wehub-resource-sync
63 lines
2.5 KiB
Python
63 lines
2.5 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 cond."""
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import numpy as np
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import tensorflow 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.framework import test_util
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@register_make_test_function("make_cond_tests")
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@test_util.enable_control_flow_v2
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def make_cond_tests(options):
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"""Make a set of tests to do relu1."""
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# Chose a set of parameters
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test_parameters = [{
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# Note: The `tf.string` test case also serves as a regression test to
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# ensure that branch subgraph with dynamically allocated inputs/outputs
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# are handled correctly.
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"dtype": [tf.float32, tf.string],
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"pred": [False, True],
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}]
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def build_graph(parameters):
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"""Build the graph for cond tests."""
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input1 = tf.compat.v1.placeholder(dtype=parameters["dtype"], shape=(1,))
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input2 = tf.compat.v1.placeholder(dtype=parameters["dtype"], shape=(1,))
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# MLIR TFLite converter can't handle scalar inputs. This is a workaround
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# to input (1,) tensors and then reshape to scalar.
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# TODO(b/129003347): Remove the workaround after scalar inputs are
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# supported.
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pred = tf.compat.v1.placeholder(dtype=tf.bool, shape=(1,))
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pred_scalar = tf.reshape(pred, ())
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out = tf.cond(
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pred=pred_scalar, true_fn=lambda: input1, false_fn=lambda: input2)
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return [input1, input2, pred], [out]
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def build_inputs(parameters, sess, inputs, outputs):
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input_values = [
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create_tensor_data(parameters["dtype"], (1,)),
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create_tensor_data(parameters["dtype"], (1,)),
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np.array([parameters["pred"]], dtype=np.bool_),
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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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