Python mobilenet_v1.mobilenet_v1_arg_scope方法代码示例

# 需要导入模块: from nets import mobilenet_v1 [as 别名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1_arg_scope [as 别名]
def build_model():
  """Build the mobilenet_v1 model for evaluation.

  Returns:
    g: graph with rewrites after insertion of quantization ops and batch norm
    folding.
    eval_ops: eval ops for inference.
    variables_to_restore: List of variables to restore from checkpoint.
  """
  g = tf.Graph()
  with g.as_default():
    inputs, labels = imagenet_input(is_training=False)

    scope = mobilenet_v1.mobilenet_v1_arg_scope(
        is_training=False, weight_decay=0.0)
    with slim.arg_scope(scope):
      logits, _ = mobilenet_v1.mobilenet_v1(
          inputs,
          is_training=False,
          depth_multiplier=FLAGS.depth_multiplier,
          num_classes=FLAGS.num_classes)

    if FLAGS.quantize:
      tf.contrib.quantize.create_eval_graph()

    eval_ops = metrics(logits, labels)

  return g, eval_ops 

# 需要导入模块: from nets import mobilenet_v1 [as 别名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1_arg_scope [as 别名]
def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
    sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=None)
    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
        slim.batch_norm)]) 

# 需要导入模块: from nets import mobilenet_v1 [as 别名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1_arg_scope [as 别名]
def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
    sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=True)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=False)
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
    sc = mobilenet_v1.mobilenet_v1_arg_scope()
    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)]) 

# 需要导入模块: from nets import mobilenet_v1 [as 别名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1_arg_scope [as 别名]
def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    with tf.variable_scope('MobilenetV1',
                           reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v1.mobilenet_v1_arg_scope(
              is_training=None, regularize_depthwise=True)):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams
              else context_manager.IdentityContextManager()):
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
              final_endpoint='Conv2d_13_pointwise',
              min_depth=self._min_depth,
              depth_multiplier=self._depth_multiplier,
              use_explicit_padding=self._use_explicit_padding,
              scope=scope)
      with slim.arg_scope(self._conv_hyperparams_fn()):
        feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
            base_feature_map_depth=0,
            num_layers=6,
            image_features={
                'image_features': image_features['Conv2d_11_pointwise']
            })
    return feature_maps.values() 

# 需要导入模块: from nets import mobilenet_v1 [as 别名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1_arg_scope [as 别名]
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
    """Extracts second stage box classifier features.

    Args:
      proposal_feature_maps: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
        representing the feature map cropped to each proposal.
      scope: A scope name (unused).

    Returns:
      proposal_classifier_features: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, height, width, depth]
        representing box classifier features for each proposal.
    """
    net = proposal_feature_maps

    conv_depth = 1024
    if self._skip_last_stride:
      conv_depth_ratio = float(self._conv_depth_ratio_in_percentage) / 100.0
      conv_depth = int(float(conv_depth) * conv_depth_ratio)

    depth = lambda d: max(int(d * 1.0), 16)
    with tf.variable_scope('MobilenetV1', reuse=self._reuse_weights):
      with slim.arg_scope(
          mobilenet_v1.mobilenet_v1_arg_scope(
              is_training=self._train_batch_norm,
              weight_decay=self._weight_decay)):
        with slim.arg_scope(
            [slim.conv2d, slim.separable_conv2d], padding='SAME'):
          net = slim.separable_conv2d(
              net,
              depth(conv_depth), [3, 3],
              depth_multiplier=1,
              stride=2,
              scope='Conv2d_12_pointwise')
          return slim.separable_conv2d(
              net,
              depth(conv_depth), [3, 3],
              depth_multiplier=1,
              stride=1,
              scope='Conv2d_13_pointwise') 

# 需要导入模块: from nets import mobilenet_v1 [as 别名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1_arg_scope [as 别名]
def head_net(self, blocks, is_training, trainable=True):

        normal_initializer = tf.truncated_normal_initializer(0, 0.01)
        msra_initializer = tf.contrib.layers.variance_scaling_initializer()
        xavier_initializer = tf.contrib.layers.xavier_initializer()

        with slim.arg_scope(mobilenet_v1_arg_scope(is_training=is_training)):

            out = slim.conv2d_transpose(blocks, 256, [4, 4], stride=2,
                trainable=trainable, weights_initializer=normal_initializer,
                padding='SAME', activation_fn=tf.nn.relu,
                scope='up1')
            out = slim.conv2d_transpose(out, 256, [4, 4], stride=2,
                trainable=trainable, weights_initializer=normal_initializer,
                padding='SAME', activation_fn=tf.nn.relu,
                scope='up2')
            out = slim.conv2d_transpose(out, 256, [4, 4], stride=2,
                trainable=trainable, weights_initializer=normal_initializer,
                padding='SAME', activation_fn=tf.nn.relu,
                scope='up3')

            out = slim.conv2d(out, cfg.nr_skeleton, [1, 1],
                    trainable=trainable, weights_initializer=msra_initializer,
                    padding='SAME', normalizer_fn=None, activation_fn=None,
                    scope='out')

        return out