          """ Keep a queue filled to 8x Batch Size """
          logger.debug("Queue batches: (image_count: %s, batchsize: %s, side: '%s', do_shuffle: %s, "
                       "is_timelapse: %s)", len(images), batchsize, side, do_shuffle, is_timelapse)
          self.batchsize = batchsize
          queue_in, queue_out = self.make_queues(side, is_timelapse)
          training_size = self.training_opts.get("training_size", 256)
          batch_shape = list((
              (batchsize, training_size, training_size, 3),  # sample images
              (batchsize, self.model_input_size, self.model_input_size, 3),
              (batchsize, self.model_output_size, self.model_output_size, 3)))
 
</s> remove                      "is_timelapse: %s)", len(images), batchsize, side, do_shuffle, is_timelapse)
 </s> add                      "is_preview, %s, is_timelapse: %s)", len(images), batchsize, side, do_shuffle,
                     is_preview, is_timelapse) </s> remove     def minibatch_ab(self, images, batchsize, side, do_shuffle=True, is_timelapse=False):
 </s> add     def minibatch_ab(self, images, batchsize, side,
                     do_shuffle=True, is_preview=False, is_timelapse=False): </s> remove         logger.debug("Loading batch: (image_count: %s, side: '%s', is_timelapse: %s, "
                     "do_shuffle: %s)", len(images), side, is_timelapse, do_shuffle)
 </s> add         logger.debug("Loading batch: (image_count: %s, side: '%s', is_display: %s, "
                     "do_shuffle: %s)", len(images), side, is_display, do_shuffle) </s> remove     def load_batches(self, mem_gen, images, side, is_timelapse,
 </s> add     def load_batches(self, mem_gen, images, side, is_display, </s> remove         logger.debug("Finished batching: (epoch: %s, side: '%s', is_timelapse: %s)",
                     epoch, side, is_timelapse)
 </s> add         logger.debug("Finished batching: (epoch: %s, side: '%s', is_display: %s)",
                     epoch, side, is_display)
        is_display = is_preview or is_timelapse
        queue_in, queue_out = self.make_queues(side, is_preview, is_timelapse)
          """ Keep a queue filled to 8x Batch Size """
          logger.debug("Queue batches: (image_count: %s, batchsize: %s, side: '%s', do_shuffle: %s, "
                       "is_timelapse: %s)", len(images), batchsize, side, do_shuffle, is_timelapse)
          self.batchsize = batchsize
        is_display = is_preview or is_timelapse
        queue_in, queue_out = self.make_queues(side, is_preview, is_timelapse)
          training_size = self.training_opts.get("training_size", 256)
          batch_shape = list((
              (batchsize, training_size, training_size, 3),  # sample images
              (batchsize, self.model_input_size, self.model_input_size, 3),
              (batchsize, self.model_output_size, self.model_output_size, 3)))
keep keep keep keep replace keep keep keep keep keep
 Color Augmentation method (#752)

* Color Augmentation: Implement testing code

* Tweak Contrast and Lighting augmentation amounts

* Prevent color augmentation from entering timelapse

* Remove all augmentations from preview images 
lib/training_data.py