GitLab

* Merged commit includes the following changes:
207771702  by Zhichao Lu:

    Refactoring evaluation utilities so that it is easier to introduce new DetectionEvaluators with eval_metric_ops.

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207758641  by Zhichao Lu:

    Require tensorflow version 1.9+ for running object detection API.

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207641470  by Zhichao Lu:

    Clip `num_groundtruth_boxes` in pad_input_data_to_static_shapes() to `max_num_boxes`. This prevents a scenario where tensors are sliced to an invalid range in model_lib.unstack_batch().

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207621728  by Zhichao Lu:

    This CL adds a FreezableBatchNorm that inherits from the Keras BatchNormalization layer, but supports freezing the `training` parameter at construction time instead of having to do it in the `call` method.

    It also adds a method to the `KerasLayerHyperparams` class that will build an appropriate FreezableBatchNorm layer according to the hyperparameter configuration. If batch_norm is disabled, this method returns and Identity layer.

    These will be used to simplify the conversion to Keras APIs.

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207610524  by Zhichao Lu:

    Update anchor generators and box predictors for python3 compatibility.

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207585122  by Zhichao Lu:

    Refactoring convolutional box predictor into separate prediction heads.

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207549305  by Zhichao Lu:

    Pass all 1s for batch weights if nothing is specified in GT.

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207336575  by Zhichao Lu:

    Move the new argument 'target_assigner_instance' to the end of the list of arguments to the ssd_meta_arch constructor for backwards compatibility.

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207327862  by Zhichao Lu:

    Enable support for float output in quantized custom op for postprocessing in SSD Mobilenet model.

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207323154  by Zhichao Lu:

    Bug fix: change dict.iteritems() to dict.items()

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207301109  by Zhichao Lu:

    Integrating expected_classification_loss_under_sampling op as an option in the ssd_meta_arch

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207286221  by Zhichao Lu:

    Adding an option to weight regression loss with foreground scores from the ground truth labels.

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207231739  by Zhichao Lu:

    Explicitly mentioning the argument names when calling the batch target assigner.

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207206356  by Zhichao Lu:

    Add include_trainable_variables field to train config to better handle trainable variables.

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207135930  by Zhichao Lu:

    Internal change.

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206862541  by Zhichao Lu:

    Do not unpad the outputs from batch_non_max_suppression before sampling.

    Since BalancedPositiveNegativeSampler takes an indicator for valid positions to sample from we can pass the output from NMS directly into Sampler.

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PiperOrigin-RevId: 207771702

* Remove unused doc.

196161788  by Zhichao Lu:

    Add eval_on_train_steps parameter.

    Since the number of samples in train dataset is usually different to the number of samples in the eval dataset.

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196151742  by Zhichao Lu:

    Add an optional random sampling process for SSD meta arch and update mean stddev coder to use default std dev when corresponding tensor is not added to boxlist field.

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196148940  by Zhichao Lu:

    Release ssdlite mobilenet v2 coco trained model.

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196058528  by Zhichao Lu:

    Apply FPN feature map generation before we add additional layers on top of resnet feature extractor.

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195818367  by Zhichao Lu:

    Add support for exporting detection keypoints.

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195745420  by Zhichao Lu:

    Introduce include_metrics_per_category option to Object Detection eval_config.

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195734733  by Zhichao Lu:

    Rename SSDLite config to be more explicit.

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195717383  by Zhichao Lu:

    Add quantized training to object_detection.

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195683542  by Zhichao Lu:

    Fix documentation for the interaction of fine_tune_checkpoint_type and load_all_detection_checkpoint_vars interaction.

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195668233  by Zhichao Lu:

    Using batch size from params dictionary if present.

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195570173  by Zhichao Lu:

    A few fixes to get new estimator API eval to match legacy detection eval binary by (1) plumbing `is_crowd` annotations through to COCO evaluator, (2) setting the `sloppy` flag in tf.contrib.data.parallel_interleave based on whether shuffling is enabled, and (3) saving the original image instead of the resized original image, which allows for small/medium/large mAP metrics to be properly computed.

