per_location(self):
     anchor_strides = [(32, 32), (64, 64)]
@@ -115,29 +114,28 @@ class FlexibleGridAnchorGeneratorTest(test_case.TestCase):
     self.assertEqual(anchor_generator.num_anchors_per_location(), [6, 6])
 
   def test_construct_single_anchor_dynamic_size(self):
-    anchor_strides = [(32, 32),]
-    anchor_offsets = [(0, 0),]
-    base_sizes = [(128.0,)]
-    aspect_ratios = [(1.0,)]
-    im_height = tf.constant(64)
-    im_width = tf.constant(64)
-    feature_map_shape_list = [(2, 2)]
+    def graph_fn():
+      anchor_strides = [(32, 32),]
+      anchor_offsets = [(0, 0),]
+      base_sizes = [(128.0,)]
+      aspect_ratios = [(1.0,)]
+      im_height = tf.constant(64)
+      im_width = tf.constant(64)
+      feature_map_shape_list = [(2, 2)]
+      anchor_generator = fg.FlexibleGridAnchorGenerator(
+          base_sizes, aspect_ratios, anchor_strides, anchor_offsets,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list, im_height=im_height, im_width=im_width)
+      anchor_corners = anchors_list[0].get()
+      return anchor_corners
     # Zero offsets are used.
     exp_anchor_corners = [[-64, -64, 64, 64],
                           [-64, -32, 64, 96],
                           [-32, -64, 96, 64],
                           [-32, -32, 96, 96]]
-
-    anchor_generator = fg.FlexibleGridAnchorGenerator(
-        base_sizes, aspect_ratios, anchor_strides, anchor_offsets,
-        normalize_coordinates=False)
-    anchors_list = anchor_generator.generate(
-        feature_map_shape_list, im_height=im_height, im_width=im_width)
-    anchor_corners = anchors_list[0].get()
-
-    with self.test_session():
-      anchor_corners_out = anchor_corners.eval()
-      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+    anchor_corners_out = self.execute_cpu(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
 
   def test_construct_single_anchor_with_odd_input_dimension(self):
 

research/object_detection/anchor_generators/multiple_grid_anchor_generator.py

@@ -212,7 +212,7 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
     min_im_shape = tf.minimum(im_height, im_width)
     scale_height = min_im_shape / im_height
     scale_width = min_im_shape / im_width
-    if not tf.contrib.framework.is_tensor(self._base_anchor_size):
+    if not tf.is_tensor(self._base_anchor_size):
       base_anchor_size = [
           scale_height * tf.constant(self._base_anchor_size[0],
                                      dtype=tf.float32),

research/object_detection/anchor_generators/multiscale_grid_anchor_generator.py

@@ -20,6 +20,8 @@ described in:
 T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar
 """
 
+import tensorflow as tf
+
 from object_detection.anchor_generators import grid_anchor_generator
 from object_detection.core import anchor_generator
 from object_detection.core import box_list_ops
@@ -85,8 +87,10 @@ class MultiscaleGridAnchorGenerator(anchor_generator.AnchorGenerator):
   def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
     """Generates a collection of bounding boxes to be used as anchors.
 
-    Currently we require the input image shape to be statically defined.  That
-    is, im_height and im_width should be integers rather than tensors.
+    For training, we require the input image shape to be statically defined.
+    That is, im_height and im_width should be integers rather than tensors.
+    For inference, im_height and im_width can be either integers (for fixed
+    image size), or tensors (for arbitrary image size).
 
     Args:
       feature_map_shape_list: list of pairs of convnet layer resolutions in the
@@ -124,6 +128,9 @@ class MultiscaleGridAnchorGenerator(anchor_generator.AnchorGenerator):
           anchor_offset[0] = stride / 2.0
         if im_width % 2.0**level == 0 or im_width == 1:
           anchor_offset[1] = stride / 2.0
+      if tf.is_tensor(im_height) and tf.is_tensor(im_width):
+        anchor_offset[0] = stride / 2.0
+        anchor_offset[1] = stride / 2.0
       ag = grid_anchor_generator.GridAnchorGenerator(
           scales,
           aspect_ratios,

research/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py

@@ -24,54 +24,55 @@ from object_detection.utils import test_case
 class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
 
   def test_construct_single_anchor(self):
-    min_level = 5
-    max_level = 5
-    anchor_scale = 4.0
-    aspect_ratios = [1.0]
-    scales_per_octave = 1
-    im_height = 64
-    im_width = 64
-    feature_map_shape_list = [(2, 2)]
+    def graph_fn():
+      min_level = 5
+      max_level = 5
+      anchor_scale = 4.0
+      aspect_ratios = [1.0]
+      scales_per_octave = 1
+      im_height = 64
+      im_width = 64
+      feature_map_shape_list = [(2, 2)]
+      anchor_generator = mg.MultiscaleGridAnchorGenerator(
+          min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list, im_height=im_height, im_width=im_width)
+      anchor_corners = anchors_list[0].get()
+      return anchor_corners
+
     exp_anchor_corners = [[-48, -48, 80, 80],
                           [-48, -16, 80, 112],
                           [-16, -48, 112, 80],
                           [-16, -16, 112, 112]]
-    anchor_generator = mg.MultiscaleGridAnchorGenerator(
-        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
-        normalize_coordinates=False)
-    anchors_list = anchor_generator.generate(
-        feature_map_shape_list, im_height=im_height, im_width=im_width)
-    anchor_corners = anchors_list[0].get()
-
-    with self.test_session():
-      anchor_corners_out = anchor_corners.eval()
-      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
 
   def test_construct_single_anchor_unit_dimensions(self):
-    min_level = 5
-    max_level = 5
-    anchor_scale = 1.0
-    aspect_ratios = [1.0]
-    scales_per_octave = 1
-    im_height = 1
-    im_width = 1
-    feature_map_shape_list = [(2, 2)]
+    def graph_fn():
+      min_level = 5
+      max_level = 5
+      anchor_scale = 1.0
+      aspect_ratios = [1.0]
+      scales_per_octave = 1
+      im_height = 1
+      im_width = 1
+      feature_map_shape_list = [(2, 2)]
+      anchor_generator = mg.MultiscaleGridAnchorGenerator(
+          min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list, im_height=im_height, im_width=im_width)
+      anchor_corners = anchors_list[0].get()
+      return anchor_corners
+
     # Positive offsets are produced.
     exp_anchor_corners = [[0, 0, 32, 32],
                           [0, 32, 32, 64],
                           [32, 0, 64, 32],
                           [32, 32, 64, 64]]
-
-    anchor_generator = mg.MultiscaleGridAnchorGenerator(
-        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
-        normalize_coordinates=False)
-    anchors_list = anchor_generator.generate(
-        feature_map_shape_list, im_height=im_height, im_width=im_width)
-    anchor_corners = anchors_list[0].get()
-
-    with self.test_session():
-      anchor_corners_out = anchor_corners.eval()
-      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
 
   def test_construct_normalized_anchors_fails_with_unit_dimensions(self):
     anchor_generator = mg.MultiscaleGridAnchorGenerator(
@@ -82,28 +83,29 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
           feature_map_shape_list=[(2, 2)], im_height=1, im_width=1)
 
   def test_construct_single_anchor_in_normalized_coordinates(self):
-    min_level = 5
-    max_level = 5
-    anchor_scale = 4.0
-    aspect_ratios = [1.0]
-    scales_per_octave = 1
-    im_height = 64
-    im_width = 128
-    feature_map_shape_list = [(2, 2)]
+    def graph_fn():
+      min_level = 5
+      max_level = 5
+      anchor_scale = 4.0
+      aspect_ratios = [1.0]
+      scales_per_octave = 1
+      im_height = 64
+      im_width = 128
+      feature_map_shape_list = [(2, 2)]
+      anchor_generator = mg.MultiscaleGridAnchorGenerator(
+          min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+          normalize_coordinates=True)
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list, im_height=im_height, im_width=im_width)
+      anchor_corners = anchors_list[0].get()
+      return anchor_corners
+
     exp_anchor_corners = [[-48./64, -48./128, 80./64, 80./128],
                           [-48./64, -16./128, 80./64, 112./128],
                           [-16./64, -48./128, 112./64, 80./128],
                           [-16./64, -16./128, 112./64, 112./128]]
-    anchor_generator = mg.MultiscaleGridAnchorGenerator(
-        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
-        normalize_coordinates=True)
-    anchors_list = anchor_generator.generate(
-        feature_map_shape_list, im_height=im_height, im_width=im_width)
-    anchor_corners = anchors_list[0].get()
-
-    with self.test_session():
-      anchor_corners_out = anchor_corners.eval()
-      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
 
   def test_num_anchors_per_location(self):
     min_level = 5
@@ -117,30 +119,34 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
     self.assertEqual(anchor_generator.num_anchors_per_location(), [6, 6])
 
