diff --git a/docs/en_US/tutorials/prenet.md b/docs/en_US/tutorials/prenet.md
index dff8dc3d723f3c438649dd1b5a561e4f625ab36b..fbe141c6bd4b519107bdd533fc8e6c7cb3b1edb0 100644
--- a/docs/en_US/tutorials/prenet.md
+++ b/docs/en_US/tutorials/prenet.md
@@ -41,7 +41,6 @@
                     .
 ```
 
-
 ### 2.2 Train/Test
 
 
@@ -70,6 +69,7 @@ Output:
 </div>
 
 ## 4 Model Download
+
 | model | dataset |
 |---|---|
 | [PReNet](https://paddlegan.bj.bcebos.com/models/PReNet.pdparams)  | [RainH.zip](https://pan.baidu.com/s/1_vxCatOV3sOA6Vkx1l23eA?pwd=vitu) |
@@ -77,6 +77,7 @@ Output:
 
 
 
+
 # References
 
 - 1. [Progressive Image Deraining Networks: A Better and Simpler Baseline](https://arxiv.org/pdf/1901.09221v3.pdf)
@@ -89,4 +90,4 @@ Output:
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
    year={2019},
  }
-```
+
diff --git a/test_tipc/configs/PReNet/train_infer_python.txt b/test_tipc/configs/PReNet/train_infer_python.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cf30f759816db817f9a63d5ea9e9b96e2b975e44
--- /dev/null
+++ b/test_tipc/configs/PReNet/train_infer_python.txt
@@ -0,0 +1,59 @@
+===========================train_params===========================
+model_name:prenet
+python:python3.7
+gpu_list:0
+##
+auto_cast:null
+total_iters:lite_train_lite_infer=10|lite_train_whole_infer=10|whole_train_whole_infer=200
+output_dir:./output/
+dataset.train.batch_size:lite_train_lite_infer=1|whole_train_whole_infer=1
+pretrained_model:null
+train_model_name:prenet*/*checkpoint.pdparams
+train_infer_img_dir:./data/prenet/test
+null:null
+##
+trainer:norm_train
+norm_train:tools/main.py -c configs/prenet.yaml --seed 123 -o dataset.train.num_workers=0 log_config.interval=1 snapshot_config.interval=5
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:null
+null:null
+##
+===========================infer_params===========================
+--output_dir:./output/
+load:null
+norm_export:tools/export_model.py -c configs/prenet.yaml  --inputs_size="-1,3,-1,-1" --model_name inference --load
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:inference
+train_model:./inference/prenet/prenet_generator
+infer_export:null
+infer_quant:False
+inference:tools/inference.py --model_type prenet -c configs/prenet.yaml --seed 123  --output_path test_tipc/output/
+--device:gpu
+null:null
+null:null
+null:null
+null:null
+null:null
+--model_path:
+null:null
+null:null
+--benchmark:True
+null:null
+===========================train_benchmark_params==========================
+batch_size:2|4
+fp_items:fp32
+total_iters:50
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[6,3,180,320]}]
diff --git a/tools/inference.py b/tools/inference.py
index 0f79ebcb26def517518c645be5bea6a0c6c41237..6ee2108adbc98c164645f61113df2db4a959d29c 100644
--- a/tools/inference.py
+++ b/tools/inference.py
@@ -313,6 +313,22 @@ def main():
             metric_file = os.path.join(args.output_path, "singan/metric.txt")
             for metric in metrics.values():
                 metric.update(prediction, data['A'])
+         elif model_type == "prenet":
+            lq = data['lq'].numpy()
+            gt = data['gt'].numpy()
+            input_handles[0].copy_from_cpu(lq)
+            predictor.run()
+            prediction = output_handle.copy_to_cpu()
+            prediction = paddle.to_tensor(prediction)
+            gt = paddle.to_tensor(gt)
+            image_numpy = tensor2img(prediction, min_max)
+            gt_img = tensor2img(gt, min_max)
+            save_image(
+                image_numpy,
+                os.path.join(args.output_path, "prenet/{}.png".format(i)))
+            metric_file = os.path.join(args.output_path, "prenet/metric.txt")
+            for metric in metrics.values():
+                metric.update(image_numpy, gt_img)
 
         elif model_type == "prenet":
             lq = data['lq'].numpy()