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195316756  by Zhichao Lu:

    Internal change

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PiperOrigin-RevId: 196161788

* Adding option for one_box_for_all_classes to the box_predictor

PiperOrigin-RevId: 192813444

* Extend to accept different ratios of conv channels.

PiperOrigin-RevId: 192837477

* Remove inaccurate caveat from proto file.

PiperOrigin-RevId: 192850747

* Add option to set dropout for classification net in weight shared box predictor.

PiperOrigin-RevId: 192922089

* fix flakiness in testSSDRandomCropWithMultiClassScores due to randomness.

PiperOrigin-RevId: 193067658

* Post-process now works again in train mode.

PiperOrigin-RevId: 193087707

* Adding support for reading in logits as groundtruth labels and applying an optional temperature (scaling) before softmax in support of distillation.

PiperOrigin-RevId: 193119411

* Add a util function to visualize value histogram as a tf.summary.image.

PiperOrigin-RevId: 193137342

* Do not add batch norm parameters to final conv2d ops that predict boxes encodings and class...

* Force cast of num_classes to integer

PiperOrigin-RevId: 188335318

* Updating config util to allow overwriting of cosine decay learning rates.

PiperOrigin-RevId: 188338852

* Make box_list_ops.py and box_list_ops_test.py work with C API enabled.

The C API has improved shape inference over the original Python
code. This causes some previously-working conds to fail. Switching to smart_cond fixes this.

Another effect of the improved shape inference is that one of the
failures tested gets caught earlier, so I modified the test to reflect
this.

PiperOrigin-RevId: 188409792

* Fix parallel event file writing issue.

Without this change, the event files might get corrupted when multiple evaluations are run in parallel.

PiperOrigin-RevId: 188502560

* Deprecating the boolean flag of from_detection_checkpoint.

Replace with a string field fine_tune_checkpoint_type to train_config to provide extensibility. The fine_tune_checkpoint_type can currently take value of `detection`, `classification`, or others when the restore_map is overwritten.

PiperOrigin-RevId: 188518685

* Automated g4 rollback of changelist 188502560

PiperOrigin-RevId: 188519969

* Introducing eval metrics specs for Coco Mask metrics. This allows metrics to be computed in tensorflow using the tf.learn Estimator.

PiperOrigin-RevId: 188528485

* Minor fix to make object_detection/metrics/coco_evaluation.py python3 compatible.

PiperOrigin-RevId: 188550683

* Updating eval_util to handle eval_metric_ops from multiple `DetectionEvaluator`s.

PiperOrigin-RevId: 188560474

* Allow tensor input for new_height and new_width for resize_image.

PiperOrigin-RevId: 188561908

* Fix typo in fine_tune_checkpoint_type name in trainer.

PiperOrigin-RevId: 188799033

* Adding mobilenet feature extractor to object detection.

PiperOrigin-RevId: 188916897

* Allow label maps to optionally contain an explicit background class with id zero.

PiperOrigin-RevId: 188951089

* Fix boundary conditions in random_pad_to_aspect_ratio to ensure that min_scale is always less than max_scale.

PiperOrigin-RevId: 189026868

* Fallback on from_detection_checkpoint option if fine_tune_checkpoint_type isn't set.

PiperOrigin-RevId: 189052833

* Add proper names for learning rate schedules so we don't see cryptic names on tensorboard.

PiperOrigin-RevId: 189069837

* Enforcing that all datasets are batched (and then unbatched in the model) with batch_size >= 1.

PiperOrigin-RevId: 189117178

* Adding regularization to total loss returned from DetectionModel.loss().

PiperOrigin-RevId: 189189123

* Standardize the names of loss scalars (for SSD, Faster R-CNN and R-FCN) in both training and eval so they can be compared on tensorboard.

Log localization and classification losses in evaluation.

PiperOrigin-RevId: 189189940

* Remove negative test from box list ops test.

PiperOrigin-RevId: 189229327

* Add an option to warmup learning rate in manual stepping schedule.

PiperOrigin-RevId: 189361039

* Replace tf.contrib.slim.tfexample_decoder.LookupTensor with object_detection.data_decoders.tf_example_decoder.LookupTensor.

