wehub-resource-sync
78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
# Copyright 2021 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 gelu."""
|
|
import functools
|
|
|
|
import tensorflow as tf
|
|
from tensorflow.lite.python import lite
|
|
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
|
|
|
|
|
|
def _tflite_convert_verify_op(tflite_convert_function, *args, **kwargs):
|
|
"""Verifies that the result of the conversion contains Gelu op."""
|
|
result = tflite_convert_function(*args, **kwargs)
|
|
tflite_model_binary = result[0]
|
|
if not result[0]:
|
|
tf.compat.v1.logging.error(result[1]) # stderr from running tflite_convert.
|
|
raise RuntimeError("Failed to build model: \n\n" + result[1])
|
|
interpreter = lite.Interpreter(model_content=tflite_model_binary)
|
|
interpreter.allocate_tensors()
|
|
for op in interpreter._get_ops_details(): # pylint: disable=protected-access
|
|
if op["op_name"] == "GELU":
|
|
return result
|
|
raise RuntimeError("Expected to generate GELU op node in graph.")
|
|
|
|
|
|
@register_make_test_function()
|
|
def make_gelu_tests(options):
|
|
"""Makes a set of tests for gelu."""
|
|
|
|
test_parameters = [{
|
|
"input_dtype": [tf.float32],
|
|
"input_shape": [[], [1], [2, 3], [1, 1, 1, 1], [1, 3, 4, 3],
|
|
[3, 15, 14, 3], [3, 1, 2, 4, 6], [2, 2, 3, 4, 5, 6]],
|
|
"fully_quantize": [False, True],
|
|
"input_range": [(-10, 10)],
|
|
"approximate": [True, False],
|
|
}]
|
|
|
|
def build_graph(parameters):
|
|
"""Builds the gelu op testing graph."""
|
|
input_tensor = tf.compat.v1.placeholder(
|
|
dtype=parameters["input_dtype"],
|
|
name="input",
|
|
shape=parameters["input_shape"])
|
|
|
|
out = tf.nn.gelu(input_tensor, approximate=parameters["approximate"])
|
|
return [input_tensor], [out]
|
|
|
|
def build_inputs(parameters, sess, inputs, outputs):
|
|
values = [
|
|
create_tensor_data(
|
|
parameters["input_dtype"],
|
|
parameters["input_shape"],
|
|
min_value=-8,
|
|
max_value=8)
|
|
]
|
|
return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
|
|
|
|
if not options.run_with_flex:
|
|
options.tflite_convert_function = functools.partial(
|
|
_tflite_convert_verify_op,
|
|
options.tflite_convert_function)
|
|
make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
|