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