add onnx

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.

research/cv/ghostnet/eval_onnx.py

+# 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))

research/cv/ghostnet/export.py

-# 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)

research/cv/ghostnet/run_eval_onnx.sh

+#!/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

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)