OCDC

[mygpt.git] / picoclvr.py

diff --git a/picoclvr.py b/picoclvr.py
index 774ae3b..f097eb0 100755 (executable)
--- a/picoclvr.py
+++ b/picoclvr.py
@@ -67,6 +67,7 @@ color_names = [
     'azure', 'snow', 'silver', 'gainsboro', 'white_smoke',
 ]
 
+color_id = dict( [ (n, k) for k, n in enumerate(color_names) ] )
 color_tokens = dict( [ (n, c) for n, c in zip(color_names, colors) ] )
 
 ######################################################################
@@ -92,11 +93,11 @@ def all_properties(height, width, nb_squares, square_i, square_j, square_c):
 
 ######################################################################
 
-def generate(nb, height = 6, width = 8,
+def generate(nb, height, width,
              max_nb_squares = 5, max_nb_properties = 10,
-             many_colors = False):
+             nb_colors = 5):
 
-    nb_colors =  len(color_tokens) - 1 if many_colors else max_nb_squares
+    assert nb_colors >= max_nb_squares and nb_colors <= len(color_tokens) - 1
 
     descr = [ ]
 
@@ -128,10 +129,10 @@ def generate(nb, height = 6, width = 8,
 
 ######################################################################
 
-def descr2img(descr, height = 6, width = 8):
+def descr2img(descr, height, width):
 
     if type(descr) == list:
-        return torch.cat([ descr2img(d) for d in descr ], 0)
+        return torch.cat([ descr2img(d, height, width) for d in descr ], 0)
 
     def token2color(t):
         try:
@@ -151,9 +152,60 @@ def descr2img(descr, height = 6, width = 8):
 
 ######################################################################
 
+def descr2properties(descr, height, width):
+
+    if type(descr) == list:
+        return [ descr2properties(d, height, width) for d in descr ]
+
+    d = descr.split('<img>', 1)
+    d = d[-1] if len(d) > 1 else ''
+    d = d.strip().split(' ')[:height * width]
+
+    seen = {}
+    if len(d) != height * width: return []
+
+    for k, x in enumerate(d):
+        if x != color_names[0]:
+            if x in color_tokens:
+                if x in seen: return []
+            else:
+                return []
+            seen[x] = (color_id[x], k // width, k % width)
+
+    square_infos = zip(*seen.values())
+    square_c = torch.tensor(square_infos[0])
+    square_i = torch.tensor(square_infos[1])
+    square_j = torch.tensor(square_infos[2])
+
+    s = all_properties(height, width, len(seen), square_i, square_j, square_c)
+
+    return s
+
+######################################################################
+
+def nb_properties(descr, height, width):
+    if type(descr) == list:
+        return [ nb_properties(d, height, width) for d in descr ]
+
+    d = descr.split('<img>', 1)
+    if len(d) == 0: return 0
+    d = d[0].strip().split('<sep>')
+    d = [ x.strip() for x in d ]
+
+    requested_properties = set(d)
+    all_properties = set(descr2properties(descr, height, width))
+    missing_properties = requested_properties - all_properties
+
+    return (len(requested_properties), len(all_properties), len(missing_properties))
+
+######################################################################
+
 if __name__ == '__main__':
     descr = generate(nb = 5)
 
+    #print(descr2properties(descr))
+    print(nb_properties(descr))
+
     with open('picoclvr_example.txt', 'w') as f:
         for d in descr:
             f.write(f'{d}\n\n')
@@ -165,7 +217,7 @@ if __name__ == '__main__':
     import time
 
     start_time = time.perf_counter()
-    descr = generate(10000)
+    descr = generate(nb = 1000)
     end_time = time.perf_counter()
     print(f'{len(descr) / (end_time - start_time):.02f} samples per second')