5.模型评估首先是导包以及设置context：from mindspore import context

from dataset.voc2012_dataset import create_voc2012_dataset
from model.yolo import YOLOV3DarkNet53
from utils.utils_yolov3 import DetectionEngine, load_yolov3
from yolov3_eval_conf import EvalYOLOv3Conf

set context

context.set_context(mode=context.GRAPH_MODE,

                device_target="GPU", save_graphs=False)

set_graph_kernel_context

context.set_context(enable_graph_kernel=True)
context.set_context(graph_kernel_flags="--enable_parallel_fusion "

                                   "--enable_trans_op_optimize "                                   "--disable_cluster_ops=ReduceMax,Reshape "                                   "--enable_expand_ops=Conv2D")

Set mempool block size for improving memory utilization, which will not take effect in GRAPH_MODE

if context.get_context("mode") == context.PYNATIVE_MODE:

context.set_context(mempool_block_size="31GB")

config

config = EvalYOLOv3Conf()评估模型时的配置如下（也可见 附件\yolov3_eval_conf.py）：class EvalYOLOv3Conf():

def __init__(self):        # ---- dataset ----        self.data_path = "VOCdevkit/VOC2012/"    self.data_usage = "my_person_val"       # 评估数据集    self.data_training = False        self.num_classes = 1        self.class_to_idx = {}        self.anchor_scales = [[15, 38],                          [34, 86],                          [84, 127],                                                      [51, 192],                          [91, 257],                          [173, 195],                                                      [142, 319],                          [221, 339],                          [351, 365]]        self.batch_size = 48                      # 评估时的batch    self.max_box = 32        # test    self.test_img_shape = [416, 416]          # 图片缩放大小        # ---- Model ----        self.out_channel = 3 * ( 5 + self.num_classes)        self.keep_detect = True        self.ckpt_path = "./train_ckpt/yolov3-???.ckpt" # 评估的模型        # ---- Detect ----        self.nms_thresh = 0.5                     # nms 算法去重的IoU阈值    self.eval_ignore_threshold = 0.001        # 检测置信度阈值        self.detcte_result_dir = "det_res/"       # 分类检测框的长期后果寄存        self.image_id_idx = []                    # xml编号的列表，与图片序号对应，eval_yolov3.py中设置        self.anno_path = "VOCdevkit/VOC2012/Annotations/{}.xml"               # Annotations目录的地位，留神最初的格局    self.val_path = "VOCdevkit/VOC2012/ImageSets/Main/my_person_val.txt"  # 模型评估xml文件读数据集：# dataset

voc2012_dat, data_size = create_voc2012_dataset(config, 2)
config.steps_per_epoch = int(data_size / config.batch_size)
image_id_idx = {}
with open(config.val_path) as f:

lines = f.readlines()for i, line in enumerate(lines):    image_id_idx[i] = line.strip()

config.image_id_idx = image_id_idx
print("dataset size: ",data_size)
print("bath num in 1 epoch: ", config.steps_per_epoch)定义网络和检测器：# network
network = YOLOV3DarkNet53(is_training=config.data_training, config=config)
load_yolov3(network,config.ckpt_path)
network.set_train(False)

init detection engine

detection = DetectionEngine(config)这里咱们不须要损失函数，所以只用了网络结构YOLOV3DarkNet53，而后用load_yolov3将后面训练好的模型加载进来。DetectionEngine类次要解决模型前向流传后的输入，包含置信度阈值筛选、NMS算法去除重叠等等工作，最初计算AP的工作也是它实现，对这些计算感兴趣的敌人能够看 附件\utils\utils_yolov3.py 以及 附件\utils\eval_utils.py。最初对模型进行测试评估：print('Start inference....')
for i, data in enumerate(voc2012_dat.create_dict_iterator(num_epochs=1)):

image = data["image"]image_shape = data["image_shape"]image_id = data["img_id"]prediction = network(image)output_big, output_me, output_small = predictionoutput_big = output_big.asnumpy()output_me = output_me.asnumpy()output_small = output_small.asnumpy()image_id = image_id.asnumpy()image_shape = image_shape.asnumpy()detection.detect([output_small, output_me, output_big], config.batch_size, image_shape, image_id)if i % 2 == 0:    print('Processing... {:.2f}% '.format(i * config.batch_size / data_size * 100))

print("Finish")

print('Calculating mAP...')
detection.do_nms_for_results()
result_file_path = detection.write_result()
print('result file path: ', result_file_path)

detection.get_eval_result()

for k, t in detection.eval_res.items():

print(k, " AP : ", t['ap'])

import matplotlib.pyplot as plt

plt.title("P-R curve",fontsize=14)
plt.xlabel("recall", fontsize=14)
plt.ylabel("precision", fontsize=14)
res = detection.eval_res['person']
plt.plot(res['prec'], res['rec'])

plt.savefig("p_r.png")最初我设置检测置信度的阈值为 0.01，nms 算法中的 IoU 阈值为 0.5时，失去的后果是AP为0.609，P-R曲线如下：