wehub-resource-sync
279 lines
8.4 KiB
Python
279 lines
8.4 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 reduce operators."""
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import numpy as np
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import tensorflow 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_reduce_tests(reduce_op,
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min_value=-10,
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max_value=10,
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boolean_tensor_only=False,
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allow_fully_quantize=False):
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"""Make a set of tests to do reduce operation.
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Args:
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reduce_op: TensorFlow reduce operation to test, i.e. `tf.reduce_mean`.
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min_value: min value for created tensor data.
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max_value: max value for created tensor data.
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boolean_tensor_only: If true, will only generate tensor with boolean value.
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allow_fully_quantize: bool, whether fully_quantize is allowed.
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Returns:
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a function representing the true generator with `reduce_op_in` curried.
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"""
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def f(options):
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"""Actual function that generates examples."""
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test_parameters = [
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{
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"input_dtype": [tf.float32, tf.int32, tf.int64],
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"input_shape": [[3, 3, 2, 4]],
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"axis": [
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0,
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1,
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2,
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[0, 1],
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[0, 2],
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[1, 2],
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[0, 1, 2],
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[1, 0],
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[2, 0],
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[2, 1],
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[2, 1, 0],
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[2, 0, 1],
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-1,
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-2,
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-3,
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[1, -1],
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[0, -1],
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[-1, 0],
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[-1, -2, -3],
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],
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"const_axis": [True, False],
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"keepdims": [True, False],
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"fully_quantize": [False],
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},
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{
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"input_dtype": [tf.float32],
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"input_shape": [[1, 8, 8, 3]],
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"axis": [
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0,
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1,
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2,
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3,
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[1, 2],
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[0, 3],
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[1, 2, 3],
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[0, 1, 2, 3],
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[3, 2, 1, 0],
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[3, 1, 0, 2],
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[2, 0],
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[3, 0],
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[3, 1],
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[1, 0],
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-1,
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-2,
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-3,
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-4,
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[0, -2],
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[2, 3, 1, 0],
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[3, 1, 2],
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[3, -4],
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],
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"const_axis": [True, False],
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"keepdims": [True, False],
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"fully_quantize": [False],
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},
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{
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"input_dtype": [tf.float32],
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"input_shape": [[], [1, 8, 8, 3], [3, 2, 4]],
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"axis": [[]], # shape is: [0]
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"const_axis": [False],
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"keepdims": [True, False],
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"fully_quantize": [False],
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},
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{
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"input_dtype": [tf.float32],
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"input_shape": [[], [1, 8, 8, 3], [3, 2, 4]],
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"axis": [None], # shape is: []
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"const_axis": [True],
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"keepdims": [True, False],
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"fully_quantize": [False],
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},
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{
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"input_dtype": [tf.float32],
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"input_shape": [[3, 3, 2, 4]],
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"axis": [
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0,
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1,
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2,
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[0, 1],
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[0, 2],
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[1, 2],
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[0, 1, 2],
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[1, 0],
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[2, 0],
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[2, 1],
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[2, 1, 0],
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[2, 0, 1],
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-1,
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-2,
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-3,
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[1, -1],
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[0, -1],
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[-1, 0],
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[-1, -2, -3],
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],
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"const_axis": [True],
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"keepdims": [True, False],
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"fully_quantize": [True],
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},
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{
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"input_dtype": [tf.float32],
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"input_shape": [[1, 8, 8, 4], [1, 8, 8, 3]],
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"axis": [
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0, 1, 2, 3, [0], [1], [2], [3], [-1], [-2], [-3], [1, 2],
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[0, 3], [1, 2, 3], [1, 3], [2, 3]
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],
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"const_axis": [True],
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"keepdims": [True, False],
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"fully_quantize": [True],
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},
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{
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"input_dtype": [tf.float32, tf.int32],
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"input_shape": [[2, 0, 2], [0]],
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"axis": [0],
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"const_axis": [True],
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"keepdims": [True, False],
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"fully_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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"""Build the mean op testing graph."""
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dtype = parameters["input_dtype"]
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if boolean_tensor_only:
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dtype = tf.bool
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input_tensor = tf.compat.v1.placeholder(
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dtype=dtype, name="input", shape=parameters["input_shape"])
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# Get axis as either a placeholder or constants.
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if parameters["const_axis"]:
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axis = parameters["axis"]
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input_tensors = [input_tensor]
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else:
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if isinstance(parameters["axis"], list):
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shape = [len(parameters["axis"])]
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else:
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shape = [] # shape for None or integers.
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axis = tf.compat.v1.placeholder(
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dtype=tf.int32, name="axis", shape=shape)
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input_tensors = [input_tensor, axis]
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out = reduce_op(input_tensor, axis=axis, keepdims=parameters["keepdims"])
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return input_tensors, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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"""Build the inputs for reduced operators."""
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dtype = parameters["input_dtype"]
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if boolean_tensor_only:
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dtype = tf.bool
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values = [
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create_tensor_data(
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dtype,
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parameters["input_shape"],
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min_value=min_value,
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max_value=max_value)
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]
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if not parameters["const_axis"]:
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values.append(np.array(parameters["axis"]))
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return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
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return f
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@register_make_test_function()
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def make_mean_tests(options):
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"""Make a set of tests to do mean."""
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return make_reduce_tests(
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tf.reduce_mean,
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min_value=-1,
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max_value=1,
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boolean_tensor_only=False,
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allow_fully_quantize=True)(
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options)
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@register_make_test_function()
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def make_sum_tests(options):
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"""Make a set of tests to do sum."""
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return make_reduce_tests(
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tf.reduce_sum,
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min_value=-1,
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max_value=1,
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boolean_tensor_only=False,
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allow_fully_quantize=True)(
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options)
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@register_make_test_function()
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def make_reduce_prod_tests(options):
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"""Make a set of tests to do prod."""
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# set min max value to be -2, 2 to avoid overflow.
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return make_reduce_tests(tf.reduce_prod, -2, 2)(options)
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@register_make_test_function()
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def make_reduce_max_tests(options):
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"""Make a set of tests to do max."""
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return make_reduce_tests(
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tf.reduce_max, allow_fully_quantize=True, min_value=-1, max_value=1)(
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options)
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@register_make_test_function()
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def make_reduce_min_tests(options):
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"""Make a set of tests to do min."""
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return make_reduce_tests(
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tf.reduce_min, allow_fully_quantize=True, min_value=-1, max_value=1)(
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options)
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@register_make_test_function()
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def make_reduce_any_tests(options):
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"""Make a set of tests to do any."""
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return make_reduce_tests(tf.reduce_any, boolean_tensor_only=True)(options)
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@register_make_test_function()
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def make_reduce_all_tests(options):
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"""Make a set of tests to do all."""
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return make_reduce_tests(tf.reduce_all, boolean_tensor_only=True)(options)
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