wehub-resource-sync
114 lines
4.1 KiB
Python
114 lines
4.1 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 where."""
|
|
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 register_make_test_function
|
|
|
|
|
|
@register_make_test_function()
|
|
def make_where_tests(options):
|
|
"""Make a set of tests to do where."""
|
|
|
|
test_parameters = [
|
|
{
|
|
"input_dtype": [tf.float32, tf.int32],
|
|
"input_shape_set": [([1, 2, 3, 4], [1, 2, 3, 4]),],
|
|
"use_where_v2": [False, True],
|
|
"fully_quantize": [False],
|
|
},
|
|
{
|
|
"input_dtype": [tf.float32, tf.int32],
|
|
"input_shape_set": [([], []),],
|
|
"use_where_v2": [],
|
|
"fully_quantize": [False],
|
|
},
|
|
{
|
|
"input_dtype": [tf.float32],
|
|
"input_shape_set": [
|
|
([1, 2, 3, 4], [1, 2, 3, 4]),
|
|
([], []),
|
|
],
|
|
"use_where_v2": [False, True],
|
|
"fully_quantize": [True],
|
|
},
|
|
# High dimension broadcasting support in MLIR converter.
|
|
{
|
|
"input_dtype": [tf.float32, tf.int32],
|
|
"input_shape_set": [([8, 7, 6, 5, 4, 3, 2, 1], [4, 3, 2, 1]),
|
|
([8, 7, 6, 5, 4, 3, 2, 1], [None, 3, 2, 1]),
|
|
([8, 7, 6, 5, None, 3, 2, 1], [None, 3, 2, 1])],
|
|
"use_where_v2": [True],
|
|
"fully_quantize": [False],
|
|
"dynamic_size_value": [4, 1],
|
|
},
|
|
{
|
|
"input_dtype": [tf.float32],
|
|
"input_shape_set": [([8, 7, 6, 5, 4, 3, 2, 1], [4, 3, 2, 1])],
|
|
"use_where_v2": [True],
|
|
"fully_quantize": [True],
|
|
"dynamic_size_value": [4],
|
|
},
|
|
{
|
|
"input_dtype": [tf.float32, tf.int32],
|
|
"input_shape_set": [([], []), ([1], []), ([], [1])],
|
|
"use_where_v2": [False, True],
|
|
"fully_quantize": [False],
|
|
},
|
|
]
|
|
|
|
def populate_dynamic_shape(parameters, input_shape):
|
|
return [
|
|
parameters["dynamic_size_value"] if x is None else x
|
|
for x in input_shape
|
|
]
|
|
|
|
def build_graph(parameters):
|
|
"""Build the where op testing graph."""
|
|
input_value1 = tf.compat.v1.placeholder(
|
|
dtype=parameters["input_dtype"],
|
|
name="input2",
|
|
shape=parameters["input_shape_set"][0])
|
|
input_value2 = tf.compat.v1.placeholder(
|
|
dtype=parameters["input_dtype"],
|
|
name="input3",
|
|
shape=parameters["input_shape_set"][1])
|
|
less = tf.less(input_value1, input_value2)
|
|
where = tf.compat.v2.where if parameters[
|
|
"use_where_v2"] else tf.compat.v1.where
|
|
out = where(less, input_value1, input_value2)
|
|
return [input_value1, input_value2], [out]
|
|
|
|
def build_inputs(parameters, sess, inputs, outputs):
|
|
input_shape_1 = populate_dynamic_shape(parameters,
|
|
parameters["input_shape_set"][0])
|
|
input_shape_2 = populate_dynamic_shape(parameters,
|
|
parameters["input_shape_set"][1])
|
|
|
|
input_value1 = create_tensor_data(
|
|
parameters["input_dtype"], input_shape_1, min_value=-1, max_value=1)
|
|
input_value2 = create_tensor_data(
|
|
parameters["input_dtype"], input_shape_2, min_value=-1, max_value=1)
|
|
return [input_value1, input_value2], sess.run(
|
|
outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
|
|
|
|
make_zip_of_tests(
|
|
options,
|
|
test_parameters,
|
|
build_graph,
|
|
build_inputs,
|
|
expected_tf_failures=4)
|