| import mxnet as mx |
| import numpy as np |
| |
| |
| class MultiBoxMetric(mx.metric.EvalMetric): |
| """Calculate metrics for Multibox training """ |
| def __init__(self, eps=1e-8): |
| super(MultiBoxMetric, self).__init__(['CrossEntropy', 'SmoothL1'], 2) |
| self.eps = eps |
| |
| 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() |
| valid_count = np.sum(cls_label >= 0) |
| # overall accuracy & object accuracy |
| label = cls_label.flatten() |
| mask = np.where(label >= 0)[0] |
| indices = np.int64(label[mask]) |
| prob = cls_prob.transpose((0, 2, 1)).reshape((-1, cls_prob.shape[1])) |
| prob = prob[mask, indices] |
| self.sum_metric[0] += (-np.log(prob + self.eps)).sum() |
| self.num_inst[0] += valid_count |
| # smoothl1loss |
| self.sum_metric[1] += np.sum(loc_loss) |
| self.num_inst[1] += valid_count |
| |
| 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) |
