tasks.py

   1 #!/usr/bin/env python
   2
   3 # Any copyright is dedicated to the Public Domain.
   4 # https://creativecommons.org/publicdomain/zero/1.0/
   5
   6 # Written by Francois Fleuret <francois@fleuret.org>
   7
   8 import math, os, tqdm
   9
  10 import torch, torchvision
  11
  12 from torch import nn
  13 from torch.nn import functional as F
  14
  15 ######################################################################
  16
  17
  18 def masked_inplace_autoregression(
  19     model,
  20     batch_size,
  21     input,
  22     ar_mask,
  23     deterministic_synthesis,
  24     forbidden_tokens=None,
  25     progress_bar_desc="autoregression",
  26     device=torch.device("cpu"),
  27 ):
  28     assert input.size() == ar_mask.size()
  29
  30     batches = zip(input.split(batch_size), ar_mask.split(batch_size))
  31
  32     if progress_bar_desc is not None:
  33         batches = tqdm.tqdm(
  34             batches,
  35             dynamic_ncols=True,
  36             desc=progress_bar_desc,
  37             total=(input.size(0) + batch_size - 1) // batch_size,
  38         )
  39
  40     with torch.autograd.no_grad():
  41         t = model.training
  42         model.eval()
  43
  44         for input, ar_mask in batches:
  45             model.masked_inplace_autoregression(
  46                 input, ar_mask, forbidden_tokens, deterministic_synthesis
  47             )
  48
  49         model.train(t)
  50
  51
  52 ######################################################################
  53
  54
  55 class Task:
  56     def batches(self, split="train"):
  57         pass
  58
  59     def vocabulary_size(self):
  60         pass
  61
  62     def produce_results(
  63         self, n_epoch, model, result_dir, logger, deterministic_synthesis
  64     ):
  65         pass
  66
  67
  68 ######################################################################
  69
  70
  71 class Problem:
  72     def generate_sequences(self, nb):
  73         pass
  74
  75     def seq2str(self, seq):
  76         return "[NOT IMPLEMENTED]"
  77
  78
  79 ####################
  80
  81
  82 class ProblemLevel0(Problem):
  83     def __init__(self, nb_sentences=100, len_prompt=5, len_result=5):
  84         self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result))
  85         self.seq[:, len_prompt] = 10
  86
  87     def generate_sequences(self, nb):
  88         sequences = self.seq[torch.randint(self.seq.size(0), (nb,))]
  89         ar_mask = (sequences == 10).long()
  90         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
  91         return sequences, ar_mask
  92
  93
  94 class ProblemLevel1(Problem):
  95     def __init__(self, nb_operators=100, len_source=5, len_result=8):
  96         self.len_source = len_source
  97         self.len_result = len_result
  98         self.len_nb_operator = int(math.log(nb_operators) / math.log(10)) + 1
  99         self.operators = F.one_hot(
 100             torch.rand(nb_operators, len_result, len_source).argmax(-1),
 101             num_classes=len_source,
 102         )
 103
 104     def generate_sequences(self, nb):
 105         nb_operators = torch.randint(self.operators.size(0), (nb,))
 106         operators = self.operators[nb_operators]
 107         nb_operators = (
 108             nb_operators[:, None]
 109             // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1)
 110         ) % 10
 111         marker1 = torch.full((nb, 1), 10)
 112         # source = torch.randint(10, (nb, self.len_source))
 113         source = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
 114         marker2 = torch.full((nb, 1), 11)
 115         result = operators.bmm(source[:, :, None]).squeeze(-1)
 116         sequences = torch.cat((nb_operators, marker1, source, marker2, result), 1)
 117         ar_mask = (sequences == 11).long()
 118         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
 119         return sequences, ar_mask
 120
 121     def seq2str(self, seq):
 122         return "".join("0123456789|>"[x.item()] for x in seq)
 123
 124
 125 class ProblemLevel2(Problem):
 126     def __init__(self, len_source=5, len_result=8):
 127         self.len_source = len_source
 128         self.len_result = len_result
 129
 130     def generate_sequences(self, nb):
 131         operators = F.one_hot(
 132             torch.rand(nb, self.len_result, self.len_source).argmax(-1),
 133             num_classes=self.len_source,
 134         )
 135         source1 = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
 136         # source1 = torch.randint(10, (nb, self.len_source))
