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Unverified Commit 8518d053 authored by pkulzc's avatar pkulzc Committed by GitHub
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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.

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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.

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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

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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.

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303444542  by rathodv:

    Py3 update to metrics module

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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.

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301888316  by Zhichao Lu:

    Fix checkpoint_filepath not defined error.

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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.

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300836556  by Zhichao Lu:

    Internal changes to add keypoint estimation parameters in CenterNet proto.

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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.

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298695964  by Zhichao Lu:

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

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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
parent ac5fff19
Hide whitespace changes
Inline Side-by-side
Showing with 983 additions and 131 deletions
research/object_detection/core/standard_fields.py
View file @ 8518d053
......@@ -42,6 +42,8 @@ class InputDataFields(object):
filename: original filename of the dataset (without common path).
groundtruth_image_classes: image-level class labels.
groundtruth_image_confidences: image-level class confidences.
groundtruth_labeled_classes: image-level annotation that indicates the
classes for which an image has been labeled.
groundtruth_boxes: coordinates of the ground truth boxes in the image.
groundtruth_classes: box-level class labels.
groundtruth_confidences: box-level class confidences. The shape should be
......@@ -61,6 +63,7 @@ class InputDataFields(object):
groundtruth_instance_classes: instance mask-level class labels.
groundtruth_keypoints: ground truth keypoints.
groundtruth_keypoint_visibilities: ground truth keypoint visibilities.
groundtruth_keypoint_weights: groundtruth weight factor for keypoints.
groundtruth_label_weights: groundtruth label weights.
groundtruth_weights: groundtruth weight factor for bounding boxes.
num_groundtruth_boxes: number of groundtruth boxes.
......@@ -68,6 +71,11 @@ class InputDataFields(object):
true_image_shapes: true shapes of images in the resized images, as resized
images can be padded with zeros.
multiclass_scores: the label score per class for each box.
context_features: a flattened list of contextual features.
context_feature_length: the fixed length of each feature in
context_features, used for reshaping.
valid_context_size: the valid context size, used in filtering the padded
context features.
"""
image = 'image'
image_additional_channels = 'image_additional_channels'
......@@ -78,6 +86,7 @@ class InputDataFields(object):
filename = 'filename'
groundtruth_image_classes = 'groundtruth_image_classes'
groundtruth_image_confidences = 'groundtruth_image_confidences'
groundtruth_labeled_classes = 'groundtruth_labeled_classes'
groundtruth_boxes = 'groundtruth_boxes'
groundtruth_classes = 'groundtruth_classes'
groundtruth_confidences = 'groundtruth_confidences'
......@@ -93,12 +102,16 @@ class InputDataFields(object):
groundtruth_instance_classes = 'groundtruth_instance_classes'
groundtruth_keypoints = 'groundtruth_keypoints'
groundtruth_keypoint_visibilities = 'groundtruth_keypoint_visibilities'
groundtruth_keypoint_weights = 'groundtruth_keypoint_weights'
groundtruth_label_weights = 'groundtruth_label_weights'
groundtruth_weights = 'groundtruth_weights'
num_groundtruth_boxes = 'num_groundtruth_boxes'
is_annotated = 'is_annotated'
true_image_shape = 'true_image_shape'
multiclass_scores = 'multiclass_scores'
context_features = 'context_features'
context_feature_length = 'context_feature_length'
valid_context_size = 'valid_context_size'
class DetectionResultFields(object):
......@@ -115,6 +128,7 @@ class DetectionResultFields(object):
detection_masks: contains a segmentation mask for each detection box.
detection_boundaries: contains an object boundary for each detection box.
detection_keypoints: contains detection keypoints for each detection box.
detection_keypoint_scores: contains detection keypoint scores.
num_detections: number of detections in the batch.
raw_detection_boxes: contains decoded detection boxes without Non-Max
suppression.
......@@ -134,6 +148,7 @@ class DetectionResultFields(object):
detection_masks = 'detection_masks'
detection_boundaries = 'detection_boundaries'
detection_keypoints = 'detection_keypoints'
detection_keypoint_scores = 'detection_keypoint_scores'
num_detections = 'num_detections'
raw_detection_boxes = 'raw_detection_boxes'
raw_detection_scores = 'raw_detection_scores'
......@@ -164,6 +179,7 @@ class BoxListFields(object):
masks = 'masks'
boundaries = 'boundaries'
keypoints = 'keypoints'
keypoint_visibilities = 'keypoint_visibilities'
keypoint_heatmaps = 'keypoint_heatmaps'
is_crowd = 'is_crowd'
......@@ -201,6 +217,7 @@ class TfExampleFields(object):
source_id: original source of the image
image_class_text: image-level label in text format
image_class_label: image-level label in numerical format
image_class_confidence: image-level confidence of the label
object_class_text: labels in text format, e.g. ["person", "cat"]
object_class_label: labels in numbers, e.g. [16, 8]
object_bbox_xmin: xmin coordinates of groundtruth box, e.g. 10, 30
......@@ -237,6 +254,7 @@ class TfExampleFields(object):
source_id = 'image/source_id'
image_class_text = 'image/class/text'
image_class_label = 'image/class/label'
image_class_confidence = 'image/class/confidence'
object_class_text = 'image/object/class/text'
object_class_label = 'image/object/class/label'
object_bbox_ymin = 'image/object/bbox/ymin'
......
research/object_detection/core/target_assigner.py
View file @ 8518d053
......@@ -44,12 +44,17 @@ from object_detection.box_coders import mean_stddev_box_coder
from object_detection.core import box_coder
from object_detection.core import box_list
from object_detection.core import box_list_ops
from object_detection.core import keypoint_ops
from object_detection.core import matcher as mat
from object_detection.core import region_similarity_calculator as sim_calc
from object_detection.core import standard_fields as fields
from object_detection.matchers import argmax_matcher
from object_detection.matchers import bipartite_matcher
from object_detection.utils import shape_utils
from object_detection.utils import target_assigner_utils as ta_utils
_DEFAULT_KEYPOINT_OFFSET_STD_DEV = 1.0
class TargetAssigner(object):
......
research/object_detection/core/target_assigner_test.py
View file @ 8518d053
......@@ -25,6 +25,7 @@ from object_detection.core import standard_fields as fields
from object_detection.core import target_assigner as targetassigner
from object_detection.matchers import argmax_matcher
from object_detection.matchers import bipartite_matcher
from object_detection.utils import np_box_ops
from object_detection.utils import test_case
......@@ -65,10 +66,10 @@ class TargetAssignerTest(test_case.TestCase):
self.assertAllClose(cls_weights_out, exp_cls_weights)
self.assertAllClose(reg_targets_out, exp_reg_targets)
self.assertAllClose(reg_weights_out, exp_reg_weights)
self.assertEquals(cls_targets_out.dtype, np.float32)
self.assertEquals(cls_weights_out.dtype, np.float32)
self.assertEquals(reg_targets_out.dtype, np.float32)
self.assertEquals(reg_weights_out.dtype, np.float32)
self.assertEqual(cls_targets_out.dtype, np.float32)
self.assertEqual(cls_weights_out.dtype, np.float32)
self.assertEqual(reg_targets_out.dtype, np.float32)
self.assertEqual(reg_weights_out.dtype, np.float32)
def test_assign_class_agnostic_with_ignored_matches(self):
# Note: test is very similar to above. The third box matched with an IOU
......@@ -108,10 +109,10 @@ class TargetAssignerTest(test_case.TestCase):
self.assertAllClose(cls_weights_out, exp_cls_weights)
self.assertAllClose(reg_targets_out, exp_reg_targets)
self.assertAllClose(reg_weights_out, exp_reg_weights)
self.assertEquals(cls_targets_out.dtype, np.float32)
self.assertEquals(cls_weights_out.dtype, np.float32)
self.assertEquals(reg_targets_out.dtype, np.float32)
self.assertEquals(reg_weights_out.dtype, np.float32)
self.assertEqual(cls_targets_out.dtype, np.float32)
self.assertEqual(cls_weights_out.dtype, np.float32)
self.assertEqual(reg_targets_out.dtype, np.float32)
self.assertEqual(reg_weights_out.dtype, np.float32)
def test_assign_agnostic_with_keypoints(self):
def graph_fn(anchor_means, groundtruth_box_corners,
......@@ -158,10 +159,10 @@ class TargetAssignerTest(test_case.TestCase):
self.assertAllClose(cls_weights_out, exp_cls_weights)
self.assertAllClose(reg_targets_out, exp_reg_targets)
self.assertAllClose(reg_weights_out, exp_reg_weights)
