84 lines
3.2 KiB
Python
84 lines
3.2 KiB
Python
# Copyright 2019 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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"""Test configs for hardswish."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import functools
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import numpy as np
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import tensorflow.compat.v1 as tf
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from tensorflow.lite.testing.zip_test_utils import create_tensor_data
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from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
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from tensorflow.lite.testing.zip_test_utils import register_make_test_function
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def _tflite_convert_verify_num_ops(tflite_convert_function, *args, **kwargs):
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"""Verifies that the result of the conversion is a single op."""
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num_ops = kwargs.pop("num_ops", 2)
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result = tflite_convert_function(*args, **kwargs)
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tflite_model_binary = result[0]
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if not result[0]:
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tf.compat.v1.logging.error(result[1]) # stderr from running tflite_convert.
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raise RuntimeError("Failed to build model: \n\n" + result[1])
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interpreter = tf.lite.Interpreter(model_content=tflite_model_binary)
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interpreter.allocate_tensors()
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if len(interpreter.get_tensor_details()) != num_ops:
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raise RuntimeError(
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"Expected to generate two node graph got %s " %
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"\n".join(str(x) for x in interpreter.get_tensor_details()))
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return result
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@register_make_test_function()
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def make_hardswish_tests(options):
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"""Make a set of tests to do hardswish."""
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# Chose a set of parameters
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if options.run_with_flex:
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# Only Flex is able to execute on the data bigger than four dimension.
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test_parameters = [{
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"input_shape": [[], [1], [2, 3], [1, 1, 1, 1], [1, 3, 4, 3],
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[3, 15, 14, 3], [3, 1, 2, 4, 6], [2, 2, 3, 4, 5, 6]],
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}]
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else:
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test_parameters = [{
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"input_shape": [[], [1], [2, 3], [1, 1, 1, 1], [1, 3, 4, 3],
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[3, 15, 14, 3]],
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}]
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def build_graph(parameters):
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inp = tf.compat.v1.placeholder(
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dtype=tf.float32, name="input", shape=parameters["input_shape"])
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out = inp * tf.nn.relu6(inp + np.float32(3)) * np.float32(1. / 6.)
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return [inp], [out]
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def build_inputs(parameters, sess, inputs, outputs):
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input_values = create_tensor_data(
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np.float32, parameters["input_shape"], min_value=-10, max_value=10)
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return [input_values], sess.run(
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outputs, feed_dict=dict(zip(inputs, [input_values])))
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# Add additional validation if we are using toco.
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# Flex doesn't yet support this.
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if not options.run_with_flex:
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options.tflite_convert_function = functools.partial(
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_tflite_convert_verify_num_ops,
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options.tflite_convert_function,
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num_ops=2)
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
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