# Copyright 2019 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 minimum.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow.compat.v1 as tf from tensorflow.lite.testing.zip_test_utils import create_tensor_data 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_minimum_tests(options): """Make a set of tests to do minimum.""" test_parameters = [{ "input_dtype": [tf.float32], "input_shape_1": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3], [5, 32, 32, 1, 1], [5, 32, 32, 1, 1]], "input_shape_2": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3], [5, 32, 32, 1, 1], [5, 32, 32, 1, 3]], "fully_quantize": [False, True], }] def build_graph(parameters): """Build the minimum op testing graph.""" input_tensor_1 = tf.compat.v1.placeholder( dtype=parameters["input_dtype"], name="input_1", shape=parameters["input_shape_1"]) input_tensor_2 = tf.compat.v1.placeholder( dtype=parameters["input_dtype"], name="input_2", shape=parameters["input_shape_2"]) out = tf.minimum(input_tensor_1, input_tensor_2) return [input_tensor_1, input_tensor_2], [out] def build_inputs(parameters, sess, inputs, outputs): """Builds the inputs for the model above.""" values = [ create_tensor_data( parameters["input_dtype"], parameters["input_shape_1"], min_value=-1, max_value=1), create_tensor_data( parameters["input_dtype"], parameters["input_shape_2"], min_value=-1, max_value=1) ] return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) make_zip_of_tests( options, test_parameters, build_graph, build_inputs, expected_tf_failures=44)