STT-tensorflow/tensorflow/lite/testing/op_tests/strided_slice.py
Thai Nguyen 85504f9555 Fix error when end_mask and shrink_mask are set at the same axis
PiperOrigin-RevId: 350255382
Change-Id: I1ac180e02a22b62570fe4491fc1e08c6e8fda1de
2021-01-05 17:49:16 -08:00

272 lines
9.0 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 strided_slice operators."""
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
def _make_strided_slice_tests(options, test_parameters, expected_tf_failures=0):
"""Utility function to make strided_slice_tests based on parameters."""
def build_graph(parameters):
"""Build graph for stride_slice test."""
input_tensor = tf.compat.v1.placeholder(
dtype=parameters["dtype"],
name="input",
shape=parameters["input_shape"])
if parameters["constant_indices"]:
begin = parameters["begin"]
end = parameters["end"]
strides = parameters["strides"]
tensors = [input_tensor]
else:
begin = tf.compat.v1.placeholder(
dtype=parameters["index_type"],
name="begin",
shape=[len(parameters["begin"])])
end = tf.compat.v1.placeholder(
dtype=parameters["index_type"],
name="end",
shape=[len(parameters["end"])])
strides = None
if parameters["strides"] is not None:
strides = tf.compat.v1.placeholder(
dtype=parameters["index_type"],
name="strides",
shape=[len(parameters["strides"])])
tensors = [input_tensor, begin, end]
if strides is not None:
tensors.append(strides)
out = tf.strided_slice(
input_tensor,
begin,
end,
strides,
begin_mask=parameters["begin_mask"],
end_mask=parameters["end_mask"],
shrink_axis_mask=parameters["shrink_axis_mask"])
return tensors, [out]
def build_inputs(parameters, sess, inputs, outputs):
"""Build inputs for stride_slice test."""
input_values = create_tensor_data(
parameters["dtype"],
parameters["input_shape"],
min_value=-1,
max_value=1)
index_type = TF_TYPE_INFO[parameters["index_type"]][0]
values = [input_values]
if not parameters["constant_indices"]:
begin_values = np.array(parameters["begin"]).astype(index_type)
end_values = np.array(parameters["end"]).astype(index_type)
stride_values = (
np.array(parameters["strides"]).astype(index_type)
if parameters["strides"] is not None else None)
values.append(begin_values)
values.append(end_values)
if stride_values is not None:
values.append(stride_values)
return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
make_zip_of_tests(
options,
test_parameters,
build_graph,
build_inputs,
expected_tf_failures=expected_tf_failures)
@register_make_test_function()
def make_strided_slice_tests(options):
"""Make a set of tests to do strided_slice."""
