本文整理匯總了Python中object_detection.core.box_predictor.RfcnBoxPredictor方法的典型用法代碼示例。如果您正苦於以下問題：Python box_predictor.RfcnBoxPredictor方法的具體用法？Python box_predictor.RfcnBoxPredictor怎麽用？Python box_predictor.RfcnBoxPredictor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.core.box_predictor的用法示例。
在下文中一共展示了box_predictor.RfcnBoxPredictor方法的4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_get_correct_box_encoding_and_class_prediction_shapes
# 需要導入模塊: from object_detection.core import box_predictor [as 別名]
# 或者: from object_detection.core.box_predictor import RfcnBoxPredictor [as 別名]
def test_get_correct_box_encoding_and_class_prediction_shapes(self):
image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32)
proposal_boxes = tf.random_normal([4, 2, 4], dtype=tf.float32)
rfcn_box_predictor = box_predictor.RfcnBoxPredictor(
is_training=False,
num_classes=2,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
num_spatial_bins=[3, 3],
depth=4,
crop_size=[12, 12],
box_code_size=4
)
box_predictions = rfcn_box_predictor.predict(
image_features, num_predictions_per_location=1, scope='BoxPredictor',
proposal_boxes=proposal_boxes)
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_shape) = sess.run(
[tf.shape(box_encodings),
tf.shape(class_predictions_with_background)])
self.assertAllEqual(box_encodings_shape, [8, 1, 2, 4])
self.assertAllEqual(class_predictions_shape, [8, 1, 3]) 示例2: test_get_correct_box_encoding_and_class_prediction_shapes
# 需要導入模塊: from object_detection.core import box_predictor [as 別名]
# 或者: from object_detection.core.box_predictor import RfcnBoxPredictor [as 別名]
def test_get_correct_box_encoding_and_class_prediction_shapes(self):
image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32)
proposal_boxes = tf.random_normal([4, 2, 4], dtype=tf.float32)
rfcn_box_predictor = box_predictor.RfcnBoxPredictor(
is_training=False,
num_classes=2,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
num_spatial_bins=[3, 3],
depth=4,
crop_size=[12, 12],
box_code_size=4
)
box_predictions = rfcn_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor',
proposal_boxes=proposal_boxes)
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
class_predictions_shape) = sess.run(
[tf.shape(box_encodings),
tf.shape(class_predictions_with_background)])
self.assertAllEqual(box_encodings_shape, [8, 1, 2, 4])
self.assertAllEqual(class_predictions_shape, [8, 1, 3]) 示例3: test_get_correct_box_encoding_and_class_prediction_shapes
# 需要導入模塊: from object_detection.core import box_predictor [as 別名]
# 或者: from object_detection.core.box_predictor import RfcnBoxPredictor [as 別名]
def test_get_correct_box_encoding_and_class_prediction_shapes(self):
image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32)
proposal_boxes = tf.random_normal([4, 2, 4], dtype=tf.float32)
rfcn_box_predictor = box_predictor.RfcnBoxPredictor(
is_training=False,
num_classes=2,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
num_spatial_bins=[3, 3],
depth=4,
crop_size=[12, 12],
box_code_size=4
)
box_predictions = rfcn_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor',
proposal_boxes=proposal_boxes)
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
class_predictions_shape) = sess.run(
[tf.shape(box_encodings),
tf.shape(class_predictions_with_background)])
self.assertAllEqual(box_encodings_shape, [8, 1, 2, 4])
self.assertAllEqual(class_predictions_shape, [8, 1, 3]) 示例4: test_get_correct_box_encoding_and_class_prediction_shapes
# 需要導入模塊: from object_detection.core import box_predictor [as 別名]
# 或者: from object_detection.core.box_predictor import RfcnBoxPredictor [as 別名]
def test_get_correct_box_encoding_and_class_prediction_shapes(self):
image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32)
proposal_boxes = tf.random_normal([4, 2, 4], dtype=tf.float32)
rfcn_box_predictor = box_predictor.RfcnBoxPredictor(
is_training=False,
num_classes=2,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
num_spatial_bins=[3, 3],
depth=4,
crop_size=[12, 12],
box_code_size=4
)
box_predictions = rfcn_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor',
proposal_boxes=proposal_boxes)
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_shape) = sess.run(
[tf.shape(box_encodings),
tf.shape(class_predictions_with_background)])
self.assertAllEqual(box_encodings_shape, [8, 1, 2, 4])
self.assertAllEqual(class_predictions_shape, [8, 1, 3])