Adjust directory structure

2021-06-03 17:38:11 +08:00
parent 92301257f3
commit 89a2236b18
1993 changed files with 40 additions and 0 deletions
@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+# ==========================================================================================
+# Copyright (c) 2020 AIIT XUOS Lab
+# XiOS is licensed under Mulan PSL v2.
+# You can use this software according to the terms and conditions of the Mulan PSL v2.
+# You may obtain a copy of Mulan PSL v2 at:
+#        http://license.coscl.org.cn/MulanPSL2
+# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
+# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+# See the Mulan PSL v2 for more details.
+
+#  @file:    mnist-inference.py
+#  @brief:   load data amd start model omferemce
+#  @version: 1.0
+#  @author:  AIIT XUOS Lab
+#  @date:    2021/4/30
+# ==========================================================================================
+
+
+import tensorflow as tf
+
+print("TensorFlow version %s" % (tf.__version__))
+
+MODEL_NAME_H5 = 'mnist.h5'
+MODEL_NAME_TFLITE = 'mnist.tflite'
+DEFAULT_QUAN_MODEL_NAME_TFLITE = 'mnist-default-quan.tflite'
+FULL_QUAN_MODEL_NAME_TFLITE = 'mnist-full-quan.tflite'
+
+
+def show(image):
+    for i in range(28):
+        for j in range(28):
+            if image[i][j][0] > 0.3:
+                print('#', end = '')
+            else:
+                print(' ', end = '')
+        print()
+
+
+if __name__ == '__main__':
+    mnist = tf.keras.datasets.mnist
+    (_, _), (test_images, test_labels) = mnist.load_data()
+    test_images = test_images.reshape(10000, 28, 28, 1)
+    index = 0
+    input_image = test_images[index].astype('float32')/255
+    target_label = test_labels[index]
+
+    interpreter = tf.lite.Interpreter(model_path = DEFAULT_QUAN_MODEL_NAME_TFLITE)
+    interpreter.allocate_tensors()
+    input_details = interpreter.get_input_details()[0]
+    output_details = interpreter.get_output_details()[0]
+    interpreter.set_tensor(input_details['index'], [input_image])
+    interpreter.invoke()
+    output = interpreter.get_tensor(output_details['index'])[0]
+
+    show(input_image)
+    print('target label: %d, predict label: %d' % (target_label, output.argmax()))