# 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 slice.""" 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.lite.testing.zip_test_utils import TF_TYPE_INFO @register_make_test_function() def make_slice_tests(options): """Make a set of tests to do slice.""" # TODO(renjieliu): add test/support for uint8. test_parameters = [ # 4-D { "dtype": [tf.float32, tf.int32, tf.int64, tf.string], "index_type": [tf.int32, tf.int64], "input_shape": [[12, 2, 2, 5]], "begin": [[0, 0, 0, 0], [1, 0, 1, 0]], "size": [[8, 2, 2, 3], [11, 2, 1, 5]], "constant_indices": [False], "fully_quantize": [False], }, # 5-D { "dtype": [tf.float32], "index_type": [tf.int32], "input_shape": [[6, 2, 2, 2, 5]], "begin": [[0, 0, 0, 0, 0], [0, 1, 0, 1, 0]], "size": [[4, 2, 2, 2, 3], [5, 2, 1, 1, 5]], "constant_indices": [False], "fully_quantize": [False], }, # 2-D { "dtype": [tf.float32, tf.int32, tf.int64, tf.string], "index_type": [tf.int32, tf.int64], "input_shape": [[2, 3]], "begin": [[0, 0], [1, 0]], "size": [[2, 3], [2, 2]], "constant_indices": [False], "fully_quantize": [False], }, # 4-D with size -1 { "dtype": [tf.float32], "index_type": [tf.int32], "input_shape": [[4, 4, 4, 4]], "begin": [[0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], "size": [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1], [1, 1, 1, -1]], "constant_indices": [False, True], "fully_quantize": [False], }, # last dimension out of index { "dtype": [tf.float32], "index_type": [tf.int32], "input_shape": [[4, 4, 4]], "begin": [[3, 3, 4]], "size": [[-1, -1, -1]], "constant_indices": [False, True], "fully_quantize": [False], }, # 4-D { "dtype": [tf.float32], "index_type": [tf.int32], "input_shape": [[12, 2, 2, 5]], "begin": [[0, 0, 0, 0], [1, 0, 1, 0]], "size": [[8, 2, 2, 3], [11, 2, 1, 5]], "constant_indices": [True], "fully_quantize": [True], }, # 2-D { "dtype": [tf.float32], "index_type": [tf.int32], "input_shape": [[2, 3]], "begin": [[0, 0], [1, 0]], "size": [[2, 3], [2, 2]], "constant_indices": [True], "fully_quantize": [True], }, # 4-D with size -1 { "dtype": [tf.float32], "index_type": [tf.int32], "input_shape": [[4, 4, 4, 4]], "begin": [[0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], "size": [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1], [1, 1, 1, -1]], "constant_indices": [True], "fully_quantize": [True], }, { "dtype": [tf.float32], "index_type": [tf.int32], "input_shape": [[1, 4, 4, 4]], "begin": [[0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], "size": [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1], [1, 1, 1, -1]], "constant_indices": [True], "fully_quantize": [True], }, ] def build_graph(parameters): """Build graph for slice test.""" input_tensor = tf.compat.v1.placeholder( dtype=parameters["dtype"], name="input", shape=parameters["input_shape"]) if parameters["constant_indices"]: index_type = TF_TYPE_INFO[parameters["index_type"]][0] begin_values = np.array(parameters["begin"]).astype(index_type) size_values = np.array(parameters["size"]).astype(index_type) out = tf.slice(input_tensor, begin_values, size_values) return [input_tensor], [out] else: begin = tf.compat.v1.placeholder( dtype=parameters["index_type"], name="begin", shape=[len(parameters["input_shape"])]) size = tf.compat.v1.placeholder( dtype=parameters["index_type"], name="size", shape=[len(parameters["input_shape"])]) tensors = [input_tensor, begin, size] out = tf.slice(input_tensor, begin, size) return tensors, [out] def build_inputs(parameters, sess, inputs, outputs): """Build inputs for slice test.""" input_values = create_tensor_data( parameters["dtype"], parameters["input_shape"], min_value=-1, max_value=1) if parameters["constant_indices"]: return [input_values], sess.run( outputs, feed_dict=dict(zip(inputs, [input_values]))) else: index_type = TF_TYPE_INFO[parameters["index_type"]][0] begin_values = np.array(parameters["begin"]).astype(index_type) size_values = np.array(parameters["size"]).astype(index_type) values = [input_values, begin_values, size_values] return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) # Note: Not all [begin x size] permutations are compatible for each grouping # of test_parameters, but for brevity we ignore the failures rather than # separating out each compatible set into separate test_parameters entries. make_zip_of_tests( options, test_parameters, build_graph, build_inputs, expected_tf_failures=29)