From: Francois Fleuret <francois@fleuret.org>
Date: Thu, 12 May 2022 06:28:24 +0000 (+0200)
Subject: Update.
X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=mygpt.git;a=commitdiff_plain;h=1d49fa4d3aeb8e311a145d6dbd3d5c640d7d16da

Update.
---

diff --git a/picoclvr.py b/picoclvr.py
index 2342016..1215764 100755
--- a/picoclvr.py
+++ b/picoclvr.py
@@ -34,34 +34,40 @@ def generate(nb, height = 6, width = 8, max_nb_statements = 10):
     descr = [ ]
 
     for n in range(nb):
-        nb_shapes = torch.randint(len(color_tokens) - 1, (1,)) + 1
-        shape_position = torch.randperm(height * width)[:nb_shapes]
-        shape_c = torch.randperm(len(color_tokens) - 1)[:nb_shapes] + 1
-        shape_i = shape_position.div(width, rounding_mode = 'floor')
-        shape_j = shape_position % width
+
+        nb_squares = torch.randint(len(color_tokens) - 1, (1,)) + 1
+        square_position = torch.randperm(height * width)[:nb_squares]
+        square_c = torch.randperm(len(color_tokens) - 1)[:nb_squares] + 1
+        square_i = square_position.div(width, rounding_mode = 'floor')
+        square_j = square_position % width
 
         img = [ 0 ] * height * width
-        for k in range(nb_shapes): img[shape_position[k]] = shape_c[k]
+        for k in range(nb_squares): img[square_position[k]] = square_c[k]
+
+        # generates all the true relations
 
         s = [ ]
 
-        for r, c in [ (k, color_names[shape_c[k]]) for k in range(nb_shapes) ]:
+        for r, c in [ (k, color_names[square_c[k]]) for k in range(nb_squares) ]:
             s += [ f'there is {c}' ]
 
-            if shape_i[r] >= height - height//3: s += [ f'{c} bottom' ]
-            if shape_i[r] < height//3: s += [ f'{c} top' ]
-            if shape_j[r] >= width - width//3: s += [ f'{c} right' ]
-            if shape_j[r] < width//3: s += [ f'{c} left' ]
+            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}' ]
 
-            for t, d in [ (k, color_names[shape_c[k]]) for k in range(nb_shapes) ]:
-                if shape_i[r] > shape_i[t]: s += [ f'{c} below {d}' ]
-                if shape_i[r] < shape_i[t]: s += [ f'{c} above {d}' ]
-                if shape_j[r] > shape_j[t]: s += [ f'{c} right of {d}' ]
-                if shape_j[r] < shape_j[t]: s += [ f'{c} left of {d}' ]
+        # pick at most max_nb_statements 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] ] )
         s += ' <img> ' + ' '.join([ f'{color_names[n]}' for n in img ])
+
         descr += [ s ]
 
     return descr
@@ -94,10 +100,15 @@ def descr2img(descr, height = 6, width = 8):
 
 if __name__ == '__main__':
     descr = generate(5)
+    for d in descr:
+        print(d)
+        print()
+
     img = descr2img(descr)
-    print(descr, img.size())
+    print(img.size())
+
     torchvision.utils.save_image(img / 255.,
-                                 'example.png', nrow = 16, pad_value = 0.8)
+                                 'picoclvr_example.png', nrow = 16, pad_value = 0.8)
 
     import time