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 conv with activations.""" 16from __future__ import absolute_import 17from __future__ import division 18from __future__ import print_function 19 20import numpy as np 21import tensorflow as tf 22from tensorflow.lite.testing.zip_test_utils import create_tensor_data 23from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests 24from tensorflow.lite.testing.zip_test_utils import register_make_test_function 25 26 27def make_conv_activation_tests(activation_op): 28 """Make a set of tests to do convolution with activation.""" 29 30 def f(options): 31 """Actual function that generates examples.""" 32 test_parameters = [ 33 { 34 "input_shape": [[1, 3, 4, 3], [4, 6, 6, 1]], 35 "filter_shape": [[1, 1], [2, 3], [3, 3]], 36 "strides": [[1, 1, 1, 1], [1, 2, 3, 1]], 37 "dilations": [[1, 1, 1, 1], [1, 3, 2, 1], [1, 2, 2, 1]], 38 "padding": ["SAME", "VALID"], 39 "data_format": ["NHWC"], # TODO(aselle): NCHW would be good 40 "constant_filter": [True, False], 41 "channel_multiplier": [1, 2], 42 "fully_quantize": [False], 43 }, 44 # TODO(b/134702301): The fully_quantize param is just ignored by the 45 # MLIR testing path now, resulting in duplicate tests. Either ignore 46 # these tests or handle it properly in the mlir_convert() function. 47 { 48 "input_shape": [[1, 3, 4, 3], [4, 6, 6, 1]], 49 "filter_shape": [[1, 1], [2, 3], [3, 3]], 50 "strides": [[1, 1, 1, 1], [1, 2, 3, 1]], 51 "dilations": [[1, 1, 1, 1], [1, 3, 2, 1], [1, 2, 2, 1]], 52 "padding": ["SAME", "VALID"], 53 "data_format": ["NHWC"], # TODO(aselle): NCHW would be good 54 "constant_filter": [True], 55 "channel_multiplier": [1, 2], 56 "fully_quantize": [True], 57 } 58 ] 59 60 def get_tensor_shapes(parameters): 61 input_shape = parameters["input_shape"] 62 filter_size = parameters["filter_shape"] 63 filter_shape = filter_size + [ 64 input_shape[3], parameters["channel_multiplier"] 65 ] 66 return [input_shape, filter_shape] 67 68 def build_graph(parameters): 69 """Build a conv graph given `parameters`.""" 70 input_shape, filter_shape = get_tensor_shapes(parameters) 71 input_tensor = tf.compat.v1.placeholder( 72 dtype=tf.float32, name="input", shape=input_shape) 73 74 # Get filter input either as a placeholder or constants. Also get a list 75 # of the input tensors that are represented as placeholders. 76 if parameters["constant_filter"]: 77 filter_input = create_tensor_data( 78 np.float32, filter_shape, min_value=-10, max_value=10) 79 input_tensors = [input_tensor] 80 else: 81 filter_input = tf.compat.v1.placeholder( 82 dtype=tf.float32, name="filter", shape=filter_shape) 83 input_tensors = [input_tensor, filter_input] 84 85 out = tf.nn.conv2d( 86 input_tensor, 87 filter_input, 88 strides=parameters["strides"], 89 dilations=parameters["dilations"], 90 padding=parameters["padding"], 91 data_format=parameters["data_format"]) 92 out = activation_op(out) 93 return input_tensors, [out] 94 95 def build_inputs(parameters, sess, inputs, outputs): 96 """Build inputs for conv with activation.""" 97 98 input_shape, filter_shape = get_tensor_shapes(parameters) 99 values = [ 100 create_tensor_data( 101 np.float32, input_shape, min_value=-1, max_value=1) 102 ] 103 if not parameters["constant_filter"]: 104 values.append(create_tensor_data(np.float32, filter_shape)) 105 return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) 106 107 make_zip_of_tests( 108 options, 109 test_parameters, 110 build_graph, 111 build_inputs, 112 expected_tf_failures=60) 113 114 return f 115 116 117@register_make_test_function() 118def make_conv_relu6_tests(options): 119 """Make a set of tests to do conv_relu6.""" 120 return make_conv_activation_tests(tf.nn.relu6)(options) 121 122 123@register_make_test_function() 124def make_conv_relu_tests(options): 125 """Make a set of tests to do conv_relu.""" 126 return make_conv_activation_tests(tf.nn.relu)(options) 127 128 129def relu1(input_tensor): 130 # Note that the following is not supported: 131 # out = tf.maximum(-1.0, tf.minimum(input_tensor, 1.0)) 132 out = tf.minimum(1.0, tf.maximum(input_tensor, -1.0)) 133 return out 134 135 136@register_make_test_function() 137def make_conv_relu1_tests(options): 138 """Make a set of tests to do conv_relu1.""" 139 return make_conv_activation_tests(relu1)(options) 140