text: _base_ = [
      '../_base_/models/cyclegan_lsgan_resnet.py',
      '../_base_/datasets/unpaired_imgs_256x256.py',
      '../_base_/default_runtime.py'
  ]
  test_cfg = dict(test_direction='b2a', show_input=False)
  model = dict(id_loss=dict(type='L1Loss', loss_weight=0, reduction='mean'))
  dataroot = './data/summer2winter_yosemite'
  data = dict(
      train=dict(dataroot=dataroot),
      val=dict(dataroot=dataroot),
      test=dict(dataroot=dataroot))
  
  optimizer = dict(
      generators=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)),
      discriminators=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)))
  lr_config = None
  checkpoint_config = dict(interval=10000, save_optimizer=True, by_epoch=False)
  custom_hooks = [
      dict(
          type='VisualizationHook',
          output_dir='training_samples',
          res_name_list=['fake_a'],
          interval=5000)
  ]
  
  runner = None
  use_ddp_wrapper = True
  total_iters = 250000
  workflow = [('train', 1)]
  exp_name = 'cyclegan_summer2winter_id0'
  work_dir = f'./work_dirs/experiments/{exp_name}'
  # testA: 309, testB:238
  metrics = dict(
      FID=dict(type='FID', num_images=309, image_shape=(3, 256, 256)),
      IS=dict(type='IS', num_images=309, image_shape=(3, 256, 256)))
  