本文整理汇总了Python中object_detection.core.box_predictor.MaskRCNNBoxPredictor方法的典型用法代码示例。如果您正苦于以下问题:Python box_predictor.MaskRCNNBoxPredictor方法的具体用法?Python box_predictor.MaskRCNNBoxPredictor怎么用?Python box_predictor.MaskRCNNBoxPredictor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.core.box_predictor
的用法示例。
在下文中一共展示了box_predictor.MaskRCNNBoxPredictor方法的8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: test_get_boxes_with_five_classes
# 需要导入模块: from object_detection.core import box_predictor [as 别名]
# 或者: from object_detection.core.box_predictor import MaskRCNNBoxPredictor [as 别名]
def test_get_boxes_with_five_classes(self):
image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_arg_scope_with_hyperparams(),
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
)
box_predictions = mask_box_predictor.predict(
image_features, num_predictions_per_location=1, scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
class_predictions_with_background = box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND]
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
class_predictions_with_background_shape) = sess.run(
[tf.shape(box_encodings),
tf.shape(class_predictions_with_background)])
self.assertAllEqual(box_encodings_shape, [2, 1, 5, 4])
self.assertAllEqual(class_predictions_with_background_shape, [2, 1, 6])
示例2: test_get_instance_masks
# 需要导入模块: from object_detection.core import box_predictor [as 别名]
# 或者: from object_detection.core.box_predictor import MaskRCNNBoxPredictor [as 别名]
def test_get_instance_masks(self):
image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_arg_scope_with_hyperparams(),
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
conv_hyperparams=self._build_arg_scope_with_hyperparams(
op_type=hyperparams_pb2.Hyperparams.CONV),
predict_instance_masks=True)
box_predictions = mask_box_predictor.predict(
image_features, num_predictions_per_location=1, scope='BoxPredictor')
mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
self.assertListEqual([2, 1, 5, 14, 14],
mask_predictions.get_shape().as_list())
示例3: test_get_boxes_with_five_classes
# 需要导入模块: from object_detection.core import box_predictor [as 别名]
# 或者: from object_detection.core.box_predictor import MaskRCNNBoxPredictor [as 别名]
def test_get_boxes_with_five_classes(self):
image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_arg_scope_with_hyperparams(),
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
)
box_predictions = mask_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
class_predictions_with_background = box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND]
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
class_predictions_with_background_shape) = sess.run(
[tf.shape(box_encodings),
tf.shape(class_predictions_with_background)])
self.assertAllEqual(box_encodings_shape, [2, 1, 5, 4])
self.assertAllEqual(class_predictions_with_background_shape, [2, 1, 6])
示例4: test_get_instance_masks
# 需要导入模块: from object_detection.core import box_predictor [as 别名]
# 或者: from object_detection.core.box_predictor import MaskRCNNBoxPredictor [as 别名]
def test_get_instance_masks(self):
image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_arg_scope_with_hyperparams(),
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
conv_hyperparams=self._build_arg_scope_with_hyperparams(
op_type=hyperparams_pb2.Hyperparams.CONV),
predict_instance_masks=True)
box_predictions = mask_box_predictor.predict(
[image_features],
num_predictions_per_location=[1],
scope='BoxPredictor',
predict_boxes_and_classes=True,
predict_auxiliary_outputs=True)
mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
self.assertListEqual([2, 1, 5, 14, 14],
mask_predictions.get_shape().as_list())
示例5: test_do_not_return_instance_masks_without_request
# 需要导入模块: from object_detection.core import box_predictor [as 别名]
# 或者: from object_detection.core.box_predictor import MaskRCNNBoxPredictor [as 别名]
def test_do_not_return_instance_masks_without_request(self):
image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_arg_scope_with_hyperparams(),
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4)
box_predictions = mask_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
self.assertEqual(len(box_predictions), 2)
self.assertTrue(box_predictor.BOX_ENCODINGS in box_predictions)
self.assertTrue(box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND
in box_predictions)
示例6: test_get_boxes_with_five_classes
# 需要导入模块: from object_detection.core import box_predictor [as 别名]
# 或者: from object_detection.core.box_predictor import MaskRCNNBoxPredictor [as 别名]
def test_get_boxes_with_five_classes(self):
image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
is_training=False,
num_classes=5,
fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
)
box_predictions = mask_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
class_predictions_with_background = box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND]
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
class_predictions_with_background_shape) = sess.run(
[tf.shape(box_encodings),
tf.shape(class_predictions_with_background)])
self.assertAllEqual(box_encodings_shape, [2, 1, 5, 4])
self.assertAllEqual(class_predictions_with_background_shape, [2, 1, 6])
示例7: test_get_instance_masks
# 需要导入模块: from object_detection.core import box_predictor [as 别名]
# 或者: from object_detection.core.box_predictor import MaskRCNNBoxPredictor [as 别名]
def test_get_instance_masks(self):
image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
is_training=False,
num_classes=5,
fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
conv_hyperparams_fn=self._build_arg_scope_with_hyperparams(
op_type=hyperparams_pb2.Hyperparams.CONV),
predict_instance_masks=True)
box_predictions = mask_box_predictor.predict(
[image_features],
num_predictions_per_location=[1],
scope='BoxPredictor',
predict_boxes_and_classes=True,
predict_auxiliary_outputs=True)
mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
self.assertListEqual([2, 1, 5, 14, 14],
mask_predictions.get_shape().as_list())
示例8: test_do_not_return_instance_masks_without_request
# 需要导入模块: from object_detection.core import box_predictor [as 别名]
# 或者: from object_detection.core.box_predictor import MaskRCNNBoxPredictor [as 别名]
def test_do_not_return_instance_masks_without_request(self):
image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
is_training=False,
num_classes=5,
fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4)
box_predictions = mask_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
self.assertEqual(len(box_predictions), 2)
self.assertTrue(box_predictor.BOX_ENCODINGS in box_predictions)
self.assertTrue(box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND
in box_predictions)