本文整理汇总了Python中object_detection.inputs.create_eval_input_fn方法的典型用法代码示例。如果您正苦于以下问题：Python inputs.create_eval_input_fn方法的具体用法？Python inputs.create_eval_input_fn怎么用？Python inputs.create_eval_input_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.inputs的用法示例。
在下文中一共展示了inputs.create_eval_input_fn方法的9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 inputs [as 别名]
# 或者: from object_detection.inputs import create_eval_input_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_outputs_for_predict
# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_eval_input_fn [as 别名]
def _assert_outputs_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.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_predict
# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_eval_input_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4: _assert_outputs_for_predict
# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_eval_input_fn [as 别名]
def _assert_outputs_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.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5: test_error_with_bad_eval_config
# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_eval_input_fn [as 别名]
def test_error_with_bad_eval_config(self):
"""Tests that a TypeError is raised with improper eval config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
eval_input_fn = inputs.create_eval_input_fn(
eval_config=configs['train_config'], # Expecting `EvalConfig`.
eval_input_config=configs['eval_input_configs'][0],
model_config=configs['model'])
with self.assertRaises(TypeError):
eval_input_fn() 示例6: test_error_with_bad_eval_input_config
# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_eval_input_fn [as 别名]
def test_error_with_bad_eval_input_config(self):
"""Tests that a TypeError is raised with improper eval input config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
eval_input_fn = inputs.create_eval_input_fn(
eval_config=configs['eval_config'],
eval_input_config=configs['model'], # Expecting `InputReader`.
model_config=configs['model'])
with self.assertRaises(TypeError):
eval_input_fn() 示例7: test_error_with_bad_eval_model_config
# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_eval_input_fn [as 别名]
def test_error_with_bad_eval_model_config(self):
"""Tests that a TypeError is raised with improper eval model config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
eval_input_fn = inputs.create_eval_input_fn(
eval_config=configs['eval_config'],
eval_input_config=configs['eval_input_configs'][0],
model_config=configs['eval_config']) # Expecting `DetectionModel`.
with self.assertRaises(TypeError):
eval_input_fn() 示例8: test_error_with_bad_eval_config
# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_eval_input_fn [as 别名]
def test_error_with_bad_eval_config(self):
"""Tests that a TypeError is raised with improper eval config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
eval_input_fn = inputs.create_eval_input_fn(
eval_config=configs['train_config'], # Expecting `EvalConfig`.
eval_input_config=configs['eval_input_config'],
model_config=configs['model'])
with self.assertRaises(TypeError):
eval_input_fn() 示例9: test_error_with_bad_eval_model_config
# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_eval_input_fn [as 别名]
def test_error_with_bad_eval_model_config(self):
"""Tests that a TypeError is raised with improper eval model config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
eval_input_fn = inputs.create_eval_input_fn(
eval_config=configs['eval_config'],
eval_input_config=configs['eval_input_config'],
model_config=configs['eval_config']) # Expecting `DetectionModel`.
with self.assertRaises(TypeError):
eval_input_fn()