本文整理汇总了Python中object_detection.builders.box_predictor_builder.build_weight_shared_convolutional_box_predictor方法的典型用法代码示例。如果您正苦于以下问题：Python box_predictor_builder.build_weight_shared_convolutional_box_predictor方法的具体用法？Python box_predictor_builder.build_weight_shared_convolutional_box_predictor怎么用？Python box_predictor_builder.build_weight_shared_convolutional_box_predictor使用的例子？那么, 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.builders.box_predictor_builder的用法示例。
在下文中一共展示了box_predictor_builder.build_weight_shared_convolutional_box_predictor方法的6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.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build_weight_shared_convolutional_box_predictor [as 别名]
def test_get_boxes_for_five_aspect_ratios_per_location(self):
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=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, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1]) 示例2: test_bias_predictions_to_background_with_sigmoid_score_conversion
# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build_weight_shared_convolutional_box_predictor [as 别名]
def test_bias_predictions_to_background_with_sigmoid_score_conversion(self):
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=True,
num_classes=2,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
class_prediction_bias_init=-4.6,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
class_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (tf.nn.sigmoid(class_predictions),)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
class_predictions = self.execute(graph_fn, [image_features])
self.assertAlmostEqual(np.mean(class_predictions), 0.01, places=3) 示例3: test_get_multi_class_predictions_for_five_aspect_ratios_per_location
# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build_weight_shared_convolutional_box_predictor [as 别名]
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
self):
num_classes_without_background = 6
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=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)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features])
self.assertAllEqual(box_encodings.shape, [4, 320, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 320, num_classes_without_background+1]) 示例4: test_get_multi_class_predictions_from_two_feature_maps
# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build_weight_shared_convolutional_box_predictor [as 别名]
def test_get_multi_class_predictions_from_two_feature_maps(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2],
num_predictions_per_location=[5, 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)
image_features1 = np.random.rand(4, 8, 8, 64).astype(np.float32)
image_features2 = np.random.rand(4, 8, 8, 64).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features1, image_features2])
self.assertAllEqual(box_encodings.shape, [4, 640, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 640, num_classes_without_background+1]) 示例5: test_get_multi_class_predictions_from_feature_maps_of_different_depth
# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build_weight_shared_convolutional_box_predictor [as 别名]
def test_get_multi_class_predictions_from_feature_maps_of_different_depth(
self):
num_classes_without_background = 6
def graph_fn(image_features1, image_features2, image_features3):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor.predict(
[image_features1, image_features2, image_features3],
num_predictions_per_location=[5, 5, 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)
image_features1 = np.random.rand(4, 8, 8, 64).astype(np.float32)
image_features2 = np.random.rand(4, 8, 8, 64).astype(np.float32)
image_features3 = np.random.rand(4, 8, 8, 32).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features1, image_features2, image_features3])
self.assertAllEqual(box_encodings.shape, [4, 960, 4])
self.assertAllEqual(class_predictions_with_background.shape,
[4, 960, num_classes_without_background+1]) 示例6: test_get_predictions_with_feature_maps_of_dynamic_shape
# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build_weight_shared_convolutional_box_predictor [as 别名]
def test_get_predictions_with_feature_maps_of_dynamic_shape(
self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=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)
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, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1])