text: 
  import os
  import albumentations as A
  import cv2
  import numpy as np
  
  abs_path = os.path.dirname(__file__)
  
  args = {
      'model_path':'../models/',
      'data_path':'/ssd/kaggle_landmark/input/',
      'data_path_2019':'/ssd/kaggle_landmark/2019/',
      'valid_csv_fn':'recognition_solution_v2.1.csv',
      'train_csv_fn':'train.csv',
  
      'filter_warnings':True,
      'logger': 'neptune',
      'num_sanity_val_steps': 0,
  
      'gpus':'0,1',
      'distributed_backend': "ddp",
      'sync_batchnorm': True,
  
      'gradient_accumulation_steps': 4,
      'precision':16,
  
      'seed':1337,
      
      'drop_last_n': 0,
      
  
      'save_weights_only': False,
      'resume_from_checkpoint': None,
  
      'p_trainable': True,
  
      'normalization':'imagenet',
  
      'backbone':'tf_efficientnet_b3_ns',
      'embedding_size': 512,
      'pool': "gem",
      'arcface_s': 45,
      'arcface_m': 0.35,
  
      'head': 'arc_margin',
      'neck': 'option-D',
  
      'loss': 'arcface',
      'crit': "bce",
     # 'focal_loss_gamma': 2,
      'class_weights': "log",
      'class_weights_norm': "batch",
      
      'optimizer': "sgd",
      'lr': 0.05,
      'weight_decay': 1e-4,
      'batch_size': 32,
  
      'max_epochs': 10,
      
      'scheduler': {"method": "cosine", "warmup_epochs": 1},
      
      'pretrained_weights': None,
  
      'n_classes':81313,
      'data_frac':1.,
          
      'num_workers': 8,
      
      'crop_size': 448,
  
      'neptune_project':'xx/kaggle-landmark',
  }
  
  args['tr_aug'] = A.Compose([ A.LongestMaxSize(512,p=1),
                              A.PadIfNeeded(512, 512, border_mode=cv2.BORDER_CONSTANT,p=1),
                              A.RandomCrop(always_apply=False, p=1.0, height=args['crop_size'], width=args['crop_size']), 
                              A.HorizontalFlip(always_apply=False, p=0.5), 
                             ],
                              p=1.0
                              )
  
  args['val_aug'] = A.Compose([ A.LongestMaxSize(512,p=1),
                               A.PadIfNeeded(512, 512, border_mode=cv2.BORDER_CONSTANT,p=1),
                              A.CenterCrop(always_apply=False, p=1.0, height=args['crop_size'], width=args['crop_size']), 
                              ], 
                              p=1.0
                              )
  
  