本文整理汇总了Python中object_detection.model_lib.create_model_fn方法的典型用法代码示例。如果您正苦于以下问题：Python model_lib.create_model_fn方法的具体用法？Python model_lib.create_model_fn怎么用？Python model_lib.create_model_fn使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.model_lib的用法示例。
在下文中一共展示了model_lib.create_model_fn方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _assert_model_fn_for_predict
# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import create_model_fn [as 别名]
def _assert_model_fn_for_predict(self, configs):
model_config = configs['model']
with tf.Graph().as_default():
features, _ = _make_initializable_iterator(
inputs.create_eval_input_fn(configs['eval_config'],
configs['eval_input_config'],
configs['model'])()).get_next()
detection_model_fn = functools.partial(
model_builder.build, model_config=model_config, is_training=False)
hparams = model_hparams.create_hparams(
hparams_overrides='load_pretrained=false')
model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
estimator_spec = model_fn(features, None, tf.estimator.ModeKeys.PREDICT)
self.assertIsNone(estimator_spec.loss)
self.assertIsNone(estimator_spec.train_op)
self.assertIsNotNone(estimator_spec.predictions)
self.assertIsNotNone(estimator_spec.export_outputs)
self.assertIn(tf.saved_model.signature_constants.PREDICT_METHOD_NAME,
estimator_spec.export_outputs) 示例2: _assert_model_fn_for_predict
# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import create_model_fn [as 别名]
def _assert_model_fn_for_predict(self, configs):
model_config = configs['model']
with tf.Graph().as_default():
features, _ = inputs.create_eval_input_fn(
configs['eval_config'],
configs['eval_input_config'],
configs['model'])()
detection_model_fn = functools.partial(
model_builder.build, model_config=model_config, is_training=False)
hparams = model_hparams.create_hparams(
hparams_overrides='load_pretrained=false')
model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
estimator_spec = model_fn(features, None, tf.estimator.ModeKeys.PREDICT)
self.assertIsNone(estimator_spec.loss)
self.assertIsNone(estimator_spec.train_op)
self.assertIsNotNone(estimator_spec.predictions)
self.assertIsNotNone(estimator_spec.export_outputs)
self.assertIn(tf.saved_model.signature_constants.PREDICT_METHOD_NAME,
estimator_spec.export_outputs) 示例3: _assert_model_fn_for_train_eval
# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import create_model_fn [as 别名]
def _assert_model_fn_for_train_eval(self, configs, mode,
class_agnostic=False):
model_config = configs['model']
train_config = configs['train_config']
with tf.Graph().as_default():
if mode == 'train':
features, labels = _make_initializable_iterator(
inputs.create_train_input_fn(configs['train_config'],
configs['train_input_config'],
configs['model'])()).get_next()
model_mode = tf.estimator.ModeKeys.TRAIN
batch_size = train_config.batch_size
elif mode == 'eval':
features, labels = _make_initializable_iterator(
inputs.create_eval_input_fn(configs['eval_config'],
configs['eval_input_config'],
configs['model'])()).get_next()
model_mode = tf.estimator.ModeKeys.EVAL
batch_size = 1
elif mode == 'eval_on_train':
features, labels = _make_initializable_iterator(
inputs.create_eval_input_fn(configs['eval_config'],
configs['train_input_config'],
configs['model'])()).get_next()
model_mode = tf.estimator.ModeKeys.EVAL
batch_size = 1
detection_model_fn = functools.partial(
model_builder.build, model_config=model_config, is_training=True)
hparams = model_hparams.create_hparams(
hparams_overrides='load_pretrained=false')
model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
estimator_spec = model_fn(features, labels, model_mode)
self.assertIsNotNone(estimator_spec.loss)
self.assertIsNotNone(estimator_spec.predictions)
if mode == 'eval' or mode == 'eval_on_train':
if class_agnostic:
self.assertNotIn('detection_classes', estimator_spec.predictions)
else:
detection_classes = estimator_spec.predictions['detection_classes']
self.assertEqual(batch_size, detection_classes.shape.as_list()[0])
self.assertEqual(tf.float32, detection_classes.dtype)
detection_boxes = estimator_spec.predictions['detection_boxes']
detection_scores = estimator_spec.predictions['detection_scores']
num_detections = estimator_spec.predictions['num_detections']
self.assertEqual(batch_size, detection_boxes.shape.as_list()[0])
self.assertEqual(tf.float32, detection_boxes.dtype)
self.assertEqual(batch_size, detection_scores.shape.as_list()[0])
self.assertEqual(tf.float32, detection_scores.dtype)
self.assertEqual(tf.float32, num_detections.dtype)
if mode == 'eval':
self.assertIn('Detections_Left_Groundtruth_Right/0',
estimator_spec.eval_metric_ops)
