return properties
- def generate_example(self):
+ def generate_scene_and_questions(self):
while True:
while True:
scene = self.generate_scene()
if len(false) >= self.nb_questions:
break
+ # print(f"{a=}")
+
if a < 10:
break
true = [true[k] for k in torch.randperm(len(true))[: self.nb_questions]]
false = [false[k] for k in torch.randperm(len(false))[: self.nb_questions]]
- true = [(q, "yes") for q in true]
- false = [(q, "no") for q in false]
+ true = ["<prop> " + q + " <true>" for q in true]
+ false = ["<prop> " + q + " <false>" for q in false]
union = true + false
questions = [union[k] for k in torch.randperm(len(union))[: self.nb_questions]]
- return scene, questions
+ result = " ".join(
+ ["<obj> " + x for x in self.grid_positions(scene)] + questions
+ )
+
+ return scene, result
+
+ def generate_samples(self, nb, progress_bar=None):
+ result = []
+
+ r = range(nb)
+ if progress_bar is not None:
+ r = progress_bar(r)
+
+ for _ in r:
+ result.append(self.generate_scene_and_questions()[1])
+
+ return result
######################################################################
if __name__ == "__main__":
+ import time
+
grid_factory = GridFactory()
- scene, questions = grid_factory.generate_example()
+
+ start_time = time.perf_counter()
+ samples = grid_factory.generate_samples(10000)
+ end_time = time.perf_counter()
+ print(f"{len(samples) / (end_time - start_time):.02f} samples per second")
+
+ scene, questions = grid_factory.generate_scene_and_questions()
grid_factory.print_scene(scene)
print(questions)