   def test_construct_single_anchor_dynamic_size(self):
-    min_level = 5
-    max_level = 5
-    anchor_scale = 4.0
-    aspect_ratios = [1.0]
-    scales_per_octave = 1
-    im_height = tf.constant(64)
-    im_width = tf.constant(64)
-    feature_map_shape_list = [(2, 2)]
-    # Zero offsets are used.
+    def graph_fn():
+      min_level = 5
+      max_level = 5
+      anchor_scale = 4.0
+      aspect_ratios = [1.0]
+      scales_per_octave = 1
+      im_height = tf.constant(64)
+      im_width = tf.constant(64)
+      feature_map_shape_list = [(2, 2)]
+      anchor_generator = mg.MultiscaleGridAnchorGenerator(
+          min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+          normalize_coordinates=False)
+      anchors_list = anchor_generator.generate(
+          feature_map_shape_list, im_height=im_height, im_width=im_width)
+      anchor_corners = anchors_list[0].get()
+      return anchor_corners
+
     exp_anchor_corners = [[-64, -64, 64, 64],
                           [-64, -32, 64, 96],
                           [-32, -64, 96, 64],
                           [-32, -32, 96, 96]]
-
-    anchor_generator = mg.MultiscaleGridAnchorGenerator(
-        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
-        normalize_coordinates=False)
-    anchors_list = anchor_generator.generate(
-        feature_map_shape_list, im_height=im_height, im_width=im_width)
-    anchor_corners = anchors_list[0].get()
-
-    with self.test_session():
-      anchor_corners_out = anchor_corners.eval()
-      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+    # Add anchor offset.
+    anchor_offset = 2.0**5 / 2.0
+    exp_anchor_corners = [
+        [b + anchor_offset for b in a] for a in exp_anchor_corners
+    ]
+    anchor_corners_out = self.execute(graph_fn, [])
+    self.assertAllClose(anchor_corners_out, exp_anchor_corners)
 
   def test_construct_single_anchor_with_odd_input_dimension(self):
 

research/object_detection/box_coders/faster_rcnn_box_coder_test.py

@@ -14,80 +14,99 @@
 # ==============================================================================
 
 """Tests for object_detection.box_coder.faster_rcnn_box_coder."""
-
+import numpy as np
 import tensorflow as tf
 
 from object_detection.box_coders import faster_rcnn_box_coder
 from object_detection.core import box_list
+from object_detection.utils import test_case
 
 
-class FasterRcnnBoxCoderTest(tf.test.TestCase):
+class FasterRcnnBoxCoderTest(test_case.TestCase):
 
   def test_get_correct_relative_codes_after_encoding(self):
-    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
-    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
+    boxes = np.array([[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]],
+                     np.float32)
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]],
+                       np.float32)
     expected_rel_codes = [[-0.5, -0.416666, -0.405465, -0.182321],
                           [-0.083333, -0.222222, -0.693147, -1.098612]]
-    boxes = box_list.BoxList(tf.constant(boxes))
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
-    rel_codes = coder.encode(boxes, anchors)
-    with self.test_session() as sess:
-      rel_codes_out, = sess.run([rel_codes])
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, anchors):
+      boxes = box_list.BoxList(boxes)
+      anchors = box_list.BoxList(anchors)
+      coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+    rel_codes_out = self.execute(graph_fn, [boxes, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_get_correct_relative_codes_after_encoding_with_scaling(self):
-    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
-    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
-    scale_factors = [2, 3, 4, 5]
+    boxes = np.array([[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]],
+                     np.float32)
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]],
+                       np.float32)
     expected_rel_codes = [[-1., -1.25, -1.62186, -0.911608],
                           [-0.166667, -0.666667, -2.772588, -5.493062]]
-    boxes = box_list.BoxList(tf.constant(boxes))
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
-        scale_factors=scale_factors)
-    rel_codes = coder.encode(boxes, anchors)
-    with self.test_session() as sess:
-      rel_codes_out, = sess.run([rel_codes])
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, anchors):
+      scale_factors = [2, 3, 4, 5]
+      boxes = box_list.BoxList(boxes)
+      anchors = box_list.BoxList(anchors)
+      coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
+          scale_factors=scale_factors)
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+    rel_codes_out = self.execute(graph_fn, [boxes, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_get_correct_boxes_after_decoding(self):
-    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
-    rel_codes = [[-0.5, -0.416666, -0.405465, -0.182321],
-                 [-0.083333, -0.222222, -0.693147, -1.098612]]
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]],
+                       np.float32)
+    rel_codes = np.array([[-0.5, -0.416666, -0.405465, -0.182321],
+                          [-0.083333, -0.222222, -0.693147, -1.098612]],
+                         np.float32)
     expected_boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
-    boxes = coder.decode(rel_codes, anchors)
-    with self.test_session() as sess:
-      boxes_out, = sess.run([boxes.get()])
-      self.assertAllClose(boxes_out, expected_boxes)
+    def graph_fn(rel_codes, anchors):
+      anchors = box_list.BoxList(anchors)
+      coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
+      boxes = coder.decode(rel_codes, anchors)
+      return boxes.get()
+    boxes_out = self.execute(graph_fn, [rel_codes, anchors])
+    self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_get_correct_boxes_after_decoding_with_scaling(self):
-    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
-    rel_codes = [[-1., -1.25, -1.62186, -0.911608],
-                 [-0.166667, -0.666667, -2.772588, -5.493062]]
-    scale_factors = [2, 3, 4, 5]
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]],
+                       np.float32)
+    rel_codes = np.array([[-1., -1.25, -1.62186, -0.911608],
+                          [-0.166667, -0.666667, -2.772588, -5.493062]],
+                         np.float32)
     expected_boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
-        scale_factors=scale_factors)
-    boxes = coder.decode(rel_codes, anchors)
-    with self.test_session() as sess:
-      boxes_out, = sess.run([boxes.get()])
-      self.assertAllClose(boxes_out, expected_boxes)
+    def graph_fn(rel_codes, anchors):
+      scale_factors = [2, 3, 4, 5]
+      anchors = box_list.BoxList(anchors)
+      coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
+          scale_factors=scale_factors)
+      boxes = coder.decode(rel_codes, anchors).get()
+      return boxes
+    boxes_out = self.execute(graph_fn, [rel_codes, anchors])
+    self.assertAllClose(expected_boxes, boxes_out, rtol=1e-04,
+                        atol=1e-04)
 
   def test_very_small_Width_nan_after_encoding(self):
-    boxes = [[10.0, 10.0, 10.0000001, 20.0]]
-    anchors = [[15.0, 12.0, 30.0, 18.0]]
+    boxes = np.array([[10.0, 10.0, 10.0000001, 20.0]], np.float32)
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0]], np.float32)
     expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826]]
-    boxes = box_list.BoxList(tf.constant(boxes))
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
-    rel_codes = coder.encode(boxes, anchors)
-    with self.test_session() as sess:
-      rel_codes_out, = sess.run([rel_codes])
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, anchors):
+      boxes = box_list.BoxList(boxes)
+      anchors = box_list.BoxList(anchors)
+      coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+    rel_codes_out = self.execute(graph_fn, [boxes, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
+                        atol=1e-04)
 
 
 if __name__ == '__main__':

research/object_detection/box_coders/keypoint_box_coder_test.py

@@ -14,126 +14,137 @@
 # ==============================================================================
 
 """Tests for object_detection.box_coder.keypoint_box_coder."""
-
+import numpy as np
 import tensorflow as tf
 
 from object_detection.box_coders import keypoint_box_coder
 from object_detection.core import box_list
 from object_detection.core import standard_fields as fields
+from object_detection.utils import test_case
 
 
-class KeypointBoxCoderTest(tf.test.TestCase):
+class KeypointBoxCoderTest(test_case.TestCase):
 