PiperOrigin-RevId: 189388556

* Force regularization summary variables under specific family names.

PiperOrigin-RevId: 189393190

* Automated g4 rollback of changelist 188619139

PiperOrigin-RevId: 189396001

* Remove step 0 schedule since we do a hard check for it after cl/189361039

PiperOrigin-RevId: 189396697

* PiperOrigin-RevId: 189040463

* PiperOrigin-RevId: 189059229

* PiperOrigin-RevId: 189214402

* Force regularization summary variables under specific family names.

PiperOrigin-RevId: 189393190

* Automated g4 rollback of changelist 188619139

PiperOrigin-RevId: 189396001

* Make slim python3 compatible.

* Monir fixes.

* Add TargetAssignment summaries in a separate family.

PiperOrigin-RevId: 189407487

* 1. Setting `family` keyword arg prepends the summary names twice with the same name. Directly adding family suffix to the name gets rid of this problem.
2. Make sure the eval losses have the same name.

PiperOrigin-RevId: 189434618

* Minor fixes to make object detection tf 1.4 compatible.

PiperOrigin-RevId: 189437519

* Call the base of mobilenet_v1 feature extractor under the right arg scope and set batchnorm is_training based on the value passed in the constructor.

PiperOrigin-RevId: 189460890

* Automated g4 rollback of changelist 188409792

PiperOrigin-RevId: 189463882

* Update object detection syncing.

PiperOrigin-RevId: 189601955

* Add an option to warmup learning rate, hold it constant for a certain number of steps and cosine decay it.

PiperOrigin-RevId: 189606169

* Let the proposal feature extractor function in faster_rcnn meta architectures return the activations (end_points).

PiperOrigin-RevId: 189619301

* Fixed bug which caused masks to be mostly zeros (caused by detection_boxes being in absolute coordinates if scale_to_absolute=True.

PiperOrigin-RevId: 189641294

* Open sourcing Mobilenetv2 + SSDLite.

PiperOrigin-RevId: 189654520

* Remove unused files.

PiperOrigin-RevId: 187527188

185215255  by Zhichao Lu:

    Stop populating image/object/class/text field when generating COCO tf record.

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185213306  by Zhichao Lu:

    Use the params batch size and not the one from train_config in input_fn

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185209081  by Zhichao Lu:

    Handle the case when there are no ground-truth masks for an image.

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185195531  by Zhichao Lu:

    Remove unstack and stack operations on features from third_party/object_detection/model.py.

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185195017  by Zhichao Lu:

    Matrix multiplication based gather op implementation.

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185187744  by Zhichao Lu:

    Fix eval_util minor issue.

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185098733  by Zhichao Lu:

    Internal change

185076656  by Zhichao Lu:

    Increment the amount of boxes for coco17.

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185074199  by Zhichao Lu:

    Add config for SSD Resnet50 v1 with FPN.

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185060199  by Zhichao Lu:

    Fix a bug in clear_detections.
    This method set detection_keys to an empty dictionary instead of an empty set. I've refactored so that this ...

184048729  by Zhichao Lu:

    Modify target_assigner so that it creates regression targets taking keypoints into account.

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184027183  by Zhichao Lu:

    Resnet V1 FPN based feature extractors for SSD meta architecture in Object Detection V2 API.

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184004730  by Zhichao Lu:

    Expose a lever to override the configured mask_type.

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183933113  by Zhichao Lu:

    Weight shared convolutional box predictor as described in https://arxiv.org/abs/1708.02002

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183929669  by Zhichao Lu:

    Expanding box list operations for future data augmentations.

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183916792  by Zhichao Lu:

    Fix unrecognized assertion function in tests.

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183906851  by Zhichao Lu:

    - Change ssd meta architecture to use regression weights to compute loss normalizer.

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183871003  by Zhichao Lu:

    Fix config_util_test wrong dependency.

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183782120  by Zhichao Lu:

    Add __init__ file to third_party directories.

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183779109  by Zhichao Lu:

    Setup regular version s...