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# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
import tflite_runtime.interpreter as tflite
import numpy as np
import os
def run_mock_model(delegate, test_data_folder):
model_path = os.path.join(test_data_folder, 'mock_model.tflite')
interpreter = tflite.Interpreter(model_path=model_path,
experimental_delegates=[delegate])
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.uint8)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
def run_inference(test_data_folder, model_filename, inputs, delegates=None):
model_path = os.path.join(test_data_folder, model_filename)
interpreter = tflite.Interpreter(model_path=model_path,
experimental_delegates=delegates)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Set inputs to tensors.
for i in range(len(inputs)):
interpreter.set_tensor(input_details[i]['index'], inputs[i])
interpreter.invoke()
results = []
for output in output_details:
results.append(interpreter.get_tensor(output['index']))
return results
def compare_outputs(outputs, expected_outputs):
assert len(outputs) == len(expected_outputs), 'Incorrect number of outputs'
for i in range(len(expected_outputs)):
assert outputs[i].shape == expected_outputs[i].shape, 'Incorrect output shape on output#{}'.format(i)
assert outputs[i].dtype == expected_outputs[i].dtype, 'Incorrect output data type on output#{}'.format(i)
assert outputs[i].all() == expected_outputs[i].all(), 'Incorrect output value on output#{}'.format(i)
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