178 lines
6.4 KiB
Python
178 lines
6.4 KiB
Python
# 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)
|