 137         marker1 = torch.full((nb, 1), 10)
 138         result1 = operators.bmm(source1[:, :, None]).squeeze(-1)
 139         marker2 = torch.full((nb, 1), 11)
 140         source2 = torch.randint(10, (nb, self.len_source))
 141         marker3 = torch.full((nb, 1), 12)
 142         result2 = operators.bmm(source2[:, :, None]).squeeze(-1)
 143
 144         sequences = torch.cat(
 145             (source1, marker1, result1, marker2, source2, marker3, result2), 1
 146         )
 147         ar_mask = (sequences == 12).long()
 148         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
 149         return sequences, ar_mask
 150
 151     def seq2str(self, seq):
 152         return "".join("0123456789>|~"[x.item()] for x in seq)
 153
 154
 155 ####################
 156
 157
 158 class ProblemAddition(Problem):
 159     def __init__(self, nb_digits=10, zero_padded=False, inverted_result=False):
 160         self.nb_digits = nb_digits
 161         self.zero_padded = zero_padded
 162         self.inverted_result = inverted_result
 163         self.char2id = dict([(c, n) for n, c in enumerate("0123456789+=$")])
 164         self.id2char = dict([(n, c) for c, n in self.char2id.items()])
 165
 166     def tensorize(self, strings):
 167         len_max = max([len(x) for x in strings])
 168         return torch.cat(
 169             [
 170                 torch.tensor(
 171                     [
 172                         [self.char2id[c] for c in s + "$" * (len_max - len(s))]
 173                         for s in strings
 174                     ]
 175                 )
 176             ],
 177             0,
 178         )
 179
 180     def generate_sequences(self, nb):
 181         sequences = []
 182         for k in range(nb):
 183             a, b = torch.randint(10**self.nb_digits, (2,))
 184             c = a + b
 185             a, b, c = str(a.item()), str(b.item()), str(c.item())
 186             if self.zero_padded:
 187                 a = "0" * (self.nb_digits - len(a)) + a
 188                 b = "0" * (self.nb_digits - len(b)) + b
 189                 c = "0" * (self.nb_digits + 1 - len(c)) + c
 190             if self.inverted_result:
 191                 c = c[::-1]
 192             sequences.append(f"{a}+{b}={c}$")
 193
 194         sequences = self.tensorize(sequences)
 195         ar_mask = (sequences == self.char2id["="]).long()
 196         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
 197         return sequences, ar_mask
 198
 199     def seq2str(self, seq):
 200         return "".join(self.id2char[x.item()] for x in seq)
 201
 202
 203 # class ProblemUnion(Problem):
 204 # problems = [ProblemByheart()]
 205 # nb_common_codes = 100
 206
 207 # def generate_sequences(nb_samples):
 208 # problem_indexes = torch.randint(len(problems), (nb_samples,))
 209 # nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
 210 # print(f"{nb_samples_per_problem}")
 211 # all_seq = []
 212 # for nb, p in zip(nb_samples_per_problem, problems):
 213 # all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
 214 # return all_seq
 215
 216 # for strain, stest in zip(train_seq, test_seq):
 217 # s = torch.cat((strain, stest), 0)
 218
 219 ####################
 220
 221
 222 class SandBox(Task):
 223     def __init__(
 224         self,
 225         problem,
 226         nb_train_samples,
 227         nb_test_samples,
 228         batch_size,
 229         logger=None,
 230         device=torch.device("cpu"),
 231         max_nb_codes=1024,
 232     ):
 233         super().__init__()
 234
 235         self.batch_size = batch_size
 236         self.device = device
 237         self.problem = problem
 238
 239         self.train_input, self.train_ar_mask = self.problem.generate_sequences(
 240             nb_train_samples
 241         )
 242         self.test_input, self.test_ar_mask = self.problem.generate_sequences(
 243             nb_test_samples
 244         )
 245
 246         self.train_input, self.train_ar_mask = self.train_input.to(
 247             device
 248         ), self.train_ar_mask.to(device)
 249         self.test_input, self.test_ar_mask = self.test_input.to(
 250             device
 251         ), self.test_ar_mask.to(device)
 252
 253         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 254
 255         # A bit of paranoia never hurts
 256         assert (
 257             self.nb_codes <= max_nb_codes
 258             and self.train_input.min() >= 0
 259             and self.test_input.min() >= 0
 260             and tuple(self.train_ar_mask.unique()) == (0, 1)
 261             and tuple(self.test_ar_mask.unique()) == (0, 1)
