我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用mxnet.Symbol()。
def get_symbol(num_classes=20, nms_thresh=0.5, force_suppress=False, nms_topk=400): """ Single-shot multi-box detection with VGG 16 layers ConvNet This is a modified version, with fc6/fc7 layers replaced by conv layers And the network is slightly smaller than original VGG 16 network This is the detection network Parameters: ---------- num_classes: int number of object classes not including background nms_thresh : float threshold of overlap for non-maximum suppression force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns: ---------- mx.Symbol """ net = get_symbol_train(num_classes) cls_preds = net.get_internals()["multibox_cls_pred_output"] loc_preds = net.get_internals()["multibox_loc_pred_output"] anchor_boxes = net.get_internals()["multibox_anchors_output"] cls_prob = mx.symbol.SoftmaxActivation(data=cls_preds, mode='channel', \ name='cls_prob') out = mx.contrib.symbol.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk) return out
def get_symbol(num_classes=20, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """ Single-shot multi-box detection with VGG 16 layers ConvNet This is a modified version, with fc6/fc7 layers replaced by conv layers And the network is slightly smaller than original VGG 16 network This is the detection network Parameters: ---------- num_classes: int number of object classes not including background nms_thresh : float threshold of overlap for non-maximum suppression force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns: ---------- mx.Symbol """ net = get_symbol_train(num_classes) cls_preds = net.get_internals()["multibox_cls_pred_output"] loc_preds = net.get_internals()["multibox_loc_pred_output"] anchor_boxes = net.get_internals()["multibox_anchors_output"] cls_prob = mx.symbol.SoftmaxActivation(data=cls_preds, mode='channel', \ name='cls_prob') out = mx.contrib.symbol.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk) return out
def get_symbol(num_classes=20, nms_thresh=0.5, force_suppress=True): """ Single-shot multi-box detection with VGG 16 layers ConvNet This is a modified version, with fc6/fc7 layers replaced by conv layers And the network is slightly smaller than original VGG 16 network This is the detection network Parameters: ---------- num_classes: int number of object classes not including background nms_thresh : float threshold of overlap for non-maximum suppression Returns: ---------- mx.Symbol """ net = get_symbol_train(num_classes) # print net.get_internals().list_outputs() cls_preds = net.get_internals()["multibox_cls_pred_output"] loc_preds = net.get_internals()["multibox_loc_pred_output"] anchor_boxes = net.get_internals()["multibox_anchors_output"] cls_prob = mx.symbol.SoftmaxActivation(data=cls_preds, mode='channel', \ name='cls_prob') # group output # out = mx.symbol.Group([loc_preds, cls_preds, anchor_boxes]) out = mx.symbol.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, variances=(0.1, 0.1, 0.2, 0.2)) return out
