Python mobilenet_v2.mobilenet_base方法代码示例

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet_base [as 别名]
def testWithOutputStride8(self):
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=8,
        scope='MobilenetV2')
    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet_base [as 别名]
def testMobilenetBase(self):
    tf.reset_default_graph()
    # Verifies that mobilenet_base returns pre-pooling layer.
    with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32):
      net, _ = mobilenet_v2.mobilenet_base(
          tf.placeholder(tf.float32, (10, 224, 224, 16)),
          conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1)
      self.assertEqual(net.get_shape().as_list(), [10, 7, 7, 128]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet_base [as 别名]
def testWithOutputStride16(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=16)
    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet_base [as 别名]
def testWithOutputStride8AndExplicitPadding(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=8,
        use_explicit_padding=True,
        scope='MobilenetV2')
    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet_base [as 别名]
def testWithOutputStride16AndExplicitPadding(self):
    tf.reset_default_graph()
    out, _ = mobilenet.mobilenet_base(
        tf.placeholder(tf.float32, (10, 224, 224, 16)),
        conv_defs=mobilenet_v2.V2_DEF,
        output_stride=16,
        use_explicit_padding=True)
    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet_base [as 别名]
def _image_to_head(self, is_training, reuse=None):
        with slim.arg_scope(mobilenet_v2.training_scope(is_training=is_training)):
            net, endpoints = mobilenet_v2.mobilenet_base(self._image, conv_defs=CTPN_DEF)

        self.variables_to_restore = slim.get_variables_to_restore()

        self._act_summaries.append(net)
        self._layers['head'] = net

        return net 

# 需要导入模块: from nets.mobilenet import mobilenet_v2 [as 别名]
# 或者: from nets.mobilenet.mobilenet_v2 import mobilenet_base [as 别名]
def _mobilenet_v2(net,
                  depth_multiplier,
                  output_stride,
                  reuse=None,
                  scope=None,
                  final_endpoint=None):
    """Auxiliary function to add support for 'reuse' to mobilenet_v2.

    Args:
      net: Input tensor of shape [batch_size, height, width, channels].
      depth_multiplier: Float multiplier for the depth (number of channels)
        for all convolution ops. The value must be greater than zero. Typical
        usage will be to set this value in (0, 1) to reduce the number of
        parameters or computation cost of the model.
      output_stride: An integer that specifies the requested ratio of input to
        output spatial resolution. If not None, then we invoke atrous convolution
        if necessary to prevent the network from reducing the spatial resolution
        of the activation maps. Allowed values are 8 (accurate fully convolutional
        mode), 16 (fast fully convolutional mode), 32 (classification mode).
      reuse: Reuse model variables.
      scope: Optional variable scope.
      final_endpoint: The endpoint to construct the network up to.

    Returns:
      Features extracted by MobileNetv2.
    """
    with tf.variable_scope(
            scope, 'MobilenetV2', [net], reuse=reuse) as scope:
        return mobilenet_v2.mobilenet_base(
            net,
            conv_defs=mobilenet_v2.V2_DEF,
            depth_multiplier=depth_multiplier,
            min_depth=8 if depth_multiplier == 1.0 else 1,
            divisible_by=8 if depth_multiplier == 1.0 else 1,
            final_endpoint=final_endpoint or _MOBILENET_V2_FINAL_ENDPOINT,
            output_stride=output_stride,
            scope=scope)


# A map from network name to network function.