   def test_get_correct_relative_codes_after_encoding(self):
-    boxes = [[10., 10., 20., 15.],
-             [0.2, 0.1, 0.5, 0.4]]
-    keypoints = [[[15., 12.], [10., 15.]],
-                 [[0.5, 0.3], [0.2, 0.4]]]
+    boxes = np.array([[10., 10., 20., 15.],
+                      [0.2, 0.1, 0.5, 0.4]], np.float32)
+    keypoints = np.array([[[15., 12.], [10., 15.]],
+                          [[0.5, 0.3], [0.2, 0.4]]], np.float32)
     num_keypoints = len(keypoints[0])
-    anchors = [[15., 12., 30., 18.],
-               [0.1, 0.0, 0.7, 0.9]]
+    anchors = np.array([[15., 12., 30., 18.],
+                        [0.1, 0.0, 0.7, 0.9]], np.float32)
     expected_rel_codes = [
         [-0.5, -0.416666, -0.405465, -0.182321,
          -0.5, -0.5, -0.833333, 0.],
         [-0.083333, -0.222222, -0.693147, -1.098612,
          0.166667, -0.166667, -0.333333, -0.055556]
     ]
-    boxes = box_list.BoxList(tf.constant(boxes))
-    boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
-    rel_codes = coder.encode(boxes, anchors)
-    with self.test_session() as sess:
-      rel_codes_out, = sess.run([rel_codes])
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, keypoints, anchors):
+      boxes = box_list.BoxList(boxes)
+      boxes.add_field(fields.BoxListFields.keypoints, keypoints)
+      anchors = box_list.BoxList(anchors)
+      coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+    rel_codes_out = self.execute(graph_fn, [boxes, keypoints, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_get_correct_relative_codes_after_encoding_with_scaling(self):
-    boxes = [[10., 10., 20., 15.],
-             [0.2, 0.1, 0.5, 0.4]]
-    keypoints = [[[15., 12.], [10., 15.]],
-                 [[0.5, 0.3], [0.2, 0.4]]]
+    boxes = np.array([[10., 10., 20., 15.],
+                      [0.2, 0.1, 0.5, 0.4]], np.float32)
+    keypoints = np.array([[[15., 12.], [10., 15.]],
+                          [[0.5, 0.3], [0.2, 0.4]]], np.float32)
     num_keypoints = len(keypoints[0])
-    anchors = [[15., 12., 30., 18.],
-               [0.1, 0.0, 0.7, 0.9]]
-    scale_factors = [2, 3, 4, 5]
+    anchors = np.array([[15., 12., 30., 18.],
+                        [0.1, 0.0, 0.7, 0.9]], np.float32)
     expected_rel_codes = [
         [-1., -1.25, -1.62186, -0.911608,
          -1.0, -1.5, -1.666667, 0.],
         [-0.166667, -0.666667, -2.772588, -5.493062,
          0.333333, -0.5, -0.666667, -0.166667]
     ]
-    boxes = box_list.BoxList(tf.constant(boxes))
-    boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = keypoint_box_coder.KeypointBoxCoder(
-        num_keypoints, scale_factors=scale_factors)
-    rel_codes = coder.encode(boxes, anchors)
-    with self.test_session() as sess:
-      rel_codes_out, = sess.run([rel_codes])
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, keypoints, anchors):
+      scale_factors = [2, 3, 4, 5]
+      boxes = box_list.BoxList(boxes)
+      boxes.add_field(fields.BoxListFields.keypoints, keypoints)
+      anchors = box_list.BoxList(anchors)
+      coder = keypoint_box_coder.KeypointBoxCoder(
+          num_keypoints, scale_factors=scale_factors)
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+    rel_codes_out = self.execute(graph_fn, [boxes, keypoints, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_get_correct_boxes_after_decoding(self):
-    anchors = [[15., 12., 30., 18.],
-               [0.1, 0.0, 0.7, 0.9]]
-    rel_codes = [
+    anchors = np.array([[15., 12., 30., 18.],
+                        [0.1, 0.0, 0.7, 0.9]], np.float32)
+    rel_codes = np.array([
         [-0.5, -0.416666, -0.405465, -0.182321,
          -0.5, -0.5, -0.833333, 0.],
         [-0.083333, -0.222222, -0.693147, -1.098612,
          0.166667, -0.166667, -0.333333, -0.055556]
-    ]
+    ], np.float32)
     expected_boxes = [[10., 10., 20., 15.],
                       [0.2, 0.1, 0.5, 0.4]]
     expected_keypoints = [[[15., 12.], [10., 15.]],
                           [[0.5, 0.3], [0.2, 0.4]]]
     num_keypoints = len(expected_keypoints[0])
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
-    boxes = coder.decode(rel_codes, anchors)
-    with self.test_session() as sess:
-      boxes_out, keypoints_out = sess.run(
-          [boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
-      self.assertAllClose(boxes_out, expected_boxes)
-      self.assertAllClose(keypoints_out, expected_keypoints)
+    def graph_fn(rel_codes, anchors):
+      anchors = box_list.BoxList(anchors)
+      coder = keypoint_box_coder.KeypointBoxCoder(num_keypoints)
+      boxes = coder.decode(rel_codes, anchors)
+      return boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)
+    boxes_out, keypoints_out = self.execute(graph_fn, [rel_codes, anchors])
+    self.assertAllClose(keypoints_out, expected_keypoints, rtol=1e-04,
+                        atol=1e-04)
+    self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_get_correct_boxes_after_decoding_with_scaling(self):
-    anchors = [[15., 12., 30., 18.],
-               [0.1, 0.0, 0.7, 0.9]]
-    rel_codes = [
+    anchors = np.array([[15., 12., 30., 18.],
+                        [0.1, 0.0, 0.7, 0.9]], np.float32)
+    rel_codes = np.array([
         [-1., -1.25, -1.62186, -0.911608,
          -1.0, -1.5, -1.666667, 0.],
         [-0.166667, -0.666667, -2.772588, -5.493062,
          0.333333, -0.5, -0.666667, -0.166667]
-    ]
-    scale_factors = [2, 3, 4, 5]
+    ], np.float32)
     expected_boxes = [[10., 10., 20., 15.],
                       [0.2, 0.1, 0.5, 0.4]]
     expected_keypoints = [[[15., 12.], [10., 15.]],
                           [[0.5, 0.3], [0.2, 0.4]]]
     num_keypoints = len(expected_keypoints[0])
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = keypoint_box_coder.KeypointBoxCoder(
-        num_keypoints, scale_factors=scale_factors)
-    boxes = coder.decode(rel_codes, anchors)
-    with self.test_session() as sess:
-      boxes_out, keypoints_out = sess.run(
-          [boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)])
-      self.assertAllClose(boxes_out, expected_boxes)
-      self.assertAllClose(keypoints_out, expected_keypoints)
+    def graph_fn(rel_codes, anchors):
+      scale_factors = [2, 3, 4, 5]
+      anchors = box_list.BoxList(anchors)
+      coder = keypoint_box_coder.KeypointBoxCoder(
+          num_keypoints, scale_factors=scale_factors)
+      boxes = coder.decode(rel_codes, anchors)
+      return boxes.get(), boxes.get_field(fields.BoxListFields.keypoints)
+    boxes_out, keypoints_out = self.execute(graph_fn, [rel_codes, anchors])
+    self.assertAllClose(keypoints_out, expected_keypoints, rtol=1e-04,
+                        atol=1e-04)
+    self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_very_small_width_nan_after_encoding(self):
-    boxes = [[10., 10., 10.0000001, 20.]]
-    keypoints = [[[10., 10.], [10.0000001, 20.]]]
-    anchors = [[15., 12., 30., 18.]]
+    boxes = np.array([[10., 10., 10.0000001, 20.]], np.float32)
+    keypoints = np.array([[[10., 10.], [10.0000001, 20.]]], np.float32)
+    anchors = np.array([[15., 12., 30., 18.]], np.float32)
     expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826,
                            -0.833333, -0.833333, -0.833333, 0.833333]]
-    boxes = box_list.BoxList(tf.constant(boxes))
-    boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = keypoint_box_coder.KeypointBoxCoder(2)
-    rel_codes = coder.encode(boxes, anchors)
-    with self.test_session() as sess:
-      rel_codes_out, = sess.run([rel_codes])
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, keypoints, anchors):
+      boxes = box_list.BoxList(boxes)
+      boxes.add_field(fields.BoxListFields.keypoints, keypoints)
+      anchors = box_list.BoxList(anchors)
+      coder = keypoint_box_coder.KeypointBoxCoder(2)
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+    rel_codes_out = self.execute(graph_fn, [boxes, keypoints, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
+                        atol=1e-04)
 
 
 if __name__ == '__main__':

research/object_detection/box_coders/mean_stddev_box_coder_test.py

@@ -14,40 +14,47 @@
 # ==============================================================================
 
 """Tests for object_detection.box_coder.mean_stddev_boxcoder."""
-
+import numpy as np
 import tensorflow as tf
 
 from object_detection.box_coders import mean_stddev_box_coder
 from object_detection.core import box_list
+from object_detection.utils import test_case
 
 
-class MeanStddevBoxCoderTest(tf.test.TestCase):
+class MeanStddevBoxCoderTest(test_case.TestCase):
 
   def testGetCorrectRelativeCodesAfterEncoding(self):
-    box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
-    boxes = box_list.BoxList(tf.constant(box_corners))
+    boxes = np.array([[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]], np.float32)
+    anchors = np.array([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]], np.float32)
     expected_rel_codes = [[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]]
-    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
-    priors = box_list.BoxList(prior_means)
 
-    coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
-    rel_codes = coder.encode(boxes, priors)
-    with self.test_session() as sess:
-      rel_codes_out = sess.run(rel_codes)
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, anchors):
+      anchors = box_list.BoxList(anchors)
+      boxes = box_list.BoxList(boxes)
+      coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+
+    rel_codes_out = self.execute(graph_fn, [boxes, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
+                        atol=1e-04)
 
   def testGetCorrectBoxesAfterDecoding(self):
-    rel_codes = tf.constant([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]])
+    rel_codes = np.array([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]],
+                         np.float32)
     expected_box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
-    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
-    priors = box_list.BoxList(prior_means)
-
-    coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
-    decoded_boxes = coder.decode(rel_codes, priors)
-    decoded_box_corners = decoded_boxes.get()
-    with self.test_session() as sess:
-      decoded_out = sess.run(decoded_box_corners)
-      self.assertAllClose(decoded_out, expected_box_corners)
+    anchors = np.array([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]], np.float32)
+
+    def graph_fn(rel_codes, anchors):
+      anchors = box_list.BoxList(anchors)
+      coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
+      decoded_boxes = coder.decode(rel_codes, anchors).get()
+      return decoded_boxes
+
+    decoded_boxes_out = self.execute(graph_fn, [rel_codes, anchors])
+    self.assertAllClose(decoded_boxes_out, expected_box_corners, rtol=1e-04,
+                        atol=1e-04)
 
 
 if __name__ == '__main__':

research/object_detection/box_coders/square_box_coder_test.py

@@ -14,83 +14,100 @@
 # ==============================================================================
 
