本文整理汇总了Python中object_detection.models.feature_map_generators.KerasMultiResolutionFeatureMaps方法的典型用法代码示例。如果您正苦于以下问题：Python feature_map_generators.KerasMultiResolutionFeatureMaps方法的具体用法？Python feature_map_generators.KerasMultiResolutionFeatureMaps怎么用？Python feature_map_generators.KerasMultiResolutionFeatureMaps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.models.feature_map_generators的用法示例。
在下文中一共展示了feature_map_generators.KerasMultiResolutionFeatureMaps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: _build_feature_map_generator
# 需要导入模块: from object_detection.models import feature_map_generators [as 别名]
# 或者: from object_detection.models.feature_map_generators import KerasMultiResolutionFeatureMaps [as 别名]
def _build_feature_map_generator(self, feature_map_layout, use_keras,
pool_residual=False):
if use_keras:
return feature_map_generators.KerasMultiResolutionFeatureMaps(
feature_map_layout=feature_map_layout,
depth_multiplier=1,
min_depth=32,
insert_1x1_conv=True,
freeze_batchnorm=False,
is_training=True,
conv_hyperparams=self._build_conv_hyperparams(),
name='FeatureMaps'
)
else:
def feature_map_generator(image_features):
return feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=feature_map_layout,
depth_multiplier=1,
min_depth=32,
insert_1x1_conv=True,
image_features=image_features,
pool_residual=pool_residual)
return feature_map_generator 示例2: _build_feature_map_generator
# 需要导入模块: from object_detection.models import feature_map_generators [as 别名]
# 或者: from object_detection.models.feature_map_generators import KerasMultiResolutionFeatureMaps [as 别名]
def _build_feature_map_generator(self, feature_map_layout,
pool_residual=False):
if tf_version.is_tf2():
return feature_map_generators.KerasMultiResolutionFeatureMaps(
feature_map_layout=feature_map_layout,
depth_multiplier=1,
min_depth=32,
insert_1x1_conv=True,
freeze_batchnorm=False,
is_training=True,
conv_hyperparams=self._build_conv_hyperparams(),
name='FeatureMaps'
)
else:
def feature_map_generator(image_features):
return feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=feature_map_layout,
depth_multiplier=1,
min_depth=32,
insert_1x1_conv=True,
image_features=image_features,
pool_residual=pool_residual)
return feature_map_generator 示例3: build
# 需要导入模块: from object_detection.models import feature_map_generators [as 别名]
# 或者: from object_detection.models.feature_map_generators import KerasMultiResolutionFeatureMaps [as 别名]
def build(self, input_shape):
full_mobilenet_v2 = mobilenet_v2.mobilenet_v2(
batchnorm_training=(self._is_training and not self._freeze_batchnorm),
conv_hyperparams=(self._conv_hyperparams
if self._override_base_feature_extractor_hyperparams
else None),
weights=None,
use_explicit_padding=self._use_explicit_padding,
alpha=self._depth_multiplier,
min_depth=self._min_depth,
include_top=False)
conv2d_11_pointwise = full_mobilenet_v2.get_layer(
name='block_13_expand_relu').output
conv2d_13_pointwise = full_mobilenet_v2.get_layer(name='out_relu').output
self.mobilenet_v2 = tf.keras.Model(
inputs=full_mobilenet_v2.inputs,
outputs=[conv2d_11_pointwise, conv2d_13_pointwise])
self.feature_map_generator = (
feature_map_generators.KerasMultiResolutionFeatureMaps(
feature_map_layout=self._feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
name='FeatureMaps'))
self.built = True 开发者ID:ahmetozlu，项目名称:vehicle_counting_tensorflow，代码行数:30，代码来源:ssd_mobilenet_v2_keras_feature_extractor.py
示例4: build
# 需要导入模块: from object_detection.models import feature_map_generators [as 别名]
# 或者: from object_detection.models.feature_map_generators import KerasMultiResolutionFeatureMaps [as 别名]
def build(self, input_shape):
full_mobilenet_v1 = mobilenet_v1.mobilenet_v1(
batchnorm_training=(self._is_training and not self._freeze_batchnorm),
conv_hyperparams=(self._conv_hyperparams
if self._override_base_feature_extractor_hyperparams
else None),
weights=None,
use_explicit_padding=self._use_explicit_padding,
alpha=self._depth_multiplier,
min_depth=self._min_depth,
include_top=False)
conv2d_11_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_11_relu').output
conv2d_13_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_13_relu').output
self._mobilenet_v1 = tf.keras.Model(
inputs=full_mobilenet_v1.inputs,
outputs=[conv2d_11_pointwise, conv2d_13_pointwise])
self._feature_map_generator = (
feature_map_generators.KerasMultiResolutionFeatureMaps(
feature_map_layout=self._feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
name='FeatureMaps'))
self.built = True 开发者ID:ShivangShekhar，项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代码行数:31，代码来源:ssd_mobilenet_v1_keras_feature_extractor.py
示例5: build
# 需要导入模块: from object_detection.models import feature_map_generators [as 别名]
# 或者: from object_detection.models.feature_map_generators import KerasMultiResolutionFeatureMaps [as 别名]
def build(self, input_shape):
full_mobilenet_v1 = mobilenet_v1.mobilenet_v1(
batchnorm_training=(self._is_training and not self._freeze_batchnorm),
conv_hyperparams=(self._conv_hyperparams
if self._override_base_feature_extractor_hyperparams
else None),
weights=None,
use_explicit_padding=self._use_explicit_padding,
alpha=self._depth_multiplier,
min_depth=self._min_depth,
include_top=False)
conv2d_11_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_11_relu').output
conv2d_13_pointwise = full_mobilenet_v1.get_layer(
name='conv_pw_13_relu').output
self.classification_backbone = tf.keras.Model(
inputs=full_mobilenet_v1.inputs,
outputs=[conv2d_11_pointwise, conv2d_13_pointwise])
self._feature_map_generator = (
feature_map_generators.KerasMultiResolutionFeatureMaps(
feature_map_layout=self._feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
name='FeatureMaps'))
self.built = True 示例6: build
# 需要导入模块: from object_detection.models import feature_map_generators [as 别名]
# 或者: from object_detection.models.feature_map_generators import KerasMultiResolutionFeatureMaps [as 别名]
def build(self, input_shape):
full_mobilenet_v2 = mobilenet_v2.mobilenet_v2(
batchnorm_training=(self._is_training and not self._freeze_batchnorm),
conv_hyperparams=(self._conv_hyperparams
if self._override_base_feature_extractor_hyperparams
else None),
weights=None,
use_explicit_padding=self._use_explicit_padding,
alpha=self._depth_multiplier,
min_depth=self._min_depth,
include_top=False)
conv2d_11_pointwise = full_mobilenet_v2.get_layer(
name='block_13_expand_relu').output
conv2d_13_pointwise = full_mobilenet_v2.get_layer(name='out_relu').output
self.classification_backbone = tf.keras.Model(
inputs=full_mobilenet_v2.inputs,
outputs=[conv2d_11_pointwise, conv2d_13_pointwise])
self.feature_map_generator = (
feature_map_generators.KerasMultiResolutionFeatureMaps(
feature_map_layout=self._feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
name='FeatureMaps'))
self.built = True