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# Lint as: python2, python3
# Copyright 2017 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 object_detection.models.model_builder."""
from absl.testing import parameterized
from google.protobuf import text_format
from object_detection.builders import model_builder
from object_detection.meta_architectures import faster_rcnn_meta_arch
from object_detection.meta_architectures import rfcn_meta_arch
from object_detection.meta_architectures import ssd_meta_arch
from object_detection.protos import hyperparams_pb2
from object_detection.protos import losses_pb2
from object_detection.protos import model_pb2
from object_detection.utils import test_case
class ModelBuilderTest(test_case.TestCase, parameterized.TestCase):
def default_ssd_feature_extractor(self):
raise NotImplementedError
def default_faster_rcnn_feature_extractor(self):
raise NotImplementedError
def ssd_feature_extractors(self):
raise NotImplementedError
def faster_rcnn_feature_extractors(self):
raise NotImplementedError
def create_model(self, model_config, is_training=True):
"""Builds a DetectionModel based on the model config.
Args:
model_config: A model.proto object containing the config for the desired
DetectionModel.
is_training: True if this model is being built for training purposes.
Returns:
DetectionModel based on the config.
"""
return model_builder.build(model_config, is_training=is_training)
def create_default_ssd_model_proto(self):
"""Creates a DetectionModel proto with ssd model fields populated."""
model_text_proto = """
ssd {
feature_extractor {
conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
override_base_feature_extractor_hyperparams: true
}
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 {
}
}
}
}
}
loss {
classification_loss {
weighted_softmax {
}
}
localization_loss {
weighted_smooth_l1 {
}
}
}
}"""
model_proto = model_pb2.DetectionModel()
text_format.Merge(model_text_proto, model_proto)
model_proto.ssd.feature_extractor.type = (self.
default_ssd_feature_extractor())
return model_proto
def create_default_faster_rcnn_model_proto(self):
"""Creates a DetectionModel proto with FasterRCNN model fields populated."""
model_text_proto = """
faster_rcnn {
inplace_batchnorm_update: false
num_classes: 3
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.01
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
}"""
model_proto = model_pb2.DetectionModel()
text_format.Merge(model_text_proto, model_proto)
(model_proto.faster_rcnn.feature_extractor.type
) = self.default_faster_rcnn_feature_extractor()
return model_proto
def test_create_ssd_models_from_config(self):
model_proto = self.create_default_ssd_model_proto()
for extractor_type, extractor_class in self.ssd_feature_extractors().items(
):
model_proto.ssd.feature_extractor.type = extractor_type
model = model_builder.build(model_proto, is_training=True)
self.assertIsInstance(model, ssd_meta_arch.SSDMetaArch)
self.assertIsInstance(model._feature_extractor, extractor_class)
def test_create_ssd_fpn_model_from_config(self):
model_proto = self.create_default_ssd_model_proto()
model_proto.ssd.feature_extractor.fpn.min_level = 3
model_proto.ssd.feature_extractor.fpn.max_level = 7
model = model_builder.build(model_proto, is_training=True)
self.assertEqual(model._feature_extractor._fpn_min_level, 3)
self.assertEqual(model._feature_extractor._fpn_max_level, 7)
@parameterized.named_parameters(
{
'testcase_name': 'mask_rcnn_with_matmul',
'use_matmul_crop_and_resize': False,
'enable_mask_prediction': True
},
{
'testcase_name': 'mask_rcnn_without_matmul',
'use_matmul_crop_and_resize': True,
'enable_mask_prediction': True
},
{
'testcase_name': 'faster_rcnn_with_matmul',
'use_matmul_crop_and_resize': False,
'enable_mask_prediction': False
},
{
'testcase_name': 'faster_rcnn_without_matmul',
'use_matmul_crop_and_resize': True,
'enable_mask_prediction': False
},
)
def test_create_faster_rcnn_models_from_config(self,
use_matmul_crop_and_resize,
enable_mask_prediction):
model_proto = self.create_default_faster_rcnn_model_proto()
faster_rcnn_config = model_proto.faster_rcnn
faster_rcnn_config.use_matmul_crop_and_resize = use_matmul_crop_and_resize
if enable_mask_prediction:
faster_rcnn_config.second_stage_mask_prediction_loss_weight = 3.0
mask_predictor_config = (