 """Tests for object_detection.box_coder.square_box_coder."""
-
+import numpy as np
 import tensorflow as tf
 
 from object_detection.box_coders import square_box_coder
 from object_detection.core import box_list
+from object_detection.utils import test_case
 
 
-class SquareBoxCoderTest(tf.test.TestCase):
+class SquareBoxCoderTest(test_case.TestCase):
 
   def test_correct_relative_codes_with_default_scale(self):
-    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
-    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
-    scale_factors = None
+    boxes = np.array([[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]],
+                     np.float32)
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]],
+                       np.float32)
     expected_rel_codes = [[-0.790569, -0.263523, -0.293893],
                           [-0.068041, -0.272166, -0.89588]]
-
-    boxes = box_list.BoxList(tf.constant(boxes))
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
-    rel_codes = coder.encode(boxes, anchors)
-    with self.test_session() as sess:
-      (rel_codes_out,) = sess.run([rel_codes])
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, anchors):
+      scale_factors = None
+      boxes = box_list.BoxList(boxes)
+      anchors = box_list.BoxList(anchors)
+      coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+    rel_codes_out = self.execute(graph_fn, [boxes, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_correct_relative_codes_with_non_default_scale(self):
-    boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
-    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
-    scale_factors = [2, 3, 4]
+    boxes = np.array([[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]],
+                     np.float32)
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]],
+                       np.float32)
     expected_rel_codes = [[-1.581139, -0.790569, -1.175573],
                           [-0.136083, -0.816497, -3.583519]]
-    boxes = box_list.BoxList(tf.constant(boxes))
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
-    rel_codes = coder.encode(boxes, anchors)
-    with self.test_session() as sess:
-      (rel_codes_out,) = sess.run([rel_codes])
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, anchors):
+      scale_factors = [2, 3, 4]
+      boxes = box_list.BoxList(boxes)
+      anchors = box_list.BoxList(anchors)
+      coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+    rel_codes_out = self.execute(graph_fn, [boxes, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-03,
+                        atol=1e-03)
 
   def test_correct_relative_codes_with_small_width(self):
-    boxes = [[10.0, 10.0, 10.0000001, 20.0]]
-    anchors = [[15.0, 12.0, 30.0, 18.0]]
-    scale_factors = None
+    boxes = np.array([[10.0, 10.0, 10.0000001, 20.0]], np.float32)
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0]], np.float32)
     expected_rel_codes = [[-1.317616, 0., -20.670586]]
-    boxes = box_list.BoxList(tf.constant(boxes))
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
-    rel_codes = coder.encode(boxes, anchors)
-    with self.test_session() as sess:
-      (rel_codes_out,) = sess.run([rel_codes])
-      self.assertAllClose(rel_codes_out, expected_rel_codes)
+    def graph_fn(boxes, anchors):
+      scale_factors = None
+      boxes = box_list.BoxList(boxes)
+      anchors = box_list.BoxList(anchors)
+      coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+      rel_codes = coder.encode(boxes, anchors)
+      return rel_codes
+    rel_codes_out = self.execute(graph_fn, [boxes, anchors])
+    self.assertAllClose(rel_codes_out, expected_rel_codes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_correct_boxes_with_default_scale(self):
-    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
-    rel_codes = [[-0.5, -0.416666, -0.405465],
-                 [-0.083333, -0.222222, -0.693147]]
-    scale_factors = None
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]],
+                       np.float32)
+    rel_codes = np.array([[-0.5, -0.416666, -0.405465],
+                          [-0.083333, -0.222222, -0.693147]], np.float32)
     expected_boxes = [[14.594306, 7.884875, 20.918861, 14.209432],
                       [0.155051, 0.102989, 0.522474, 0.470412]]
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
-    boxes = coder.decode(rel_codes, anchors)
-    with self.test_session() as sess:
-      (boxes_out,) = sess.run([boxes.get()])
-      self.assertAllClose(boxes_out, expected_boxes)
+    def graph_fn(rel_codes, anchors):
+      scale_factors = None
+      anchors = box_list.BoxList(anchors)
+      coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+      boxes = coder.decode(rel_codes, anchors).get()
+      return boxes
+    boxes_out = self.execute(graph_fn, [rel_codes, anchors])
+    self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04,
+                        atol=1e-04)
 
   def test_correct_boxes_with_non_default_scale(self):
-    anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
-    rel_codes = [[-1., -1.25, -1.62186], [-0.166667, -0.666667, -2.772588]]
-    scale_factors = [2, 3, 4]
+    anchors = np.array([[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]],
+                       np.float32)
+    rel_codes = np.array(
+        [[-1., -1.25, -1.62186], [-0.166667, -0.666667, -2.772588]], np.float32)
     expected_boxes = [[14.594306, 7.884875, 20.918861, 14.209432],
                       [0.155051, 0.102989, 0.522474, 0.470412]]
-    anchors = box_list.BoxList(tf.constant(anchors))
-    coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
-    boxes = coder.decode(rel_codes, anchors)
-    with self.test_session() as sess:
-      (boxes_out,) = sess.run([boxes.get()])
-      self.assertAllClose(boxes_out, expected_boxes)
+    def graph_fn(rel_codes, anchors):
+      scale_factors = [2, 3, 4]
+      anchors = box_list.BoxList(anchors)
+      coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
+      boxes = coder.decode(rel_codes, anchors).get()
+      return boxes
+    boxes_out = self.execute(graph_fn, [rel_codes, anchors])
+    self.assertAllClose(boxes_out, expected_boxes, rtol=1e-04,
+                        atol=1e-04)
 
 
 if __name__ == '__main__':

research/object_detection/builders/anchor_generator_builder.py

+# Lint as: python2, python3
 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,6 +16,10 @@
 
 """A function to build an object detection anchor generator from config."""
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from six.moves import zip
 from object_detection.anchor_generators import flexible_grid_anchor_generator
 from object_detection.anchor_generators import grid_anchor_generator
 from object_detection.anchor_generators import multiple_grid_anchor_generator
@@ -58,12 +63,14 @@ def build(anchor_generator_config):
     ssd_anchor_generator_config = anchor_generator_config.ssd_anchor_generator
     anchor_strides = None
     if ssd_anchor_generator_config.height_stride:
-      anchor_strides = zip(ssd_anchor_generator_config.height_stride,
-                           ssd_anchor_generator_config.width_stride)
+      anchor_strides = list(
+          zip(ssd_anchor_generator_config.height_stride,
+              ssd_anchor_generator_config.width_stride))
     anchor_offsets = None
     if ssd_anchor_generator_config.height_offset:
-      anchor_offsets = zip(ssd_anchor_generator_config.height_offset,
-                           ssd_anchor_generator_config.width_offset)
+      anchor_offsets = list(
+          zip(ssd_anchor_generator_config.height_offset,
+              ssd_anchor_generator_config.width_offset))
     return multiple_grid_anchor_generator.create_ssd_anchors(
         num_layers=ssd_anchor_generator_config.num_layers,
         min_scale=ssd_anchor_generator_config.min_scale,

research/object_detection/builders/anchor_generator_builder_test.py

+# Lint as: python2, python3
 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,8 +16,14 @@
 
 """Tests for anchor_generator_builder."""
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import math
 
+from six.moves import range
+from six.moves import zip
 import tensorflow as tf
 
 from google.protobuf import text_format

research/object_detection/builders/box_predictor_builder_test.py

+# Lint as: python2, python3
 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -487,8 +488,8 @@ class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase):
     self.assertEqual(box_predictor.num_classes, 90)
     self.assertTrue(box_predictor._is_training)
     self.assertEqual(box_head._box_code_size, 4)
-    self.assertTrue(
-        mask_rcnn_box_predictor.MASK_PREDICTIONS in third_stage_heads)
+    self.assertIn(
+        mask_rcnn_box_predictor.MASK_PREDICTIONS, third_stage_heads)
     self.assertEqual(
         third_stage_heads[mask_rcnn_box_predictor.MASK_PREDICTIONS]
         ._mask_prediction_conv_depth, 512)
@@ -527,8 +528,8 @@ class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase):
     self.assertEqual(box_predictor.num_classes, 90)
     self.assertTrue(box_predictor._is_training)
     self.assertEqual(box_head._box_code_size, 4)
-    self.assertTrue(
-        mask_rcnn_box_predictor.MASK_PREDICTIONS in third_stage_heads)
+    self.assertIn(
+        mask_rcnn_box_predictor.MASK_PREDICTIONS, third_stage_heads)
     self.assertEqual(
         third_stage_heads[mask_rcnn_box_predictor.MASK_PREDICTIONS]
         ._mask_prediction_conv_depth, 512)

research/object_detection/builders/calibration_builder_test.py

+# Lint as: python2, python3
 # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,8 +16,12 @@
 
 """Tests for calibration_builder."""
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import numpy as np
 from scipy import interpolate
+from six.moves import zip
 import tensorflow as tf
 from object_detection.builders import calibration_builder
 from object_detection.protos import calibration_pb2

research/object_detection/builders/dataset_builder.py

+# Lint as: python2, python3
 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -21,10 +22,15 @@ Note: If users wishes to also use their own InputReaders with the Object
 Detection configuration framework, they should define their own builder function
 that wraps the build function.
 """
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import functools
 import tensorflow as tf
 
-from object_detection.data_decoders import tf_example_decoder
+from tensorflow.contrib import data as tf_data
+from object_detection.builders import decoder_builder
 from object_detection.protos import input_reader_pb2
 
 
@@ -45,14 +51,20 @@ def make_initializable_iterator(dataset):
   return iterator
 
 
-def read_dataset(file_read_func, input_files, config):
+def read_dataset(file_read_func, input_files, config,
+                 filename_shard_fn=None):
   """Reads a dataset, and handles repetition and shuffling.
 
