本文整理匯總了Python中object_detection.protos.post_processing_pb2.PostProcessing方法的典型用法代碼示例。如果您正苦於以下問題：Python post_processing_pb2.PostProcessing方法的具體用法？Python post_processing_pb2.PostProcessing怎麽用？Python post_processing_pb2.PostProcessing使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.protos.post_processing_pb2的用法示例。
在下文中一共展示了post_processing_pb2.PostProcessing方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_build_non_max_suppressor_with_correct_parameters
# 需要導入模塊: from object_detection.protos import post_processing_pb2 [as 別名]
# 或者: from object_detection.protos.post_processing_pb2 import PostProcessing [as 別名]
def test_build_non_max_suppressor_with_correct_parameters(self):
post_processing_text_proto = """
batch_non_max_suppression {
score_threshold: 0.7
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
"""
post_processing_config = post_processing_pb2.PostProcessing()
text_format.Merge(post_processing_text_proto, post_processing_config)
non_max_suppressor, _ = post_processing_builder.build(
post_processing_config)
self.assertEqual(non_max_suppressor.keywords['max_size_per_class'], 100)
self.assertEqual(non_max_suppressor.keywords['max_total_size'], 300)
self.assertAlmostEqual(non_max_suppressor.keywords['score_thresh'], 0.7)
self.assertAlmostEqual(non_max_suppressor.keywords['iou_thresh'], 0.6) 示例2: _build_score_converter
# 需要導入模塊: from object_detection.protos import post_processing_pb2 [as 別名]
# 或者: from object_detection.protos.post_processing_pb2 import PostProcessing [as 別名]
def _build_score_converter(score_converter_config):
"""Builds score converter based on the config.
Builds one of [tf.identity, tf.sigmoid, tf.softmax] score converters based on
the config.
Args:
score_converter_config: post_processing_pb2.PostProcessing.score_converter.
Returns:
Callable score converter op.
Raises:
ValueError: On unknown score converter.
"""
if score_converter_config == post_processing_pb2.PostProcessing.IDENTITY:
return tf.identity
if score_converter_config == post_processing_pb2.PostProcessing.SIGMOID:
return tf.sigmoid
if score_converter_config == post_processing_pb2.PostProcessing.SOFTMAX:
return tf.nn.softmax
raise ValueError('Unknown score converter.') 示例3: test_build_identity_score_converter_with_logit_scale
# 需要導入模塊: from object_detection.protos import post_processing_pb2 [as 別名]
# 或者: from object_detection.protos.post_processing_pb2 import PostProcessing [as 別名]
def test_build_identity_score_converter_with_logit_scale(self):
post_processing_text_proto = """
score_converter: IDENTITY
logit_scale: 2.0
"""
post_processing_config = post_processing_pb2.PostProcessing()
text_format.Merge(post_processing_text_proto, post_processing_config)
_, score_converter = post_processing_builder.build(post_processing_config)
self.assertEqual(score_converter.__name__, 'identity_with_logit_scale')
inputs = tf.constant([1, 1], tf.float32)
outputs = score_converter(inputs)
with self.test_session() as sess:
converted_scores = sess.run(outputs)
expected_converted_scores = sess.run(tf.constant([.5, .5], tf.float32))
self.assertAllClose(converted_scores, expected_converted_scores) 示例4: _build_score_converter
# 需要導入模塊: from object_detection.protos import post_processing_pb2 [as 別名]
# 或者: from object_detection.protos.post_processing_pb2 import PostProcessing [as 別名]
def _build_score_converter(score_converter_config, logit_scale):
"""Builds score converter based on the config.
Builds one of [tf.identity, tf.sigmoid, tf.softmax] score converters based on
the config.
Args:
score_converter_config: post_processing_pb2.PostProcessing.score_converter.
logit_scale: temperature to use for SOFTMAX score_converter.
Returns:
Callable score converter op.
Raises:
ValueError: On unknown score converter.
"""
if score_converter_config == post_processing_pb2.PostProcessing.IDENTITY:
return _score_converter_fn_with_logit_scale(tf.identity, logit_scale)
if score_converter_config == post_processing_pb2.PostProcessing.SIGMOID:
return _score_converter_fn_with_logit_scale(tf.sigmoid, logit_scale)
if score_converter_config == post_processing_pb2.PostProcessing.SOFTMAX:
return _score_converter_fn_with_logit_scale(tf.nn.softmax, logit_scale)
raise ValueError('Unknown score converter.') 示例5: test_build_identity_score_converter
# 需要導入模塊: from object_detection.protos import post_processing_pb2 [as 別名]
# 或者: from object_detection.protos.post_processing_pb2 import PostProcessing [as 別名]
def test_build_identity_score_converter(self):
post_processing_text_proto = """
score_converter: IDENTITY
"""
post_processing_config = post_processing_pb2.PostProcessing()
text_format.Merge(post_processing_text_proto, post_processing_config)
_, score_converter = post_processing_builder.build(post_processing_config)
self.assertEqual(score_converter, tf.identity)