faster_rcnn_config.second_stage_box_predictor.mask_rcnn_box_predictor)
mask_predictor_config.predict_instance_masks = True
for extractor_type, extractor_class in (
self.faster_rcnn_feature_extractors().items()):
faster_rcnn_config.feature_extractor.type = extractor_type
model = model_builder.build(model_proto, is_training=True)
self.assertIsInstance(model, faster_rcnn_meta_arch.FasterRCNNMetaArch)
self.assertIsInstance(model._feature_extractor, extractor_class)
if enable_mask_prediction:
self.assertAlmostEqual(model._second_stage_mask_loss_weight, 3.0)
def test_create_faster_rcnn_model_from_config_with_example_miner(self):
model_proto = self.create_default_faster_rcnn_model_proto()
model_proto.faster_rcnn.hard_example_miner.num_hard_examples = 64
model = model_builder.build(model_proto, is_training=True)
self.assertIsNotNone(model._hard_example_miner)
def test_create_rfcn_model_from_config(self):
model_proto = self.create_default_faster_rcnn_model_proto()
rfcn_predictor_config = (
model_proto.faster_rcnn.second_stage_box_predictor.rfcn_box_predictor)
rfcn_predictor_config.conv_hyperparams.op = hyperparams_pb2.Hyperparams.CONV
for extractor_type, extractor_class in (
self.faster_rcnn_feature_extractors().items()):
model_proto.faster_rcnn.feature_extractor.type = extractor_type
model = model_builder.build(model_proto, is_training=True)
self.assertIsInstance(model, rfcn_meta_arch.RFCNMetaArch)
self.assertIsInstance(model._feature_extractor, extractor_class)
@parameterized.parameters(True, False)
def test_create_faster_rcnn_from_config_with_crop_feature(
self, output_final_box_features):
model_proto = self.create_default_faster_rcnn_model_proto()
model_proto.faster_rcnn.output_final_box_features = (
output_final_box_features)
_ = model_builder.build(model_proto, is_training=True)
def test_invalid_model_config_proto(self):
model_proto = ''
with self.assertRaisesRegex(
ValueError, 'model_config not of type model_pb2.DetectionModel.'):
model_builder.build(model_proto, is_training=True)
def test_unknown_meta_architecture(self):
model_proto = model_pb2.DetectionModel()
with self.assertRaisesRegex(ValueError, 'Unknown meta architecture'):
model_builder.build(model_proto, is_training=True)
def test_unknown_ssd_feature_extractor(self):
model_proto = self.create_default_ssd_model_proto()
model_proto.ssd.feature_extractor.type = 'unknown_feature_extractor'
with self.assertRaises(ValueError):
model_builder.build(model_proto, is_training=True)
def test_unknown_faster_rcnn_feature_extractor(self):
model_proto = self.create_default_faster_rcnn_model_proto()
model_proto.faster_rcnn.feature_extractor.type = 'unknown_feature_extractor'
with self.assertRaises(ValueError):
model_builder.build(model_proto, is_training=True)
def test_invalid_first_stage_nms_iou_threshold(self):
model_proto = self.create_default_faster_rcnn_model_proto()
model_proto.faster_rcnn.first_stage_nms_iou_threshold = 1.1
with self.assertRaisesRegex(ValueError,
r'iou_threshold not in \[0, 1\.0\]'):
model_builder.build(model_proto, is_training=True)
model_proto.faster_rcnn.first_stage_nms_iou_threshold = -0.1
with self.assertRaisesRegex(ValueError,
r'iou_threshold not in \[0, 1\.0\]'):
model_builder.build(model_proto, is_training=True)
def test_invalid_second_stage_batch_size(self):
model_proto = self.create_default_faster_rcnn_model_proto()
model_proto.faster_rcnn.first_stage_max_proposals = 1
model_proto.faster_rcnn.second_stage_batch_size = 2
with self.assertRaisesRegex(
ValueError, 'second_stage_batch_size should be no greater '
'than first_stage_max_proposals.'):
model_builder.build(model_proto, is_training=True)
def test_invalid_faster_rcnn_batchnorm_update(self):
model_proto = self.create_default_faster_rcnn_model_proto()
model_proto.faster_rcnn.inplace_batchnorm_update = True
with self.assertRaisesRegex(ValueError,
'inplace batchnorm updates not supported'):
model_builder.build(model_proto, is_training=True)
def test_create_experimental_model(self):
model_text_proto = """
experimental_model {
name: 'model42'
}"""
build_func = lambda *args: 42
model_builder.EXPERIMENTAL_META_ARCH_BUILDER_MAP['model42'] = build_func
model_proto = model_pb2.DetectionModel()
text_format.Merge(model_text_proto, model_proto)
self.assertEqual(model_builder.build(model_proto, is_training=True), 42)
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