   Args:
-    file_read_func: Function to use in tf.contrib.data.parallel_interleave, to
+    file_read_func: Function to use in tf_data.parallel_interleave, to
       read every individual file into a tf.data.Dataset.
     input_files: A list of file paths to read.
     config: A input_reader_builder.InputReader object.
+    filename_shard_fn: optional, A funciton used to shard filenames across
+      replicas. This function takes as input a TF dataset of filenames and
+      is expected to return its sharded version. It is useful when the
+      dataset is being loaded on one of possibly many replicas and we want
+      to evenly shard the files between the replicas.
 
   Returns:
     A tf.data.Dataset of (undecoded) tf-records based on config.
@@ -77,9 +89,12 @@ def read_dataset(file_read_func, input_files, config):
   elif num_readers > 1:
     tf.logging.warning('`shuffle` is false, but the input data stream is '
                        'still slightly shuffled since `num_readers` > 1.')
+  if filename_shard_fn:
+    filename_dataset = filename_shard_fn(filename_dataset)
+
   filename_dataset = filename_dataset.repeat(config.num_epochs or None)
   records_dataset = filename_dataset.apply(
-      tf.contrib.data.parallel_interleave(
+      tf_data.parallel_interleave(
           file_read_func,
           cycle_length=num_readers,
           block_length=config.read_block_length,
@@ -89,7 +104,21 @@ def read_dataset(file_read_func, input_files, config):
   return records_dataset
 
 
-def build(input_reader_config, batch_size=None, transform_input_data_fn=None):
+def shard_function_for_context(input_context):
+  """Returns a function that shards filenames based on the input context."""
+
+  if input_context is None:
+    return None
+
+  def shard_fn(dataset):
+    return dataset.shard(
+        input_context.num_input_pipelines, input_context.input_pipeline_id)
+
+  return shard_fn
+
+
+def build(input_reader_config, batch_size=None, transform_input_data_fn=None,
+          input_context=None):
   """Builds a tf.data.Dataset.
 
   Builds a tf.data.Dataset by applying the `transform_input_data_fn` on all
@@ -100,6 +129,9 @@ def build(input_reader_config, batch_size=None, transform_input_data_fn=None):
     batch_size: Batch size. If batch size is None, no batching is performed.
     transform_input_data_fn: Function to apply transformation to all records,
       or None if no extra decoding is required.
+    input_context: optional, A tf.distribute.InputContext object used to
+      shard filenames and compute per-replica batch_size when this function
+      is being called per-replica.
 
   Returns:
     A tf.data.Dataset based on the input_reader_config.
@@ -112,23 +144,14 @@ def build(input_reader_config, batch_size=None, transform_input_data_fn=None):
     raise ValueError('input_reader_config not of type '
                      'input_reader_pb2.InputReader.')
 
+  decoder = decoder_builder.build(input_reader_config)
+
   if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
     config = input_reader_config.tf_record_input_reader
     if not config.input_path:
       raise ValueError('At least one input path must be specified in '
                        '`input_reader_config`.')
 
-    label_map_proto_file = None
-    if input_reader_config.HasField('label_map_path'):
-      label_map_proto_file = input_reader_config.label_map_path
-    decoder = tf_example_decoder.TfExampleDecoder(
-        load_instance_masks=input_reader_config.load_instance_masks,
-        load_multiclass_scores=input_reader_config.load_multiclass_scores,
-        instance_mask_type=input_reader_config.mask_type,
-        label_map_proto_file=label_map_proto_file,
-        use_display_name=input_reader_config.use_display_name,
-        num_additional_channels=input_reader_config.num_additional_channels)
-
     def process_fn(value):
       """Sets up tf graph that decodes, transforms and pads input data."""
       processed_tensors = decoder.decode(value)
@@ -136,9 +159,13 @@ def build(input_reader_config, batch_size=None, transform_input_data_fn=None):
         processed_tensors = transform_input_data_fn(processed_tensors)
       return processed_tensors
 
+    shard_fn = shard_function_for_context(input_context)
+    if input_context is not None:
+      batch_size = input_context.get_per_replica_batch_size(batch_size)
+
     dataset = read_dataset(
         functools.partial(tf.data.TFRecordDataset, buffer_size=8 * 1000 * 1000),
-        config.input_path[:], input_reader_config)
+        config.input_path[:], input_reader_config, filename_shard_fn=shard_fn)
     if input_reader_config.sample_1_of_n_examples > 1:
       dataset = dataset.shard(input_reader_config.sample_1_of_n_examples, 0)
     # TODO(rathodv): make batch size a required argument once the old binaries
@@ -155,7 +182,7 @@ def build(input_reader_config, batch_size=None, transform_input_data_fn=None):
     dataset = data_map_fn(process_fn, num_parallel_calls=num_parallel_calls)
     if batch_size:
       dataset = dataset.apply(
-          tf.contrib.data.batch_and_drop_remainder(batch_size))
+          tf_data.batch_and_drop_remainder(batch_size))
     dataset = dataset.prefetch(input_reader_config.num_prefetch_batches)
     return dataset
 

research/object_detection/builders/dataset_builder_test.py

+# Lint as: python2, python3
 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,8 +15,13 @@
 # ==============================================================================
 """Tests for dataset_builder."""
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import os
 import numpy as np
+from six.moves import range
 import tensorflow as tf
 
 from google.protobuf import text_format
@@ -24,25 +30,63 @@ from object_detection.builders import dataset_builder
 from object_detection.core import standard_fields as fields
 from object_detection.protos import input_reader_pb2
 from object_detection.utils import dataset_util
+from object_detection.utils import test_case
 
 
-class DatasetBuilderTest(tf.test.TestCase):
+# pylint: disable=g-import-not-at-top
+try:
+  from tensorflow.contrib import lookup as contrib_lookup
+except ImportError:
+  # TF 2.0 doesn't ship with contrib.
+  pass
+# pylint: enable=g-import-not-at-top
 
-  def create_tf_record(self, has_additional_channels=False, num_examples=1):
-    path = os.path.join(self.get_temp_dir(), 'tfrecord')
-    writer = tf.python_io.TFRecordWriter(path)
 
-    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
-    additional_channels_tensor = np.random.randint(
-        255, size=(4, 5, 1)).astype(np.uint8)
-    flat_mask = (4 * 5) * [1.0]
-    with self.test_session():
-      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
+def get_iterator_next_for_testing(dataset, is_tf2):
+
+  # In TF2, lookup tables are not supported in one shot iterators, but
+  # initialization is implicit.
+  if is_tf2:
+    return dataset.make_initializable_iterator().get_next()
+  # In TF1, we use one shot iterator because it does not require running
+  # a separate init op.
+  else:
+    return dataset.make_one_shot_iterator().get_next()
+
+
+class DatasetBuilderTest(test_case.TestCase):
+
+  def create_tf_record(self, has_additional_channels=False, num_shards=1,
+                       num_examples_per_shard=1):
+
+    def dummy_jpeg_fn():
+      image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
+      additional_channels_tensor = np.random.randint(
+          255, size=(4, 5, 1)).astype(np.uint8)
+      encoded_jpeg = tf.image.encode_jpeg(image_tensor)
       encoded_additional_channels_jpeg = tf.image.encode_jpeg(
-          tf.constant(additional_channels_tensor)).eval()
-      for i in range(num_examples):
+          additional_channels_tensor)
+
+      return encoded_jpeg, encoded_additional_channels_jpeg
+
+    encoded_jpeg, encoded_additional_channels_jpeg = self.execute(
+        dummy_jpeg_fn, [])
+
+    tmp_dir = self.get_temp_dir()
+    flat_mask = (4 * 5) * [1.0]
+
+    for i in range(num_shards):
+      path = os.path.join(tmp_dir, '%05d.tfrecord' % i)
+      writer = tf.python_io.TFRecordWriter(path)
+
+      for j in range(num_examples_per_shard):
+        if num_shards > 1:
+          source_id = (str(i) + '_' + str(j)).encode()
+        else:
+          source_id = str(j).encode()
+
         features = {
-            'image/source_id': dataset_util.bytes_feature(str(i)),
+            'image/source_id': dataset_util.bytes_feature(source_id),
             'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
             'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
             'image/height': dataset_util.int64_feature(4),
@@ -54,15 +98,18 @@ class DatasetBuilderTest(tf.test.TestCase):
             'image/object/class/label': dataset_util.int64_list_feature([2]),
             'image/object/mask': dataset_util.float_list_feature(flat_mask),
         }
+
         if has_additional_channels:
           additional_channels_key = 'image/additional_channels/encoded'
           features[additional_channels_key] = dataset_util.bytes_list_feature(
               [encoded_additional_channels_jpeg] * 2)
+
         example = tf.train.Example(features=tf.train.Features(feature=features))
         writer.write(example.SerializeToString())
+
       writer.close()
 
