1# Copyright 2019 The TensorFlow Authors. All Rights Reserved. 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================== 15"""Test configs for concat.""" 16from __future__ import absolute_import 17from __future__ import division 18from __future__ import print_function 19 20import tensorflow.compat.v1 as tf 21from tensorflow.lite.testing.zip_test_utils import create_tensor_data 22from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests 23from tensorflow.lite.testing.zip_test_utils import register_make_test_function 24 25 26@register_make_test_function() 27def make_concat_tests(options): 28 """Make a set of tests to do concatenation.""" 29 30 test_parameters = [{ 31 "base_shape": [[1, 3, 4, 3], [3, 4]], 32 "num_tensors": [1, 2, 3, 4, 5, 6], 33 "axis": [0, 1, 2, 3, -3, -2, -1], 34 "type": [tf.float32, tf.uint8, tf.int32, tf.int64], 35 "fully_quantize": [False], 36 "quant_16x8": [False], 37 "dynamic_range_quantize": [False], 38 }, { 39 "base_shape": [[1, 3, 4, 3], [3, 4], [2, 3, 4, 3]], 40 "num_tensors": [1, 2, 3, 4, 5, 6], 41 "axis": [1, 2, 3, -3, -2, -1], 42 "type": [tf.float32], 43 "fully_quantize": [True], 44 "quant_16x8": [False], 45 "dynamic_range_quantize": [False], 46 }, { 47 "base_shape": [[1, 3, 4, 3]], 48 "num_tensors": [6], 49 "axis": [-1], 50 "type": [tf.float32], 51 "fully_quantize": [True], 52 "quant_16x8": [True], 53 "dynamic_range_quantize": [False], 54 }, { 55 "base_shape": [[1, 3, 4, 3]], 56 "num_tensors": [6], 57 "axis": [1], 58 "type": [tf.float32], 59 "fully_quantize": [False], 60 "quant_16x8": [False], 61 "dynamic_range_quantize": [True], 62 }, { 63 "base_shape": [[1, 3, 4, 3]], 64 "num_tensors": [6], 65 "axis": [1], 66 "type": [tf.bool], 67 "fully_quantize": [False], 68 "quant_16x8": [False], 69 "dynamic_range_quantize": [True], 70 }] 71 72 def get_shape(parameters, delta): 73 """Return a tweaked version of 'base_shape'.""" 74 axis = parameters["axis"] 75 shape = parameters["base_shape"][:] 76 if axis < 0: 77 axis += len(shape) 78 if axis < len(shape): 79 shape[axis] += delta 80 return shape 81 82 def build_graph(parameters): 83 all_tensors = [] 84 for n in range(0, parameters["num_tensors"]): 85 input_tensor = tf.compat.v1.placeholder( 86 dtype=parameters["type"], 87 name=("input%d" % n), 88 shape=get_shape(parameters, n)) 89 all_tensors.append(input_tensor) 90 out = tf.concat(all_tensors, parameters["axis"]) 91 return all_tensors, [out] 92 93 def build_inputs(parameters, sess, inputs, outputs): 94 all_values = [] 95 for n in range(0, parameters["num_tensors"]): 96 input_values = create_tensor_data( 97 parameters["type"], 98 get_shape(parameters, n), 99 min_value=-1, 100 max_value=1) 101 all_values.append(input_values) 102 return all_values, sess.run( 103 outputs, feed_dict=dict(zip(inputs, all_values))) 104 105 make_zip_of_tests( 106 options, 107 test_parameters, 108 build_graph, 109 build_inputs, 110 expected_tf_failures=75) 111