diff --git a/research/cv/ibnnet/README_CN.md b/research/cv/ibnnet/README_CN.md
index d24f8659bcf87b288916527cf42765785e493a66..37402f7c55f38980c11bc6f424c213f655040be6 100644
--- a/research/cv/ibnnet/README_CN.md
+++ b/research/cv/ibnnet/README_CN.md
@@ -91,7 +91,8 @@ bash scripts/run_eval.sh
├── run_standalone_train.sh // 用于单机训练的shell脚本
├── run_standalone_train.sh // 用于GPU单机训练的shell脚本
├── run_eval.sh // 用于评估的shell脚本
- └── run_eval.sh // 用于GPU评估的shell脚本
+ ├── run_eval.sh // 用于GPU评估的shell脚本
+ └── run_eval_onnx.sh // 用于评估onnx的shell脚本
├── src
├── loss.py //损失函数
├── lr_generator.py //生成学习率
@@ -102,6 +103,7 @@ bash scripts/run_eval.sh
├── pth2ckpt.py //转换pth文件为ckpt文件
├── export.py
├── eval.py // 测试脚本
+ ├── eval_onnx.py // onnx模型评估脚本
├── train.py // 训练脚本
├── preprocess.py // 310推理数据预处理
├── preprocess.py // 310推理数据后处理
@@ -210,6 +212,31 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [
参数`ckpt_file` 是必需的，`FILE_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。
+## onnx模型导出与推理
+
+- 导出 ONNX:
+
+```shell
+python export.py --ckpt_file [CKPT_PATH] --file_format [FILE_FORMAT] --device_target [DEVICE_TARGET]
+e.g. python export.py --ckpt_file ibnnet_top1acc77.12_top5acc93.58.ckpt --file_format ONNX --device_target GPU
+```
+
+- 运行推理-python方式:
+
+```shell
+python ./eval_onnx.py --onnx_path=[ONNX_PATH] --dataset_path=[DATASET_PATH] --device_target=[DEVICE_TARGET]
+e.g. python ./eval_onnx.py --onnx_path=./ibnnet.onnx --dataset_path=../ghost/dataset/ILSVRC2012_img_val --device_target=GPU
+```
+
+- 运行推理-bash方式:
+
+```shell
+bash run_eval_onnx.sh [ONNX_PATH] [DATASET_PATH] [DEVICE_TARGET]
+bash run_eval_onnx.sh ./ibnnet.onnx ../ghost/dataset/ILSVRC2012_img_val/ GPU
+```
+
+- 评估结果将存放在 eval_onnx.log 中.
+
# 推理过程
## 用法
@@ -270,4 +297,3 @@ bash run_310_infer.sh [MINDIR_PATH] [DATASET_PATH]
# ModelZoo主页
请浏览官网[主页](https://gitee.com/mindspore/models)。
-
diff --git a/research/cv/ibnnet/eval_onnx.py b/research/cv/ibnnet/eval_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b21b12862a829b7b937a04414aa46d59389bc4b
--- /dev/null
+++ b/research/cv/ibnnet/eval_onnx.py
@@ -0,0 +1,74 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+eval
+"""
+
+import argparse
+import mindspore.nn as nn
+import onnxruntime as ort
+
+from src.dataset import create_evalset
+
+
+
+def create_session(onnx_path, target_device):
+ if target_device == 'GPU':
+ providers = ['CUDAExecutionProvider']
+ elif target_device == 'CPU':
+ providers = ['CPUExecutionProvider']
+ else:
+ raise ValueError(
+ f'Unsupported target device {target_device}, '
+ f'Expected one of: "CPU", "GPU"'
+ )
+ session = ort.InferenceSession(onnx_path, providers=providers)
+ input_name = session.get_inputs()[0].name
+ return session, input_name
+
+def onnx_eval(onnx_path, dataset_path, batch_size, device_target):
+ # create dataset
+ ds = create_evalset(dataset_path=dataset_path,
+ do_train=False,
+ repeat_num=1,
+ batch_size=batch_size,
+ target=device_target)
+ # load onnx
+ session, input_name = create_session(onnx_path=onnx_path,
+ target_device=device_target)
+ # evaluation
+ metrics = {
+ 'top-1 accuracy': nn.Top1CategoricalAccuracy(),
+ 'top-5 accuracy': nn.Top5CategoricalAccuracy(),
+ }
+ for batch in ds.create_dict_iterator(num_epochs=1, output_numpy=True):
+ y_pred = session.run(None, {input_name: batch['image']})[0]
+ for metric in metrics.values():
+ metric.update(y_pred, batch['label'])
+ return {name: metric.eval() for name, metric in metrics.items()}
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description='Image classification')
+ # onnx parameter
+ parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+ parser.add_argument('--onnx_path', type=str, default=None, help='ONNX file path')
+ parser.add_argument('--device_target', type=str, default='GPU', help='Device target')
+ parser.add_argument('--batch_size', type=int, default=1, help='Batch size')
+
+ args = parser.parse_args()
+
+ results = onnx_eval(args.onnx_path, args.dataset_path, args.batch_size, args.device_target)
+ for name, value in results.items():
+ print(f'{name}: {value:.4f}')
diff --git a/research/cv/ibnnet/requirements.txt b/research/cv/ibnnet/requirements.txt
index 08ed5eeb4b9f080b780db7d3e0af6712866c0493..5de5f677ee9d5e41e096fbd38c6016f7774f85f4 100644
--- a/research/cv/ibnnet/requirements.txt
+++ b/research/cv/ibnnet/requirements.txt
@@ -1 +1,2 @@
-torch
\ No newline at end of file
+torch
+onnxruntime-gpu
\ No newline at end of file
diff --git a/research/cv/ibnnet/scripts/run_eval_onnx.sh b/research/cv/ibnnet/scripts/run_eval_onnx.sh
new file mode 100644
index 0000000000000000000000000000000000000000..563b05a23178c4cbf83edd24d833bc87b5a536d0
--- /dev/null
+++ b/research/cv/ibnnet/scripts/run_eval_onnx.sh
@@ -0,0 +1,47 @@
+#!/bin/bash
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [[ $# -ne 3 ]]; then
+ echo "Usage: bash run_infer_onnx.sh [ONNX_PATH] [DATASET_PATH] [DEVICE_TARGET]"
+exit 1
+fi
+
+get_real_path(){
+ if [ "${1:0:1}" == "/" ]; then
+ echo "$1"
+ else
+ echo "$(realpath -m $PWD/$1)"
+ fi
+}
+onnx_path=$(get_real_path $1)
+dataset_path=$(get_real_path $2)
+device_target=$3
+
+echo "onnx_path: "$onnx_path
+echo "dataset_path: "$dataset_path
+echo "device_target: "$device_target
+
+function infer()
+{
+ python ./eval_onnx.py --onnx_path=$onnx_path \
+ --dataset_path=$dataset_path \
+ --device_target=$device_target &> eval_onnx.log 2>&1
+}
+infer
+if [ $? -ne 0 ]; then
+ echo " execute inference failed"
+ exit 1
+fi
\ No newline at end of file