 262         )
 263
 264     def batches(self, split="train", nb_to_use=-1, desc=None):
 265         assert split in {"train", "test"}
 266         input = self.train_input if split == "train" else self.test_input
 267         if nb_to_use > 0:
 268             input = input[:nb_to_use]
 269         if desc is None:
 270             desc = f"epoch-{split}"
 271         for batch in tqdm.tqdm(
 272             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 273         ):
 274             yield batch
 275
 276     def vocabulary_size(self):
 277         return self.nb_codes
 278
 279     def produce_results(
 280         self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
 281     ):
 282         def compute_accuracy(input, ar_mask, logger=None):
 283             input, ar_mask = input[:nmax], ar_mask[:nmax]
 284             result = input.clone() * (1 - ar_mask)
 285
 286             masked_inplace_autoregression(
 287                 model,
 288                 self.batch_size,
 289                 result,
 290                 ar_mask,
 291                 deterministic_synthesis,
 292                 progress_bar_desc=None,
 293                 device=self.device,
 294             )
 295
 296             if logger is not None:
 297                 for sp, st in zip(result[:10], input[:10]):
 298                     logger(
 299                         f"test_sequences {n_epoch} prediction   {self.problem.seq2str(sp)}"
 300                     )
 301                     logger(
 302                         f"               {n_epoch} ground truth {self.problem.seq2str(st)}"
 303                     )
 304
 305             nb_total = ar_mask.sum().item()
 306             nb_correct = ((result == input).long() * ar_mask).sum().item()
 307
 308             return nb_total, nb_correct
 309
 310         train_nb_total, train_nb_correct = compute_accuracy(
 311             self.train_input, self.train_ar_mask
 312         )
 313
 314         logger(
 315             f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
 316         )
 317
 318         test_nb_total, test_nb_correct = compute_accuracy(
 319             self.test_input, self.test_ar_mask, logger
 320         )
 321
 322         logger(
 323             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 324         )
 325
 326
 327 ######################################################################
 328
 329 import picoclvr
 330
 331
 332 class PicoCLVR(Task):
 333     # Make a tensor from a list of strings
 334     def tensorize(self, descr):
 335         token_descr = [s.strip().split(" ") for s in descr]
 336         l = max([len(s) for s in token_descr])
 337         token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
 338         id_descr = [[self.token2id[u] for u in s] for s in token_descr]
 339         return torch.tensor(id_descr, device=self.device)
 340
 341     # Make a list of strings from a tensor
 342     def detensorize(self, x):
 343         return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
 344
 345     # trim all the tensors in the tuple z to remove as much token from
 346     # left and right in the first tensor. If z is a tuple, all its
 347     # elements are trimed according to the triming for the first
 348     def trim(self, z, token="<nul>"):
 349         n = self.token2id[token]
 350         if type(z) == tuple:
 351             x = z[0]
 352             i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
 353             a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
 354             return tuple([t[:, a:b] for t in z])
 355         else:
 356             i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
 357             a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
 358             return z[:, a:b]
 359
 360     ######################
 361
 362     def __init__(
 363         self,
 364         nb_train_samples,
 365         nb_test_samples,
 366         batch_size,
 367         height,
 368         width,
 369         nb_colors=5,
 370         logger=None,
 371         device=torch.device("cpu"),
 372         pruner_train=None,
 373         pruner_eval=None,
 374     ):
 375         super().__init__()
 376
 377         def generate_descr(nb, cache_suffix, pruner):
 378             return picoclvr.generate(
 379                 nb,
 380                 height=self.height,
 381                 width=self.width,
 382                 nb_colors=nb_colors,
 383                 pruner=pruner,
 384             )
 385
 386         self.height = height
 387         self.width = width
 388         self.batch_size = batch_size
