def test_postprocess_per_class(self):
"""Test postprocess with per class 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
outputs = postprocess.generate_detections(self.params,
cls_outputs_list,
box_outputs_list, scales,
ids)
self.params['disable_pyfun'] = False
score_thresh = 0.5
max_output_size = self.params['nms_configs']['max_output_size']
self.params['batch_size'] = len(scales)
legacy_outputs = inference.det_post_process(self.params, cls_outputs,
box_outputs, scales,
score_thresh,
max_output_size)
self.assertAllClose(outputs, legacy_outputs)
def test_postprocess_global(self):
"""Test the postprocess with global 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]
self.params['max_detection_points'] = 10
boxes, scores, classes, valid_len = postprocess.postprocess_global(
self.params, cls_outputs_list, box_outputs_list, scales)
self.assertAllClose(valid_len, [2, 2])
self.params['disable_pyfun'] = True
score_thresh = 0.5
self.params['batch_size'] = len(scales)
max_output_size = self.params['nms_configs']['max_output_size']
legacy_detections = inference.det_post_process(self.params,
cls_outputs,
box_outputs, scales,
score_thresh,
max_output_size)
legacy_boxes = legacy_detections[:, :, 1:5]
legacy_scores = legacy_detections[:, :, 5]
legacy_classes = legacy_detections[:, :, 6]
self.assertAllClose(boxes, legacy_boxes)
self.assertAllClose(scores, legacy_scores)
self.assertAllClose(classes, legacy_classes)