79 lines
3.1 KiB
Python
79 lines
3.1 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 identity."""
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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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from tensorflow.python.ops import array_ops
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@register_make_test_function()
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def make_identity_tests(options):
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"""Make a set of tests to do identity."""
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# Chose a set of parameters
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test_parameters = [{
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"input_shape": [[], [1], [3, 3]],
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"op_to_use": [
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"identity", "identity_n", "snapshot", "identity_n_with_2_inputs"
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],
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}]
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def build_graph(parameters):
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"""Make a set of tests to do identity."""
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input_tensors = []
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input_count = (2 if parameters["op_to_use"] == "identity_n_with_2_inputs"
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else 1)
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input_tensors = [
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tf.compat.v1.placeholder(
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dtype=tf.float32, name="input", shape=parameters["input_shape"])
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for _ in range(input_count)
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]
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# We add the Multiply before Identity just as a walk-around to make the test
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# pass when input_shape is scalar.
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# During graph transformation, TOCO will replace the Identity op with
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# Reshape when input has shape. However, currently TOCO can't distinguish
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# between missing shape and scalar shape. As a result, when input has scalar
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# shape, this conversion still fails.
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# TODO(b/129197312), remove the walk-around code once the bug is fixed.
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inputs_doubled = [input_tensor * 2.0 for input_tensor in input_tensors]
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if parameters["op_to_use"] == "identity":
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identity_outputs = [tf.identity(inputs_doubled[0])]
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elif parameters["op_to_use"] == "snapshot":
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identity_outputs = [array_ops.snapshot(inputs_doubled[0])]
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elif parameters["op_to_use"] in ("identity_n", "identity_n_with_2_inputs"):
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identity_outputs = tf.identity_n(inputs_doubled)
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return input_tensors, identity_outputs
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def build_inputs(parameters, sess, inputs, outputs):
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input_values = [
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create_tensor_data(
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np.float32, parameters["input_shape"], min_value=-4, max_value=10)
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for _ in range(len(inputs))
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]
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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(options, test_parameters, build_graph, build_inputs)
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