From: François Fleuret Date: Sun, 22 Oct 2023 13:35:46 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=503298855a80bde0bf856f1a34b532079d3c7ef6;p=culture.git Update. --- diff --git a/main.py b/main.py index 6e87cda..496a603 100755 --- a/main.py +++ b/main.py @@ -33,7 +33,7 @@ parser.add_argument( "--task", type=str, default="twotargets", - help="byheart, learnop, guessop, degradation, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp", + help="byheart, learnop, guessop, mixing, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp", ) parser.add_argument("--log_filename", type=str, default="train.log", help=" ") @@ -162,7 +162,7 @@ parser.add_argument("--expr_input_file", type=str, default=None) ############################## # Misc -parser.add_argument("--degradation_hard", action="store_true", default=False) +parser.add_argument("--mixing_hard", action="store_true", default=False) ###################################################################### @@ -254,7 +254,7 @@ default_task_args = { "nb_train_samples": 50000, "nb_test_samples": 10000, }, - "degradation": { + "mixing": { "model": "37M", "batch_size": 25, "nb_train_samples": 250000, @@ -414,9 +414,9 @@ elif args.task == "twotargets": device=device, ) -elif args.task == "degradation": +elif args.task == "mixing": task = tasks.SandBox( - problem=problems.ProblemDegradation(hard=args.degradation_hard), + problem=problems.ProblemMixing(hard=args.mixing_hard), nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, batch_size=args.batch_size, diff --git a/problems.py b/problems.py index 22b6517..28e4f7b 100755 --- a/problems.py +++ b/problems.py @@ -24,8 +24,6 @@ class Problem: #################### - - class ProblemDegradation(Problem): def __init__(self, nb_state_tokens=5, nb_time_steps=12, value_max=25, hard=False): assert value_max // nb_state_tokens >= 2 @@ -285,9 +283,100 @@ class ProblemAddition(Problem): return "".join(self.id2char[x.item()] for x in seq) +#################### + + +class ProblemMixing(Problem): + def __init__(self, height=3, width=3, nb_time_steps=12, hard=False): + self.height = height + self.width = width + self.nb_time_steps = nb_time_steps + self.hard = hard + + def start(self, nb): + return ( + torch.arange(self.height * self.width) + .reshape(1, 1, self.height, self.width) + .expand(nb, -1, -1, -1) + ) + + def moves(self, x): + y = ( + x[:, None, :, :] + .expand(-1, self.height * 2 + self.width * 2, -1, -1) + .clone() + ) + k = 0 + + for i in range(self.height): + y[:, k, i, :] = y[:, k, i, :].roll(dims=-1, shifts=-1) + k += 1 + y[:, k, i, :] = y[:, k, i, :].roll(dims=-1, shifts=1) + k += 1 + + for j in range(self.width): + y[:, k, :, j] = y[:, k, :, j].roll(dims=-1, shifts=-1) + k += 1 + y[:, k, :, j] = y[:, k, :, j].roll(dims=-1, shifts=1) + k += 1 + + return y + + def generate_sequences(self, nb): + y = self.start(nb) + x = y[torch.arange(nb), torch.randint(y.size(1), (nb,))] + + seq = [x.flatten(1)] + + for t in range(self.nb_time_steps - 1): + y = self.moves(x) + x = y[torch.arange(nb), torch.randint(y.size(1), (nb,))] + seq.append(x.flatten(1)) + + if self.hard: + seq.reverse() + + seq = torch.cat(seq, dim=1) + return seq, seq.new_full(seq.size(), 1, dtype=torch.int64) + + def compute_nb_correct(self, input, ar_mask, result): + a = [ + x.reshape(result.size(0), self.height, self.width) + for x in result.split(self.height * self.width, dim=1) + ] + if self.hard: + a.reverse() + + x = a[0] + + y = self.start(result.size(0)).to(x.device) + d = (x[:, None] - y).abs().sum((-1, -2)).min(dim=-1).values + + for t in range(self.nb_time_steps - 1): + x0, x = a[t], a[t + 1] + y = self.moves(x0) + d = d + (x[:, None] - y).abs().sum((-1, -2)).min(dim=-1).values + + nb_total, nb_correct = result.size(0), (d == 0).long().sum().item() + + return nb_total, nb_correct + + def seq2str(self, seq): + return " | ".join( + [ + " ".join( + ["-".join([f"{x:02d}" for x in s]) for s in r.split(self.width)] + ) + for r in seq.split(self.height * self.width) + ] + ) + + +#################### + if __name__ == "__main__": - p = ProblemDegradation(hard=False) + p = ProblemMixing(hard=True) s, m = p.generate_sequences(10000) - for x in s[:100]: + for x in s[:5]: print(p.seq2str(x)) print(p.compute_nb_correct(None, None, s))