boxes = []
for score, geo, index in zip(quad_scores, quad_after_nms,
range(len(quad_scores))):
if np.amin(score) > 0:
geo = reorder_vertexes(geo)[[0, 3, 2, 1]] # 顺时针排序
rescaled_geo = geo / [
width * 4 / int(batch['shape'][0][1]),
height * 4 / int(batch['shape'][0][0])
]
boxes.append(rescaled_geo.astype(np.int16))
boxes = np.array(boxes).astype(np.int16)
boxes_batch.append(boxes)
raw_metric = QuadMeasurer().validate_measure(
batch, [boxes_batch], False)
raw_metrics.append(raw_metric)
metrics = QuadMeasurer().gather_measure(raw_metrics)
for key, metric in metrics.items():
logger.metrics('%d : %s : %f (%d)' %
(epoch, key, metric.avg, metric.count))
state_dict = model.state_dict()
torch.save(state_dict, 'model/resnest_east_weights_{}_{}.h5'.format(\
cfg.train_task_id, epoch))
