def model_fn(images, labels):
cls_outputs, box_outputs = model(images, training=False)
detections = postprocess.generate_detections(
config, cls_outputs, box_outputs, labels['image_scales'],
labels['source_ids'])
tf.numpy_function(evaluator.update_state, [
labels['groundtruth_data'],
postprocess.transform_detections(detections)
], [])
def _get_detections(self, images, labels):
cls_outputs, box_outputs = util_keras.fp16_to_fp32_nested(
self.model(images, training=False))
detections = postprocess.generate_detections(self.config,
cls_outputs,
box_outputs,
labels['image_scales'],
labels['source_ids'])
tf.numpy_function(self.evaluator.update_state,
[labels['groundtruth_data'],
postprocess.transform_detections(detections)], [])
def test_postprocess_per_class_tf_nms(self):
"""Test postprocess with per class nms using the tensorflow nms."""
tf.random.set_seed(1111)
cls_outputs = {
1: tf.random.normal([2, 4, 4, 2]),
2: tf.random.normal([2, 2, 2, 2])
}
box_outputs = {
1: tf.random.normal([2, 4, 4, 4]),
2: tf.random.normal([2, 2, 2, 4])
}
cls_outputs_list = [cls_outputs[1], cls_outputs[2]]
box_outputs_list = [box_outputs[1], box_outputs[2]]
scales = [1.0, 2.0]
ids = [0, 1]
self.params['max_detection_points'] = 10
self.params['nms_configs']['pyfunc'] = False
outputs = postprocess.generate_detections(self.params,
cls_outputs_list,
box_outputs_list, scales,
ids)
self.assertAllClose(
outputs.numpy(),
[[[0., -1.177383, 1.793507, 8.340945, 4.418388, 0.901576, 2.],
[0., 5.676410, 6.102146, 7.785691, 8.537168, 0.888125, 1.]],
[[1., 5.885427, 13.529362, 11.410081, 14.154047, 0.884544, 1.],
[1., 8.145872, -9.660868, 14.173973, 10.41237, 0.815883, 2.]]])
outputs_flipped = postprocess.generate_detections(
self.params, cls_outputs_list, box_outputs_list, scales, ids, True)
self.assertAllClose(
outputs_flipped.numpy(),
[[[0., -0.340945, 1.793507, 9.177383, 4.418388, 0.901576, 2.],
[0., 0.214309, 6.102146, 2.32359, 8.537168, 0.888125, 1.]],
[[1., 4.589919, 13.529362, 10.114573, 14.154047, 0.884544, 1.],
[1., 1.826027, -9.660868, 7.854128, 10.41237, 0.815883, 2.]]])
def main(_):
config = hparams_config.get_efficientdet_config(FLAGS.model_name)
config.override(FLAGS.hparams)
config.val_json_file = FLAGS.val_json_file
config.nms_configs.max_nms_inputs = anchors.MAX_DETECTION_POINTS
config.drop_remainder = False # eval all examples w/o drop.
config.image_size = utils.parse_image_size(config['image_size'])
# Evaluator for AP calculation.
label_map = label_util.get_label_map(config.label_map)
evaluator = coco_metric.EvaluationMetric(filename=config.val_json_file,
label_map=label_map)
# dataset
batch_size = 1
ds = dataloader.InputReader(
FLAGS.val_file_pattern,
is_training=False,
max_instances_per_image=config.max_instances_per_image)(
config, batch_size=batch_size)
eval_samples = FLAGS.eval_samples
if eval_samples:
ds = ds.take((eval_samples + batch_size - 1) // batch_size)
# Network
lite_runner = LiteRunner(FLAGS.tflite_path, FLAGS.only_network)
eval_samples = FLAGS.eval_samples or 5000
pbar = tf.keras.utils.Progbar(
(eval_samples + batch_size - 1) // batch_size)
for i, (images, labels) in enumerate(ds):
if not FLAGS.only_network:
nms_boxes_bs, nms_classes_bs, nms_scores_bs, _ = lite_runner.run(
images)
nms_classes_bs += postprocess.CLASS_OFFSET
height, width = utils.parse_image_size(config.image_size)
normalize_factor = tf.constant([height, width, height, width],
dtype=tf.float32)
nms_boxes_bs *= normalize_factor
if labels['image_scales'] is not None:
scales = tf.expand_dims(
tf.expand_dims(labels['image_scales'], -1), -1)
nms_boxes_bs = nms_boxes_bs * tf.cast(scales,
nms_boxes_bs.dtype)
detections = postprocess.generate_detections_from_nms_output(
nms_boxes_bs, nms_classes_bs, nms_scores_bs,
labels['source_ids'])
else:
cls_outputs, box_outputs = lite_runner.run(images)
detections = postprocess.generate_detections(
config,
cls_outputs,
box_outputs,
labels['image_scales'],
labels['source_ids'],
pre_class_nms=FLAGS.pre_class_nms)
detections = postprocess.transform_detections(detections)
evaluator.update_state(labels['groundtruth_data'].numpy(),
detections.numpy())
pbar.update(i)
# compute the final eval results.
metrics = evaluator.result()
metric_dict = {}
for i, name in enumerate(evaluator.metric_names):
metric_dict[name] = metrics[i]
if label_map:
for i, cid in enumerate(sorted(label_map.keys())):
name = 'AP_/%s' % label_map[cid]
metric_dict[name] = metrics[i + len(evaluator.metric_names)]
print(FLAGS.model_name, metric_dict)