              var_x_sr = UpscaleBlock(self._filters,
                                      kernel_size=self._kernel_size,
                                      padding=self._padding,
                                      scale_factor=self._scale_factor,
                                      res_block_follows=self._res_block_follows,
                                      **self._kwargs)(var_x)
          if self._fast or (not self._fast and self._filters > 0):
              var_x2 = Conv2D(self._filters, 3,
                              padding=self._padding,
                              name="{}_conv2d".format(self._name),
 
</s> remove                                       res_block_follows=self._res_block_follows,
 </s> add                                       activation=self._activation, </s> remove         var_x = Conv2D(self._filters,
                       self._kernel_size,
                       strides=self._strides,
                       padding=self._padding,
                       name="{}_conv2d".format(self._name),
                       **self._kwargs)(inputs)
        if self._use_instance_norm:
 </s> add         conv = DepthwiseConv2D if self._use_depthwise else Conv2D
        var_x = conv(*self._args,
                     strides=self._strides,
                     padding=self._padding,
                     name="{}_{}conv2d".format(self._name, "dw" if self._use_depthwise else ""),
                     **self._kwargs)(inputs)
        # normalization
        if self._normalization == "instance": </s> remove                                          kernel_size=self._kernel_size,
 </s> add                                          kernel_size=self._args[-1], </s> remove                             use_instance_norm=self._use_instance_norm,
                            res_block_follows=self._res_block_follows,
 </s> add                             normalization=self._normalization,
                            activation=self._activation, </s> remove                  res_block_follows=False, sr_ratio=0.5, scale_factor=2, fast=False, **kwargs):
 </s> add                  activation="leakyrelu", sr_ratio=0.5, scale_factor=2, fast=False, **kwargs):
                                    activation=self._activation,
              var_x_sr = UpscaleBlock(self._filters,
                                      kernel_size=self._kernel_size,
                                      padding=self._padding,
                                      scale_factor=self._scale_factor,
                                    activation=self._activation,
                                      **self._kwargs)(var_x)
          if self._fast or (not self._fast and self._filters > 0):
              var_x2 = Conv2D(self._filters, 3,
                              padding=self._padding,
                              name="{}_conv2d".format(self._name),
keep keep keep keep replace keep keep keep keep keep
 lib.model - Maintenance
  - Add Depthwise option to Conv Block
  - Add Swish Activation function
  - remove res_block_follows and add_instance_norm_args
  - Add explicit normalization and activation args
  - Add K.resize_images layer
plugins.train.model - Inference creation bugfix 
lib/model/nn_blocks.py