Add data_to_mindrecord.py to resnet50_adv_purning net.

model_zoo/research/cv/resnet50_adv_pruning/Readme.md

@@ -25,6 +25,15 @@ The Adversarial Pruning method is a reliable neural network pruning algorithm by
 
 Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
 
+- step 1: Download dataset
+
+- step 2: Convert the dataset to mindrecord:
+
+    ```bash
+    cd ./src
+    python data_to_mindrecord_test.py
+    ```
+
 - Dataset size: 7049 colorful images in 1000 classes
     - Train:  3680 images
     - Test: 3369 images

model_zoo/research/cv/resnet50_adv_pruning/src/data_to_mindrecord_test.py

@@ -0,0 +1,62 @@
+# Copyright 2020 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.
+# ============================================================================
+"""
+data transform to mindrecord format
+"""
+import os
+import argparse
+import numpy as np
+from mindspore.mindrecord import FileWriter
+
+parser = argparse.ArgumentParser(description='Export parameter')
+parser.add_argument('--image_root_dir', type=str,
+                    default='/home/dataset/Oxford_IIIT_Pet/images/', help='Dataset path')
+parser.add_argument('--annotation_dir', type=str,
+                    default='/home/dataset/Oxford_IIIT_Pet/annotations', help='Annotation path')
+parser.add_argument('--mindrecord_file_name', type=str,
+                    default='/home/dataset/Oxford_IIIT_Pet/test.mindrecord', help='Mindrecord path')
+parser.add_argument('--mindrecord_num', type=int, default=1, help='Mindrecord num')
+args_opt = parser.parse_args()
+
+if __name__ == '__main__':
+    txt_path = os.path.join(args_opt.annotation_dir, "test.txt")
+    writer = FileWriter(args_opt.mindrecord_file_name, args_opt.mindrecord_num)
+    test_json = {
+        "image": {"type": "bytes"},
+        "label_list": {"type": "int32"},
+    }
+    writer.add_schema(test_json, "test_json")
+
+    image_path_list = []
+    label_list = []
+    with open(txt_path, "r") as rf:
+        for line in rf:
+            str_list = line.strip().split(' ')
+            img_name = str_list[0]
+            image_path_list.append(os.path.join(args_opt.image_root_dir, img_name + ".jpg"))
+            label = int(str_list[1]) - 1
+            label_list.append(label)
+
+    for index, _ in enumerate(image_path_list):
+        path = image_path_list[index]
+        target = label_list[index]
+        img = open(path, 'rb').read()
+        if target is None:
+            target = np.zeros(1)
+        row = {"image": img, "label_list": target}
+        writer.write_raw_data([row])
+
+    print('total test images: ', len(image_path_list))
+    writer.commit()