# Add layers that generate anchors (bounding boxes proposals).
priorBoxes = []
+ boxCoder = config['box_coder'][0]
+ fasterRcnnBoxCoder = boxCoder['faster_rcnn_box_coder'][0]
+ boxCoderVariance = [1.0/float(fasterRcnnBoxCoder['x_scale'][0]), 1.0/float(fasterRcnnBoxCoder['y_scale'][0]), 1.0/float(fasterRcnnBoxCoder['width_scale'][0]), 1.0/float(fasterRcnnBoxCoder['height_scale'][0])]
for i in range(num_layers):
priorBox = NodeDef()
priorBox.name = 'PriorBox_%d' % i
priorBox.addAttr('width', widths)
priorBox.addAttr('height', heights)
- priorBox.addAttr('variance', [0.1, 0.1, 0.2, 0.2])
+ priorBox.addAttr('variance', boxCoderVariance)
graph_def.node.extend([priorBox])
priorBoxes.append(priorBox.name)
detectionOut.addAttr('num_classes', num_classes + 1)
detectionOut.addAttr('share_location', True)
detectionOut.addAttr('background_label_id', 0)
- detectionOut.addAttr('nms_threshold', 0.6)
- detectionOut.addAttr('top_k', 100)
+
+ postProcessing = config['post_processing'][0]
+ batchNMS = postProcessing['batch_non_max_suppression'][0]
+
+ if 'iou_threshold' in batchNMS:
+ detectionOut.addAttr('nms_threshold', float(batchNMS['iou_threshold'][0]))
+ else:
+ detectionOut.addAttr('nms_threshold', 0.6)
+
+ if 'score_threshold' in batchNMS:
+ detectionOut.addAttr('confidence_threshold', float(batchNMS['score_threshold'][0]))
+ else:
+ detectionOut.addAttr('confidence_threshold', 0.01)
+
+ if 'max_detections_per_class' in batchNMS:
+ detectionOut.addAttr('top_k', int(batchNMS['max_detections_per_class'][0]))
+ else:
+ detectionOut.addAttr('top_k', 100)
+
+ if 'max_total_detections' in batchNMS:
+ detectionOut.addAttr('keep_top_k', int(batchNMS['max_total_detections'][0]))
+ else:
+ detectionOut.addAttr('keep_top_k', 100)
+
detectionOut.addAttr('code_type', "CENTER_SIZE")
- detectionOut.addAttr('keep_top_k', 100)
- detectionOut.addAttr('confidence_threshold', 0.01)
graph_def.node.extend([detectionOut])