Update.

diff --git a/main.py b/main.py
index 3bf6b52..ace376d 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -72,9 +72,18 @@ parser.add_argument('--synthesis_sampling',
 parser.add_argument('--checkpoint_name',
                     type = str, default = 'checkpoint.pth')
 
+##############################
+# picoclvr options
+
 parser.add_argument('--picoclvr_many_colors',
                     action='store_true', default = False)
 
+parser.add_argument('--picoclvr_height',
+                    type = int, default = 12)
+
+parser.add_argument('--picoclvr_width',
+                    type = int, default = 16)
+
 ######################################################################
 
 args = parser.parse_args()
@@ -118,16 +127,18 @@ import picoclvr
 class TaskPicoCLVR(Task):
 
     def __init__(self, batch_size,
-                 height = 6, width = 8, many_colors = False,
+                 height, width, many_colors = False,
                  device = torch.device('cpu')):
 
+        self.height = height
+        self.width = width
         self.batch_size = batch_size
         self.device = device
         nb = args.data_size if args.data_size > 0 else 250000
 
         descr = picoclvr.generate(
             nb,
-            height = height, width = width,
+            height = self.height, width = self.width,
             many_colors = many_colors
         )
 
@@ -180,7 +191,9 @@ class TaskPicoCLVR(Task):
 
         return ' '.join(t_primer + t_generated)
 
-    def produce_results(self, n_epoch, model, nb_tokens = 50):
+    def produce_results(self, n_epoch, model, nb_tokens = None):
+        if nb_tokens is None:
+            nb_tokens = self.height * self.width + 3
         descr = [ ]
         nb_per_primer = 8
 
@@ -194,14 +207,14 @@ class TaskPicoCLVR(Task):
             for k in range(nb_per_primer):
                 descr.append(self.generate(primer, model, nb_tokens))
 
-        img = [ picoclvr.descr2img(d) for d in descr ]
+        img = [ picoclvr.descr2img(d, height = self.height, width = self.width) for d in descr ]
         img = torch.cat(img, 0)
         file_name = f'result_picoclvr_{n_epoch:04d}.png'
         torchvision.utils.save_image(img / 255.,
                                      file_name, nrow = nb_per_primer, pad_value = 0.8)
         log_string(f'wrote {file_name}')
 
-        nb_missing = sum( [ x[2] for x in picoclvr.nb_missing_properties(descr) ] )
+        nb_missing = sum( [ x[2] for x in picoclvr.nb_missing_properties(descr, height = self.height, width = self.width) ] )
         log_string(f'nb_missing {nb_missing / len(descr):.02f}')
 
 ######################################################################
@@ -365,7 +378,11 @@ if args.data == 'wiki103':
 elif args.data == 'mnist':
     task = TaskMNIST(batch_size = args.batch_size, device = device)
 elif args.data == 'picoclvr':
-    task = TaskPicoCLVR(batch_size = args.batch_size, many_colors = args.picoclvr_many_colors, device = device)
+    task = TaskPicoCLVR(batch_size = args.batch_size,
+                        height = args.picoclvr_height,
+                        width = args.picoclvr_width,
+                        many_colors = args.picoclvr_many_colors,
+                        device = device)
 else:
     raise ValueError(f'Unknown dataset {args.data}.')