Spaces:
Running
Running
File size: 9,319 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 |
# Copyright 2018 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.
# ==============================================================================
"""Tests for lstm_object_detection.tensorflow.model_builder."""
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
from lstm_object_detection import model_builder
from lstm_object_detection.meta_architectures import lstm_ssd_meta_arch
from lstm_object_detection.protos import pipeline_pb2 as internal_pipeline_pb2
from object_detection.protos import pipeline_pb2
class ModelBuilderTest(tf.test.TestCase):
def create_train_model(self, model_config, lstm_config):
"""Builds a DetectionModel based on the model config.
Args:
model_config: A model.proto object containing the config for the desired
DetectionModel.
lstm_config: LstmModel config proto that specifies LSTM train/eval
configs.
Returns:
DetectionModel based on the config.
"""
return model_builder.build(model_config, lstm_config, is_training=True)
def create_eval_model(self, model_config, lstm_config):
"""Builds a DetectionModel based on the model config.
Args:
model_config: A model.proto object containing the config for the desired
DetectionModel.
lstm_config: LstmModel config proto that specifies LSTM train/eval
configs.
Returns:
DetectionModel based on the config.
"""
return model_builder.build(model_config, lstm_config, is_training=False)
def get_model_configs_from_proto(self):
"""Creates a model text proto for testing.
Returns:
A dictionary of model configs.
"""
model_text_proto = """
[lstm_object_detection.protos.lstm_model] {
train_unroll_length: 4
eval_unroll_length: 4
}
model {
ssd {
feature_extractor {
type: 'lstm_ssd_mobilenet_v1'
conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
}
negative_class_weight: 2.0
box_coder {
faster_rcnn_box_coder {
}
}
matcher {
argmax_matcher {
}
}
similarity_calculator {
iou_similarity {
}
}
anchor_generator {
ssd_anchor_generator {
aspect_ratios: 1.0
}
}
image_resizer {
fixed_shape_resizer {
height: 320
width: 320
}
}
box_predictor {
convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
}
}
normalize_loc_loss_by_codesize: true
loss {
classification_loss {
weighted_softmax {
}
}
localization_loss {
weighted_smooth_l1 {
}
}
}
}
}"""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
text_format.Merge(model_text_proto, pipeline_config)
configs = {}
configs['model'] = pipeline_config.model
configs['lstm_model'] = pipeline_config.Extensions[
internal_pipeline_pb2.lstm_model]
return configs
def get_interleaved_model_configs_from_proto(self):
"""Creates an interleaved model text proto for testing.
Returns:
A dictionary of model configs.
"""
model_text_proto = """
[lstm_object_detection.protos.lstm_model] {
train_unroll_length: 4
eval_unroll_length: 10
lstm_state_depth: 320
depth_multipliers: 1.4
depth_multipliers: 0.35
pre_bottleneck: true
low_res: true
train_interleave_method: 'RANDOM_SKIP_SMALL'
eval_interleave_method: 'SKIP3'
}
model {
ssd {
feature_extractor {
type: 'lstm_ssd_interleaved_mobilenet_v2'
conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
}
negative_class_weight: 2.0
box_coder {
faster_rcnn_box_coder {
}
}
matcher {
argmax_matcher {
}
}
similarity_calculator {
iou_similarity {
}
}
anchor_generator {
ssd_anchor_generator {
aspect_ratios: 1.0
}
}
image_resizer {
fixed_shape_resizer {
height: 320
width: 320
}
}
box_predictor {
convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
}
}
normalize_loc_loss_by_codesize: true
loss {
classification_loss {
weighted_softmax {
}
}
localization_loss {
weighted_smooth_l1 {
}
}
}
}
}"""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
text_format.Merge(model_text_proto, pipeline_config)
configs = {}
configs['model'] = pipeline_config.model
configs['lstm_model'] = pipeline_config.Extensions[
internal_pipeline_pb2.lstm_model]
return configs
def test_model_creation_from_valid_configs(self):
configs = self.get_model_configs_from_proto()
# Test model properties.
self.assertEqual(configs['model'].ssd.negative_class_weight, 2.0)
self.assertTrue(configs['model'].ssd.normalize_loc_loss_by_codesize)
self.assertEqual(configs['model'].ssd.feature_extractor.type,
'lstm_ssd_mobilenet_v1')
model = self.create_train_model(configs['model'], configs['lstm_model'])
# Test architechture type.
self.assertIsInstance(model, lstm_ssd_meta_arch.LSTMSSDMetaArch)
# Test LSTM unroll length.
self.assertEqual(model.unroll_length, 4)
model = self.create_eval_model(configs['model'], configs['lstm_model'])
# Test architechture type.
self.assertIsInstance(model, lstm_ssd_meta_arch.LSTMSSDMetaArch)
# Test LSTM configs.
self.assertEqual(model.unroll_length, 4)
def test_interleaved_model_creation_from_valid_configs(self):
configs = self.get_interleaved_model_configs_from_proto()
# Test model properties.
self.assertEqual(configs['model'].ssd.negative_class_weight, 2.0)
self.assertTrue(configs['model'].ssd.normalize_loc_loss_by_codesize)
self.assertEqual(configs['model'].ssd.feature_extractor.type,
'lstm_ssd_interleaved_mobilenet_v2')
model = self.create_train_model(configs['model'], configs['lstm_model'])
# Test architechture type.
self.assertIsInstance(model, lstm_ssd_meta_arch.LSTMSSDMetaArch)
# Test LSTM configs.
self.assertEqual(model.unroll_length, 4)
self.assertEqual(model._feature_extractor.lstm_state_depth, 320)
self.assertAllClose(model._feature_extractor.depth_multipliers, (1.4, 0.35))
self.assertTrue(model._feature_extractor.pre_bottleneck)
self.assertTrue(model._feature_extractor.low_res)
self.assertEqual(model._feature_extractor.interleave_method,
'RANDOM_SKIP_SMALL')
model = self.create_eval_model(configs['model'], configs['lstm_model'])
# Test architechture type.
self.assertIsInstance(model, lstm_ssd_meta_arch.LSTMSSDMetaArch)
# Test LSTM configs.
self.assertEqual(model.unroll_length, 10)
self.assertEqual(model._feature_extractor.lstm_state_depth, 320)
self.assertAllClose(model._feature_extractor.depth_multipliers, (1.4, 0.35))
self.assertTrue(model._feature_extractor.pre_bottleneck)
self.assertTrue(model._feature_extractor.low_res)
self.assertEqual(model._feature_extractor.interleave_method, 'SKIP3')
def test_model_creation_from_invalid_configs(self):
configs = self.get_model_configs_from_proto()
# Test model build failure with wrong input configs.
with self.assertRaises(AttributeError):
_ = self.create_train_model(configs['model'], configs['model'])
with self.assertRaises(AttributeError):
_ = self.create_eval_model(configs['model'], configs['model'])
if __name__ == '__main__':
tf.test.main()
|