def metric_fn(**kwargs):
"""Returns a dictionary that has the evaluation metrics."""
if params['nms_configs'].get('pyfunc', True):
detections_bs = []
for index in range(kwargs['boxes'].shape[0]):
nms_configs = params['nms_configs']
detections = tf.numpy_function(
functools.partial(nms_np.per_class_nms, nms_configs=nms_configs),
[
kwargs['boxes'][index],
kwargs['scores'][index],
kwargs['classes'][index],
tf.slice(kwargs['image_ids'], [index], [1]),
tf.slice(kwargs['image_scales'], [index], [1]),
params['num_classes'],
nms_configs['max_output_size'],
], tf.float32)
detections_bs.append(detections)
else:
# These two branches should be equivalent, but currently they are not.
# TODO(tanmingxing): enable the non_pyfun path after bug fix.
nms_boxes, nms_scores, nms_classes, _ = postprocess.per_class_nms(
params, kwargs['boxes'], kwargs['scores'], kwargs['classes'],
kwargs['image_scales'])
img_ids = tf.cast(
tf.expand_dims(kwargs['image_ids'], -1), nms_scores.dtype)
detections_bs = [
img_ids * tf.ones_like(nms_scores),
nms_boxes[:, :, 1],
nms_boxes[:, :, 0],
nms_boxes[:, :, 3] - nms_boxes[:, :, 1],
nms_boxes[:, :, 2] - nms_boxes[:, :, 0],
nms_scores,
nms_classes,
]
detections_bs = tf.stack(detections_bs, axis=-1, name='detnections')
if params.get('testdev_dir', None):
logging.info('Eval testdev_dir %s', params['testdev_dir'])
eval_metric = coco_metric.EvaluationMetric(
testdev_dir=params['testdev_dir'])
coco_metrics = eval_metric.estimator_metric_fn(detections_bs,
tf.zeros([1]))
else:
logging.info('Eval val with groudtruths %s.', params['val_json_file'])
eval_metric = coco_metric.EvaluationMetric(
filename=params['val_json_file'])
coco_metrics = eval_metric.estimator_metric_fn(
detections_bs, kwargs['groundtruth_data'])
# Add metrics to output.
cls_loss = tf.metrics.mean(kwargs['cls_loss_repeat'])
box_loss = tf.metrics.mean(kwargs['box_loss_repeat'])
output_metrics = {
'cls_loss': cls_loss,
'box_loss': box_loss,
}
output_metrics.update(coco_metrics)
return output_metrics
def metric_fn(**kwargs):
"""Returns a dictionary that has the evaluation metrics."""
nms_boxes, nms_scores, nms_classes, _ = postprocess.per_class_nms(
params, kwargs['boxes'], kwargs['scores'], kwargs['classes'],
kwargs['image_scales'])
img_ids = tf.cast(tf.expand_dims(kwargs['source_ids'], -1),
nms_scores.dtype)
detections = [
img_ids * tf.ones_like(nms_scores),
nms_boxes[:, :, 1],
nms_boxes[:, :, 0],
nms_boxes[:, :, 3] - nms_boxes[:, :, 1],
nms_boxes[:, :, 2] - nms_boxes[:, :, 0],
nms_scores,
nms_classes,
]
detections = tf.stack(detections, axis=-1, name='detnections')
kwargs['detections_bs'] = detections
if params.get('testdev_dir', None):
logging.info('Eval testdev_dir %s', params['testdev_dir'])
eval_metric = coco_metric.EvaluationMetric(
testdev_dir=params['testdev_dir'])
coco_metrics = eval_metric.estimator_metric_fn(
detections, tf.zeros([1]))
else:
logging.info('Eval val with groudtruths %s.',
params['val_json_file'])
eval_metric = coco_metric.EvaluationMetric(
filename=params['val_json_file'])
coco_metrics = eval_metric.estimator_metric_fn(
detections, kwargs['groundtruth_data'])
# Add metrics to output.
cls_loss = tf.metrics.mean(kwargs['cls_loss_repeat'])
box_loss = tf.metrics.mean(kwargs['box_loss_repeat'])
output_metrics = {
'cls_loss': cls_loss,
'box_loss': box_loss,
}
output_metrics.update(coco_metrics)
return output_metrics
