Python object_detection.builders.box_coder_builder.build() Examples
The following are 30
code examples of object_detection.builders.box_coder_builder.build().
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Example #1
| Source File: model_builder.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
|
def build(model_config, is_training, add_summaries=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.
add_summaries: Whether to add tensorflow summaries in the model graph.
Returns:
DetectionModel based on the config.
Raises:
ValueError: On invalid meta architecture or model.
"""
if not isinstance(model_config, model_pb2.DetectionModel):
raise ValueError('model_config not of type model_pb2.DetectionModel.')
meta_architecture = model_config.WhichOneof('model')
if meta_architecture == 'ssd':
return _build_ssd_model(model_config.ssd, is_training, add_summaries)
if meta_architecture == 'faster_rcnn':
return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
add_summaries)
raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #2
| Source File: box_coder_builder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
|
def test_build_keypoint_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
keypoint_box_coder {
num_keypoints: 6
y_scale: 6.0
x_scale: 3.0
height_scale: 7.0
width_scale: 8.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
self.assertEqual(box_coder_object._num_keypoints, 6)
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
Example #3
| Source File: model_builder.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 6 votes |
|
def build(model_config, is_training):
"""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.
Raises:
ValueError: On invalid meta architecture or model.
"""
if not isinstance(model_config, model_pb2.DetectionModel):
raise ValueError('model_config not of type model_pb2.DetectionModel.')
meta_architecture = model_config.WhichOneof('model')
if meta_architecture == 'ssd':
return _build_ssd_model(model_config.ssd, is_training)
if meta_architecture == 'faster_rcnn':
return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #4
| Source File: model_builder.py From object_detector_app with MIT License | 6 votes |
|
def build(model_config, is_training):
"""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.
Raises:
ValueError: On invalid meta architecture or model.
"""
if not isinstance(model_config, model_pb2.DetectionModel):
raise ValueError('model_config not of type model_pb2.DetectionModel.')
meta_architecture = model_config.WhichOneof('model')
if meta_architecture == 'ssd':
return _build_ssd_model(model_config.ssd, is_training)
if meta_architecture == 'faster_rcnn':
return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #5
| Source File: box_coder_builder_test.py From vehicle_counting_tensorflow with MIT License | 6 votes |
|
def test_build_keypoint_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
keypoint_box_coder {
num_keypoints: 6
y_scale: 6.0
x_scale: 3.0
height_scale: 7.0
width_scale: 8.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
self.assertEqual(box_coder_object._num_keypoints, 6)
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
Example #6
| Source File: model_builder.py From vehicle_counting_tensorflow with MIT License | 6 votes |
|
def build(model_config, is_training, add_summaries=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.
add_summaries: Whether to add tensorflow summaries in the model graph.
Returns:
DetectionModel based on the config.
Raises:
ValueError: On invalid meta architecture or model.
"""
if not isinstance(model_config, model_pb2.DetectionModel):
raise ValueError('model_config not of type model_pb2.DetectionModel.')
meta_architecture = model_config.WhichOneof('model')
if meta_architecture == 'ssd':
return _build_ssd_model(model_config.ssd, is_training, add_summaries)
if meta_architecture == 'faster_rcnn':
return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
add_summaries)
raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #7
| Source File: box_coder_builder_test.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def test_build_keypoint_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
keypoint_box_coder {
num_keypoints: 6
y_scale: 6.0
x_scale: 3.0
height_scale: 7.0
width_scale: 8.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
self.assertEqual(box_coder_object._num_keypoints, 6)
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
Example #8
| Source File: model_builder.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def build(model_config, is_training):
"""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.
Raises:
ValueError: On invalid meta architecture or model.
"""
if not isinstance(model_config, model_pb2.DetectionModel):
raise ValueError('model_config not of type model_pb2.DetectionModel.')
meta_architecture = model_config.WhichOneof('model')
if meta_architecture == 'ssd':
return _build_ssd_model(model_config.ssd, is_training)
if meta_architecture == 'faster_rcnn':
return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #9
| Source File: model_builder.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 6 votes |
|
def build(model_config, is_training, add_summaries=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.
add_summaries: Whether to add tensorflow summaries in the model graph.
Returns:
DetectionModel based on the config.
Raises:
ValueError: On invalid meta architecture or model.