-    return path
+    return os.path.join(self.get_temp_dir(), '?????.tfrecord')
 
   def test_build_tf_record_input_reader(self):
     tf_record_path = self.create_tf_record()
@@ -76,19 +123,21 @@ class DatasetBuilderTest(tf.test.TestCase):
     """.format(tf_record_path)
     input_reader_proto = input_reader_pb2.InputReader()
     text_format.Merge(input_reader_text_proto, input_reader_proto)
-    tensor_dict = dataset_builder.make_initializable_iterator(
-        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
 
-    with tf.train.MonitoredSession() as sess:
-      output_dict = sess.run(tensor_dict)
+    def graph_fn():
+      return get_iterator_next_for_testing(
+          dataset_builder.build(input_reader_proto, batch_size=1),
+          self.is_tf2())
 
-    self.assertTrue(
-        fields.InputDataFields.groundtruth_instance_masks not in output_dict)
-    self.assertEquals((1, 4, 5, 3),
-                      output_dict[fields.InputDataFields.image].shape)
+    output_dict = self.execute(graph_fn, [])
+
+    self.assertNotIn(
+        fields.InputDataFields.groundtruth_instance_masks, output_dict)
+    self.assertEqual((1, 4, 5, 3),
+                     output_dict[fields.InputDataFields.image].shape)
     self.assertAllEqual([[2]],
                         output_dict[fields.InputDataFields.groundtruth_classes])
-    self.assertEquals(
+    self.assertEqual(
         (1, 1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
     self.assertAllEqual(
         [0.0, 0.0, 1.0, 1.0],
@@ -107,11 +156,14 @@ class DatasetBuilderTest(tf.test.TestCase):
     """.format(tf_record_path)
     input_reader_proto = input_reader_pb2.InputReader()
     text_format.Merge(input_reader_text_proto, input_reader_proto)
-    tensor_dict = dataset_builder.make_initializable_iterator(
-        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
 
-    with tf.train.MonitoredSession() as sess:
-      output_dict = sess.run(tensor_dict)
+    def graph_fn():
+      return get_iterator_next_for_testing(
+          dataset_builder.build(input_reader_proto, batch_size=1),
+          self.is_tf2()
+      )
+
+    output_dict = self.execute(graph_fn, [])
     self.assertAllEqual(
         (1, 1, 4, 5),
         output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
@@ -134,14 +186,14 @@ class DatasetBuilderTest(tf.test.TestCase):
           tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3)
       return tensor_dict
 
-    tensor_dict = dataset_builder.make_initializable_iterator(
-        dataset_builder.build(
-            input_reader_proto,
-            transform_input_data_fn=one_hot_class_encoding_fn,
-            batch_size=2)).get_next()
+    def graph_fn():
+      return dataset_builder.make_initializable_iterator(
+          dataset_builder.build(
+              input_reader_proto,
+              transform_input_data_fn=one_hot_class_encoding_fn,
+              batch_size=2)).get_next()
 
-    with tf.train.MonitoredSession() as sess:
-      output_dict = sess.run(tensor_dict)
+    output_dict = self.execute(graph_fn, [])
 
     self.assertAllEqual([2, 4, 5, 3],
                         output_dict[fields.InputDataFields.image].shape)
@@ -172,14 +224,14 @@ class DatasetBuilderTest(tf.test.TestCase):
           tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3)
       return tensor_dict
 
-    tensor_dict = dataset_builder.make_initializable_iterator(
-        dataset_builder.build(
-            input_reader_proto,
-            transform_input_data_fn=one_hot_class_encoding_fn,
-            batch_size=2)).get_next()
+    def graph_fn():
+      return dataset_builder.make_initializable_iterator(
+          dataset_builder.build(
+              input_reader_proto,
+              transform_input_data_fn=one_hot_class_encoding_fn,
+              batch_size=2)).get_next()
 
-    with tf.train.MonitoredSession() as sess:
-      output_dict = sess.run(tensor_dict)
+    output_dict = self.execute(graph_fn, [])
 
     self.assertAllEqual(
         [2, 1, 4, 5],
@@ -197,7 +249,7 @@ class DatasetBuilderTest(tf.test.TestCase):
       dataset_builder.build(input_reader_proto, batch_size=1)
 
   def test_sample_all_data(self):
-    tf_record_path = self.create_tf_record(num_examples=2)
+    tf_record_path = self.create_tf_record(num_examples_per_shard=2)
 
     input_reader_text_proto = """
       shuffle: false
@@ -209,17 +261,22 @@ class DatasetBuilderTest(tf.test.TestCase):
     """.format(tf_record_path)
     input_reader_proto = input_reader_pb2.InputReader()
     text_format.Merge(input_reader_text_proto, input_reader_proto)
-    tensor_dict = dataset_builder.make_initializable_iterator(
-        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
 
-    with tf.train.MonitoredSession() as sess:
-      output_dict = sess.run(tensor_dict)
-      self.assertAllEqual(['0'], output_dict[fields.InputDataFields.source_id])
-      output_dict = sess.run(tensor_dict)
-      self.assertEquals(['1'], output_dict[fields.InputDataFields.source_id])
+    def graph_fn():
+      dataset = dataset_builder.build(input_reader_proto, batch_size=1)
+      sample1_ds = dataset.take(1)
+      sample2_ds = dataset.skip(1)
+      iter1 = dataset_builder.make_initializable_iterator(sample1_ds)
+      iter2 = dataset_builder.make_initializable_iterator(sample2_ds)
+
+      return iter1.get_next(), iter2.get_next()
+
+    output_dict1, output_dict2 = self.execute(graph_fn, [])
+    self.assertAllEqual(['0'], output_dict1[fields.InputDataFields.source_id])
+    self.assertEqual([b'1'], output_dict2[fields.InputDataFields.source_id])
 
   def test_sample_one_of_n_shards(self):
-    tf_record_path = self.create_tf_record(num_examples=4)
+    tf_record_path = self.create_tf_record(num_examples_per_shard=4)
 
     input_reader_text_proto = """
       shuffle: false
@@ -231,17 +288,99 @@ class DatasetBuilderTest(tf.test.TestCase):
     """.format(tf_record_path)
     input_reader_proto = input_reader_pb2.InputReader()
     text_format.Merge(input_reader_text_proto, input_reader_proto)
-    tensor_dict = dataset_builder.make_initializable_iterator(
-        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
 
-    with tf.train.MonitoredSession() as sess:
-      output_dict = sess.run(tensor_dict)
-      self.assertAllEqual(['0'], output_dict[fields.InputDataFields.source_id])
-      output_dict = sess.run(tensor_dict)
-      self.assertEquals(['2'], output_dict[fields.InputDataFields.source_id])
+    def graph_fn():
+      dataset = dataset_builder.build(input_reader_proto, batch_size=1)
+      sample1_ds = dataset.take(1)
+      sample2_ds = dataset.skip(1)
+      iter1 = dataset_builder.make_initializable_iterator(sample1_ds)
+      iter2 = dataset_builder.make_initializable_iterator(sample2_ds)
+
+      return iter1.get_next(), iter2.get_next()
+
+    output_dict1, output_dict2 = self.execute(graph_fn, [])
+    self.assertAllEqual([b'0'], output_dict1[fields.InputDataFields.source_id])
+    self.assertEqual([b'2'], output_dict2[fields.InputDataFields.source_id])
+
+  def test_no_input_context(self):
+    """Test that all samples are read with no input context given."""
+    tf_record_path = self.create_tf_record(num_examples_per_shard=16,
+                                           num_shards=2)
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      num_epochs: 1
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+
+    for i in range(4):
+
+      # pylint:disable=cell-var-from-loop
+      def graph_fn():
+        dataset = dataset_builder.build(input_reader_proto, batch_size=8)
+        dataset = dataset.skip(i)
+        return get_iterator_next_for_testing(dataset, self.is_tf2())
 
+      batch = self.execute(graph_fn, [])
+      self.assertEqual(batch['image'].shape, (8, 4, 5, 3))
 