self.assertEquals(cls_targets_out.dtype, np.float32)
self.assertEquals(cls_weights_out.dtype, np.float32)
self.assertEquals(reg_targets_out.dtype, np.float32)
self.assertEquals(reg_weights_out.dtype, np.float32)
self.assertEqual(cls_targets_out.dtype, np.float32)
self.assertEqual(cls_weights_out.dtype, np.float32)
self.assertEqual(reg_targets_out.dtype, np.float32)
self.assertEqual(reg_weights_out.dtype, np.float32)
def test_assign_class_agnostic_with_keypoints_and_ignored_matches(self):
# Note: test is very similar to above. The third box matched with an IOU
......@@ -213,10 +214,10 @@ class TargetAssignerTest(test_case.TestCase):
self.assertAllClose(cls_weights_out, exp_cls_weights)
self.assertAllClose(reg_targets_out, exp_reg_targets)
self.assertAllClose(reg_weights_out, exp_reg_weights)
self.assertEquals(cls_targets_out.dtype, np.float32)
self.assertEquals(cls_weights_out.dtype, np.float32)
self.assertEquals(reg_targets_out.dtype, np.float32)
self.assertEquals(reg_weights_out.dtype, np.float32)
self.assertEqual(cls_targets_out.dtype, np.float32)
self.assertEqual(cls_weights_out.dtype, np.float32)
self.assertEqual(reg_targets_out.dtype, np.float32)
self.assertEqual(reg_weights_out.dtype, np.float32)
def test_assign_multiclass(self):
......@@ -271,10 +272,10 @@ class TargetAssignerTest(test_case.TestCase):
self.assertAllClose(cls_weights_out, exp_cls_weights)
self.assertAllClose(reg_targets_out, exp_reg_targets)
self.assertAllClose(reg_weights_out, exp_reg_weights)
self.assertEquals(cls_targets_out.dtype, np.float32)
self.assertEquals(cls_weights_out.dtype, np.float32)
self.assertEquals(reg_targets_out.dtype, np.float32)
self.assertEquals(reg_weights_out.dtype, np.float32)
self.assertEqual(cls_targets_out.dtype, np.float32)
self.assertEqual(cls_weights_out.dtype, np.float32)
self.assertEqual(reg_targets_out.dtype, np.float32)
self.assertEqual(reg_weights_out.dtype, np.float32)
def test_assign_multiclass_with_groundtruth_weights(self):
......@@ -379,10 +380,10 @@ class TargetAssignerTest(test_case.TestCase):
self.assertAllClose(cls_weights_out, exp_cls_weights)
self.assertAllClose(reg_targets_out, exp_reg_targets)
self.assertAllClose(reg_weights_out, exp_reg_weights)
self.assertEquals(cls_targets_out.dtype, np.float32)
self.assertEquals(cls_weights_out.dtype, np.float32)
self.assertEquals(reg_targets_out.dtype, np.float32)
self.assertEquals(reg_weights_out.dtype, np.float32)
self.assertEqual(cls_targets_out.dtype, np.float32)
self.assertEqual(cls_weights_out.dtype, np.float32)
self.assertEqual(reg_targets_out.dtype, np.float32)
self.assertEqual(reg_weights_out.dtype, np.float32)
def test_assign_empty_groundtruth(self):
......@@ -431,10 +432,10 @@ class TargetAssignerTest(test_case.TestCase):
self.assertAllClose(cls_weights_out, exp_cls_weights)
self.assertAllClose(reg_targets_out, exp_reg_targets)
self.assertAllClose(reg_weights_out, exp_reg_weights)
self.assertEquals(cls_targets_out.dtype, np.float32)
self.assertEquals(cls_weights_out.dtype, np.float32)
self.assertEquals(reg_targets_out.dtype, np.float32)
self.assertEquals(reg_weights_out.dtype, np.float32)
self.assertEqual(cls_targets_out.dtype, np.float32)
self.assertEqual(cls_weights_out.dtype, np.float32)
self.assertEqual(reg_targets_out.dtype, np.float32)
self.assertEqual(reg_weights_out.dtype, np.float32)
def test_raises_error_on_incompatible_groundtruth_boxes_and_labels(self):
similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
......@@ -1228,5 +1229,7 @@ class CreateTargetAssignerTest(tf.test.TestCase):
stage='invalid_stage')
if __name__ == '__main__':
tf.test.main()
research/object_detection/data/face_person_with_keypoints_label_map.pbtxt 0 → 100644
View file @ 8518d053
item: {
id: 1
name: 'face'
display_name: 'face'
keypoints {
id: 0
label: "left_eye_center"
}
keypoints {
id: 1
label: "right_eye_center"
}
keypoints {
id: 2
label: "nose_tip"
}
keypoints {
id: 3
label: "mouth_center"
}
keypoints {
id: 4
label: "left_ear_tragion"
}
keypoints {
id: 5
label: "right_ear_tragion"
}
}
item: {
id: 2
name: 'Person'
display_name: 'PERSON'
keypoints {
id: 6
label: "NOSE_TIP"
}
keypoints {
id: 7
label: "LEFT_EYE"
}
keypoints {
id: 8
label: "RIGHT_EYE"
}
keypoints {
id: 9
label: "LEFT_EAR_TRAGION"
}
keypoints {
id: 10
label: "RIGHT_EAR_TRAGION"
}
keypoints {
id: 11
label: "LEFT_SHOULDER"
}
keypoints {
id: 12
label: "RIGHT_SHOULDER"
}
keypoints {
id: 13
label: "LEFT_ELBOW"
}
keypoints {
id: 14
label: "RIGHT_ELBOW"
}
keypoints {
id: 15
label: "LEFT_WRIST"
}
keypoints {
id: 16
label: "RIGHT_WRIST"
}
keypoints {
id: 17
label: "LEFT_HIP"
}
keypoints {
id: 18
label: "RIGHT_HIP"
}
keypoints {
id: 19
label: "LEFT_KNEE"
}
keypoints {
id: 20
label: "RIGHT_KNEE"
}
keypoints {
id: 21
label: "LEFT_ANKLE"
}
keypoints {
id: 22
label: "RIGHT_ANKLE"
}
}
research/object_detection/data_decoders/tf_example_decoder.py
View file @ 8518d053
......@@ -21,6 +21,8 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import enum
import numpy as np
from six.moves import zip
import tensorflow as tf
......@@ -29,7 +31,27 @@ from object_detection.core import standard_fields as fields
from object_detection.protos import input_reader_pb2
from object_detection.utils import label_map_util
slim_example_decoder = tf.contrib.slim.tfexample_decoder
# pylint: disable=g-import-not-at-top
try:
from tensorflow.contrib import lookup as contrib_lookup
from tensorflow.contrib.slim import tfexample_decoder as slim_example_decoder
except ImportError:
# TF 2.0 doesn't ship with contrib.
pass
# pylint: enable=g-import-not-at-top
class Visibility(enum.Enum):
"""Visibility definitions.
This follows the MS Coco convention (http://cocodataset.org/#format-data).
"""
# Keypoint is not labeled.
UNLABELED = 0
# Keypoint is labeled but falls outside the object segment (e.g. occluded).
NOT_VISIBLE = 1
# Keypoint is labeled and visible.
VISIBLE = 2
class _ClassTensorHandler(slim_example_decoder.Tensor):
......@@ -69,8 +91,8 @@ class _ClassTensorHandler(slim_example_decoder.Tensor):
lookup = tf.compat.v2.lookup
hash_table_class = tf.compat.v2.lookup.StaticHashTable
except AttributeError:
lookup = tf.contrib.lookup
hash_table_class = tf.contrib.lookup.HashTable
lookup = contrib_lookup
hash_table_class = contrib_lookup.HashTable
name_to_id_table = hash_table_class(
initializer=lookup.KeyValueTensorInitializer(
keys=tf.constant(list(name_to_id.keys())),
......@@ -144,7 +166,8 @@ class TfExampleDecoder(data_decoder.DataDecoder):
dct_method='',
num_keypoints=0,
num_additional_channels=0,
load_multiclass_scores=False):
load_multiclass_scores=False,
load_context_features=False):
"""Constructor sets keys_to_features and items_to_handlers.
Args:
......@@ -168,6 +191,9 @@ class TfExampleDecoder(data_decoder.DataDecoder):
num_additional_channels: how many additional channels to use.
load_multiclass_scores: Whether to load multiclass scores associated with
boxes.
load_context_features: Whether to load information from context_features,
to provide additional context to a detection model for training and/or
inference
Raises:
ValueError: If `instance_mask_type` option is not one of
......@@ -197,6 +223,8 @@ class TfExampleDecoder(data_decoder.DataDecoder):
tf.VarLenFeature(tf.string),
'image/class/label':
tf.VarLenFeature(tf.int64),
'image/class/confidence':
tf.VarLenFeature(tf.float32),
# Object boxes and classes.
'image/object/bbox/xmin':
tf.VarLenFeature(tf.float32),
......@@ -253,6 +281,9 @@ class TfExampleDecoder(data_decoder.DataDecoder):
slim_example_decoder.Tensor('image/key/sha256')),
fields.InputDataFields.filename: (
slim_example_decoder.Tensor('image/filename')),
# Image-level labels.
fields.InputDataFields.groundtruth_image_confidences: (
slim_example_decoder.Tensor('image/class/confidence')),
# Object boxes and classes.
fields.InputDataFields.groundtruth_boxes: (
slim_example_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'],
......@@ -274,6 +305,20 @@ class TfExampleDecoder(data_decoder.DataDecoder):
'image/object/class/multiclass_scores'] = tf.VarLenFeature(tf.float32)
self.items_to_handlers[fields.InputDataFields.multiclass_scores] = (
slim_example_decoder.Tensor('image/object/class/multiclass_scores'))
if load_context_features:
self.keys_to_features[
'image/context_features'] = tf.VarLenFeature(tf.float32)
self.items_to_handlers[fields.InputDataFields.context_features] = (
slim_example_decoder.ItemHandlerCallback(
['image/context_features', 'image/context_feature_length'],
self._reshape_context_features))
self.keys_to_features[
'image/context_feature_length'] = tf.FixedLenFeature((), tf.int64)
self.items_to_handlers[fields.InputDataFields.context_feature_length] = (
slim_example_decoder.Tensor('image/context_feature_length'))
if num_additional_channels > 0:
self.keys_to_features[
'image/additional_channels/encoded'] = tf.FixedLenFeature(
......@@ -287,10 +332,17 @@ class TfExampleDecoder(data_decoder.DataDecoder):
tf.VarLenFeature(tf.float32))
self.keys_to_features['image/object/keypoint/y'] = (
tf.VarLenFeature(tf.float32))
self.keys_to_features['image/object/keypoint/visibility'] = (
tf.VarLenFeature(tf.int64))
self.items_to_handlers[fields.InputDataFields.groundtruth_keypoints] = (
slim_example_decoder.ItemHandlerCallback(
['image/object/keypoint/y', 'image/object/keypoint/x'],
self._reshape_keypoints))
kpt_vis_field = fields.InputDataFields.groundtruth_keypoint_visibilities