"""
if not isinstance(model_config, model_pb2.DetectionModel):
raise ValueError('model_config not of type model_pb2.DetectionModel.')
meta_architecture = model_config.WhichOneof('model')
if meta_architecture == 'ssd':
return _build_ssd_model(model_config.ssd, is_training, add_summaries)
if meta_architecture == 'faster_rcnn':
return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
add_summaries)
raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #10
| Source File: model_builder.py From HereIsWally with MIT License | 6 votes |
|
def build(model_config, is_training):
"""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.
Raises:
ValueError: On invalid meta architecture or model.
"""
if not isinstance(model_config, model_pb2.DetectionModel):
raise ValueError('model_config not of type model_pb2.DetectionModel.')
meta_architecture = model_config.WhichOneof('model')
if meta_architecture == 'ssd':
return _build_ssd_model(model_config.ssd, is_training)
if meta_architecture == 'faster_rcnn':
return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #11
| Source File: model_builder.py From garbage-object-detection-tensorflow with MIT License | 6 votes |
|
def build(model_config, is_training):
"""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.
Raises:
ValueError: On invalid meta architecture or model.
"""
if not isinstance(model_config, model_pb2.DetectionModel):
raise ValueError('model_config not of type model_pb2.DetectionModel.')
meta_architecture = model_config.WhichOneof('model')
if meta_architecture == 'ssd':
return _build_ssd_model(model_config.ssd, is_training)
if meta_architecture == 'faster_rcnn':
return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #12
| Source File: model_builder.py From DOTA_models with Apache License 2.0 | 6 votes |
|
def build(model_config, is_training):
"""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.
Raises:
ValueError: On invalid meta architecture or model.
"""
if not isinstance(model_config, model_pb2.DetectionModel):
raise ValueError('model_config not of type model_pb2.DetectionModel.')
meta_architecture = model_config.WhichOneof('model')
if meta_architecture == 'ssd':
return _build_ssd_model(model_config.ssd, is_training)
if meta_architecture == 'faster_rcnn':
return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #13
| Source File: box_coder_builder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
|
def test_raise_error_on_empty_box_coder(self):
box_coder_text_proto = """
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
with self.assertRaises(ValueError):
box_coder_builder.build(box_coder_proto)
Example #14
| Source File: box_coder_builder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
|
def test_build_mean_stddev_box_coder(self):
box_coder_text_proto = """
mean_stddev_box_coder {
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(
isinstance(box_coder_object,
mean_stddev_box_coder.MeanStddevBoxCoder))
Example #15
| Source File: model_builder.py From HereIsWally with MIT License | 5 votes |
|
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
reuse_weights=None):
"""Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
Args:
feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
is_training: True if this feature extractor is being built for training.
reuse_weights: if the feature extractor should reuse weights.
Returns:
ssd_meta_arch.SSDFeatureExtractor based on config.
Raises:
ValueError: On invalid feature extractor type.
"""
feature_type = feature_extractor_config.type
depth_multiplier = feature_extractor_config.depth_multiplier
min_depth = feature_extractor_config.min_depth
conv_hyperparams = hyperparams_builder.build(
feature_extractor_config.conv_hyperparams, is_training)
if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
return feature_extractor_class(depth_multiplier, min_depth, conv_hyperparams,
reuse_weights)
Example #16
| Source File: box_coder_builder_test.py From HereIsWally with MIT License | 5 votes |
|
def test_build_faster_rcnn_box_coder_with_defaults(self):
box_coder_text_proto = """
faster_rcnn_box_coder {
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(isinstance(box_coder_object,
faster_rcnn_box_coder.FasterRcnnBoxCoder))
self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0, 5.0])
Example #17
| Source File: box_coder_builder_test.py From HereIsWally with MIT License | 5 votes |
|
def test_build_faster_rcnn_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
faster_rcnn_box_coder {
y_scale: 6.0
x_scale: 3.0
height_scale: 7.0
width_scale: 8.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(isinstance(box_coder_object,
faster_rcnn_box_coder.FasterRcnnBoxCoder))
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
Example #18
| Source File: box_coder_builder_test.py From HereIsWally with MIT License | 5 votes |
|
def test_build_square_box_coder_with_defaults(self):
box_coder_text_proto = """
square_box_coder {
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(
isinstance(box_coder_object, square_box_coder.SquareBoxCoder))
self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0])
Example #19
| Source File: box_coder_builder_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
|
def test_build_mean_stddev_box_coder(self):
box_coder_text_proto = """
mean_stddev_box_coder {
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(
isinstance(box_coder_object,
mean_stddev_box_coder.MeanStddevBoxCoder))
