From b5efc396f45c23b7de0fe11f618731ac2b900d99 Mon Sep 17 00:00:00 2001
From: Francois Fleuret <francois@fleuret.org>
Date: Fri, 15 Jul 2022 18:10:14 +0200
Subject: [PATCH] Update.

---
 picoclvr.py | 31 +++++++++++++++----------------
 1 file changed, 15 insertions(+), 16 deletions(-)

diff --git a/picoclvr.py b/picoclvr.py
index 26f53ab..774ae3b 100755
--- a/picoclvr.py
+++ b/picoclvr.py
@@ -93,7 +93,7 @@ def all_properties(height, width, nb_squares, square_i, square_j, square_c):
 ######################################################################
 
 def generate(nb, height = 6, width = 8,
-             max_nb_squares = 5, max_nb_statements = 10,
+             max_nb_squares = 5, max_nb_properties = 10,
              many_colors = False):
 
     nb_colors =  len(color_tokens) - 1 if many_colors else max_nb_squares
@@ -112,14 +112,14 @@ def generate(nb, height = 6, width = 8,
         img = [ 0 ] * height * width
         for k in range(nb_squares): img[square_position[k]] = square_c[k]
 
-        # generates all the true relations
+        # generates all the true properties
 
         s = all_properties(height, width, nb_squares, square_i, square_j, square_c)
 
-        # pick at most max_nb_statements at random
+        # pick at most max_nb_properties at random
 
-        nb_statements = torch.randint(max_nb_statements, (1,)) + 1
-        s = ' <sep> '.join([ s[k] for k in torch.randperm(len(s))[:nb_statements] ] )
+        nb_properties = torch.randint(max_nb_properties, (1,)) + 1
+        s = ' <sep> '.join([ s[k] for k in torch.randperm(len(s))[:nb_properties] ] )
         s += ' <img> ' + ' '.join([ f'{color_names[n]}' for n in img ])
 
         descr += [ s ]
@@ -130,23 +130,22 @@ def generate(nb, height = 6, width = 8,
 
 def descr2img(descr, height = 6, width = 8):
 
+    if type(descr) == list:
+        return torch.cat([ descr2img(d) for d in descr ], 0)
+
     def token2color(t):
         try:
             return color_tokens[t]
         except KeyError:
             return [ 128, 128, 128 ]
 
-    def img_descr(x):
-        u = x.split('<img>', 1)
-        return u[1] if len(u) > 1 else ''
-
-    img = torch.full((len(descr), 3, height, width), 255)
-    d = [ img_descr(x) for x in descr ]
-    d = [ u.strip().split(' ')[:height * width] for u in d ]
-    d = [ u + [ '<unk>' ] * (height * width - len(u)) for u in d ]
-    d = [ [ token2color(t) for t in u ] for u in d ]
-    img = torch.tensor(d).permute(0, 2, 1)
-    img = img.reshape(img.size(0), 3, height, width)
+    d = descr.split('<img>', 1)
+    d = d[-1] if len(d) > 1 else ''
+    d = d.strip().split(' ')[:height * width]
+    d = d + [ '<unk>' ] * (height * width - len(d))
+    d = [ token2color(t) for t in d ]
+    img = torch.tensor(d).permute(1, 0)
+    img = img.reshape(1, 3, height, width)
 
     return img
 
-- 
2.39.5