self.items_to_handlers[kpt_vis_field] = (
slim_example_decoder.ItemHandlerCallback(
['image/object/keypoint/x', 'image/object/keypoint/visibility'],
self._reshape_keypoint_visibilities))
if load_instance_masks:
if instance_mask_type in (input_reader_pb2.DEFAULT,
input_reader_pb2.NUMERICAL_MASKS):
......@@ -363,6 +415,9 @@ class TfExampleDecoder(data_decoder.DataDecoder):
[None] indicating if the boxes enclose a crowd.
Optional:
fields.InputDataFields.groundtruth_image_confidences - 1D float tensor of
shape [None] indicating if a class is present in the image (1.0) or
a class is not present in the image (0.0).
fields.InputDataFields.image_additional_channels - 3D uint8 tensor of
shape [None, None, num_additional_channels]. 1st dim is height; 2nd dim
is width; 3rd dim is the number of additional channels.
......@@ -371,8 +426,10 @@ class TfExampleDecoder(data_decoder.DataDecoder):
fields.InputDataFields.groundtruth_group_of - 1D bool tensor of shape
[None] indicating if the boxes represent `group_of` instances.
fields.InputDataFields.groundtruth_keypoints - 3D float32 tensor of
shape [None, None, 2] containing keypoints, where the coordinates of
the keypoints are ordered (y, x).
shape [None, num_keypoints, 2] containing keypoints, where the
coordinates of the keypoints are ordered (y, x).
fields.InputDataFields.groundtruth_keypoint_visibilities - 2D bool
tensor of shape [None, num_keypoints] containing keypoint visibilites.
fields.InputDataFields.groundtruth_instance_masks - 3D float32 tensor of
shape [None, None, None] containing instance masks.
fields.InputDataFields.groundtruth_image_classes - 1D uint64 of shape
......@@ -380,6 +437,10 @@ class TfExampleDecoder(data_decoder.DataDecoder):
fields.InputDataFields.multiclass_scores - 1D float32 tensor of shape
[None * num_classes] containing flattened multiclass scores for
groundtruth boxes.
fields.InputDataFields.context_features - 1D float32 tensor of shape
[context_feature_length * num_context_features]
fields.InputDataFields.context_feature_length - int32 tensor specifying
the length of each feature in context_features
"""
serialized_example = tf.reshape(tf_example_string_tensor, shape=[])
decoder = slim_example_decoder.TFExampleDecoder(self.keys_to_features,
......@@ -410,20 +471,34 @@ class TfExampleDecoder(data_decoder.DataDecoder):
tensor_dict[fields.InputDataFields.groundtruth_weights])[0],
0), lambda: tensor_dict[fields.InputDataFields.groundtruth_weights],
default_groundtruth_weights)
if fields.InputDataFields.groundtruth_keypoints in tensor_dict:
# Set all keypoints that are not labeled to NaN.
gt_kpt_fld = fields.InputDataFields.groundtruth_keypoints
gt_kpt_vis_fld = fields.InputDataFields.groundtruth_keypoint_visibilities
visibilities_tiled = tf.tile(
tf.expand_dims(tensor_dict[gt_kpt_vis_fld], -1),
[1, 1, 2])
tensor_dict[gt_kpt_fld] = tf.where(
visibilities_tiled,
tensor_dict[gt_kpt_fld],
np.nan * tf.ones_like(tensor_dict[gt_kpt_fld]))
return tensor_dict
def _reshape_keypoints(self, keys_to_tensors):
"""Reshape keypoints.
The instance segmentation masks are reshaped to [num_instances,
num_keypoints, 2].
The keypoints are reshaped to [num_instances, num_keypoints, 2].
Args:
keys_to_tensors: a dictionary from keys to tensors.
keys_to_tensors: a dictionary from keys to tensors. Expected keys are:
'image/object/keypoint/x'
'image/object/keypoint/y'
Returns:
A 3-D float tensor of shape [num_instances, num_keypoints, 2] with values
in {0, 1}.
in [0, 1].
"""
y = keys_to_tensors['image/object/keypoint/y']
if isinstance(y, tf.SparseTensor):
......@@ -437,6 +512,54 @@ class TfExampleDecoder(data_decoder.DataDecoder):
keypoints = tf.reshape(keypoints, [-1, self._num_keypoints, 2])
return keypoints
def _reshape_keypoint_visibilities(self, keys_to_tensors):
"""Reshape keypoint visibilities.
The keypoint visibilities are reshaped to [num_instances,
num_keypoints].
The raw keypoint visibilities are expected to conform to the
MSCoco definition. See Visibility enum.
The returned boolean is True for the labeled case (either
Visibility.NOT_VISIBLE or Visibility.VISIBLE). These are the same categories
that COCO uses to evaluate keypoint detection performance:
http://cocodataset.org/#keypoints-eval
If image/object/keypoint/visibility is not provided, visibilities will be
set to True for finite keypoint coordinate values, and 0 if the coordinates
are NaN.
Args:
keys_to_tensors: a dictionary from keys to tensors. Expected keys are:
'image/object/keypoint/x'
'image/object/keypoint/visibility'
Returns:
A 2-D bool tensor of shape [num_instances, num_keypoints] with values
in {0, 1}. 1 if the keypoint is labeled, 0 otherwise.
"""
x = keys_to_tensors['image/object/keypoint/x']
vis = keys_to_tensors['image/object/keypoint/visibility']
if isinstance(vis, tf.SparseTensor):
vis = tf.sparse_tensor_to_dense(vis)
if isinstance(x, tf.SparseTensor):
x = tf.sparse_tensor_to_dense(x)
default_vis = tf.where(
tf.math.is_nan(x),
Visibility.UNLABELED.value * tf.ones_like(x, dtype=tf.int64),
Visibility.VISIBLE.value * tf.ones_like(x, dtype=tf.int64))
# Use visibility if provided, otherwise use the default visibility.
vis = tf.cond(tf.equal(tf.size(x), tf.size(vis)),
true_fn=lambda: vis,
false_fn=lambda: default_vis)
vis = tf.math.logical_or(
tf.math.equal(vis, Visibility.NOT_VISIBLE.value),
tf.math.equal(vis, Visibility.VISIBLE.value))
vis = tf.reshape(vis, [-1, self._num_keypoints])
return vis
def _reshape_instance_masks(self, keys_to_tensors):
"""Reshape instance segmentation masks.
......@@ -460,6 +583,26 @@ class TfExampleDecoder(data_decoder.DataDecoder):
tf.cast(tf.greater(masks, 0.0), dtype=tf.float32), to_shape)
return tf.cast(masks, tf.float32)
def _reshape_context_features(self, keys_to_tensors):
"""Reshape context features.
The instance context_features are reshaped to
[num_context_features, context_feature_length]
Args:
keys_to_tensors: a dictionary from keys to tensors.
Returns:
A 2-D float tensor of shape [num_context_features, context_feature_length]
"""
context_feature_length = keys_to_tensors['image/context_feature_length']
to_shape = tf.cast(tf.stack([-1, context_feature_length]), tf.int32)
context_features = keys_to_tensors['image/context_features']
if isinstance(context_features, tf.SparseTensor):
context_features = tf.sparse_tensor_to_dense(context_features)
context_features = tf.reshape(context_features, to_shape)
return context_features
def _decode_png_instance_masks(self, keys_to_tensors):
"""Decode PNG instance segmentation masks and stack into dense tensor.
......
research/object_detection/data_decoders/tf_example_decoder_test.py
View file @ 8518d053
......@@ -24,8 +24,6 @@ from object_detection.data_decoders import tf_example_decoder
from object_detection.protos import input_reader_pb2
from object_detection.utils import dataset_util
slim_example_decoder = tf.contrib.slim.tfexample_decoder
class TfExampleDecoderTest(tf.test.TestCase):
......@@ -265,6 +263,68 @@ class TfExampleDecoderTest(tf.test.TestCase):
tensor_dict[fields.InputDataFields.groundtruth_boxes])
def testDecodeKeypoint(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_ymins = [0.0, 4.0]
bbox_xmins = [1.0, 5.0]
bbox_ymaxs = [2.0, 6.0]
bbox_xmaxs = [3.0, 7.0]
keypoint_ys = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
keypoint_xs = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
keypoint_visibility = [1, 2, 0, 1, 0, 2]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(six.b('jpeg')),
'image/object/bbox/ymin':
dataset_util.float_list_feature(bbox_ymins),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bbox_xmins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(bbox_ymaxs),
'image/object/bbox/xmax':
dataset_util.float_list_feature(bbox_xmaxs),
'image/object/keypoint/y':
dataset_util.float_list_feature(keypoint_ys),
'image/object/keypoint/x':
dataset_util.float_list_feature(keypoint_xs),
'image/object/keypoint/visibility':
dataset_util.int64_list_feature(keypoint_visibility),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(num_keypoints=3)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_boxes]
.get_shape().as_list()), [None, 4])
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.groundtruth_keypoints].get_shape()
.as_list()), [2, 3, 2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
expected_boxes = np.vstack([bbox_ymins, bbox_xmins, bbox_ymaxs,
bbox_xmaxs]).transpose()
self.assertAllEqual(expected_boxes,
tensor_dict[fields.InputDataFields.groundtruth_boxes])
expected_keypoints = [
[[0.0, 1.0], [1.0, 2.0], [np.nan, np.nan]],
[[3.0, 4.0], [np.nan, np.nan], [5.0, 6.0]]]
self.assertAllClose(
expected_keypoints,
tensor_dict[fields.InputDataFields.groundtruth_keypoints])
expected_visibility = (
(np.array(keypoint_visibility) > 0).reshape((2, 3)))
self.assertAllEqual(
expected_visibility,
tensor_dict[fields.InputDataFields.groundtruth_keypoint_visibilities])
def testDecodeKeypointNoVisibilities(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_ymins = [0.0, 4.0]
......@@ -316,6 +376,11 @@ class TfExampleDecoderTest(tf.test.TestCase):
expected_keypoints,
tensor_dict[fields.InputDataFields.groundtruth_keypoints])
expected_visibility = np.ones((2, 3))
self.assertAllEqual(
expected_visibility,
tensor_dict[fields.InputDataFields.groundtruth_keypoint_visibilities])