-class ReadDatasetTest(tf.test.TestCase):
+    def graph_fn_last_batch():
+      dataset = dataset_builder.build(input_reader_proto, batch_size=8)
+      dataset = dataset.skip(4)
+      return get_iterator_next_for_testing(dataset, self.is_tf2())
+
+    self.assertRaises(tf.errors.OutOfRangeError, self.execute,
+                      compute_fn=graph_fn_last_batch, inputs=[])
+
+  def test_with_input_context(self):
+    """Test that a subset is read with input context given."""
+    tf_record_path = self.create_tf_record(num_examples_per_shard=16,
+                                           num_shards=2)
+
+    input_reader_text_proto = """
+      shuffle: false
+      num_readers: 1
+      num_epochs: 1
+      tf_record_input_reader {{
+        input_path: '{0}'
+      }}
+    """.format(tf_record_path)
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Merge(input_reader_text_proto, input_reader_proto)
+
+    input_context = tf.distribute.InputContext(
+        num_input_pipelines=2, input_pipeline_id=0, num_replicas_in_sync=4
+    )
+
+    for i in range(8):
+
+      # pylint:disable=cell-var-from-loop
+      def graph_fn():
+
+        dataset = dataset_builder.build(input_reader_proto, batch_size=8,
+                                        input_context=input_context)
+        dataset = dataset.skip(i)
+        return get_iterator_next_for_testing(dataset, self.is_tf2())
+
+      batch = self.execute(graph_fn, [])
+      self.assertEqual(batch['image'].shape, (2, 4, 5, 3))
+
+    def graph_fn_last_batch():
+      dataset = dataset_builder.build(input_reader_proto, batch_size=8,
+                                      input_context=input_context)
+      dataset = dataset.skip(8)
+      return get_iterator_next_for_testing(dataset, self.is_tf2())
+
+    self.assertRaises(tf.errors.OutOfRangeError, self.execute,
+                      compute_fn=graph_fn_last_batch, inputs=[])
+
+
+class ReadDatasetTest(test_case.TestCase):
 
   def setUp(self):
     self._path_template = os.path.join(self.get_temp_dir(), 'examples_%s.txt')
@@ -258,7 +397,9 @@ class ReadDatasetTest(tf.test.TestCase):
       with tf.gfile.Open(path, 'wb') as f:
         f.write('\n'.join([str(i)] * 5))
 
-  def _get_dataset_next(self, files, config, batch_size):
+    super(ReadDatasetTest, self).setUp()
+
+  def _get_dataset_next(self, files, config, batch_size, num_batches_skip=0):
 
     def decode_func(value):
       return [tf.string_to_number(value, out_type=tf.int32)]
@@ -267,50 +408,62 @@ class ReadDatasetTest(tf.test.TestCase):
                                            config)
     dataset = dataset.map(decode_func)
     dataset = dataset.batch(batch_size)
-    return dataset.make_one_shot_iterator().get_next()
+
+    if num_batches_skip > 0:
+      dataset = dataset.skip(num_batches_skip)
+
+    return get_iterator_next_for_testing(dataset, self.is_tf2())
 
   def test_make_initializable_iterator_with_hashTable(self):
-    keys = [1, 0, -1]
-    dataset = tf.data.Dataset.from_tensor_slices([[1, 2, -1, 5]])
-    table = tf.contrib.lookup.HashTable(
-        initializer=tf.contrib.lookup.KeyValueTensorInitializer(
-            keys=keys, values=list(reversed(keys))),
-        default_value=100)
-    dataset = dataset.map(table.lookup)
-    data = dataset_builder.make_initializable_iterator(dataset).get_next()
-    init = tf.tables_initializer()
-
-    with self.test_session() as sess:
-      sess.run(init)
-      self.assertAllEqual(sess.run(data), [-1, 100, 1, 100])
+
+    def graph_fn():
+      keys = [1, 0, -1]
+      dataset = tf.data.Dataset.from_tensor_slices([[1, 2, -1, 5]])
+      table = contrib_lookup.HashTable(
+          initializer=contrib_lookup.KeyValueTensorInitializer(
+              keys=keys, values=list(reversed(keys))),
+          default_value=100)
+      dataset = dataset.map(table.lookup)
+      return dataset_builder.make_initializable_iterator(dataset).get_next()
+
+    result = self.execute(graph_fn, [])
+    self.assertAllEqual(result, [-1, 100, 1, 100])
 
   def test_read_dataset(self):
     config = input_reader_pb2.InputReader()
     config.num_readers = 1
     config.shuffle = False
 
-    data = self._get_dataset_next(
-        [self._path_template % '*'], config, batch_size=20)
-    with self.test_session() as sess:
-      self.assertAllEqual(
-          sess.run(data), [[
-              1, 10, 2, 20, 3, 30, 4, 40, 5, 50, 1, 10, 2, 20, 3, 30, 4, 40, 5,
-              50
-          ]])
+    def graph_fn():
+      return self._get_dataset_next(
+          [self._path_template % '*'], config, batch_size=20)
+
+    data = self.execute(graph_fn, [])
+    # Note that the execute function extracts single outputs if the return
+    # value is of size 1.
+    self.assertAllEqual(
+        data, [
+            1, 10, 2, 20, 3, 30, 4, 40, 5, 50, 1, 10, 2, 20, 3, 30, 4, 40, 5,
+            50
+        ])
 
   def test_reduce_num_reader(self):
     config = input_reader_pb2.InputReader()
     config.num_readers = 10
     config.shuffle = False
 
-    data = self._get_dataset_next(
-        [self._path_template % '*'], config, batch_size=20)
-    with self.test_session() as sess:
-      self.assertAllEqual(
-          sess.run(data), [[
-              1, 10, 2, 20, 3, 30, 4, 40, 5, 50, 1, 10, 2, 20, 3, 30, 4, 40, 5,
-              50
-          ]])
+    def graph_fn():
+      return self._get_dataset_next(
+          [self._path_template % '*'], config, batch_size=20)
+
+    data = self.execute(graph_fn, [])
+    # Note that the execute function extracts single outputs if the return
+    # value is of size 1.
+    self.assertAllEqual(
+        data, [
+            1, 10, 2, 20, 3, 30, 4, 40, 5, 50, 1, 10, 2, 20, 3, 30, 4, 40, 5,
+            50
+        ])
 
   def test_enable_shuffle(self):
     config = input_reader_pb2.InputReader()
@@ -318,25 +471,30 @@ class ReadDatasetTest(tf.test.TestCase):
     config.shuffle = True
 
     tf.set_random_seed(1)  # Set graph level seed.
-    data = self._get_dataset_next(
-        [self._shuffle_path_template % '*'], config, batch_size=10)
+
+    def graph_fn():
+      return self._get_dataset_next(
+          [self._shuffle_path_template % '*'], config, batch_size=10)
     expected_non_shuffle_output = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
+    data = self.execute(graph_fn, [])
 
-    with self.test_session() as sess:
-      self.assertTrue(
-          np.any(np.not_equal(sess.run(data), expected_non_shuffle_output)))
+    self.assertTrue(
+        np.any(np.not_equal(data, expected_non_shuffle_output)))
 
   def test_disable_shuffle_(self):
     config = input_reader_pb2.InputReader()
     config.num_readers = 1
     config.shuffle = False
 
-    data = self._get_dataset_next(
-        [self._shuffle_path_template % '*'], config, batch_size=10)
+    def graph_fn():
+      return self._get_dataset_next(
+          [self._shuffle_path_template % '*'], config, batch_size=10)
     expected_non_shuffle_output = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
 
-    with self.test_session() as sess:
-      self.assertAllEqual(sess.run(data), [expected_non_shuffle_output])
+    # Note that the execute function extracts single outputs if the return
+    # value is of size 1.
+    data = self.execute(graph_fn, [])
+    self.assertAllEqual(data, expected_non_shuffle_output)
 
   def test_read_dataset_single_epoch(self):
     config = input_reader_pb2.InputReader()
@@ -344,12 +502,24 @@ class ReadDatasetTest(tf.test.TestCase):
     config.num_readers = 1
     config.shuffle = False
 
-    data = self._get_dataset_next(
-        [self._path_template % '0'], config, batch_size=30)
-    with self.test_session() as sess:
-      # First batch will retrieve as much as it can, second batch will fail.
-      self.assertAllEqual(sess.run(data), [[1, 10]])
-      self.assertRaises(tf.errors.OutOfRangeError, sess.run, data)
+    def graph_fn():
+      return self._get_dataset_next(
+          [self._path_template % '0'], config, batch_size=30)
+
+    data = self.execute(graph_fn, [])
+
+    # Note that the execute function extracts single outputs if the return
+    # value is of size 1.
+    self.assertAllEqual(data, [1, 10])
+
+    # First batch will retrieve as much as it can, second batch will fail.
+    def graph_fn_second_batch():
+      return self._get_dataset_next(
+          [self._path_template % '0'], config, batch_size=30,
+          num_batches_skip=1)
+
+    self.assertRaises(tf.errors.OutOfRangeError, self.execute,
+                      compute_fn=graph_fn_second_batch, inputs=[])
 
 
 if __name__ == '__main__':

research/object_detection/builders/decoder_builder.py

+# Lint as: python2, python3
+# 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.
+# ==============================================================================
+"""DataDecoder builder.
+
+Creates DataDecoders from InputReader configs.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from object_detection.data_decoders import tf_example_decoder
+from object_detection.protos import input_reader_pb2
+
+
+def build(input_reader_config):
+  """Builds a DataDecoder based only on the open source config proto.
+
+  Args:
+    input_reader_config: An input_reader_pb2.InputReader object.
+
+  Returns:
+    A DataDecoder based on the input_reader_config.
+
+  Raises:
+    ValueError: On invalid input reader proto.
+  """
+  if not isinstance(input_reader_config, input_reader_pb2.InputReader):
+    raise ValueError('input_reader_config not of type '
+                     'input_reader_pb2.InputReader.')
+
+  if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
+    label_map_proto_file = None
+    if input_reader_config.HasField('label_map_path'):
+      label_map_proto_file = input_reader_config.label_map_path
+    decoder = tf_example_decoder.TfExampleDecoder(
+        load_instance_masks=input_reader_config.load_instance_masks,
+        load_multiclass_scores=input_reader_config.load_multiclass_scores,
+        load_context_features=input_reader_config.load_context_features,
+        instance_mask_type=input_reader_config.mask_type,
+        label_map_proto_file=label_map_proto_file,
+        use_display_name=input_reader_config.use_display_name,
+        num_additional_channels=input_reader_config.num_additional_channels,
+        num_keypoints=input_reader_config.num_keypoints)
+
+    return decoder
+
+  raise ValueError('Unsupported input_reader_config.')

