Python object_detection.models.feature_map_generators.KerasMultiResolutionFeatureMaps() Examples

The following are 14 code examples of object_detection.models.feature_map_generators.KerasMultiResolutionFeatureMaps(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.models.feature_map_generators , or try the search function .
Example #1
Source File: feature_map_generators_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
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
Example #2
Source File: feature_map_generators_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
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
Example #3
Source File: feature_map_generators_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
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
Example #4
Source File: feature_map_generators_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
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
Example #5
Source File: feature_map_generators_test.py    From models with Apache License 2.0 6 votes vote down vote up 
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
Example #6
Source File: feature_map_generators_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
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
Example #7
Source File: ssd_mobilenet_v2_keras_feature_extractor.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
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
Example #8
Source File: ssd_mobilenet_v1_keras_feature_extractor.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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
Example #9
Source File: ssd_mobilenet_v2_keras_feature_extractor.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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
Example #10
Source File: ssd_mobilenet_v2_keras_feature_extractor.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
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
Example #11
Source File: ssd_mobilenet_v2_keras_feature_extractor.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
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
Example #12
Source File: ssd_mobilenet_v1_keras_feature_extractor.py    From models with Apache License 2.0 5 votes vote down vote up 
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
Example #13
Source File: ssd_mobilenet_v2_keras_feature_extractor.py    From models with Apache License 2.0 5 votes vote down vote up 
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
Example #14
Source File: ssd_mobilenet_v2_keras_feature_extractor.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
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