def testDecodeDefaultGroundtruthWeights(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
......@@ -841,6 +906,34 @@ class TfExampleDecoderTest(tf.test.TestCase):
self.assertAllEqual(object_weights,
tensor_dict[fields.InputDataFields.groundtruth_weights])
def testDecodeClassConfidence(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
class_confidence = [0.0, 1.0, 0.0]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(six.b('jpeg')),
'image/class/confidence':
dataset_util.float_list_feature(class_confidence),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.groundtruth_image_confidences]
.get_shape().as_list()), [3])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
class_confidence,
tensor_dict[fields.InputDataFields.groundtruth_image_confidences])
def testDecodeInstanceSegmentation(self):
num_instances = 4
image_height = 5
......@@ -992,6 +1085,87 @@ class TfExampleDecoderTest(tf.test.TestCase):
tensor_dict[fields.InputDataFields.groundtruth_image_classes],
np.array([1, 3]))
def testDecodeContextFeatures(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_ymins = [0.0, 4.0]
bbox_xmins = [1.0, 5.0]
bbox_ymaxs = [2.0, 6.0]
bbox_xmaxs = [3.0, 7.0]
num_features = 8
context_feature_length = 10
context_features = np.random.random(num_features*context_feature_length)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(six.b('jpeg')),
'image/context_features':
dataset_util.float_list_feature(context_features),
'image/context_feature_length':
dataset_util.int64_feature(context_feature_length),
'image/object/bbox/ymin':
dataset_util.float_list_feature(bbox_ymins),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bbox_xmins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(bbox_ymaxs),
'image/object/bbox/xmax':
dataset_util.float_list_feature(bbox_xmaxs),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_context_features=True)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllClose(
context_features.reshape(num_features, context_feature_length),
tensor_dict[fields.InputDataFields.context_features])
self.assertAllEqual(
context_feature_length,
tensor_dict[fields.InputDataFields.context_feature_length])
def testContextFeaturesNotAvailableByDefault(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_ymins = [0.0, 4.0]
bbox_xmins = [1.0, 5.0]
bbox_ymaxs = [2.0, 6.0]
bbox_xmaxs = [3.0, 7.0]
num_features = 10
context_feature_length = 10
context_features = np.random.random(num_features*context_feature_length)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature(six.b('jpeg')),
'image/context_features':
dataset_util.float_list_feature(context_features),
'image/context_feature_length':
dataset_util.int64_feature(context_feature_length),
'image/object/bbox/ymin':
dataset_util.float_list_feature(bbox_ymins),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bbox_xmins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(bbox_ymaxs),
'image/object/bbox/xmax':
dataset_util.float_list_feature(bbox_xmaxs),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertNotIn(fields.InputDataFields.context_features,
tensor_dict)
if __name__ == '__main__':
tf.test.main()
research/object_detection/dataset_tools/create_coco_tf_record.py
View file @ 8518d053
......@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Convert raw COCO dataset to TFRecord for object_detection.
Please note that this tool creates sharded output files.
......@@ -34,6 +33,7 @@ from __future__ import print_function
import hashlib
import io
import json
import logging
import os
import contextlib2
import numpy as np
......@@ -46,57 +46,69 @@ from object_detection.dataset_tools import tf_record_creation_util
from object_detection.utils import dataset_util
from object_detection.utils import label_map_util
flags = tf.app.flags
tf.flags.DEFINE_boolean('include_masks', False,
'Whether to include instance segmentations masks '
'(PNG encoded) in the result. default: False.')
tf.flags.DEFINE_string('train_image_dir', '',
'Training image directory.')
tf.flags.DEFINE_string('val_image_dir', '',
'Validation image directory.')
tf.flags.DEFINE_string('test_image_dir', '',
'Test image directory.')
tf.flags.DEFINE_boolean(
'include_masks', False, 'Whether to include instance segmentations masks '
'(PNG encoded) in the result. default: False.')
tf.flags.DEFINE_string('train_image_dir', '', 'Training image directory.')
tf.flags.DEFINE_string('val_image_dir', '', 'Validation image directory.')
tf.flags.DEFINE_string('test_image_dir', '', 'Test image directory.')
tf.flags.DEFINE_string('train_annotations_file', '',
'Training annotations JSON file.')
tf.flags.DEFINE_string('val_annotations_file', '',
'Validation annotations JSON file.')
tf.flags.DEFINE_string('testdev_annotations_file', '',
'Test-dev annotations JSON file.')
tf.flags.DEFINE_string('train_keypoint_annotations_file', '',
'Training annotations JSON file.')
tf.flags.DEFINE_string('val_keypoint_annotations_file', '',
'Validation annotations JSON file.')
tf.flags.DEFINE_string('output_dir', '/tmp/', 'Output data directory.')
FLAGS = flags.FLAGS
tf.logging.set_verbosity(tf.logging.INFO)
logger = tf.get_logger()
logger.setLevel(logging.INFO)
_COCO_KEYPOINT_NAMES = [
b'nose', b'left_eye', b'right_eye', b'left_ear', b'right_ear',
b'left_shoulder', b'right_shoulder', b'left_elbow', b'right_elbow',
b'left_wrist', b'right_wrist', b'left_hip', b'right_hip',
b'left_knee', b'right_knee', b'left_ankle', b'right_ankle'
]
def create_tf_example(image,
annotations_list,
image_dir,
category_index,
include_masks=False):
include_masks=False,
keypoint_annotations_dict=None):
"""Converts image and annotations to a tf.Example proto.
Args:
image: dict with keys:
[u'license', u'file_name', u'coco_url', u'height', u'width',
u'date_captured', u'flickr_url', u'id']
image: dict with keys: [u'license', u'file_name', u'coco_url', u'height',
u'width', u'date_captured', u'flickr_url', u'id']
annotations_list:
list of dicts with keys:
[u'segmentation', u'area', u'iscrowd', u'image_id',
u'bbox', u'category_id', u'id']
Notice that bounding box coordinates in the official COCO dataset are
given as [x, y, width, height] tuples using absolute coordinates where
x, y represent the top-left (0-indexed) corner. This function converts
to the format expected by the Tensorflow Object Detection API (which is
which is [ymin, xmin, ymax, xmax] with coordinates normalized relative
to image size).
list of dicts with keys: [u'segmentation', u'area', u'iscrowd',
u'image_id', u'bbox', u'category_id', u'id'] Notice that bounding box
coordinates in the official COCO dataset are given as [x, y, width,
height] tuples using absolute coordinates where x, y represent the
top-left (0-indexed) corner. This function converts to the format
expected by the Tensorflow Object Detection API (which is which is
[ymin, xmin, ymax, xmax] with coordinates normalized relative to image
size).
image_dir: directory containing the image files.
category_index: a dict containing COCO category information keyed
by the 'id' field of each category. See the
label_map_util.create_category_index function.
category_index: a dict containing COCO category information keyed by the
'id' field of each category. See the label_map_util.create_category_index
function.
include_masks: Whether to include instance segmentations masks
(PNG encoded) in the result. default: False.
keypoint_annotations_dict: A dictionary that maps from annotation_id to a
dictionary with keys: [u'keypoints', u'num_keypoints'] represeting the
keypoint information for this person object annotation. If None, then
no keypoint annotations will be populated.
Returns:
example: The converted tf.Example
num_annotations_skipped: Number of (invalid) annotations that were ignored.
......@@ -125,7 +137,15 @@ def create_tf_example(image,
category_ids = []
area = []
encoded_mask_png = []
keypoints_x = []
keypoints_y = []
keypoints_visibility = []
keypoints_name = []
num_keypoints = []
include_keypoint = keypoint_annotations_dict is not None
num_annotations_skipped = 0
num_keypoint_annotation_used = 0
num_keypoint_annotation_skipped = 0
for object_annotations in annotations_list:
(x, y, width, height) = tuple(object_annotations['bbox'])
if width <= 0 or height <= 0:
......@@ -154,6 +174,29 @@ def create_tf_example(image,
output_io = io.BytesIO()
pil_image.save(output_io, format='PNG')
encoded_mask_png.append(output_io.getvalue())
if include_keypoint:
annotation_id = object_annotations['id']
if annotation_id in keypoint_annotations_dict:
num_keypoint_annotation_used += 1
keypoint_annotations = keypoint_annotations_dict[annotation_id]
keypoints = keypoint_annotations['keypoints']
num_kpts = keypoint_annotations['num_keypoints']
keypoints_x_abs = keypoints[::3]
keypoints_x.extend(
[float(x_abs) / image_width for x_abs in keypoints_x_abs])
keypoints_y_abs = keypoints[1::3]
keypoints_y.extend(
[float(y_abs) / image_height for y_abs in keypoints_y_abs])
keypoints_visibility.extend(keypoints[2::3])
keypoints_name.extend(_COCO_KEYPOINT_NAMES)
num_keypoints.append(num_kpts)
else:
keypoints_x.extend([0.0] * len(_COCO_KEYPOINT_NAMES))
keypoints_y.extend([0.0] * len(_COCO_KEYPOINT_NAMES))
keypoints_visibility.extend([0] * len(_COCO_KEYPOINT_NAMES))
keypoints_name.extend(_COCO_KEYPOINT_NAMES)
num_keypoints.append(0)
feature_dict = {
'image/height':
dataset_util.int64_feature(image_height),
