wehub-resource-sync
80 lines
2.7 KiB
Python
80 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 topk."""
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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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@register_make_test_function()
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def make_topk_tests(options):
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"""Make a set of tests to do topk."""
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test_parameters = [{
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"input_dtype": [tf.float32, tf.int32, tf.int16],
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"input_k_dtype": [tf.int32, tf.int16],
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"input_shape": [[10], [5, 20]],
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"input_k": [None, 1, 3],
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"output_index_dtype": [tf.int32, tf.int16],
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}]
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def build_graph(parameters):
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"""Build the topk op testing graph."""
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input_value = tf.compat.v1.placeholder(
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dtype=parameters["input_dtype"],
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name="input",
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shape=parameters["input_shape"],
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)
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if parameters["input_k"] is not None:
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k = tf.compat.v1.placeholder(
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dtype=parameters["input_k_dtype"], name="input_k", shape=[]
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)
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inputs = [input_value, k]
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else:
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k = tf.constant(3, name="k", dtype=parameters["input_k_dtype"])
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inputs = [input_value]
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out = tf.nn.top_k(
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input_value, k, index_type=parameters["output_index_dtype"]
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)
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return inputs, [out[1]]
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def build_inputs(parameters, sess, inputs, outputs):
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input_value = create_tensor_data(
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parameters["input_dtype"], parameters["input_shape"]
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)
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if parameters["input_k"] is not None:
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k = np.array(
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parameters["input_k"],
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dtype=parameters["input_k_dtype"].as_numpy_dtype,
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)
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return [input_value, k], sess.run(
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outputs, feed_dict=dict(zip(inputs, [input_value, k]))
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)
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else:
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return [input_value], sess.run(
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outputs, feed_dict=dict(zip(inputs, [input_value]))
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)
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# TF currently does not support infering int16 scalar from tensor,
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# i.e. input_k = None x input_k_dtype = int16 cases.
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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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)
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