| # 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 cond.""" |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import numpy as np |
| 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 |
| from tensorflow.python.framework import test_util |
| |
| |
| @register_make_test_function("make_cond_tests") |
| @test_util.enable_control_flow_v2 |
| def make_cond_tests(options): |
| """Make a set of tests to do relu1.""" |
| |
| # Chose a set of parameters |
| test_parameters = [{ |
| # Note: The `tf.string` test case also serves as a regression test to |
| # ensure that branch subgraph with dynamically allocated inputs/outputs |
| # are handled correctly. |
| "dtype": [tf.float32, tf.string], |
| "pred": [False, True], |
| }] |
| |
| def build_graph(parameters): |
| """Build the graph for cond tests.""" |
| input1 = tf.placeholder(dtype=parameters["dtype"], shape=(1,)) |
| input2 = tf.placeholder(dtype=parameters["dtype"], shape=(1,)) |
| # MLIR TFLite converter can't handle scalar inputs. This is a workaround |
| # to input (1,) tensors and then reshape to scalar. |
| # TODO(b/129003347): Remove the workaround after scalar inputs are |
| # supported. |
| pred = tf.placeholder(dtype=tf.bool, shape=(1,)) |
| pred_scalar = tf.reshape(pred, ()) |
| |
| out = tf.cond(pred_scalar, lambda: input1, lambda: input2) |
| return [input1, input2, pred], [out] |
| |
| def build_inputs(parameters, sess, inputs, outputs): |
| input_values = [ |
| create_tensor_data(parameters["dtype"], (1,)), |
| create_tensor_data(parameters["dtype"], (1,)), |
| np.array([parameters["pred"]], dtype=np.bool_), |
| ] |
| return input_values, sess.run( |
| outputs, feed_dict=dict(zip(inputs, input_values))) |
| |
| make_zip_of_tests(options, test_parameters, build_graph, build_inputs) |