......@@ -187,12 +230,28 @@ def create_tf_example(image,
if include_masks:
feature_dict['image/object/mask'] = (
dataset_util.bytes_list_feature(encoded_mask_png))
if include_keypoint:
feature_dict['image/object/keypoint/x'] = (
dataset_util.float_list_feature(keypoints_x))
feature_dict['image/object/keypoint/y'] = (
dataset_util.float_list_feature(keypoints_y))
feature_dict['image/object/keypoint/num'] = (
dataset_util.int64_list_feature(num_keypoints))
feature_dict['image/object/keypoint/visibility'] = (
dataset_util.int64_list_feature(keypoints_visibility))
feature_dict['image/object/keypoint/text'] = (
dataset_util.bytes_list_feature(keypoints_name))
num_keypoint_annotation_skipped = (
len(keypoint_annotations_dict) - num_keypoint_annotation_used)
example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
return key, example, num_annotations_skipped
return key, example, num_annotations_skipped, num_keypoint_annotation_skipped
def _create_tf_record_from_coco_annotations(
annotations_file, image_dir, output_path, include_masks, num_shards):
def _create_tf_record_from_coco_annotations(annotations_file, image_dir,
output_path, include_masks,
num_shards,
keypoint_annotations_file=''):
"""Loads COCO annotation json files and converts to tf.Record format.
Args:
......@@ -202,6 +261,9 @@ def _create_tf_record_from_coco_annotations(
include_masks: Whether to include instance segmentations masks
(PNG encoded) in the result. default: False.
num_shards: number of output file shards.
keypoint_annotations_file: JSON file containing the person keypoint
annotations. If empty, then no person keypoint annotations will be
generated.
"""
with contextlib2.ExitStack() as tf_record_close_stack, \
tf.gfile.GFile(annotations_file, 'r') as fid:
......@@ -214,8 +276,7 @@ def _create_tf_record_from_coco_annotations(
annotations_index = {}
if 'annotations' in groundtruth_data:
tf.logging.info(
'Found groundtruth annotations. Building annotations index.')
logging.info('Found groundtruth annotations. Building annotations index.')
for annotation in groundtruth_data['annotations']:
image_id = annotation['image_id']
if image_id not in annotations_index:
......@@ -227,21 +288,43 @@ def _create_tf_record_from_coco_annotations(
if image_id not in annotations_index:
missing_annotation_count += 1
annotations_index[image_id] = []
tf.logging.info('%d images are missing annotations.',
missing_annotation_count)
logging.info('%d images are missing annotations.', missing_annotation_count)
keypoint_annotations_index = {}
if keypoint_annotations_file:
with tf.gfile.GFile(keypoint_annotations_file, 'r') as kid:
keypoint_groundtruth_data = json.load(kid)
if 'annotations' in keypoint_groundtruth_data:
for annotation in keypoint_groundtruth_data['annotations']:
image_id = annotation['image_id']
if image_id not in keypoint_annotations_index:
keypoint_annotations_index[image_id] = {}
keypoint_annotations_index[image_id][annotation['id']] = annotation
total_num_annotations_skipped = 0
total_num_keypoint_annotations_skipped = 0
for idx, image in enumerate(images):
if idx % 100 == 0:
tf.logging.info('On image %d of %d', idx, len(images))
logging.info('On image %d of %d', idx, len(images))
annotations_list = annotations_index[image['id']]
_, tf_example, num_annotations_skipped = create_tf_example(
image, annotations_list, image_dir, category_index, include_masks)
keypoint_annotations_dict = None
if keypoint_annotations_file:
keypoint_annotations_dict = {}
if image['id'] in keypoint_annotations_index:
keypoint_annotations_dict = keypoint_annotations_index[image['id']]
(_, tf_example, num_annotations_skipped,
num_keypoint_annotations_skipped) = create_tf_example(
image, annotations_list, image_dir, category_index, include_masks,
keypoint_annotations_dict)
total_num_annotations_skipped += num_annotations_skipped
total_num_keypoint_annotations_skipped += num_keypoint_annotations_skipped
shard_idx = idx % num_shards
output_tfrecords[shard_idx].write(tf_example.SerializeToString())
tf.logging.info('Finished writing, skipped %d annotations.',
total_num_annotations_skipped)
logging.info('Finished writing, skipped %d annotations.',
total_num_annotations_skipped)
if keypoint_annotations_file:
logging.info('Finished writing, skipped %d keypoint annotations.',
total_num_keypoint_annotations_skipped)
def main(_):
......@@ -263,13 +346,15 @@ def main(_):
FLAGS.train_image_dir,
train_output_path,
FLAGS.include_masks,
num_shards=100)
num_shards=100,
keypoint_annotations_file=FLAGS.train_keypoint_annotations_file)
_create_tf_record_from_coco_annotations(
FLAGS.val_annotations_file,
FLAGS.val_image_dir,
val_output_path,
FLAGS.include_masks,
num_shards=10)
num_shards=100,
keypoint_annotations_file=FLAGS.val_keypoint_annotations_file)
_create_tf_record_from_coco_annotations(
FLAGS.testdev_annotations_file,
FLAGS.test_image_dir,
......
research/object_detection/dataset_tools/create_coco_tf_record_test.py
View file @ 8518d053
......@@ -20,6 +20,7 @@ import os
import numpy as np
import PIL.Image
import six
import tensorflow as tf
from object_detection.dataset_tools import create_coco_tf_record
......@@ -37,6 +38,16 @@ class CreateCocoTFRecordTest(tf.test.TestCase):
proto_list = [p for p in proto_field]
self.assertListEqual(proto_list, expectation)
def _assertProtoClose(self, proto_field, expectation):
"""Helper function to assert if a proto field nearly equals some value.
Args:
proto_field: The protobuf field to compare.
expectation: The expected value of the protobuf field.
"""
proto_list = [p for p in proto_field]
self.assertAllClose(proto_list, expectation)
def test_create_tf_example(self):
image_file_name = 'tmp_image.jpg'
image_data = np.random.rand(256, 256, 3)
......@@ -78,7 +89,7 @@ class CreateCocoTFRecordTest(tf.test.TestCase):
}
(_, example,
num_annotations_skipped) = create_coco_tf_record.create_tf_example(
num_annotations_skipped, _) = create_coco_tf_record.create_tf_example(
image, annotations_list, image_dir, category_index)
self.assertEqual(num_annotations_skipped, 0)
......@@ -88,12 +99,13 @@ class CreateCocoTFRecordTest(tf.test.TestCase):
example.features.feature['image/width'].int64_list.value, [256])
self._assertProtoEqual(
example.features.feature['image/filename'].bytes_list.value,
[image_file_name])
[six.b(image_file_name)])
self._assertProtoEqual(
example.features.feature['image/source_id'].bytes_list.value,
[str(image['id'])])
[six.b(str(image['id']))])
self._assertProtoEqual(
example.features.feature['image/format'].bytes_list.value, ['jpeg'])
example.features.feature['image/format'].bytes_list.value,
[six.b('jpeg')])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmin'].float_list.value,
[0.25])
......@@ -108,7 +120,7 @@ class CreateCocoTFRecordTest(tf.test.TestCase):
[0.75])
self._assertProtoEqual(
example.features.feature['image/object/class/text'].bytes_list.value,
['cat'])
[six.b('cat')])
def test_create_tf_example_with_instance_masks(self):
image_file_name = 'tmp_image.jpg'
......@@ -144,7 +156,7 @@ class CreateCocoTFRecordTest(tf.test.TestCase):
}
(_, example,
num_annotations_skipped) = create_coco_tf_record.create_tf_example(
num_annotations_skipped, _) = create_coco_tf_record.create_tf_example(
image, annotations_list, image_dir, category_index, include_masks=True)
self.assertEqual(num_annotations_skipped, 0)
......@@ -154,12 +166,13 @@ class CreateCocoTFRecordTest(tf.test.TestCase):
example.features.feature['image/width'].int64_list.value, [8])
self._assertProtoEqual(
example.features.feature['image/filename'].bytes_list.value,
[image_file_name])
[six.b(image_file_name)])
self._assertProtoEqual(
example.features.feature['image/source_id'].bytes_list.value,
[str(image['id'])])
[six.b(str(image['id']))])
self._assertProtoEqual(
example.features.feature['image/format'].bytes_list.value, ['jpeg'])
example.features.feature['image/format'].bytes_list.value,
[six.b('jpeg')])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmin'].float_list.value,
[0])
......@@ -174,7 +187,7 @@ class CreateCocoTFRecordTest(tf.test.TestCase):
[1])
self._assertProtoEqual(
example.features.feature['image/object/class/text'].bytes_list.value,
['dog'])
[six.b('dog')])
encoded_mask_pngs = [
io.BytesIO(encoded_masks) for encoded_masks in example.features.feature[
'image/object/mask'].bytes_list.value
......@@ -183,13 +196,120 @@ class CreateCocoTFRecordTest(tf.test.TestCase):
np.array(PIL.Image.open(encoded_mask_png))
for encoded_mask_png in encoded_mask_pngs
]
self.assertTrue(len(pil_masks) == 1)
self.assertEqual(len(pil_masks), 1)
self.assertAllEqual(pil_masks[0],
[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 1]])
def test_create_tf_example_with_keypoints(self):
image_dir = self.get_temp_dir()
image_file_name = 'tmp_image.jpg'
image_data = np.random.randint(low=0, high=256, size=(256, 256, 3)).astype(
np.uint8)
save_path = os.path.join(image_dir, image_file_name)
image = PIL.Image.fromarray(image_data, 'RGB')
image.save(save_path)
image = {
'file_name': image_file_name,
'height': 256,
'width': 256,
'id': 11,
}
min_x, min_y = 64, 64
max_x, max_y = 128, 128
keypoints = []
num_visible_keypoints = 0
xv = []
yv = []
vv = []
for _ in range(17):
xc = min_x + int(np.random.rand()*(max_x - min_x))
yc = min_y + int(np.random.rand()*(max_y - min_y))
vis = np.random.randint(0, 3)
xv.append(xc)
yv.append(yc)
vv.append(vis)
keypoints.extend([xc, yc, vis])
