| # 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_gradient_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 ExtraTocoOptions |
| 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() |
| def make_broadcast_gradient_args_tests(options): |
| """Make a set of tests to do broadcast_gradient_args.""" |
| |
| test_parameters = [{ |
| 'input_case': ['ALL_EQUAL', 'ONE_DIM', 'NON_BROADCASTABLE'], |
| 'dtype': [tf.dtypes.int32, tf.dtypes.int64], |
| }] |
| |
| def build_graph(parameters): |
| """Build the op testing graph.""" |
| input1 = tf.compat.v1.placeholder(dtype=parameters['dtype'], name='input1') |
| input2 = tf.compat.v1.placeholder(dtype=parameters['dtype'], name='input2') |
| output1, output2 = tf.raw_ops.BroadcastGradientArgs(s0=input1, s1=input2) |
| return [input1, input2], [output1, output2] |
| |
| def build_inputs(parameters, sess, inputs, outputs): |
| dtype = parameters['dtype'].as_numpy_dtype() |
| |
| if parameters['input_case'] == 'ALL_EQUAL': |
| values = [ |
| np.array([2, 4, 1, 3], dtype=dtype), |
| np.array([2, 4, 1, 3], dtype=dtype) |
| ] |
| elif parameters['input_case'] == 'ONE_DIM': |
| values = [ |
| np.array([2, 4, 1, 3], dtype=dtype), |
| np.array([2, 1, 1, 3], dtype=dtype) |
| ] |
| elif parameters['input_case'] == 'NON_BROADCASTABLE': |
| values = [ |
| np.array([2, 4, 1, 3], dtype=dtype), |
| np.array([2, 5, 1, 3], dtype=dtype) |
| ] |
| return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) |
| |
| extra_toco_options = ExtraTocoOptions() |
| extra_toco_options.allow_custom_ops = True |
| make_zip_of_tests( |
| options, |
| test_parameters, |
| build_graph, |
| build_inputs, |
| extra_toco_options, |
| expected_tf_failures=2) |