预、API介绍
mxnet.metric
from mxnet import metriccls_metric = metric.Accuracy()box_metric = metric.MAE() cls_metric.update([cls_target], [class_preds.transpose((0,2,1))])box_metric.update([box_target], [box_preds * box_mask])cls_metric.get()box_metric.get()
gluon.loss.Loss
class FocalLoss(gluon.loss.Loss): ???def __init__(self, axis=-1, alpha=0.25, gamma=2, batch_axis=0, **kwargs): ???????super(FocalLoss, self).__init__(None, batch_axis, **kwargs) ???????self._axis = axis ???????self._alpha = alpha ???????self._gamma = gamma ???def hybrid_forward(self, F, output, label): ???????# Here `F` can be either mx.nd or mx.sym ???????# 这里使用F取代在forward中显式的指定两者，方便使用 ???????# 所以非hybrid无此参数 ???????output = F.softmax(output) ???????pj = output.pick(label, axis=self._axis, keepdims=True) ???????loss = - self._alpha * ((1 - pj) ** self._gamma) * pj.log() ???????return loss.mean(axis=self._batch_axis, exclude=True)
mxnet.contrib.ndarray.MultiBoxTarget
def training_targets(anchors, class_preds, labels): ???""" ???得到的全部边框坐标 ???得到的全部边框各个类别得分 ???真实类别及对应边框坐标 ???""" ???class_preds = class_preds.transpose(axes=(0,2,1)) ???return MultiBoxTarget(anchors, labels, class_preds)# Output achors: (1, 5444, 4)# Output class predictions: (1, 5444, 3)# batch.label: (1, 1, 5)out = training_targets(anchors, class_preds, batch.label[0][0:1])
mxnet.contrib.ndarray.MultiBoxDetection
『MXNet』第八弹_物体检测之SSD
原文地址：https://www.cnblogs.com/hellcat/p/9108647.html