79 lines
2.7 KiB
Python
79 lines
2.7 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 add_n."""
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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 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_add_n_tests(options):
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"""Make a set of tests for AddN op."""
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test_parameters = [
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{
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"dtype": [tf.float32, tf.int32],
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"input_shape": [[2, 5, 3, 1]],
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"num_inputs": [2, 3, 4, 5],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32, tf.int32],
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"input_shape": [[5]],
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"num_inputs": [2, 3, 4, 5],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32, tf.int32],
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"input_shape": [[]],
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"num_inputs": [2, 3, 4, 5],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape": [[]],
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"num_inputs": [2, 3, 4, 5],
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"dynamic_range_quantize": [True],
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},
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]
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def build_graph(parameters):
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"""Builds the graph given the current parameters."""
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input_tensors = []
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for i in range(parameters["num_inputs"]):
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input_tensors.append(
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tf.compat.v1.placeholder(
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dtype=parameters["dtype"],
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name="input_{}".format(i),
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shape=parameters["input_shape"]))
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out = tf.add_n(input_tensors)
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return input_tensors, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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"""Builds operand inputs for op."""
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input_data = []
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for _ in range(parameters["num_inputs"]):
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input_data.append(
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create_tensor_data(parameters["dtype"], parameters["input_shape"]))
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return input_data, sess.run(
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outputs, feed_dict={i: d for i, d in zip(inputs, input_data)})
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
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