Disable refs and centers altogether in single_image_dataset

I suspect that this might be a cause of failures on parallel datasets.
Plus it is unnecessary computation.

codes/data/chunk_with_reference.py

@@ -4,11 +4,15 @@ import torch
 import numpy as np
 
 # Iterable that reads all the images in a directory that contains a reference image, tile images and center coordinates.
+from utils.util import opt_get
+
+
 class ChunkWithReference:
     def __init__(self, opt, path):
         self.path = path.path
         self.tiles, _ = util.get_image_paths('img', self.path)
-        self.strict = opt['strict'] if 'strict' in opt.keys() else True
+        self.need_metadata = opt_get(opt, ['strict'], False) or opt_get(opt, ['needs_metadata'], False)
+        self.need_ref = opt_get(opt, ['need_ref'], False)
         if 'ignore_first' in opt.keys():
             self.tiles = self.tiles[opt['ignore_first']:]
 
@@ -21,18 +25,19 @@ class ChunkWithReference:
 
     def __getitem__(self, item):
         tile = self.read_image_or_get_zero(self.tiles[item])
-        if osp.exists(osp.join(self.path, "ref.jpg")):
+        if self.need_ref and osp.exists(osp.join(self.path, "ref.jpg")):
             tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
-            centers = torch.load(osp.join(self.path, "centers.pt"))
             ref = self.read_image_or_get_zero(osp.join(self.path, "ref.jpg"))
-            if tile_id in centers.keys():
+            if self.need_metadata:
-                center, tile_width = centers[tile_id]
+                centers = torch.load(osp.join(self.path, "centers.pt"))
-            elif self.strict:
+                if tile_id in centers.keys():
-                print("Could not find the given tile id in the accompanying centers.pt. This generally means that "
+                    center, tile_width = centers[tile_id]
-                      "centers.pt was overwritten at some point e.g. by duplicate data. If you don't care about tile "
+                else:
-                      "centers, consider passing strict=false to the dataset options. (Note: you must re-build your"
+                    print("Could not find the given tile id in the accompanying centers.pt. This generally means that "
-                      "caches for this setting change to take effect.)")
+                          "centers.pt was overwritten at some point e.g. by duplicate data. If you don't care about tile "
-                raise FileNotFoundError(tile_id, self.tiles[item])
+                          "centers, consider passing strict=false to the dataset options. (Note: you must re-build your"
+                          "caches for this setting change to take effect.)")
+                    raise FileNotFoundError(tile_id, self.tiles[item])
             else:
                 center = torch.tensor([128, 128], dtype=torch.long)
                 tile_width = 256

codes/data/image_corruptor.py

@@ -119,7 +119,7 @@ class ImageCorruptor:
                     raise NotImplementedError("specified jpeg corruption doesn't exist")
                 # JPEG compression
                 qf = (rand_int % range + lo)
-                # cv2's jpeg compression is "odd". It introduces artifacts. Use PIL instead.
+                # Use PIL to perform a mock compression to a data buffer, then swap back to cv2.
                 img = (img * 255).astype(np.uint8)
                 img = Image.fromarray(img)
                 buffer = BytesIO()

codes/data/single_image_dataset.py

@@ -50,7 +50,6 @@ if __name__ == '__main__':
         'force_multiple': 32,
         'scale': 2,
         'eval': False,
-        'strict': False,
         'fixed_corruptions': ['jpeg-broad', 'gaussian_blur'],
         'random_corruptions': ['noise-5', 'none'],
         'num_corrupts_per_image': 1,

codes/train.py

@@ -293,7 +293,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_mi1_rrdb4x_23bl_opt.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_xx_faces_glean.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()