######################################################################
-def generate(nb, height = 6, width = 8,
+def generate(nb, height, width,
max_nb_squares = 5, max_nb_properties = 10,
many_colors = False):
######################################################################
-def descr2img(descr, height = 6, width = 8):
+def descr2img(descr, height, width):
if type(descr) == list:
return torch.cat([ descr2img(d, height, width) for d in descr ], 0)
######################################################################
-def descr2properties(descr, height = 6, width = 8):
+def descr2properties(descr, height, width):
if type(descr) == list:
return [ descr2properties(d, height, width) for d in descr ]
######################################################################
-def nb_missing_properties(descr, height = 6, width = 8):
+def nb_missing_properties(descr, height, width):
if type(descr) == list:
return [ nb_missing_properties(d, height, width) for d in descr ]
d = d[0].strip().split('<sep>')
d = [ x.strip() for x in d ]
- missing_properties = set(d) - set(descr2properties(descr, height, width))
+ requested_properties = set(d)
+ all_properties = set(descr2properties(descr, height, width))
+ missing_properties = requested_properties - all_properties
- return len(missing_properties)
+ return (len(requested_properties), len(all_properties), len(missing_properties))
######################################################################