本文整理汇总了Python中object_detection.core.box_predictor.ConvolutionalBoxPredictor方法的典型用法代码示例。如果您正苦于以下问题：Python box_predictor.ConvolutionalBoxPredictor方法的具体用法？Python box_predictor.ConvolutionalBoxPredictor怎么用？Python box_predictor.ConvolutionalBoxPredictor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.ConvolutionalBoxPredictor方法的13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_for_five_aspect_ratios_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_boxes_for_five_aspect_ratios_per_location(self):
image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32)
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
image_features, num_predictions_per_location=5, scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
objectness_predictions = 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,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)])
self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4])
self.assertAllEqual(objectness_predictions_shape, [4, 320, 1]) 示例2: test_get_boxes_for_one_aspect_ratio_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_boxes_for_one_aspect_ratio_per_location(self):
image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32)
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
image_features, num_predictions_per_location=1, scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
objectness_predictions = 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,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)])
self.assertAllEqual(box_encodings_shape, [4, 64, 1, 4])
self.assertAllEqual(objectness_predictions_shape, [4, 64, 1]) 示例3: test_get_multi_class_predictions_for_five_aspect_ratios_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
self):
num_classes_without_background = 6
image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32)
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
image_features,
num_predictions_per_location=5,
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, [4, 320, 1, 4])
self.assertAllEqual(class_predictions_with_background_shape,
[4, 320, num_classes_without_background+1]) 示例4: test_get_boxes_for_five_aspect_ratios_per_location_fully_convolutional
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_boxes_for_five_aspect_ratios_per_location_fully_convolutional(
self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
image_features, num_predictions_per_location=5, scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
objectness_predictions = box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND]
init_op = tf.global_variables_initializer()
resolution = 32
expected_num_anchors = resolution*resolution*5
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)],
feed_dict={image_features:
np.random.rand(4, resolution, resolution, 64)})
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1]) 示例5: test_get_boxes_for_five_aspect_ratios_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_boxes_for_five_aspect_ratios_per_location(self):
def graph_fn(image_features):
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, objectness_predictions)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1]) 示例6: test_get_boxes_for_one_aspect_ratio_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_boxes_for_one_aspect_ratio_per_location(self):
def graph_fn(image_features):
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (box_encodings, objectness_predictions)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 64, 1, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 64, 1]) 示例7: test_get_multi_class_predictions_for_five_aspect_ratios_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
self):
num_classes_without_background = 6
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
def graph_fn(image_features):
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
[image_features],
num_predictions_per_location=[5],
scope='BoxPredictor')
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)
return (box_encodings, class_predictions_with_background)
(box_encodings,
class_predictions_with_background) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 320, num_classes_without_background+1]) 示例8: test_get_boxes_for_five_aspect_ratios_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_boxes_for_five_aspect_ratios_per_location(self):
def graph_fn(image_features):
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return (box_encodings, objectness_predictions)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1]) 示例9: test_get_boxes_for_one_aspect_ratio_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_boxes_for_one_aspect_ratio_per_location(self):
def graph_fn(image_features):
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (box_encodings, objectness_predictions)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 64, 1, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 64, 1]) 示例10: test_get_multi_class_predictions_for_five_aspect_ratios_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
self):
num_classes_without_background = 6
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
def graph_fn(image_features):
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
[image_features],
num_predictions_per_location=[5],
scope='BoxPredictor')
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)
return (box_encodings, class_predictions_with_background)
(box_encodings,
class_predictions_with_background) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 320, num_classes_without_background+1]) 示例11: test_get_boxes_for_five_aspect_ratios_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_boxes_for_five_aspect_ratios_per_location(self):
def graph_fn(image_features):
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
objectness_predictions = box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND]
return (box_encodings, objectness_predictions)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1]) 示例12: test_get_boxes_for_one_aspect_ratio_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_boxes_for_one_aspect_ratio_per_location(self):
def graph_fn(image_features):
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
objectness_predictions = box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND]
return (box_encodings, objectness_predictions)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, objectness_predictions) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 64, 1, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 64, 1]) 示例13: test_get_multi_class_predictions_for_five_aspect_ratios_per_location
# 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 ConvolutionalBoxPredictor [as 别名]
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
self):
num_classes_without_background = 6
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
def graph_fn(image_features):
conv_box_predictor = box_predictor.ConvolutionalBoxPredictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(),
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
)
box_predictions = conv_box_predictor.predict(
[image_features],
num_predictions_per_location=[5],
scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
class_predictions_with_background = box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND]
return (box_encodings, class_predictions_with_background)
(box_encodings,
class_predictions_with_background) = self.execute(graph_fn,
[image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 320, num_classes_without_background+1])