From: François Fleuret Date: Sat, 21 Oct 2023 16:02:52 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=0d4b3fe3ccd16cd72fc96fe12c85996f35233c5e;p=picoclvr.git Update. --- diff --git a/main.py b/main.py index 4a46fe6..6a1e8c1 100755 --- a/main.py +++ b/main.py @@ -33,7 +33,7 @@ parser.add_argument( "--task", type=str, default="twotargets", - help="byheart, learnop, guessop, twocuts, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp", + help="byheart, learnop, guessop, degradation, 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("--twocuts_no_global", action="store_true", default=False) +parser.add_argument("--degradation_hard", action="store_true", default=False) ###################################################################### @@ -254,7 +254,7 @@ default_task_args = { "nb_train_samples": 50000, "nb_test_samples": 10000, }, - "twocuts": { + "degradation": { "model": "37M", "batch_size": 25, "nb_train_samples": 100000, @@ -414,9 +414,9 @@ elif args.task == "twotargets": device=device, ) -elif args.task == "twocuts": +elif args.task == "degradation": task = tasks.SandBox( - problem=problems.ProblemTwoCuts(global_constraint = not args.twocuts_no_global), + problem=problems.ProblemDegradation(hard=args.degradation_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 68a46b3..ef48162 100755 --- a/problems.py +++ b/problems.py @@ -25,88 +25,61 @@ class Problem: #################### -class ProblemTwoCuts(Problem): - def __init__(self, len_total=50, nb_values=100, global_constraint=True): - self.len_total = len_total - self.nb_values = nb_values - self.global_constraint = global_constraint - - def generate_sequences_internal(self, nb): - return u,v,a,b,c +class ProblemDegradation(Problem): + def __init__(self, nb_state_tokens=5, nb_time_steps=5, value_max=25, hard=False): + self.nb_state_tokens = nb_state_tokens + self.nb_time_steps = nb_time_steps + self.value_max = value_max + self.hard = hard def generate_sequences(self,nb): - u = torch.randint(self.len_total, (nb,)) - v = torch.randint(self.len_total, (nb,)) - - a = torch.randint(self.nb_values, (nb,)) - b = torch.randint(self.nb_values, (nb,)) - c = torch.randint(self.nb_values, (nb,)) - - while True: - to_compute = torch.logical_or(u>=v-self.len_total//10,u=v).long().sum() == 0 - assert (a==b).long().sum() == 0 - assert (a==c).long().sum() == 0 - assert (c==b).long().sum() == 0 - - t = torch.arange(self.len_total) - seq = (t[None,:] < u[:,None]).long() * a[:,None] + \ - (t[None,:] >= u[:,None]).long() * (t[None,:] < v[:,None]).long() * b[:,None] + \ - (t[None,:] >= v[:,None]).long() * c[:,None] + x = (torch.rand(nb,self.nb_state_tokens).sort(dim=-1).indices == 0).long() * self.value_max + seq = [x] + + for t in range(self.nb_time_steps-1): + v = torch.rand(x.size()) * (x > 0).float() + u = (v.max(dim=-1,keepdim=True).values == v).long() + n = (u*x*torch.rand(x.size())).long().sum(dim=-1,keepdim=True) // 2 + x = x + n * (u.roll(shifts=-1,dims=-1) - 2 * u + u.roll(shifts=1,dims=-1)) + seq.append(x) + 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): nb_total = result.size(0) nb_correct = 0 - i = torch.arange(result.size(1), device=result.device) - - for k in range(nb_total): - s = result[k] - a = s[0] - uu = (s != a).nonzero() - if uu.size(0) > 0: - u = uu.min() - b = s[u] - vv = torch.logical_and(s != b, i >= u).nonzero() - if vv.size(0) > 0: - v = vv.min() - c = s[v] - ww = torch.logical_and(s != c, i >= v).nonzero() - if ww.size(0) == 0: - if not self.global_constraint or (a*u+b*(v-u)+c*(self.len_total-v)) // self.len_total == self.nb_values//2: - nb_correct += 1 + + for seq in result: + states = list(seq.split(self.nb_state_tokens)) + if self.hard: + states.reverse() + + d = states[0] + j=d.sort(descending=True).indices[0] + e=d.new_zeros(d.size()) + e[j]=self.value_max + if (d-e).abs().sum() == 0: + nb_errors = 0 + for k in range(len(states)-1): + d=states[k]-states[k+1] + j=d.sort(descending=True).indices[0] + e=d.new_zeros(d.size()) + e[j]=d[j] + e[(j+1)%e.size(0)]=-d[j]//2 + e[(j-1)%e.size(0)]=-d[j]//2 + if (d-e).abs().sum() > 0: + nb_errors += 1 + if nb_errors == 0: + nb_correct += 1 return nb_total, nb_correct def seq2str(self, seq): - return " ".join( [ f"{x:02d}" for x in seq ] ) + return " | ".join( [ " ".join([f"{x:02d}" for x in s ]) for s in seq.split(self.nb_state_tokens) ] ) #################### @@ -287,6 +260,7 @@ class ProblemAddition(Problem): if __name__ == "__main__": - p = ProblemTwoCuts(12) + p = ProblemDegradation(hard=False) s, m = p.generate_sequences(10000) + print(p.seq2str(s[0])) print(p.compute_nb_correct(None, None, s))