mkdocs issues fixed

README.md

@@ -77,7 +77,7 @@ Commands:
  train General-purpose training script for multi-task...
 ```
 
-## Training Dataset:
+## Training Dataset
 For training, all image sets must be 512x512 and combined together in 3072x512 images (six images of size 512x512 stitched
 together horizontally).
 The data need to be arranged in the following order:

deepliif/options/processing_options.py

@@ -8,21 +8,14 @@ def create_base_parser():
 
  parser.add_argument('--input_dir', required=True, help='path to input images')
  parser.add_argument('--output_dir', required=False, type=str, default='', help='path to output images')
- parser.add_argument('--validation_ratio', required=False, type=float, default=0.2,
- help='the ratio of the number of the images in the validation set to the total number of images.')
- parser.add_argument('--input_orig_dir', required=False, type=str, default='',
- help='path to original whole slide images')
- parser.add_argument('--resize_size', required=False, default=None, help='resizing size of the whole slide image')
- parser.add_argument('--image_size', required=False, default=None, help='size of the original whole slide image')
+ parser.add_argument('--name', type=str, default='experiment_name',
+ help='name of the experiment. It decides where to store samples and models')
+ parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
  parser.add_argument('--tile_size', required=False, type=int, default=512, help='size of the tiles')
  parser.add_argument('--overlap_size', required=False, type=int, default=50,
  help='size of the overlapping area between tiles')
- parser.add_argument('--resize_self', required=False, type=bool, default=True,
- help='if want to resize the whole slide to a specific size')
  parser.add_argument('--gpu_ids', required=False, type=str, default='0',
  help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
- parser.add_argument('--post_process_seg_mask', action='store_true',
- help='if user wants the post-processing script post-processes the segmentation mask')
  parser.add_argument('--epoch_count', type=int, default=0,
  help='the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...')