Update.

[mygpt.git] / picoclvr.py

diff --git a/picoclvr.py b/picoclvr.py
index 712da17..26f53ab 100755 (executable)
--- a/picoclvr.py
+++ b/picoclvr.py
@@ -71,6 +71,27 @@ color_tokens = dict( [ (n, c) for n, c in zip(color_names, colors) ] )
 
 ######################################################################
 
+def all_properties(height, width, nb_squares, square_i, square_j, square_c):
+    s = [ ]
+
+    for r, c in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]:
+        s += [ f'there is {c}' ]
+
+        if square_i[r] >= height - height//3: s += [ f'{c} bottom' ]
+        if square_i[r] < height//3: s += [ f'{c} top' ]
+        if square_j[r] >= width - width//3: s += [ f'{c} right' ]
+        if square_j[r] < width//3: s += [ f'{c} left' ]
+
+        for t, d in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]:
+            if square_i[r] > square_i[t]: s += [ f'{c} below {d}' ]
+            if square_i[r] < square_i[t]: s += [ f'{c} above {d}' ]
+            if square_j[r] > square_j[t]: s += [ f'{c} right of {d}' ]
+            if square_j[r] < square_j[t]: s += [ f'{c} left of {d}' ]
+
+    return s
+
+######################################################################
+
 def generate(nb, height = 6, width = 8,
              max_nb_squares = 5, max_nb_statements = 10,
              many_colors = False):
@@ -83,6 +104,7 @@ def generate(nb, height = 6, width = 8,
 
         nb_squares = torch.randint(max_nb_squares, (1,)) + 1
         square_position = torch.randperm(height * width)[:nb_squares]
+        # color 0 is white and reserved for the background
         square_c = torch.randperm(nb_colors)[:nb_squares] + 1
         square_i = square_position.div(width, rounding_mode = 'floor')
         square_j = square_position % width
@@ -92,21 +114,7 @@ def generate(nb, height = 6, width = 8,
 
         # generates all the true relations
 
-        s = [ ]
-
-        for r, c in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]:
-            s += [ f'there is {c}' ]
-
-            if square_i[r] >= height - height//3: s += [ f'{c} bottom' ]
-            if square_i[r] < height//3: s += [ f'{c} top' ]
-            if square_j[r] >= width - width//3: s += [ f'{c} right' ]
-            if square_j[r] < width//3: s += [ f'{c} left' ]
-
-            for t, d in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]:
-                if square_i[r] > square_i[t]: s += [ f'{c} below {d}' ]
-                if square_i[r] < square_i[t]: s += [ f'{c} above {d}' ]
-                if square_j[r] > square_j[t]: s += [ f'{c} right of {d}' ]
-                if square_j[r] < square_j[t]: s += [ f'{c} left of {d}' ]
+        s = all_properties(height, width, nb_squares, square_i, square_j, square_c)
 
         # pick at most max_nb_statements at random
 
@@ -146,13 +154,12 @@ def descr2img(descr, height = 6, width = 8):
 
 if __name__ == '__main__':
     descr = generate(nb = 5)
-    for d in descr:
-        print(d)
-        print()
 
-    img = descr2img(descr)
-    print(img.size())
+    with open('picoclvr_example.txt', 'w') as f:
+        for d in descr:
+            f.write(f'{d}\n\n')
 
+    img = descr2img(descr)
     torchvision.utils.save_image(img / 255.,
                                  'picoclvr_example.png', nrow = 16, pad_value = 0.8)