num_visible_keypoints += (vis > 0)
annotations_list = [{
'area': 0.5,
'iscrowd': False,
'image_id': 11,
'bbox': [64, 64, 128, 128],
'category_id': 1,
'id': 1000
}]
keypoint_annotations_dict = {
1000: {
'keypoints': keypoints,
'num_keypoints': num_visible_keypoints
}
}
category_index = {
1: {
'name': 'person',
'id': 1
}
}
(_, example, _,
num_keypoint_annotation_skipped) = create_coco_tf_record.create_tf_example(
image,
annotations_list,
image_dir,
category_index,
include_masks=False,
keypoint_annotations_dict=keypoint_annotations_dict)
self.assertEqual(num_keypoint_annotation_skipped, 0)
self._assertProtoEqual(
example.features.feature['image/height'].int64_list.value, [256])
self._assertProtoEqual(
example.features.feature['image/width'].int64_list.value, [256])
self._assertProtoEqual(
example.features.feature['image/filename'].bytes_list.value,
[six.b(image_file_name)])
self._assertProtoEqual(
example.features.feature['image/source_id'].bytes_list.value,
[six.b(str(image['id']))])
self._assertProtoEqual(
example.features.feature['image/format'].bytes_list.value,
[six.b('jpeg')])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmin'].float_list.value,
[0.25])
self._assertProtoEqual(
example.features.feature['image/object/bbox/ymin'].float_list.value,
[0.25])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmax'].float_list.value,
[0.75])
self._assertProtoEqual(
example.features.feature['image/object/bbox/ymax'].float_list.value,
[0.75])
self._assertProtoEqual(
example.features.feature['image/object/class/text'].bytes_list.value,
[six.b('person')])
self._assertProtoClose(
example.features.feature['image/object/keypoint/x'].float_list.value,
np.array(xv, dtype=np.float32) / 256)
self._assertProtoClose(
example.features.feature['image/object/keypoint/y'].float_list.value,
np.array(yv, dtype=np.float32) / 256)
self._assertProtoEqual(
example.features.feature['image/object/keypoint/text'].bytes_list.value,
create_coco_tf_record._COCO_KEYPOINT_NAMES)
self._assertProtoEqual(
example.features.feature[
'image/object/keypoint/visibility'].int64_list.value, vv)
def test_create_sharded_tf_record(self):
tmp_dir = self.get_temp_dir()
image_paths = ['tmp1_image.jpg', 'tmp2_image.jpg']
......
research/object_detection/dataset_tools/create_kitti_tf_record_test.py
View file @ 8518d053
......@@ -19,6 +19,7 @@ import os
import numpy as np
import PIL.Image
import six
import tensorflow as tf
from object_detection.dataset_tools import create_kitti_tf_record
......@@ -75,12 +76,13 @@ class CreateKittiTFRecordTest(tf.test.TestCase):
example.features.feature['image/width'].int64_list.value, [256])
self._assertProtoEqual(
example.features.feature['image/filename'].bytes_list.value,
[save_path])
[six.b(save_path)])
self._assertProtoEqual(
example.features.feature['image/source_id'].bytes_list.value,
[save_path])
[six.b(save_path)])
self._assertProtoEqual(
example.features.feature['image/format'].bytes_list.value, ['png'])
example.features.feature['image/format'].bytes_list.value,
[six.b('png')])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmin'].float_list.value,
[0.25])
......@@ -95,7 +97,7 @@ class CreateKittiTFRecordTest(tf.test.TestCase):
[0.75])
self._assertProtoEqual(
example.features.feature['image/object/class/text'].bytes_list.value,
['car'])
[six.b('car')])
self._assertProtoEqual(
example.features.feature['image/object/class/label'].int64_list.value,
[1])
......
research/object_detection/dataset_tools/create_pascal_tf_record_test.py
View file @ 8518d053
......@@ -19,6 +19,7 @@ import os
import numpy as np
import PIL.Image
import six
import tensorflow as tf
from object_detection.dataset_tools import create_pascal_tf_record
......@@ -80,12 +81,13 @@ class CreatePascalTFRecordTest(tf.test.TestCase):
example.features.feature['image/width'].int64_list.value, [256])
self._assertProtoEqual(
example.features.feature['image/filename'].bytes_list.value,
[image_file_name])
[six.b(image_file_name)])
self._assertProtoEqual(
example.features.feature['image/source_id'].bytes_list.value,
[image_file_name])
[six.b(image_file_name)])
self._assertProtoEqual(
example.features.feature['image/format'].bytes_list.value, ['jpeg'])
example.features.feature['image/format'].bytes_list.value,
[six.b('jpeg')])
self._assertProtoEqual(
example.features.feature['image/object/bbox/xmin'].float_list.value,
[0.25])
......@@ -100,7 +102,7 @@ class CreatePascalTFRecordTest(tf.test.TestCase):
[0.75])
self._assertProtoEqual(
example.features.feature['image/object/class/text'].bytes_list.value,
['person'])
[six.b('person')])
self._assertProtoEqual(
example.features.feature['image/object/class/label'].int64_list.value,
[1])
......@@ -111,7 +113,8 @@ class CreatePascalTFRecordTest(tf.test.TestCase):
example.features.feature['image/object/truncated'].int64_list.value,
[0])
self._assertProtoEqual(
example.features.feature['image/object/view'].bytes_list.value, [''])
example.features.feature['image/object/view'].bytes_list.value,
[six.b('')])
if __name__ == '__main__':
......
research/object_detection/dataset_tools/oid_hierarchical_labels_expansion.py
View file @ 8518d053
# Lint as: python2, python3
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -32,12 +33,15 @@ oid_hierarchical_labels_expansion.py \
--annotation_type=<1 (for boxes and segments) or 2 (for image-level labels)>
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import json
from absl import app
from absl import flags
import six
flags.DEFINE_string(
'json_hierarchy_file', None,
......@@ -136,7 +140,7 @@ class OIDHierarchicalLabelsExpansion(object):
# Row header is expected to be the following for segments:
# ImageID,LabelName,ImageWidth,ImageHeight,XMin,XMax,YMin,YMax,
# IsGroupOf,Mask
split_csv_row = csv_row.split(',')
split_csv_row = six.ensure_str(csv_row).split(',')
result = [csv_row]
assert split_csv_row[
labelname_column_index] in self._hierarchy_keyed_child
......@@ -165,7 +169,7 @@ class OIDHierarchicalLabelsExpansion(object):
"""
# Row header is expected to be exactly:
# ImageID,Source,LabelName,Confidence
split_csv_row = csv_row.split(',')
split_csv_row = six.ensure_str(csv_row).split(',')
result = [csv_row]
if int(split_csv_row[confidence_column_index]) == 1:
assert split_csv_row[
......
research/object_detection/dataset_tools/oid_tfrecord_creation.py
View file @ 8518d053
......@@ -18,6 +18,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import tensorflow as tf
from object_detection.core import standard_fields
......@@ -61,18 +62,21 @@ def tf_example_from_annotations_data_frame(annotations_data_frame, label_map,
dataset_util.float_list_feature(
filtered_data_frame_boxes.XMax.as_matrix()),
standard_fields.TfExampleFields.object_class_text:
dataset_util.bytes_list_feature(
filtered_data_frame_boxes.LabelName.as_matrix()),
dataset_util.bytes_list_feature([
six.ensure_binary(label_text)
for label_text in filtered_data_frame_boxes.LabelName.as_matrix()
]),
standard_fields.TfExampleFields.object_class_label:
dataset_util.int64_list_feature(
filtered_data_frame_boxes.LabelName.map(lambda x: label_map[x])
.as_matrix()),
filtered_data_frame_boxes.LabelName.map(
lambda x: label_map[x]).as_matrix()),
standard_fields.TfExampleFields.filename:
dataset_util.bytes_feature('{}.jpg'.format(image_id)),
dataset_util.bytes_feature(
six.ensure_binary('{}.jpg'.format(image_id))),
standard_fields.TfExampleFields.source_id:
dataset_util.bytes_feature(image_id),
dataset_util.bytes_feature(six.ensure_binary(image_id)),
standard_fields.TfExampleFields.image_encoded:
dataset_util.bytes_feature(encoded_image),
dataset_util.bytes_feature(six.ensure_binary(encoded_image)),
}
if 'IsGroupOf' in filtered_data_frame.columns:
......@@ -100,7 +104,9 @@ def tf_example_from_annotations_data_frame(annotations_data_frame, label_map,
image_class_label] = dataset_util.int64_list_feature(
filtered_data_frame_labels.LabelName.map(
lambda x: label_map[x]).as_matrix())
feature_map[standard_fields.TfExampleFields.
image_class_text] = dataset_util.bytes_list_feature(
filtered_data_frame_labels.LabelName.as_matrix()),
feature_map[standard_fields.TfExampleFields
.image_class_text] = dataset_util.bytes_list_feature([
six.ensure_binary(label_text) for label_text in
filtered_data_frame_labels.LabelName.as_matrix()
]),
return tf.train.Example(features=tf.train.Features(feature=feature_map))
research/object_detection/dataset_tools/oid_tfrecord_creation_test.py
View file @ 8518d053
......@@ -15,6 +15,7 @@
"""Tests for oid_tfrecord_creation.py."""
import pandas as pd
import six
import tensorflow as tf
from object_detection.dataset_tools import oid_tfrecord_creation
......@@ -46,8 +47,7 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
df[df.ImageID == 'i1'], label_map, 'encoded_image_test')
self.assertProtoEquals(
"""
self.assertProtoEquals(six.ensure_str("""
features {
feature {
key: "image/encoded"
......@@ -94,7 +94,7 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
feature {
key: "image/class/text"
value { bytes_list { value: ["c"] } } } }
""", tf_example)
"""), tf_example)
def test_no_attributes(self):
label_map, df = create_test_data()
......@@ -107,7 +107,7 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
df[df.ImageID == 'i2'], label_map, 'encoded_image_test')
self.assertProtoEquals("""
self.assertProtoEquals(six.ensure_str("""
features {
