STT-tensorflow/tensorflow/lite/testing/op_tests/unpack.py
A. Unique TensorFlower e3be9bbcad Refactor generate_examples_lib.py to split each op to separate files.
PiperOrigin-RevId: 275117423
Change-Id: I4ee19a6c388187fa231373eeaa4b14339e328680
2019-10-16 14:46:52 -07:00

56 lines
2.0 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 unpack."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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 register_make_test_function
@register_make_test_function()
def make_unpack_tests(options):
"""Make a set of tests to do unpack."""
test_parameters = [{
"base_shape": [[3, 4, 3], [3, 4], [5, 6, 7, 8]],
"axis": [0, 1, 2, 3],
}]
def get_valid_axis(parameters):
"""Return a tweaked version of 'axis'."""
axis = parameters["axis"]
shape = parameters["base_shape"][:]
while axis > len(shape) - 1:
axis -= 1
return axis
def build_graph(parameters):
input_tensor = tf.placeholder(
dtype=tf.float32, name=("input"), shape=parameters["base_shape"])
outs = tf.unstack(input_tensor, axis=get_valid_axis(parameters))
return [input_tensor], [outs[0]]
def build_inputs(parameters, sess, inputs, outputs):
input_value = create_tensor_data(np.float32, shape=parameters["base_shape"])
return [input_value], sess.run(
outputs, feed_dict=dict(zip(inputs, [input_value])))
make_zip_of_tests(options, test_parameters, build_graph, build_inputs)