Python object_detection.eval_util.get_eval_metric_ops_for_evaluators() Examples

The following are 30 code examples of object_detection.eval_util.get_eval_metric_ops_for_evaluators(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.eval_util , or try the search function .
Example #1
Source File: eval_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #2
Source File: eval_util_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_resized_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute,
                                           resized_groundtruth_masks=True)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #3
Source File: eval_util_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #4
Source File: eval_util_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self, batch_size=1,
                                                   max_gt_boxes=None,
                                                   scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['coco_detection_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #5
Source File: eval_util_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_resized_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute,
                                           resized_groundtruth_masks=True)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in six.iteritems(metric_ops):
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #6
Source File: eval_util_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in six.iteritems(metric_ops):
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #7
Source File: eval_util_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self, batch_size=1,
                                                   max_gt_boxes=None,
                                                   scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['coco_detection_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in six.iteritems(metric_ops):
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #8
Source File: eval_util_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_resized_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute,
                                           resized_groundtruth_masks=True)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #9
Source File: eval_util_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #10
Source File: eval_util_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self, batch_size=1,
                                                   max_gt_boxes=None,
                                                   scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['coco_detection_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #11
Source File: eval_util_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_resized_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute,
                                           resized_groundtruth_masks=True)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #12
Source File: eval_util_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #13
Source File: eval_util_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self, batch_size=1,
                                                   max_gt_boxes=None,
                                                   scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['coco_detection_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #14
Source File: eval_util_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_resized_masks(self):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(resized_groundtruth_masks=True)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #15
Source File: eval_util_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(self):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #16
Source File: eval_util_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['coco_detection_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      print(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #17
Source File: eval_util_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(self):
    evaluation_metrics = ['coco_detection_metrics',
                          'coco_mask_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        evaluation_metrics, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #18
Source File: eval_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self, batch_size=1,
                                                   max_gt_boxes=None,
                                                   scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['coco_detection_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #19
Source File: eval_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_resized_masks(
      self, batch_size=1, max_gt_boxes=None, scale_to_absolute=False):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'coco_mask_metrics'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict(batch_size=batch_size,
                                           max_gt_boxes=max_gt_boxes,
                                           scale_to_absolute=scale_to_absolute,
                                           resized_groundtruth_masks=True)
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        eval_config, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #20
Source File: eval_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self):
    evaluation_metrics = ['coco_detection_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        evaluation_metrics, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      print(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #21
Source File: eval_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(self):
    evaluation_metrics = ['coco_detection_metrics',
                          'coco_mask_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        evaluation_metrics, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #22
Source File: eval_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self):
    evaluation_metrics = ['coco_detection_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        evaluation_metrics, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      print(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #23
Source File: eval_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(self):
    evaluation_metrics = ['coco_detection_metrics',
                          'coco_mask_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        evaluation_metrics, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #24
Source File: eval_util_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self):
    evaluation_metrics = ['coco_detection_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        evaluation_metrics, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      print(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #25
Source File: eval_util_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections_and_masks(self):
    evaluation_metrics = ['coco_detection_metrics',
                          'coco_mask_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        evaluation_metrics, categories, eval_dict)
    _, update_op_boxes = metric_ops['DetectionBoxes_Precision/mAP']
    _, update_op_masks = metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op_boxes)
      sess.run(update_op_masks)
      metrics = sess.run(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertAlmostEqual(1.0, metrics['DetectionMasks_Precision/mAP'])
Example #26
Source File: eval_util_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_get_eval_metric_ops_for_coco_detections(self):
    evaluation_metrics = ['coco_detection_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
        evaluation_metrics, categories, eval_dict)
    _, update_op = metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      metrics = {}
      for key, (value_op, _) in metric_ops.iteritems():
        metrics[key] = value_op
      sess.run(update_op)
      metrics = sess.run(metrics)
      print(metrics)
      self.assertAlmostEqual(1.0, metrics['DetectionBoxes_Precision/mAP'])
      self.assertNotIn('DetectionMasks_Precision/mAP', metrics)
Example #27
Source File: eval_util_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def test_get_eval_metric_ops_raises_error_with_unsupported_metric(self):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['unsupported_metric'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    with self.assertRaises(ValueError):
      eval_util.get_eval_metric_ops_for_evaluators(
          eval_config, categories, eval_dict)
Example #28
Source File: eval_util_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def test_get_eval_metric_ops_raises_error_with_unsupported_metric(self):
    evaluation_metrics = ['unsupported_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    with self.assertRaises(ValueError):
      eval_util.get_eval_metric_ops_for_evaluators(
          evaluation_metrics, categories, eval_dict)
Example #29
Source File: eval_util_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def test_get_eval_metric_ops_raises_error_with_unsupported_metric(self):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['unsupported_metric'])
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    with self.assertRaises(ValueError):
      eval_util.get_eval_metric_ops_for_evaluators(
          eval_config, categories, eval_dict)
Example #30
Source File: eval_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_get_eval_metric_ops_raises_error_with_unsupported_metric(self):
    evaluation_metrics = ['unsupported_metrics']
    categories = self._get_categories_list()
    eval_dict = self._make_evaluation_dict()
    with self.assertRaises(ValueError):
      eval_util.get_eval_metric_ops_for_evaluators(
          evaluation_metrics, categories, eval_dict)