feature {
key: "image/encoded"
......@@ -136,7 +136,7 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
feature {
key: "image/source_id"
value { bytes_list { value: "i2" } } } }
""", tf_example)
"""), tf_example)
def test_label_filtering(self):
label_map, df = create_test_data()
......@@ -146,7 +146,7 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
df[df.ImageID == 'i1'], label_map, 'encoded_image_test')
self.assertProtoEquals(
"""
six.ensure_str("""
features {
feature {
key: "image/encoded"
......@@ -193,7 +193,7 @@ class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
feature {
key: "image/class/text"
value { bytes_list { } } } }
""", tf_example)
"""), tf_example)
if __name__ == '__main__':
......
research/object_detection/dataset_tools/tf_record_creation_util.py
View file @ 8518d053
# Lint as: python2, python3
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -18,6 +19,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import range
import tensorflow as tf
......
research/object_detection/dataset_tools/tf_record_creation_util_test.py
View file @ 8518d053
# Lint as: python2, python3
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -14,8 +15,14 @@
# ==============================================================================
"""Tests for tf_record_creation_util.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import contextlib2
import six
from six.moves import range
import tensorflow as tf
from object_detection.dataset_tools import tf_record_creation_util
......@@ -29,7 +36,7 @@ class OpenOutputTfrecordsTests(tf.test.TestCase):
tf_record_close_stack,
os.path.join(tf.test.get_temp_dir(), 'test.tfrec'), 10)
for idx in range(10):
output_tfrecords[idx].write('test_{}'.format(idx))
output_tfrecords[idx].write(six.ensure_binary('test_{}'.format(idx)))
for idx in range(10):
tf_record_path = '{}-{:05d}-of-00010'.format(
......
research/object_detection/dockerfiles/android/Dockerfile
View file @ 8518d053
......@@ -24,7 +24,8 @@ RUN git clone --depth 1 https://github.com/tensorflow/models.git && \
# Install gcloud and gsutil commands
# https://cloud.google.com/sdk/docs/quickstart-debian-ubuntu
RUN apt-get -y update && apt-get install -y gpg-agent && export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" && \
RUN apt-get -y update && apt-get install -y gpg-agent && \
export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" && \
echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - && \
apt-get update -y && apt-get install google-cloud-sdk -y
......@@ -33,8 +34,10 @@ RUN apt-get -y update && apt-get install -y gpg-agent && export CLOUD_SDK_REPO="
# Install the Tensorflow Object Detection API from here
# https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md
# Install object detection api dependencies - use non-interactive mode to set default tzdata config during installation
RUN export DEBIAN_FRONTEND=noninteractive && apt-get install -y protobuf-compiler python-pil python-lxml python-tk && \
# Install object detection api dependencies - use non-interactive mode to set
# default tzdata config during installation.
RUN export DEBIAN_FRONTEND=noninteractive && \
apt-get install -y protobuf-compiler python-pil python-lxml python-tk && \
pip install Cython && \
pip install contextlib2 && \
pip install jupyter && \
......
research/object_detection/eval_util.py
View file @ 8518d053
......@@ -26,18 +26,20 @@ import numpy as np
from six.moves import range
import tensorflow as tf
from tensorflow.contrib import slim
from object_detection.core import box_list
from object_detection.core import box_list_ops
from object_detection.core import keypoint_ops
from object_detection.core import standard_fields as fields
from object_detection.metrics import coco_evaluation
from object_detection.protos import eval_pb2
from object_detection.utils import label_map_util
from object_detection.utils import object_detection_evaluation
from object_detection.utils import ops
from object_detection.utils import shape_utils
from object_detection.utils import visualization_utils as vis_utils
slim = tf.contrib.slim
EVAL_KEYPOINT_METRIC = 'coco_keypoint_metrics'
# A dictionary of metric names to classes that implement the metric. The classes
# in the dictionary must implement
......@@ -45,6 +47,8 @@ slim = tf.contrib.slim
EVAL_METRICS_CLASS_DICT = {
'coco_detection_metrics':
coco_evaluation.CocoDetectionEvaluator,
'coco_keypoint_metrics':
coco_evaluation.CocoKeypointEvaluator,
'coco_mask_metrics':
coco_evaluation.CocoMaskEvaluator,
'oid_challenge_detection_metrics':
......@@ -324,7 +328,7 @@ def _run_checkpoint_once(tensor_dict,
counters = {'skipped': 0, 'success': 0}
aggregate_result_losses_dict = collections.defaultdict(list)
with tf.contrib.slim.queues.QueueRunners(sess):
with slim.queues.QueueRunners(sess):
try:
for batch in range(int(num_batches)):
if (batch + 1) % 100 == 0:
......@@ -591,6 +595,8 @@ def result_dict_for_single_example(image,
'groundtruth_group_of': [num_boxes] int64 tensor. (Optional)
'groundtruth_instance_masks': 3D int64 tensor of instance masks
(Optional).
'groundtruth_keypoints': [num_boxes, num_keypoints, 2] float32 tensor with
keypoints (Optional).
class_agnostic: Boolean indicating whether the detections are class-agnostic
(i.e. binary). Default False.
scale_to_absolute: Boolean indicating whether boxes and keypoints should be
......@@ -620,7 +626,8 @@ def result_dict_for_single_example(image,
'groundtruth_group_of': [num_boxes] int64 tensor. (Optional)
'groundtruth_instance_masks': 3D int64 tensor of instance masks
(Optional).
'groundtruth_keypoints': [num_boxes, num_keypoints, 2] float32 tensor with
keypoints (Optional).
"""
if groundtruth:
......@@ -675,6 +682,10 @@ def result_dict_for_batched_example(images,
Note that evaluation tools require classes that are 1-indexed, and so this
function performs the offset. If `class_agnostic` is True, all output classes
have label 1.
The groundtruth coordinates of boxes/keypoints in 'groundtruth' dictionary are
normalized relative to the (potentially padded) input image, while the
coordinates in 'detection' dictionary are normalized relative to the true
image shape.
Args:
images: A single 4D uint8 image tensor of shape [batch_size, H, W, C].
......@@ -696,6 +707,10 @@ def result_dict_for_batched_example(images,
tensor. (Optional)
'groundtruth_instance_masks': 4D int64 tensor of instance
masks (Optional).
'groundtruth_keypoints': [batch_size, max_number_of_boxes, num_keypoints,
2] float32 tensor with keypoints (Optional).
'groundtruth_keypoint_visibilities': [batch_size, max_number_of_boxes,
num_keypoints] bool tensor with keypoint visibilities (Optional).
class_agnostic: Boolean indicating whether the detections are class-agnostic
(i.e. binary). Default False.
scale_to_absolute: Boolean indicating whether boxes and keypoints should be
......@@ -724,7 +739,11 @@ def result_dict_for_batched_example(images,
'detection_classes': [batch_size, max_detections] int64 tensor of 1-indexed
classes.
'detection_masks': [batch_size, max_detections, H, W] float32 tensor of
binarized masks, reframed to full image masks.
binarized masks, reframed to full image masks. (Optional)
'detection_keypoints': [batch_size, max_detections, num_keypoints, 2]
float32 tensor containing keypoint coordinates. (Optional)
'detection_keypoint_scores': [batch_size, max_detections, num_keypoints]
float32 tensor containing keypoint scores. (Optional)
'num_detections': [batch_size] int64 tensor containing number of valid
detections.
'groundtruth_boxes': [batch_size, num_boxes, 4] float32 tensor of boxes, in
......@@ -739,6 +758,10 @@ def result_dict_for_batched_example(images,
'groundtruth_group_of': [batch_size, num_boxes] int64 tensor. (Optional)
'groundtruth_instance_masks': 4D int64 tensor of instance masks
(Optional).
'groundtruth_keypoints': [batch_size, num_boxes, num_keypoints, 2] float32
tensor with keypoints (Optional).
'groundtruth_keypoint_visibilities': [batch_size, num_boxes, num_keypoints]
bool tensor with keypoint visibilities (Optional).
'num_groundtruth_boxes': [batch_size] tensor containing the maximum number
of groundtruth boxes per image.
......@@ -828,6 +851,12 @@ def result_dict_for_batched_example(images,
_scale_keypoint_to_absolute,
elems=[detection_keypoints, original_image_spatial_shapes],
dtype=tf.float32))
if detection_fields.detection_keypoint_scores in detections:
output_dict[detection_fields.detection_keypoint_scores] = detections[
detection_fields.detection_keypoint_scores]
else:
output_dict[detection_fields.detection_keypoint_scores] = tf.ones_like(
detections[detection_fields.detection_keypoints][:, :, :, 0])
if groundtruth:
if max_gt_boxes is None:
......@@ -866,6 +895,28 @@ def result_dict_for_batched_example(images,
elems=[groundtruth_boxes, true_image_shapes], dtype=tf.float32)
output_dict[input_data_fields.groundtruth_boxes] = groundtruth_boxes
if input_data_fields.groundtruth_keypoints in groundtruth:
# If groundtruth_keypoints is in the groundtruth dictionary. Update the
# coordinates to conform with the true image shape.
def _scale_keypoints_to_normalized_true_image(args):
"""Scale the box coordinates to be relative to the true image shape."""
keypoints, true_image_shape = args
true_image_shape = tf.cast(true_image_shape, tf.float32)
true_height, true_width = true_image_shape[0], true_image_shape[1]
