| # 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 hardswish.""" |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import functools |
| |
| 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 |
| |
| |
| def _tflite_convert_verify_num_ops(tflite_convert_function, *args, **kwargs): |
| """Verifies that the result of the conversion is a single op.""" |
| num_ops = kwargs.pop("num_ops", 2) |
| 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 = tf.lite.Interpreter(model_content=tflite_model_binary) |
| interpreter.allocate_tensors() |
| if len(interpreter.get_tensor_details()) != num_ops: |
| raise RuntimeError( |
| "Expected to generate two node graph got %s " % |
| "\n".join(str(x) for x in interpreter.get_tensor_details())) |
| return result |
| |
| |
| @register_make_test_function() |
| def make_hardswish_tests(options): |
| """Make a set of tests to do hardswish.""" |
| |
| # Chose a set of parameters |
| if options.run_with_flex: |
| # Only Flex is able to execute on the data bigger than four dimension. |
| test_parameters = [{ |
| "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]], |
| }] |
| else: |
| test_parameters = [{ |
| "input_shape": [[], [1], [2, 3], [1, 1, 1, 1], [1, 3, 4, 3], |
| [3, 15, 14, 3]], |
| }] |
| |
| def build_graph(parameters): |
| inp = tf.compat.v1.placeholder( |
| dtype=tf.float32, name="input", shape=parameters["input_shape"]) |
| out = inp * tf.nn.relu6(inp + np.float32(3)) * np.float32(1. / 6.) |
| |
| return [inp], [out] |
| |
| def build_inputs(parameters, sess, inputs, outputs): |
| input_values = create_tensor_data( |
| np.float32, parameters["input_shape"], min_value=-10, max_value=10) |
| return [input_values], sess.run( |
| outputs, feed_dict=dict(zip(inputs, [input_values]))) |
| |
| # Add additional validation if we are using toco. |
| # Flex doesn't yet support this. |
| if not options.run_with_flex: |
| options.tflite_convert_function = functools.partial( |
| _tflite_convert_verify_num_ops, |
| options.tflite_convert_function, |
| num_ops=2) |
| make_zip_of_tests(options, test_parameters, build_graph, build_inputs) |