# TODO(soroosh): add test/support for uint8.
test_parameters = [
# 4-D (basic cases with const/non-const indices).
{
"dtype": [tf.float32, tf.int32, tf.int64, tf.bool],
"index_type": [tf.int32],
"input_shape": [[12, 2, 2, 5]],
"strides": [None, [2, 1, 3, 1]],
"begin": [[0, 0, 0, 0]],
"end": [[12, 2, 2, 5]],
"begin_mask": [None],
"end_mask": [None],
"shrink_axis_mask": [None],
"constant_indices": [False, True],
"fully_quantize": [False],
},
# 4-D with non-trivial begin & end.
{
"dtype": [tf.float32],
"index_type": [tf.int32],
"input_shape": [[12, 2, 2, 5]],
"begin": [[0, 0, 0, 0], [1, 0, 1, 0]],
"end": [[8, 2, 2, 3], [12, 2, 2, 5]],
"strides": [None, [2, 1, 3, 1]],
"begin_mask": [None, 8],
"end_mask": [None, 3],
"shrink_axis_mask": [None, 15, -1],
"constant_indices": [True],
"fully_quantize": [False],
},
# Begin, end, strides dim are different from input shape
{
"dtype": [tf.float32],
"index_type": [tf.int32],
"input_shape": [[12, 2, 2, 5]],
"begin": [[0]],
"end": [[1]],
"strides": [None, [1]],
"begin_mask": [0],
"end_mask": [0],
"shrink_axis_mask": [1],
"constant_indices": [True, False],
"fully_quantize": [False],
},
# 2-D
{
"dtype": [tf.float32],
"index_type": [tf.int32],
"input_shape": [[2, 3]],
"begin": [[0, 0]],
"end": [[2, 2]],
"strides": [None, [2, 2]],
"begin_mask": [None, 1, 2],
"end_mask": [None, 1, 2],
"shrink_axis_mask": [None, 1, 2, 3, -1],
"constant_indices": [False, True],
"fully_quantize": [False],
},
# Negative strides
{
"dtype": [tf.float32],
"index_type": [tf.int32],
"input_shape": [[2, 3]],
"begin": [[0, -1]],
"end": [[2, -3]],
"strides": [[1, -1]],
"begin_mask": [None, 1, 2],
"end_mask": [None, 1, 2],
"shrink_axis_mask": [None, 1, 2, 3, -1],
"constant_indices": [False],
"fully_quantize": [False],
},
# 4-D (cases with const indices and batchsize of 1).
{
"dtype": [tf.float32],
"index_type": [tf.int32],
"input_shape": [[1, 2, 2, 5]],
"strides": [None, [1, 1, 1, 1]],
"begin": [[0, 0, 0, 0], [0, 1, 1, 3]],
"end": [[1, 2, 2, 5], [1, 2, 2, 4]],
"begin_mask": [None],
"end_mask": [None],
"shrink_axis_mask": [None],
"constant_indices": [True],
"fully_quantize": [True],
},
# Begin, end, strides dim are different from input shape
{
"dtype": [tf.float32],
"index_type": [tf.int32],
"input_shape": [[12, 2, 2, 5]],
"begin": [[0]],
"end": [[1]],
"strides": [None, [1]],
"begin_mask": [0],
"end_mask": [0],
"shrink_axis_mask": [1],
"constant_indices": [True],
"fully_quantize": [True],
},
]
if options.use_experimental_converter:
test_parameters = test_parameters + [
# Begin equal to input dim.
{
"dtype": [tf.float32],
"index_type": [tf.int32],
"input_shape": [[1, 1, 2]],
"begin": [[1]],
"end": [[0]],
"strides": [[1]],
"begin_mask": [0],
"end_mask": [1],
"shrink_axis_mask": [0],
"constant_indices": [True, False],
"fully_quantize": [False],
},
{
"dtype": [tf.float32],
"index_type": [tf.int32],
"input_shape": [[1, 1, 2]],
"begin": [[1, 0, 0]],
"end": [[0, -1, -1]],
"strides": [[1, 1, 1]],
"begin_mask": [6],
"end_mask": [7],
"shrink_axis_mask": [0],
"constant_indices": [True, False],
"fully_quantize": [False],
},
# String input.
{
"dtype": [tf.string],
"index_type": [tf.int32],
"input_shape": [[12, 2, 2, 5]],
"begin": [[0, 0, 0, 0]],
"end": [[8, 2, 2, 3]],
"strides": [[2, 1, 3, 1]],
"begin_mask": [8],
"end_mask": [3],
"shrink_axis_mask": [None],
"constant_indices": [True, False],
"fully_quantize": [False],
}
]
_make_strided_slice_tests(options, test_parameters, expected_tf_failures=29)
@register_make_test_function()
def make_strided_slice_1d_exhaustive_tests(options):
"""Make a set of exhaustive tests for 1D strided_slice."""
test_parameters = [
# 1-D Exhaustive
{
"dtype": [tf.float32],
"index_type": [tf.int32],
"input_shape": [[3]],
"begin": [[-2], [-1], [0], [1], [2]],
"end": [[-2], [-1], [0], [1], [2]],
"strides": [[-2], [-1], [1], [2]],
"begin_mask": [0, 1],
"end_mask": [0, 1],
"shrink_axis_mask": [0],
"constant_indices": [False],
},
]
_make_strided_slice_tests(options, test_parameters)