research/lstm_object_detection/model_builder_test.py
# 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()