:mod:`blueoil.networks.classification.lm_resnet`
================================================

.. py:module:: blueoil.networks.classification.lm_resnet


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   blueoil.networks.classification.lm_resnet.LmResnet
   blueoil.networks.classification.lm_resnet.LmResnetQuantize



.. py:class:: LmResnet(*args, **kwargs)

   Bases: :class:`blueoil.networks.classification.base.Base`

   Residual network (ResNet) of 18-layers for classification

   This ResNet-18 is modified from the ImageNet version of ResNet-18 of the original paper
           Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385)

   - The first layer is 3x3 convolution layer with stride 1 instead of 7x7 conv with stride 2,
     like the CIFAR-10 version of ResNet-18 in the paper.
   - The 3x3 max pooling with stride 2 is not used in this architecture.
   - In each residual block, batch normalization (BN) is after the add, to be specific, a
     pre-activation variant of residual block is used.
   - Utilizing Space-to-Depth operator for each the transition layer, convolution op with
     strides of 2 is replaced with space-to-depth.
   - Note currently this ResNet-18 only supports NHWC data format.

   .. method:: _batch_norm(inputs, training)
      :staticmethod:


   .. method:: _conv2d_fix_padding(inputs, filters, kernel_size, strides)
      :staticmethod:

      Convolution layer deals with stride of 2


   .. method:: basicblock(self, x, out_ch, strides, training)

      Basic building block of single residual function


   .. method:: resnet_group(self, x, out_ch, count, strides, training, name)


   .. method:: base(self, images, is_training)

      Base network.

      :param images: Input images.
      :param is_training: A flag for if it is training or not.

      :returns: Inference result.
      :rtype: tf.Tensor



.. py:class:: LmResnetQuantize(activation_quantizer=None, activation_quantizer_kwargs={}, weight_quantizer=None, weight_quantizer_kwargs={}, *args, **kwargs)

   Bases: :class:`blueoil.networks.classification.lm_resnet.LmResnet`

   Residual network (ResNet) of 18-layers for classification

   This ResNet-18 is modified from the ImageNet version of ResNet-18 of the original paper
           Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385)

   - The first layer is 3x3 convolution layer with stride 1 instead of 7x7 conv with stride 2,
     like the CIFAR-10 version of ResNet-18 in the paper.
   - The 3x3 max pooling with stride 2 is not used in this architecture.
   - In each residual block, batch normalization (BN) is after the add, to be specific, a
     pre-activation variant of residual block is used.
   - Utilizing Space-to-Depth operator for each the transition layer, convolution op with
     strides of 2 is replaced with space-to-depth.
   - Note currently this ResNet-18 only supports NHWC data format.

   .. attribute:: version
      :annotation: = 1.0

      

   .. method:: _quantized_variable_getter(getter, name, weight_quantization=None, *args, **kwargs)
      :staticmethod:

      Get the quantized variables.
      Use if to choose or skip the target should be quantized.
      :param getter: Default from tensorflow.
      :param name: Default from tensorflow.
      :param weight_quantization: Callable object which quantize variable.
      :param args: Args.
      :param kwargs: Kwargs.