68 lines
2.6 KiB
Python
68 lines
2.6 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 eye."""
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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_scalar_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_eye_tests(options):
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"""Make a set of tests for tf.eye op."""
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test_parameters = [{
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"num_rows_shape": [[]],
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"num_cols_shape": [[]],
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"batch_shape": [[3], [2, 4], [4, 5, 6], None],
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"use_num_cols": [True, False],
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"dtype": [tf.float32, tf.int32],
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}]
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def build_graph(parameters):
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"""Make a set of tests to do eye."""
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input_tensor0 = tf.compat.v1.placeholder(
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dtype=tf.int32, name="num_rows", shape=parameters["num_rows_shape"])
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input_tensor1 = tf.compat.v1.placeholder(
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dtype=tf.int32, name="num_columns", shape=parameters["num_cols_shape"])
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if parameters["use_num_cols"]:
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outs = tf.eye(
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num_rows=input_tensor0,
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num_columns=input_tensor1,
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batch_shape=parameters["batch_shape"],
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dtype=parameters["dtype"])
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return [input_tensor0, input_tensor1], [outs]
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else:
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outs = tf.eye(num_rows=input_tensor0, dtype=parameters["dtype"])
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return [input_tensor0], [outs]
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def build_inputs(parameters, sess, inputs, outputs):
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input_value0 = create_scalar_data(dtype=np.int32, min_value=1)
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input_value1 = create_scalar_data(dtype=np.int32, min_value=1)
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if parameters["use_num_cols"]:
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return [input_value0, input_value1], sess.run(
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outputs, feed_dict=dict(zip(inputs, [input_value0, input_value1])))
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else:
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return [input_value0], sess.run(
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outputs, feed_dict=dict(zip(inputs, [input_value0])))
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
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