X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=b579177623ba6c7bbf897b06279be6f4d6ed663a;hb=02a6cbfdda55ba2ce13ff0f925009c15cc2a0a90;hp=11cf0a31401eb0c21aff0875564f80d2e7270db3;hpb=c8d0cf6842db19f84a78c1b3a4d2666b323a5d4a;p=mygpt.git

diff --git a/main.py b/main.py
index 11cf0a3..b579177 100755
--- 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()
@@ -118,19 +127,24 @@ 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
         )
 
+        # self.test_descr = descr[:nb // 5]
+        # self.train_descr = descr[nb // 5:]
+
         descr = [ s.strip().split(' ') for s in descr ]
         l = max([ len(s) for s in descr ])
         descr = [ s + [ '<unk>' ] * (l - len(s)) for s in descr ]
@@ -159,8 +173,28 @@ class TaskPicoCLVR(Task):
     def vocabulary_size(self):
         return len(self.token2id)
 
-    def produce_results(self, n_epoch, model, nb_tokens = 50):
-        img = [ ]
+    def generate(self, primer, model, nb_tokens):
+        t_primer = primer.strip().split(' ')
+        t_generated = [ ]
+
+        for j in range(nb_tokens):
+            t = [ [ self.token2id[u] for u in t_primer + t_generated ] ]
+            input = torch.tensor(t, device = self.device)
+            output = model(input)
+            logits = output[0, -1]
+            if args.synthesis_sampling:
+                dist = torch.distributions.categorical.Categorical(logits = logits)
+                t = dist.sample()
+            else:
+                t = logits.argmax()
+            t_generated.append(self.id2token[t.item()])
+
+        return ' '.join(t_primer + t_generated)
+
+    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
 
         for primer in [
@@ -171,30 +205,26 @@ class TaskPicoCLVR(Task):
         ]:
 
             for k in range(nb_per_primer):
-                t_primer = primer.strip().split(' ')
-                t_generated = [ ]
-
-                for j in range(nb_tokens):
-                    t = [ [ self.token2id[u] for u in t_primer + t_generated ] ]
-                    input = torch.tensor(t, device = self.device)
-                    output = model(input)
-                    logits = output[0, -1]
-                    if args.synthesis_sampling:
-                        dist = torch.distributions.categorical.Categorical(logits = logits)
-                        t = dist.sample()
-                    else:
-                        t = logits.argmax()
-                    t_generated.append(self.id2token[t.item()])
-
-                descr = [ ' '.join(t_primer + t_generated) ]
-                img += [ picoclvr.descr2img(descr) ]
+                descr.append(self.generate(primer, model, nb_tokens))
 
+        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)
+        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,
+                height = self.height, width = self.width
+            )
+        ] )
+
+        log_string(f'nb_missing {nb_missing / len(descr):.02f}')
+
 ######################################################################
 
 class TaskWiki103(Task):
@@ -356,7 +386,11 @@ 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}.')