!2299 [东北大学][高校贡献][Mindspore][DEM]-高性能预训练模型提交+功能

Merge pull request !2299 from 刘禹廷/master

research/cv/dem/infer/preprocess.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.
+"""preprocessing dataset for mx-base infer"""
+
+import argparse
+import os
+import scipy.io as sio
+import numpy as np
+
+
+def parse_args():
+    """set and check parameters."""
+    parser = argparse.ArgumentParser(description="dem process")
+    parser.add_argument("--data_dir", type=str, default="/home/dataset/DEM_data/",
+                        help="path where the dataset is saved")
+    args_opt = parser.parse_args()
+    return args_opt
+
+
+def dataset_cub(data_path):
+    f = sio.loadmat(data_path+'/CUB_data/test_proto.mat')
+    test_att_0 = np.array(f['test_proto'])
+    test_att_0 = test_att_0.astype("float32")
+    os.mkdir(data_path + "/CUB_data/test_att")
+    for i in range(test_att_0.shape[0]):
+        np.savetxt(data_path + '/CUB_data/test_att/test_att_%d' % i, test_att_0[i], fmt="%f")
+
+
+def dataset_AwA(data_path):
+    """input:*.mat, output:array"""
+    f = sio.loadmat(data_path+'/AwA_data/attribute/pca_te_con_10x85.mat')
+    test_att_0 = np.array(f['pca_te_con_10x85'])
+    test_att_0 = test_att_0.astype("float32")
+    os.mkdir(data_path + "/AwA_data/test_att")
+    for i in range(test_att_0.shape[0]):
+        np.savetxt(data_path + '/AwA_data/test_att/test_att_%d' % i, test_att_0[i], fmt="%f")
+    return test_att_0
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    dataset_cub(args.data_dir)
+    dataset_AwA(args.data_dir)