tensorflow/lite/testing/op_tests/slice.py
# 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."""
import numpy as np
import tensorflow 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 MAP_TF_TO_NUMPY_TYPE
from tensorflow.lite.testing.zip_test_utils import register_make_test_function
@register_make_test_function()
def make_slice_tests(options):
"""Make a set of tests to do slice."""
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 = MAP_TF_TO_NUMPY_TYPE[parameters["index_type"]]
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 = MAP_TF_TO_NUMPY_TYPE[parameters["index_type"]]
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)