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https://github.com/vee1e/tflite-micro.git
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e2cdefd520
Functionality to use the rest of the Signal Library OPs directly from python. Test with `bazel run python/tflite_micro/signal:stacker_op_test` and `bazel run python/tflite_micro/signal:filter_bank_ops_test` BUG=[287346710](http://b/287346710)
80 lines
3 KiB
Python
80 lines
3 KiB
Python
# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for stacker ops."""
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import os
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import numpy as np
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import tensorflow as tf
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from tensorflow.python.platform import resource_loader
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from tflite_micro.python.tflite_micro.signal.ops import stacker_op
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from tflite_micro.python.tflite_micro.signal.utils import util
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class StackerOpTest(tf.test.TestCase):
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_PREFIX_PATH = resource_loader.get_path_to_datafile('')
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def GetResource(self, filepath):
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full_path = os.path.join(self._PREFIX_PATH, filepath)
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with open(full_path, 'rt') as f:
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file_text = f.read()
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return file_text
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def SingleStackerTest(self, filename):
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lines = self.GetResource(filename).splitlines()
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args = lines[0].split()
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num_channels = int(args[0])
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stacker_left_context = int(args[1])
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stacker_right_context = int(args[2])
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stacker_step = int(args[3])
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func = tf.function(stacker_op.stacker)
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input_size = len(lines[1].split())
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concrete_function = func.get_concrete_function(
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tf.TensorSpec(input_size, dtype=tf.int16), num_channels,
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stacker_left_context, stacker_right_context, stacker_step)
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interpreter = util.get_tflm_interpreter(concrete_function, func)
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# Skip line 0, which contains the configuration params.
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# Read lines in triplets <input, expected output, expected valid>
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i = 1
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while i < len(lines):
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input_array = np.array([int(j) for j in lines[i].split()],
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dtype=np.int16)
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output_array_exp = [int(j) for j in lines[i + 1].split()]
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output_valid_exp = [int(j) for j in lines[i + 2].split()]
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# TFLM
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interpreter.set_input(input_array, 0)
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interpreter.invoke()
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out_frame = interpreter.get_output(0)
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out_valid = interpreter.get_output(1)
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self.assertEqual(out_valid, output_valid_exp)
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if out_valid:
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self.assertAllEqual(out_frame, output_array_exp)
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# TF
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[out_frame, out_valid] = self.evaluate(
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stacker_op.stacker(input_array, num_channels, stacker_left_context,
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stacker_right_context, stacker_step))
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self.assertEqual(out_valid, output_valid_exp)
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if out_valid:
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self.assertAllEqual(out_frame, output_array_exp)
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i += 3
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def testStacker(self):
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self.SingleStackerTest('testdata/stacker_test1.txt')
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if __name__ == '__main__':
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np.random.seed(0)
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tf.test.main()
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