309 lines
9.6 KiB
Python
309 lines
9.6 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test configs for binary_op."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow.compat.v1 as tf
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from tensorflow.lite.testing.zip_test_utils import create_tensor_data
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from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
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from tensorflow.lite.testing.zip_test_utils import register_make_test_function
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def make_binary_op_tests(options,
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binary_operator,
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allow_fully_quantize=False,
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expected_tf_failures=0,
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test_parameters=None):
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"""Make a set of tests to do binary ops with and without broadcast."""
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if test_parameters is None:
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test_parameters = []
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test_parameters = test_parameters + [
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# Avoid creating all combinations to keep the test size small.
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{
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"dtype": [tf.float32, tf.int32],
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"input_shape_1": [[1, 3, 4, 3]],
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"input_shape_2": [[1, 3, 4, 3]],
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"activation": [True],
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"fully_quantize": [False],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[5]],
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"input_shape_2": [[5]],
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"activation": [False, True],
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"fully_quantize": [False],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32, tf.int32, tf.int64],
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"input_shape_1": [[1, 3, 4, 3]],
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"input_shape_2": [[3]],
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"activation": [True, False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32, tf.int32],
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"input_shape_1": [[3]],
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"input_shape_2": [[1, 3, 4, 3]],
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"activation": [True, False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[]],
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"input_shape_2": [[]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[0]],
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"input_shape_2": [[1]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[1, 3, 4, 3]],
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"input_shape_2": [[1, 3, 4, 3]],
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"activation": [False],
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"fully_quantize": [True],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[5]],
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"input_shape_2": [[5]],
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"activation": [False],
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"fully_quantize": [True],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[1, 3, 4, 3]],
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"input_shape_2": [[3]],
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"activation": [False],
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"fully_quantize": [True],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[3]],
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"input_shape_2": [[1, 3, 4, 3]],
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"activation": [False],
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"fully_quantize": [True],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[]],
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"input_shape_2": [[]],
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"activation": [False],
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"fully_quantize": [True],
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"dynamic_range_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[1, 3, 4, 3]],
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"input_shape_2": [[1, 3, 4, 3]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [True],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[5]],
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"input_shape_2": [[5]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [True],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[1, 3, 4, 3]],
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"input_shape_2": [[3]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [True],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[3]],
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"input_shape_2": [[1, 3, 4, 3]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [True],
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},
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{
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"dtype": [tf.float32],
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"input_shape_1": [[]],
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"input_shape_2": [[]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [True],
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},
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]
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# float64 types are supported via flex only.
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if options.run_with_flex and options.use_experimental_converter:
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test_parameters = test_parameters + [
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{
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"dtype": [tf.float64],
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"input_shape_1": [[7]],
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"input_shape_2": [[7]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [False],
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},
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]
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# test_parameters include fully_quantize option only when
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# allow_fully_quantize is True.
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if not allow_fully_quantize:
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test_parameters = [
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test_parameter for test_parameter in test_parameters
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if True not in test_parameter["fully_quantize"]
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]
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def build_graph(parameters):
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"""Builds the graph given the current parameters."""
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input1 = tf.compat.v1.placeholder(
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dtype=parameters["dtype"],
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name="input1",
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shape=parameters["input_shape_1"])
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input2 = tf.compat.v1.placeholder(
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dtype=parameters["dtype"],
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name="input2",
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shape=parameters["input_shape_2"])
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out = binary_operator(input1, input2)
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# TODO(karimnosseir): Update condition after moving to new converter.
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if parameters["activation"] and (not options.use_experimental_converter or
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(parameters["dtype"] != tf.int32 and
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parameters["dtype"] != tf.int64)):
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out = tf.nn.relu(out)
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return [input1, input2], [out]
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def build_inputs(parameters, sess, inputs, outputs):
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"""Builds operand inputs for op."""
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if allow_fully_quantize:
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input1 = create_tensor_data(
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parameters["dtype"],
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parameters["input_shape_1"],
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min_value=-1,
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max_value=1)
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input2 = create_tensor_data(
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parameters["dtype"],
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parameters["input_shape_2"],
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min_value=-1,
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max_value=1)
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else:
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input1 = create_tensor_data(parameters["dtype"],
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parameters["input_shape_1"])
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input2 = create_tensor_data(parameters["dtype"],
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parameters["input_shape_2"])
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return [input1, input2], sess.run(
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outputs, feed_dict={
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inputs[0]: input1,
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inputs[1]: input2
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})
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make_zip_of_tests(
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options,
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test_parameters,
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build_graph,
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build_inputs,
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expected_tf_failures=expected_tf_failures)
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def make_binary_op_tests_func(binary_operator):
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"""Return a function that does a test on a binary operator."""
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return lambda options: make_binary_op_tests(options, binary_operator)
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@register_make_test_function()
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def make_add_tests(options):
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make_binary_op_tests(options, tf.add, allow_fully_quantize=True)
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@register_make_test_function()
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def make_div_tests(options):
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"""Make zip tests for div op with 5D case."""
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test_parameters = [
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{
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"dtype": [tf.float32],
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"input_shape_1": [[1, 3, 3, 3, 3]],
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"input_shape_2": [[3]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [False, True],
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},
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]
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make_binary_op_tests(
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options, tf.compat.v1.div, test_parameters=test_parameters)
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@register_make_test_function()
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def make_sub_tests(options):
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"""Make zip tests for sub op with additional cases."""
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test_parameters = [
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{
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"dtype": [tf.float32],
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"input_shape_1": [[1, 3, 3, 3, 3]],
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"input_shape_2": [[3]],
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"activation": [False],
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"fully_quantize": [False],
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"dynamic_range_quantize": [False, True],
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},
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]
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make_binary_op_tests(
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options,
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tf.subtract,
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allow_fully_quantize=True,
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test_parameters=test_parameters)
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@register_make_test_function()
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def make_mul_tests(options):
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make_binary_op_tests(options, tf.multiply, allow_fully_quantize=True)
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@register_make_test_function()
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def make_pow_tests(options):
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make_binary_op_tests(options, tf.pow, expected_tf_failures=7)
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@register_make_test_function()
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def make_floor_div_tests(options):
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make_binary_op_tests(options, tf.math.floordiv)
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@register_make_test_function()
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def make_floor_mod_tests(options):
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make_binary_op_tests(options, tf.math.floormod)
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@register_make_test_function()
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def make_squared_difference_tests(options):
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make_binary_op_tests(options, tf.math.squared_difference)
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