| # 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 roll.""" |
| 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 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 |
| |
| test_parameters = [ |
| # Scalar axis. |
| { |
| "input_dtype": [tf.float32, tf.int32], |
| "input_shape": [[2, 4, 5], [3, 8, 4]], |
| "shift": [1, -3, 5], |
| "axis": [0, 1, 2], |
| }, |
| # 1-D axis. |
| { |
| "input_dtype": [tf.float32, tf.int32], |
| "input_shape": [[2, 4, 5], [3, 8, 4]], |
| "shift": [[1], [-3], [5]], |
| "axis": [[0], [1], [2]], |
| }, |
| # Multiple axis. |
| { |
| "input_dtype": [tf.float32, tf.int32], |
| "input_shape": [[2, 4, 5], [3, 8, 4]], |
| "shift": [[1, 3, 2], [3, -6, 5], [-5, 7, 8]], |
| "axis": [[0, 1, 2]], |
| }, |
| # Duplicate axis. |
| { |
| "input_dtype": [tf.float32], |
| "input_shape": [[2, 4, 5], [3, 8, 4]], |
| "shift": [[1, 3, -2]], |
| "axis": [[0, 1, 1]], |
| }, |
| ] |
| |
| |
| @register_make_test_function() |
| def make_roll_with_constant_tests(options): |
| """Make a set of tests to do roll with constant shift and axis.""" |
| |
| def build_graph(parameters): |
| input_value = tf.compat.v1.placeholder( |
| dtype=parameters["input_dtype"], |
| name="input", |
| shape=parameters["input_shape"]) |
| outs = tf.roll( |
| input_value, shift=parameters["shift"], axis=parameters["axis"]) |
| return [input_value], [outs] |
| |
| def build_inputs(parameters, sess, inputs, outputs): |
| input_value = create_tensor_data(parameters["input_dtype"], |
| parameters["input_shape"]) |
| return [input_value], sess.run( |
| outputs, feed_dict=dict(zip(inputs, [input_value]))) |
| |
| make_zip_of_tests(options, test_parameters, build_graph, build_inputs) |
| |
| |
| @register_make_test_function() |
| def make_roll_tests(options): |
| """Make a set of tests to do roll.""" |
| |
| ext_test_parameters = test_parameters + [ |
| # Scalar axis. |
| { |
| "input_dtype": [tf.float32, tf.int32], |
| "input_shape": [[None, 8, 4]], |
| "shift": [-3, 5], |
| "axis": [1, 2], |
| } |
| ] |
| |
| def set_dynamic_shape(shape): |
| return [4 if x is None else x for x in shape] |
| |
| def get_shape(param): |
| if np.isscalar(param): |
| return [] |
| return [len(param)] |
| |
| def get_value(param, dtype): |
| if np.isscalar(param): |
| return np.dtype(dtype).type(param) |
| return np.array(param).astype(dtype) |
| |
| def build_graph(parameters): |
| input_tensor = tf.compat.v1.placeholder( |
| dtype=parameters["input_dtype"], |
| name="input", |
| shape=parameters["input_shape"]) |
| shift_tensor = tf.compat.v1.placeholder( |
| dtype=tf.int64, name="shift", shape=get_shape(parameters["shift"])) |
| axis_tensor = tf.compat.v1.placeholder( |
| dtype=tf.int64, name="axis", shape=get_shape(parameters["axis"])) |
| outs = tf.roll(input_tensor, shift_tensor, axis_tensor) |
| return [input_tensor, shift_tensor, axis_tensor], [outs] |
| |
| def build_inputs(parameters, sess, inputs, outputs): |
| input_value = create_tensor_data( |
| parameters["input_dtype"], set_dynamic_shape(parameters["input_shape"])) |
| shift_value = get_value(parameters["shift"], np.int64) |
| axis_value = get_value(parameters["axis"], np.int64) |
| return [input_value, shift_value, axis_value], sess.run( |
| outputs, |
| feed_dict=dict(zip(inputs, [input_value, shift_value, axis_value]))) |
| |
| extra_toco_options = ExtraTocoOptions() |
| extra_toco_options.allow_custom_ops = True |
| make_zip_of_tests(options, ext_test_parameters, build_graph, build_inputs, |
| extra_toco_options) |