Example #20
| Source File: box_coder_builder_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
|
def test_build_square_box_coder_with_defaults(self):
box_coder_text_proto = """
square_box_coder {
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(
isinstance(box_coder_object, square_box_coder.SquareBoxCoder))
self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0])
Example #21
| Source File: box_coder_builder_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
|
def test_build_square_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
square_box_coder {
y_scale: 6.0
x_scale: 3.0
length_scale: 7.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(
isinstance(box_coder_object, square_box_coder.SquareBoxCoder))
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0])
Example #22
| Source File: box_coder_builder_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
|
def test_raise_error_on_empty_box_coder(self):
box_coder_text_proto = """
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
with self.assertRaises(ValueError):
box_coder_builder.build(box_coder_proto)
Example #23
| Source File: box_coder_builder_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
|
def test_build_square_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
square_box_coder {
y_scale: 6.0
x_scale: 3.0
length_scale: 7.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(
isinstance(box_coder_object, square_box_coder.SquareBoxCoder))
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0])
Example #24
| Source File: box_coder_builder_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
|
def test_build_square_box_coder_with_defaults(self):
box_coder_text_proto = """
square_box_coder {
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(
isinstance(box_coder_object, square_box_coder.SquareBoxCoder))
self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0])
Example #25
| Source File: box_coder_builder_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
|
def test_build_faster_rcnn_box_coder_with_non_default_parameters(self):
box_coder_text_proto = """
faster_rcnn_box_coder {
y_scale: 6.0
x_scale: 3.0
height_scale: 7.0
width_scale: 8.0
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(isinstance(box_coder_object,
faster_rcnn_box_coder.FasterRcnnBoxCoder))
self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0])
Example #26
| Source File: box_coder_builder_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
|
def test_build_faster_rcnn_box_coder_with_defaults(self):
box_coder_text_proto = """
faster_rcnn_box_coder {
}
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
box_coder_object = box_coder_builder.build(box_coder_proto)
self.assertTrue(isinstance(box_coder_object,
faster_rcnn_box_coder.FasterRcnnBoxCoder))
self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0, 5.0])
Example #27
| Source File: model_builder.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
|
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
reuse_weights=None):
"""Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
Args:
feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
is_training: True if this feature extractor is being built for training.
reuse_weights: if the feature extractor should reuse weights.
Returns:
ssd_meta_arch.SSDFeatureExtractor based on config.
Raises:
ValueError: On invalid feature extractor type.
"""
feature_type = feature_extractor_config.type
depth_multiplier = feature_extractor_config.depth_multiplier
min_depth = feature_extractor_config.min_depth
conv_hyperparams = hyperparams_builder.build(
feature_extractor_config.conv_hyperparams, is_training)
if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
return feature_extractor_class(depth_multiplier, min_depth, conv_hyperparams,
reuse_weights)
Example #28
| Source File: box_coder_builder_test.py From Person-Detection-and-Tracking with MIT License | 5 votes |
|
def test_raise_error_on_empty_box_coder(self):
box_coder_text_proto = """
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
with self.assertRaises(ValueError):
box_coder_builder.build(box_coder_proto)
Example #29
| Source File: model_builder.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 5 votes |
|
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
reuse_weights=None):
"""Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
Args:
feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
is_training: True if this feature extractor is being built for training.
reuse_weights: if the feature extractor should reuse weights.
Returns:
ssd_meta_arch.SSDFeatureExtractor based on config.
Raises:
ValueError: On invalid feature extractor type.
"""
feature_type = feature_extractor_config.type
depth_multiplier = feature_extractor_config.depth_multiplier
min_depth = feature_extractor_config.min_depth
conv_hyperparams = hyperparams_builder.build(
feature_extractor_config.conv_hyperparams, is_training)
if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
return feature_extractor_class(depth_multiplier, min_depth, conv_hyperparams,
reuse_weights)
Example #30
| Source File: box_coder_builder_test.py From HereIsWally with MIT License | 5 votes |
|
def test_raise_error_on_empty_box_coder(self):
box_coder_text_proto = """
"""
box_coder_proto = box_coder_pb2.BoxCoder()
text_format.Merge(box_coder_text_proto, box_coder_proto)
with self.assertRaises(ValueError):
box_coder_builder.build(box_coder_proto)
