| # Copyright 2021 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Test configs for broadcast_args.""" |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import numpy as np |
| import tensorflow as tf |
| |
| from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests |
| from tensorflow.lite.testing.zip_test_utils import register_make_test_function |
| |
| |
| @register_make_test_function("make_broadcast_args_tests") |
| def make_broadcast_args_tests(options): |
| """Make a set of tests to do broadcast_args.""" |
| |
| # Chose a set of parameters |
| test_parameters = [{ |
| "dtype": [tf.int64, tf.int32], |
| "input1_shape": [[1], [4], [3, 4], [1, 3, 4]], |
| "input2_shape": [[6, 4, 3, 4]], |
| }, { |
| "dtype": [tf.int64, tf.int32], |
| "input1_shape": [[1, 4, 0]], |
| "input2_shape": [[3, 1, 0], [3, 4, 1]], |
| }] |
| |
| def build_graph(parameters): |
| """Build the graph for broadcast_args tests.""" |
| shape1_tensor = tf.compat.v1.placeholder( |
| dtype=parameters["dtype"], |
| name="input1", |
| shape=[len(parameters["input1_shape"])]) |
| shape2_tensor = tf.compat.v1.placeholder( |
| dtype=parameters["dtype"], |
| name="input2", |
| shape=[len(parameters["input2_shape"])]) |
| |
| out = tf.raw_ops.BroadcastArgs(s0=shape1_tensor, s1=shape2_tensor) |
| return [shape1_tensor, shape2_tensor], [out] |
| |
| def build_inputs(parameters, sess, inputs, outputs): |
| input_values = [ |
| np.array(parameters["input1_shape"]).astype( |
| parameters["dtype"].as_numpy_dtype), |
| np.array(parameters["input2_shape"]).astype( |
| parameters["dtype"].as_numpy_dtype), |
| ] |
| return input_values, sess.run( |
| outputs, feed_dict=dict(zip(inputs, input_values))) |
| |
| make_zip_of_tests(options, test_parameters, build_graph, build_inputs) |