Merge remote-tracking branch 'upstream/master' into amp_resnet50

research/lstm_object_detection/test_tflite_model.py

+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+
+"""Test a tflite model using random input data."""
+
+from __future__ import print_function
+from absl import flags
+import numpy as np
+import tensorflow as tf
+
+flags.DEFINE_string('model_path', None, 'Path to model.')
+FLAGS = flags.FLAGS
+
+
+def main(_):
+
+  flags.mark_flag_as_required('model_path')
+
+  # Load TFLite model and allocate tensors.
+  interpreter = tf.lite.Interpreter(model_path=FLAGS.model_path)
+  interpreter.allocate_tensors()
+
+  # Get input and output tensors.
+  input_details = interpreter.get_input_details()
+  print('input_details:', input_details)
+  output_details = interpreter.get_output_details()
+  print('output_details:', output_details)
+
+  # Test model on random input data.
+  input_shape = input_details[0]['shape']
+  # change the following line to feed into your own data.
+  input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
+  interpreter.set_tensor(input_details[0]['index'], input_data)
+
+  interpreter.invoke()
+  output_data = interpreter.get_tensor(output_details[0]['index'])
+  print(output_data)
+
+
+if __name__ == '__main__':
+  tf.app.run()