diff --git a/research/cv/ghostnet/README_CN.md b/research/cv/ghostnet/README_CN.md
index 3276aefbdc8a98afcd567447b1ba5d1182ead5d7..8a2ef91d21c8bb5ef1a59a460ca77f319789a889 100644
--- a/research/cv/ghostnet/README_CN.md
+++ b/research/cv/ghostnet/README_CN.md
@@ -115,11 +115,12 @@ GhostNet的总体网络架构如下：[链接](https://arxiv.org/pdf/1911.11907.
├── CMakeLists.txt # ascend310推理
├── main.cc # ascend310推理
└── utils.cc # ascend310推理
- ├── scripts
- ├── run_distribute_train.sh # 启动Ascend分布式训练（8卡）
- ├── run_eval.sh # 启动Ascend评估
- ├── run_infer_310.sh # 启动Ascend310推理
- └── run_standalone_train.sh # 启动Ascend单机训练（单卡）
+ ├── scripts
+ ├── run_distribute_train.sh # 启动Ascend分布式训练（8卡）
+ ├── run_eval.sh # 启动Ascend评估
+ ├── run_eval_onnx.sh # 启动ONNX评估
+ ├── run_infer_310.sh # 启动Ascend310推理
+ └── run_standalone_train.sh # 启动Ascend单机训练（单卡）
├── src
├── config.py # 参数配置
├── dataset.py # 数据预处理
@@ -131,6 +132,7 @@ GhostNet的总体网络架构如下：[链接](https://arxiv.org/pdf/1911.11907.
├── launch.py
└── ghostnet.py # ghostnet网络
├── eval.py # 评估网络
+ ├── eval_onnx.py # ONNX评估
├── create_imagenet2012_label.py # 创建ImageNet2012标签
├── export.py # 导出MindIR模型
├── postprocess.py # 310推理的后期处理
@@ -240,11 +242,11 @@ ckpt = /home/lzu/ghost_Mindspore/scripts/device0/ghostnet-500_1251.ckpt
## [导出MindIR](#contents)
```shell
-python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
+python export.py --device_target [DEVICE_TARGET] --file_format [FILE_FORMAT] --checkpoint_path [CKPT_PATH]
```
参数ckpt_file为必填项，
-`FILE_FORMAT` 必须在 ["AIR", "MINDIR"]中选择。
+`FILE_FORMAT` 必须在 ["AIR", "ONNX", "MINDIR"]中选择。
## 在Ascend310执行推理
@@ -258,9 +260,9 @@ bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
## 结果
-推理结果保存在脚本执行的当前路径， 你可以在acc.log中看到以下精度计算结果。
+推理结果保存在脚本执行的当前路径， 你可以在 acc.log 中看到以下精度计算结果。
-- 使用ImageNet2012数据集评估ghostnet
+- 使用 ImageNet2012 数据集评估 ghostnet
```shell
Total data: 50000, top1 accuracy: 0.73816, top5 accuracy: 0.9178.
diff --git a/research/cv/ghostnet/eval_onnx.py b/research/cv/ghostnet/eval_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7aedd43fe48ed40ad0470b7f8de2067dc1eb208
--- /dev/null
+++ b/research/cv/ghostnet/eval_onnx.py
@@ -0,0 +1,68 @@
+# 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.
+# ============================================================================
+"""
+python eval.py
+"""
+import argparse
+import time
+import mindspore.nn as nn
+import onnxruntime as ort
+from src.dataset import create_dataset
+
+
+def create_session(checkpoint_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(checkpoint_path, providers=providers)
+ input_name = session.get_inputs()[0].name
+ return session, input_name
+
+
+def run_eval(checkpoint_path, data_dir, target_device):
+ session, input_name = create_session(checkpoint_path, target_device)
+ dataset = create_dataset(dataset_path=data_dir, do_train=False, infer_910=False, batch_size=1)
+ metrics = {
+ 'top-1 accuracy': nn.Top1CategoricalAccuracy(),
+ 'top-5 accuracy': nn.Top5CategoricalAccuracy(),
+ }
+ for batch in dataset.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__':
+ start = time.time()
+ 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('--device_id', type=int, default=0, help='Device id')
+
+ args = parser.parse_args()
+ results = run_eval(args.onnx_path, args.dataset_path, args.device_target)
+ for name, value in results.items():
+ print(f'{name}: {value:.5f}')
+ end = time.time()
+ print(str(end))
diff --git a/research/cv/ghostnet/export.py b/research/cv/ghostnet/export.py
index 07456147664201ad4d8406edd2dd7aa702068c23..7e7d536c02842705ea0ead79c7aa22c2cb4042d9 100644
--- a/research/cv/ghostnet/export.py
+++ b/research/cv/ghostnet/export.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# 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.
@@ -26,7 +26,7 @@ if __name__ == '__main__':
parser.add_argument('--device_target', type=str, choices=['Ascend', 'GPU', 'CPU'], default='Ascend',
help='device where the code will be implemented')
parser.add_argument('--device_id', type=int, default=0, help='device id')
- parser.add_argument('--file_format', type=str, choices=['AIR', 'MINDIR'], default='MINDIR',
+ parser.add_argument('--file_format', type=str, choices=['AIR', 'ONNX', 'MINDIR'], default='ONNX',
help='file format')
parser.add_argument('--checkpoint_path', required=True, default=None, help='ckpt file path')
args = parser.parse_args()
@@ -41,4 +41,4 @@ if __name__ == '__main__':
input_data = Tensor(np.zeros([1, 3, 224, 224]), ms.float32)
print(input_data.shape)
- export(net, input_data, file_name='ghost', file_format=args.file_format)
+ export(net, input_data, file_name='ghostnet', file_format=args.file_format)
diff --git a/research/cv/ghostnet/run_eval_onnx.sh b/research/cv/ghostnet/run_eval_onnx.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4b2d4bb7e98c4bee7ff9f21987dda99f2d94e5a2
--- /dev/null
+++ b/research/cv/ghostnet/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 &> infer_onnx.log
+}
+infer
+if [ $? -ne 0 ]; then
+ echo " execute inference failed"
+ exit 1
+fi
diff --git a/research/cv/ghostnet/src/dataset.py b/research/cv/ghostnet/src/dataset.py
index ad05ffc335d708d04d343c3dd615723235686d9e..e3c5c2705b126e9ceca464532ee641de77f864c2 100644
--- a/research/cv/ghostnet/src/dataset.py
+++ b/research/cv/ghostnet/src/dataset.py
@@ -43,9 +43,9 @@ def create_dataset(dataset_path, do_train, infer_910=False, device_id=0, batch_s
device_num = int(os.getenv('RANK_SIZE'))
if not do_train:
- dataset_path = os.path.join(dataset_path, 'test')
+ dataset_path = os.path.join(dataset_path)
else:
- dataset_path = os.path.join(dataset_path, 'train')
+ dataset_path = os.path.join(dataset_path)
if device_num == 1:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=True)