def evaluation(self, args, down_ratio):
save_path = 'weights_' + args.dataset # 权重放的位置
self.model = self.load_model(self.model,
os.path.join(save_path, args.resume))
self.model = self.model.to(self.device)
self.model.eval()
result_path = 'result_' + args.dataset # 根据类别分别存储检测结果
if not os.path.exists(result_path):
os.mkdir(result_path)
dataset_module = self.dataset[args.dataset]
dsets = dataset_module(data_dir=args.data_dir,
phase='test',
input_h=args.input_h,
input_w=args.input_w,
down_ratio=down_ratio)
func_utils.write_results(args,
self.model,
dsets,
down_ratio,
self.device,
self.decoder,
result_path,
print_ps=True)
if args.dataset == 'dota':
merge_path = 'merge_' + args.dataset
if not os.path.exists(merge_path):
os.mkdir(merge_path)
dsets.merge_crop_image_results(result_path, merge_path)
return None
else:
ap = dsets.dec_evaluation(result_path)
return ap
def dec_eval(self, args, dsets):
result_path = 'result_' + args.dataset
if not os.path.exists(result_path):
os.mkdir(result_path)
self.model.eval()
func_utils.write_results(args, self.model, dsets, self.down_ratio,
self.device, self.decoder, result_path)
ap = dsets.dec_evaluation(result_path)
return ap