| import mxnet as mx |
| import numpy as np |
| |
| |
| class MultiBoxMetric(mx.metric.EvalMetric): |
| """Calculate metrics for Multibox training """ |
| def __init__(self): |
| super(MultiBoxMetric, self).__init__(['Acc', 'ObjectAcc', 'SmoothL1'], 3) |
| |
| def update(self, labels, preds): |
| """ |
| Implementation of updating metrics |
| """ |
| # get generated multi label from network |
| cls_prob = preds[0].asnumpy() |
| loc_loss = preds[1].asnumpy() |
| cls_label = preds[2].asnumpy() |
| # overall accuracy & object accuracy |
| label = cls_label.flatten() |
| mask = np.where(label >= 0)[0] |
| p = np.argmax(cls_prob, axis=1).flatten() |
| self.sum_metric[0] += np.sum(p[mask] == label[mask]) |
| self.num_inst[0] += mask.size |
| mask = np.where(label > 0)[0] |
| self.sum_metric[1] += np.sum(p[mask] == label[mask]) |
| self.num_inst[1] += mask.size |
| # smoothl1loss |
| self.sum_metric[2] += np.sum(loc_loss) |
| self.num_inst[2] += loc_loss.shape[0] |
| |
| def get(self): |
| """Get the current evaluation result. |
| Override the default behavior |
| |
| Returns |
| ------- |
| name : str |
| Name of the metric. |
| value : float |
| Value of the evaluation. |
| """ |
| if self.num is None: |
| if self.num_inst == 0: |
| return (self.name, float('nan')) |
| else: |
| return (self.name, self.sum_metric / self.num_inst) |
| else: |
| names = ['%s'%(self.name[i]) for i in range(self.num)] |
| values = [x / y if y != 0 else float('nan') \ |
| for x, y in zip(self.sum_metric, self.num_inst)] |
| return (names, values) |