research/object_detection/builders/decoder_builder_test.py

+# Lint as: python2, python3
+# 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.
+# ==============================================================================
+"""Tests for decoder_builder."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from google.protobuf import text_format
+from object_detection.builders import decoder_builder
+from object_detection.core import standard_fields as fields
+from object_detection.protos import input_reader_pb2
+from object_detection.utils import dataset_util
+
+
+class DecoderBuilderTest(tf.test.TestCase):
+
+  def _make_serialized_tf_example(self, has_additional_channels=False):
+    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
+    additional_channels_tensor = np.random.randint(
+        255, size=(4, 5, 1)).astype(np.uint8)
+    flat_mask = (4 * 5) * [1.0]
+    with self.test_session():
+      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
+      encoded_additional_channels_jpeg = tf.image.encode_jpeg(
+          tf.constant(additional_channels_tensor)).eval()
+    features = {
+        'image/source_id': dataset_util.bytes_feature('0'.encode()),
+        'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
+        'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
+        'image/height': dataset_util.int64_feature(4),
+        'image/width': dataset_util.int64_feature(5),
+        'image/object/bbox/xmin': dataset_util.float_list_feature([0.0]),
+        'image/object/bbox/xmax': dataset_util.float_list_feature([1.0]),
+        'image/object/bbox/ymin': dataset_util.float_list_feature([0.0]),
+        'image/object/bbox/ymax': dataset_util.float_list_feature([1.0]),
+        'image/object/class/label': dataset_util.int64_list_feature([2]),
+        'image/object/mask': dataset_util.float_list_feature(flat_mask),
+    }
+    if has_additional_channels:
+      additional_channels_key = 'image/additional_channels/encoded'
+      features[additional_channels_key] = dataset_util.bytes_list_feature(
+          [encoded_additional_channels_jpeg] * 2)
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+    return example.SerializeToString()
+
+  def test_build_tf_record_input_reader(self):
+    input_reader_text_proto = 'tf_record_input_reader {}'
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Parse(input_reader_text_proto, input_reader_proto)
+
+    decoder = decoder_builder.build(input_reader_proto)
+    tensor_dict = decoder.decode(self._make_serialized_tf_example())
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+
+    self.assertNotIn(
+        fields.InputDataFields.groundtruth_instance_masks, output_dict)
+    self.assertEqual((4, 5, 3), output_dict[fields.InputDataFields.image].shape)
+    self.assertAllEqual([2],
+                        output_dict[fields.InputDataFields.groundtruth_classes])
+    self.assertEqual(
+        (1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
+    self.assertAllEqual(
+        [0.0, 0.0, 1.0, 1.0],
+        output_dict[fields.InputDataFields.groundtruth_boxes][0])
+
+  def test_build_tf_record_input_reader_and_load_instance_masks(self):
+    input_reader_text_proto = """
+      load_instance_masks: true
+      tf_record_input_reader {}
+    """
+    input_reader_proto = input_reader_pb2.InputReader()
+    text_format.Parse(input_reader_text_proto, input_reader_proto)
+
+    decoder = decoder_builder.build(input_reader_proto)
+    tensor_dict = decoder.decode(self._make_serialized_tf_example())
+
+    with tf.train.MonitoredSession() as sess:
+      output_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(
+        (1, 4, 5),
+        output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/object_detection/builders/graph_rewriter_builder.py

@@ -16,6 +16,15 @@
 
 import tensorflow as tf
 
+# pylint: disable=g-import-not-at-top
+try:
+  from tensorflow.contrib import layers as contrib_layers
+  from tensorflow.contrib import quantize as contrib_quantize
+except ImportError:
+  # TF 2.0 doesn't ship with contrib.
+  pass
+# pylint: enable=g-import-not-at-top
+
 
 def build(graph_rewriter_config, is_training):
   """Returns a function that modifies default graph based on options.
@@ -32,14 +41,15 @@ def build(graph_rewriter_config, is_training):
 
     # Quantize the graph by inserting quantize ops for weights and activations
     if is_training:
-      tf.contrib.quantize.experimental_create_training_graph(
+      contrib_quantize.experimental_create_training_graph(
           input_graph=tf.get_default_graph(),
           quant_delay=graph_rewriter_config.quantization.delay
       )
     else:
-      tf.contrib.quantize.experimental_create_eval_graph(
+      contrib_quantize.experimental_create_eval_graph(
           input_graph=tf.get_default_graph()
       )
 
-    tf.contrib.layers.summarize_collection('quant_vars')
+    contrib_layers.summarize_collection('quant_vars')
+
   return graph_rewrite_fn

research/object_detection/builders/graph_rewriter_builder_test.py

@@ -18,14 +18,23 @@ import tensorflow as tf
 from object_detection.builders import graph_rewriter_builder
 from object_detection.protos import graph_rewriter_pb2
 
+# pylint: disable=g-import-not-at-top
+try:
+  from tensorflow.contrib import layers as contrib_layers
+  from tensorflow.contrib import quantize as contrib_quantize
+except ImportError:
+  # TF 2.0 doesn't ship with contrib.
+  pass
+# pylint: enable=g-import-not-at-top
+
 
 class QuantizationBuilderTest(tf.test.TestCase):
 
   def testQuantizationBuilderSetsUpCorrectTrainArguments(self):
     with mock.patch.object(
-        tf.contrib.quantize,
+        contrib_quantize,
         'experimental_create_training_graph') as mock_quant_fn:
-      with mock.patch.object(tf.contrib.layers,
+      with mock.patch.object(contrib_layers,
                              'summarize_collection') as mock_summarize_col:
         graph_rewriter_proto = graph_rewriter_pb2.GraphRewriter()
         graph_rewriter_proto.quantization.delay = 10
@@ -40,9 +49,9 @@ class QuantizationBuilderTest(tf.test.TestCase):
         mock_summarize_col.assert_called_with('quant_vars')
 
   def testQuantizationBuilderSetsUpCorrectEvalArguments(self):
-    with mock.patch.object(tf.contrib.quantize,
+    with mock.patch.object(contrib_quantize,
                            'experimental_create_eval_graph') as mock_quant_fn:
-      with mock.patch.object(tf.contrib.layers,
+      with mock.patch.object(contrib_layers,
                              'summarize_collection') as mock_summarize_col:
         graph_rewriter_proto = graph_rewriter_pb2.GraphRewriter()
         graph_rewriter_proto.quantization.delay = 10

research/object_detection/builders/hyperparams_builder.py

@@ -20,7 +20,14 @@ from object_detection.core import freezable_batch_norm
 from object_detection.protos import hyperparams_pb2
 from object_detection.utils import context_manager
 
-slim = tf.contrib.slim
+# pylint: disable=g-import-not-at-top
+try:
+  from tensorflow.contrib import slim
+  from tensorflow.contrib import layers as contrib_layers
+except ImportError:
+  # TF 2.0 doesn't ship with contrib.
+  pass
+# pylint: enable=g-import-not-at-top
 
 
 class KerasLayerHyperparams(object):
@@ -216,7 +223,7 @@ def build(hyperparams_config, is_training):
     batch_norm_params = _build_batch_norm_params(
         hyperparams_config.batch_norm, is_training)
   if hyperparams_config.HasField('group_norm'):
-    normalizer_fn = tf.contrib.layers.group_norm
+    normalizer_fn = contrib_layers.group_norm
   affected_ops = [slim.conv2d, slim.separable_conv2d, slim.conv2d_transpose]
   if hyperparams_config.HasField('op') and (
       hyperparams_config.op == hyperparams_pb2.Hyperparams.FC):
@@ -256,6 +263,8 @@ def _build_activation_fn(activation_fn):
     return tf.nn.relu
   if activation_fn == hyperparams_pb2.Hyperparams.RELU_6:
     return tf.nn.relu6
+  if activation_fn == hyperparams_pb2.Hyperparams.SWISH:
+    return tf.nn.swish
   raise ValueError('Unknown activation function: {}'.format(activation_fn))
 
 
@@ -301,6 +310,8 @@ def _build_keras_regularizer(regularizer):
     # weight by a factor of 2
     return tf.keras.regularizers.l2(
         float(regularizer.l2_regularizer.weight * 0.5))
+  if regularizer_oneof is None:
+    return None
   raise ValueError('Unknown regularizer function: {}'.format(regularizer_oneof))
 
 
@@ -369,6 +380,8 @@ def _build_initializer(initializer, build_for_keras=False):
           factor=initializer.variance_scaling_initializer.factor,
           mode=mode,
           uniform=initializer.variance_scaling_initializer.uniform)
+  if initializer_oneof is None:
+    return None
   raise ValueError('Unknown initializer function: {}'.format(
       initializer_oneof))