if model_mode == tf.estimator.ModeKeys.TRAIN:
self.assertIsNotNone(estimator_spec.train_op)
return estimator_spec 示例4: _assert_model_fn_for_train_eval
# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import create_model_fn [as 别名]
def _assert_model_fn_for_train_eval(self, configs, mode,
class_agnostic=False):
model_config = configs['model']
train_config = configs['train_config']
with tf.Graph().as_default():
if mode == 'train':
features, labels = inputs.create_train_input_fn(
configs['train_config'],
configs['train_input_config'],
configs['model'])()
model_mode = tf.estimator.ModeKeys.TRAIN
batch_size = train_config.batch_size
elif mode == 'eval':
features, labels = inputs.create_eval_input_fn(
configs['eval_config'],
configs['eval_input_config'],
configs['model'])()
model_mode = tf.estimator.ModeKeys.EVAL
batch_size = 1
elif mode == 'eval_on_train':
features, labels = inputs.create_eval_input_fn(
configs['eval_config'],
configs['train_input_config'],
configs['model'])()
model_mode = tf.estimator.ModeKeys.EVAL
batch_size = 1
detection_model_fn = functools.partial(
model_builder.build, model_config=model_config, is_training=True)
hparams = model_hparams.create_hparams(
hparams_overrides='load_pretrained=false')
model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
estimator_spec = model_fn(features, labels, model_mode)
self.assertIsNotNone(estimator_spec.loss)
self.assertIsNotNone(estimator_spec.predictions)
if class_agnostic:
self.assertNotIn('detection_classes', estimator_spec.predictions)
else:
detection_classes = estimator_spec.predictions['detection_classes']
self.assertEqual(batch_size, detection_classes.shape.as_list()[0])
self.assertEqual(tf.float32, detection_classes.dtype)
detection_boxes = estimator_spec.predictions['detection_boxes']
detection_scores = estimator_spec.predictions['detection_scores']
num_detections = estimator_spec.predictions['num_detections']
self.assertEqual(batch_size, detection_boxes.shape.as_list()[0])
self.assertEqual(tf.float32, detection_boxes.dtype)
self.assertEqual(batch_size, detection_scores.shape.as_list()[0])
self.assertEqual(tf.float32, detection_scores.dtype)
self.assertEqual(tf.float32, num_detections.dtype)
if model_mode == tf.estimator.ModeKeys.TRAIN:
self.assertIsNotNone(estimator_spec.train_op)
return estimator_spec 示例5: _assert_model_fn_for_train_eval
# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import create_model_fn [as 别名]
def _assert_model_fn_for_train_eval(self, configs, mode,
class_agnostic=False):
model_config = configs['model']
train_config = configs['train_config']
with tf.Graph().as_default():
if mode == 'train':
features, labels = _make_initializable_iterator(
inputs.create_train_input_fn(configs['train_config'],
configs['train_input_config'],
configs['model'])()).get_next()
model_mode = tf.estimator.ModeKeys.TRAIN
batch_size = train_config.batch_size
elif mode == 'eval':
features, labels = _make_initializable_iterator(
inputs.create_eval_input_fn(configs['eval_config'],
configs['eval_input_config'],
configs['model'])()).get_next()
model_mode = tf.estimator.ModeKeys.EVAL
batch_size = 1
elif mode == 'eval_on_train':
features, labels = _make_initializable_iterator(
inputs.create_eval_input_fn(configs['eval_config'],
configs['train_input_config'],
configs['model'])()).get_next()
model_mode = tf.estimator.ModeKeys.EVAL
batch_size = 1
detection_model_fn = functools.partial(
model_builder.build, model_config=model_config, is_training=True)
hparams = model_hparams.create_hparams(
hparams_overrides='load_pretrained=false')
model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
estimator_spec = model_fn(features, labels, model_mode)
self.assertIsNotNone(estimator_spec.loss)
self.assertIsNotNone(estimator_spec.predictions)
if mode == 'eval' or mode == 'eval_on_train':
if class_agnostic:
self.assertNotIn('detection_classes', estimator_spec.predictions)
else:
detection_classes = estimator_spec.predictions['detection_classes']
self.assertEqual(batch_size, detection_classes.shape.as_list()[0])
self.assertEqual(tf.float32, detection_classes.dtype)
detection_boxes = estimator_spec.predictions['detection_boxes']
detection_scores = estimator_spec.predictions['detection_scores']
num_detections = estimator_spec.predictions['num_detections']
self.assertEqual(batch_size, detection_boxes.shape.as_list()[0])
self.assertEqual(tf.float32, detection_boxes.dtype)
self.assertEqual(batch_size, detection_scores.shape.as_list()[0])
self.assertEqual(tf.float32, detection_scores.dtype)
self.assertEqual(tf.float32, num_detections.dtype)
if model_mode == tf.estimator.ModeKeys.TRAIN:
self.assertIsNotNone(estimator_spec.train_op)
return estimator_spec