STT-tensorflow/tensorflow/lite/testing/op_tests/add_n.py
Suharsh Sivakumar 8cf2895fcc Add dynamic range test to op_tests A-B.
PiperOrigin-RevId: 316748721
Change-Id: I2e803777a160197f3b7a6026c5e94ce11f47ab92
2020-06-16 13:55:29 -07:00

79 lines
2.7 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 add_n."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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
@register_make_test_function()
def make_add_n_tests(options):
"""Make a set of tests for AddN op."""
test_parameters = [
{
"dtype": [tf.float32, tf.int32],
"input_shape": [[2, 5, 3, 1]],
"num_inputs": [2, 3, 4, 5],
"dynamic_range_quantize": [False],
},
{
"dtype": [tf.float32, tf.int32],
"input_shape": [[5]],
"num_inputs": [2, 3, 4, 5],
"dynamic_range_quantize": [False],
},
{
"dtype": [tf.float32, tf.int32],
"input_shape": [[]],
"num_inputs": [2, 3, 4, 5],
"dynamic_range_quantize": [False],
},
{
"dtype": [tf.float32],
"input_shape": [[]],
"num_inputs": [2, 3, 4, 5],
"dynamic_range_quantize": [True],
},
]
def build_graph(parameters):
"""Builds the graph given the current parameters."""
input_tensors = []
for i in range(parameters["num_inputs"]):
input_tensors.append(
tf.compat.v1.placeholder(
dtype=parameters["dtype"],
name="input_{}".format(i),
shape=parameters["input_shape"]))
out = tf.add_n(input_tensors)
return input_tensors, [out]
def build_inputs(parameters, sess, inputs, outputs):
"""Builds operand inputs for op."""
input_data = []
for _ in range(parameters["num_inputs"]):
input_data.append(
create_tensor_data(parameters["dtype"], parameters["input_shape"]))
return input_data, sess.run(
outputs, feed_dict={i: d for i, d in zip(inputs, input_data)})
make_zip_of_tests(options, test_parameters, build_graph, build_inputs)