normalized_window = tf.stack(
[0.0, 0.0, true_height / image_height, true_width / image_width])
return keypoint_ops.change_coordinate_frame(keypoints,
normalized_window)
groundtruth_keypoints = groundtruth[
input_data_fields.groundtruth_keypoints]
groundtruth_keypoints = shape_utils.static_or_dynamic_map_fn(
_scale_keypoints_to_normalized_true_image,
elems=[groundtruth_keypoints, true_image_shapes],
dtype=tf.float32)
output_dict[
input_data_fields.groundtruth_keypoints] = groundtruth_keypoints
if scale_to_absolute:
groundtruth_boxes = output_dict[input_data_fields.groundtruth_boxes]
output_dict[input_data_fields.groundtruth_boxes] = (
......@@ -873,6 +924,14 @@ def result_dict_for_batched_example(images,
_scale_box_to_absolute,
elems=[groundtruth_boxes, original_image_spatial_shapes],
dtype=tf.float32))
if input_data_fields.groundtruth_keypoints in groundtruth:
groundtruth_keypoints = output_dict[
input_data_fields.groundtruth_keypoints]
output_dict[input_data_fields.groundtruth_keypoints] = (
shape_utils.static_or_dynamic_map_fn(
_scale_keypoint_to_absolute,
elems=[groundtruth_keypoints, original_image_spatial_shapes],
dtype=tf.float32))
# For class-agnostic models, groundtruth classes all become 1.
if class_agnostic:
......@@ -893,6 +952,8 @@ def get_evaluators(eval_config, categories, evaluator_options=None):
categories: A list of dicts, each of which has the following keys -
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name e.g., 'cat', 'dog'.
'keypoints': (optional) dict mapping this category's keypoints to unique
ids.
evaluator_options: A dictionary of metric names (see
EVAL_METRICS_CLASS_DICT) to `DetectionEvaluator` initialization
keyword arguments. For example:
......@@ -919,6 +980,32 @@ def get_evaluators(eval_config, categories, evaluator_options=None):
evaluators_list.append(EVAL_METRICS_CLASS_DICT[eval_metric_fn_key](
categories,
**kwargs_dict))
if isinstance(eval_config, eval_pb2.EvalConfig):
parameterized_metrics = eval_config.parameterized_metric
for parameterized_metric in parameterized_metrics:
assert parameterized_metric.HasField('parameterized_metric')
if parameterized_metric.WhichOneof(
'parameterized_metric') == EVAL_KEYPOINT_METRIC:
keypoint_metrics = parameterized_metric.coco_keypoint_metrics
# Create category to keypoints mapping dict.
category_keypoints = {}
class_label = keypoint_metrics.class_label
category = None
for cat in categories:
if cat['name'] == class_label:
category = cat
break
if not category:
continue
keypoints_for_this_class = category['keypoints']
category_keypoints = [{
'id': keypoints_for_this_class[kp_name], 'name': kp_name
} for kp_name in keypoints_for_this_class]
# Create keypoint evaluator for this category.
evaluators_list.append(EVAL_METRICS_CLASS_DICT[EVAL_KEYPOINT_METRIC](
category['id'], category_keypoints, class_label,
keypoint_metrics.keypoint_label_to_sigmas))
return evaluators_list
......
research/object_detection/eval_util_test.py
View file @ 8518d053
......@@ -27,6 +27,7 @@ import tensorflow as tf
from object_detection import eval_util
from object_detection.core import standard_fields as fields
from object_detection.metrics import coco_evaluation
from object_detection.protos import eval_pb2
from object_detection.utils import test_case
......@@ -38,6 +39,26 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
{'id': 2, 'name': 'dog'},
{'id': 3, 'name': 'cat'}]
def _get_categories_list_with_keypoints(self):
return [{
'id': 1,
'name': 'person',
'keypoints': {
'left_eye': 0,
'right_eye': 3
}
}, {
'id': 2,
'name': 'dog',
'keypoints': {
'tail_start': 1,
'mouth': 2
}
}, {
'id': 3,
'name': 'cat'
}]
def _make_evaluation_dict(self,
resized_groundtruth_masks=False,
batch_size=1,
......@@ -61,6 +82,7 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
groundtruth_classes = tf.constant([1])
groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
groundtruth_keypoints = tf.constant([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]])
if resized_groundtruth_masks:
groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
......@@ -72,6 +94,9 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
groundtruth_instance_masks = tf.tile(
tf.expand_dims(groundtruth_instance_masks, 0),
multiples=[batch_size, 1, 1, 1])
groundtruth_keypoints = tf.tile(
tf.expand_dims(groundtruth_keypoints, 0),
multiples=[batch_size, 1, 1])
detections = {
detection_fields.detection_boxes: detection_boxes,
......@@ -83,6 +108,7 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
groundtruth = {
input_data_fields.groundtruth_boxes: groundtruth_boxes,
input_data_fields.groundtruth_classes: groundtruth_classes,
input_data_fields.groundtruth_keypoints: groundtruth_keypoints,
input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
}
if batch_size > 1:
......@@ -255,6 +281,49 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
self.assertAlmostEqual(evaluator[1]._recall_lower_bound, 0.0)
self.assertAlmostEqual(evaluator[1]._recall_upper_bound, 1.0)
def test_get_evaluator_with_keypoint_metrics(self):
eval_config = eval_pb2.EvalConfig()
person_keypoints_metric = eval_config.parameterized_metric.add()
person_keypoints_metric.coco_keypoint_metrics.class_label = 'person'
person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'left_eye'] = 0.1
person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'right_eye'] = 0.2
dog_keypoints_metric = eval_config.parameterized_metric.add()
dog_keypoints_metric.coco_keypoint_metrics.class_label = 'dog'
dog_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'tail_start'] = 0.3
dog_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'mouth'] = 0.4
categories = self._get_categories_list_with_keypoints()
evaluator = eval_util.get_evaluators(
eval_config, categories, evaluator_options=None)
# Verify keypoint evaluator class variables.
self.assertLen(evaluator, 3)
self.assertFalse(evaluator[0]._include_metrics_per_category)
self.assertEqual(evaluator[1]._category_name, 'person')
self.assertEqual(evaluator[2]._category_name, 'dog')
self.assertAllEqual(evaluator[1]._keypoint_ids, [0, 3])
self.assertAllEqual(evaluator[2]._keypoint_ids, [1, 2])
self.assertAllClose([0.1, 0.2], evaluator[1]._oks_sigmas)
self.assertAllClose([0.3, 0.4], evaluator[2]._oks_sigmas)
def test_get_evaluator_with_unmatched_label(self):
eval_config = eval_pb2.EvalConfig()
person_keypoints_metric = eval_config.parameterized_metric.add()
person_keypoints_metric.coco_keypoint_metrics.class_label = 'unmatched'
person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
'kpt'] = 0.1
categories = self._get_categories_list_with_keypoints()
evaluator = eval_util.get_evaluators(
eval_config, categories, evaluator_options=None)
self.assertLen(evaluator, 1)
self.assertNotIsInstance(
evaluator[0], coco_evaluation.CocoKeypointEvaluator)
def test_padded_image_result_dict(self):
input_data_fields = fields.InputDataFields
......@@ -263,6 +332,8 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
detection_boxes = np.array([[[0., 0., 1., 1.]], [[0.0, 0.0, 0.5, 0.5]]],
dtype=np.float32)
detection_keypoints = np.array([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]],
dtype=np.float32)
detections = {
detection_fields.detection_boxes:
tf.constant(detection_boxes),
......@@ -271,7 +342,12 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
detection_fields.detection_classes:
tf.constant([[1], [2]]),
detection_fields.num_detections:
tf.constant([1, 1])
tf.constant([1, 1]),
detection_fields.detection_keypoints:
tf.tile(
tf.reshape(
tf.constant(detection_keypoints), shape=[1, 1, 3, 2]),
multiples=[2, 1, 1, 1])
}
gt_boxes = detection_boxes
......@@ -280,6 +356,11 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
tf.constant(gt_boxes),
input_data_fields.groundtruth_classes:
tf.constant([[1.], [1.]]),
input_data_fields.groundtruth_keypoints:
tf.tile(
tf.reshape(
tf.constant(detection_keypoints), shape=[1, 1, 3, 2]),
multiples=[2, 1, 1, 1])
}
image = tf.zeros((2, 100, 100, 3), dtype=tf.float32)
......@@ -299,11 +380,17 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
self.assertAllEqual(
[[[0., 0., 200., 200.]], [[0.0, 0.0, 150., 150.]]],
result[input_data_fields.groundtruth_boxes])
self.assertAllClose([[[[0., 0.], [100., 100.], [200., 200.]]],
[[[0., 0.], [150., 150.], [300., 300.]]]],
result[input_data_fields.groundtruth_keypoints])
# Predictions from the model are not scaled.
self.assertAllEqual(
[[[0., 0., 200., 200.]], [[0.0, 0.0, 75., 150.]]],
result[detection_fields.detection_boxes])
self.assertAllClose([[[[0., 0.], [100., 100.], [200., 200.]]],
[[[0., 0.], [75., 150.], [150., 300.]]]],
result[detection_fields.detection_keypoints])
if __name__ == '__main__':
......
research/object_detection/export_inference_graph.py
View file @ 8518d053
# Lint as: python2, python3
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -108,7 +109,6 @@ from google.protobuf import text_format
from object_detection import exporter
from object_detection.protos import pipeline_pb2
slim = tf.contrib.slim
flags = tf.app.flags
flags.DEFINE_string('input_type', 'image_tensor', 'Type of input node. Can be '
......
research/object_detection/export_tflite_ssd_graph.py
View file @ 8518d053
# Lint as: python2, python3
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
......
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