105 lines
3.6 KiB
Python
105 lines
3.6 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 prelu."""
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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 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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@register_make_test_function()
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def make_prelu_tests(options):
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"""Make a set of tests to do PReLU."""
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test_parameters = [
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{
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# The canonical case for image processing is having a 4D `input`
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# (NHWC)and `shared_axes`=[1, 2], so the alpha parameter is per
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# channel.
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"input_shape": [[1, 10, 10, 3], [3, 3, 3, 3]],
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"shared_axes": [[1, 2], [1]],
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"fully_quantize": [False],
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"input_range": [(-10, 10)],
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},
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{
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# 2D-3D example. Share the 2nd axis.
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"input_shape": [[20, 20], [20, 20, 20]],
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"shared_axes": [[1]],
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"fully_quantize": [False],
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"input_range": [(-10, 10)],
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},
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# Quantized cases.
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{
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# The canonical case for image processing is having a 4D `input`
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# (NHWC)and `shared_axes`=[1, 2], so the alpha parameter is per
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# channel.
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"input_shape": [[1, 10, 10, 3], [3, 3, 3, 3]],
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"shared_axes": [[1, 2], [1]],
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"fully_quantize": [True],
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"input_range": [(-10, 10)],
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},
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{
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# 2D-3D example. Share the 2nd axis.
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"input_shape": [[20, 20], [20, 20, 20]],
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"shared_axes": [[1]],
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"fully_quantize": [True],
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"input_range": [(-10, 10)],
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},
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]
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def build_graph(parameters):
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"""Build the graph for the test case."""
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input_tensor = tf.compat.v1.placeholder(
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dtype=tf.float32, name="input", shape=parameters["input_shape"])
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prelu = tf.keras.layers.PReLU(shared_axes=parameters["shared_axes"])
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out = prelu(input_tensor)
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return [input_tensor], [out]
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def build_inputs(parameters, sess, inputs, outputs):
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"""Build the inputs for the test case."""
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input_shape = parameters["input_shape"]
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input_values = create_tensor_data(
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np.float32, input_shape, min_value=-10, max_value=10)
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shared_axes = parameters["shared_axes"]
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alpha_shape = []
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for dim in range(1, len(input_shape)):
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alpha_shape.append(1 if dim in shared_axes else input_shape[dim])
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alpha_values = create_tensor_data(
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np.float32, alpha_shape, min_value=-5, max_value=5)
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# There should be only 1 trainable variable tensor.
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variables = tf.compat.v1.all_variables()
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assert len(variables) == 1
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sess.run(variables[0].assign(alpha_values))
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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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make_zip_of_tests(
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options,
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test_parameters,
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build_graph,
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build_inputs,
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use_frozen_graph=True)
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