 389         self.device = device
 390         self.pruner_train = pruner_train
 391         self.pruner_eval = pruner_eval
 392
 393         if logger is not None:
 394             logger(
 395                 f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
 396             )
 397
 398         self.train_descr = generate_descr(
 399             nb_train_samples, "train", pruner=self.pruner_train
 400         )
 401         self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
 402
 403         # Build the tokenizer
 404         tokens = {"<nul>", "<img>"}
 405         for d in [self.train_descr, self.test_descr]:
 406             for s in d:
 407                 for t in s.strip().split(" "):
 408                     tokens.add(t)
 409         # make this set a sorted list to get the same tensors given
 410         # the same descr
 411         tokens = list(tokens)
 412         tokens.sort()
 413         self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
 414         self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
 415         self.t_img, self.t_nul = self.token2id["<img>"], self.token2id["<nul>"]
 416
 417         # Tokenize the train and test sets
 418         self.train_input = self.tensorize(self.train_descr)
 419         self.test_input = self.tensorize(self.test_descr)
 420
 421     def batches(self, split="train"):
 422         assert split in {"train", "test"}
 423         input = self.train_input if split == "train" else self.test_input
 424         for batch in tqdm.tqdm(
 425             input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
 426         ):
 427             yield self.trim(batch)
 428
 429     def vocabulary_size(self):
 430         return len(self.token2id)
 431
 432     def compute_missing_properties(
 433         self, n_epoch, model, logger, deterministic_synthesis, pruner=None
 434     ):
 435         acc_nb_requested_properties = []
 436         acc_nb_missing_properties = []
 437         acc_nb_results = 0
 438
 439         for input in tqdm.tqdm(
 440             self.test_input.split(self.batch_size),
 441             dynamic_ncols=True,
 442             desc=f"test-properties",
 443         ):
 444             result = input.clone()
 445             ar_mask = (result == self.t_img).long().cumsum(dim=1).clamp(max=1)
 446             result = (1 - ar_mask) * result + ar_mask * self.t_nul
 447             masked_inplace_autoregression(
 448                 model,
 449                 self.batch_size,
 450                 result,
 451                 ar_mask,
 452                 deterministic_synthesis,
 453                 progress_bar_desc=None,
 454                 device=self.device,
 455             )
 456
 457             result_descr = self.detensorize(result)
 458             np = picoclvr.nb_properties(
 459                 result_descr,
 460                 height=self.height,
 461                 width=self.width,
 462                 pruner=pruner,
 463             )
 464             nb_requested_properties, _, nb_missing_properties = zip(*np)
 465             acc_nb_requested_properties += nb_requested_properties
 466             acc_nb_missing_properties += nb_missing_properties
 467             acc_nb_results += len(result_descr)
 468
 469         nb_requested_properties = sum(acc_nb_requested_properties)
 470         nb_missing_properties = sum(acc_nb_missing_properties)
 471
 472         prefix = "" if pruner is None else "pruned_"
 473         logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
 474         logger(
 475             f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
 476         )
 477         logger(
 478             f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
 479         )
 480
 481     ######################################################################
 482
 483     def produce_results(
 484         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 485     ):
 486         self.compute_missing_properties(n_epoch, model, logger, deterministic_synthesis)
 487
 488         if self.pruner_eval is not None:
 489             self.compute_missing_properties(n_epoch, model, self.pruner_eval)
 490
 491         nb_tokens_to_generate = self.height * self.width + 3
 492         result_descr = []
 493         nb_per_primer = 8
 494         primer = []
 495
 496         for primer_descr in [
 497             "red above green <sep> green top <sep> blue right of red",
 498             "there is red <sep> there is yellow <sep> there is blue",
 499             "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
 500             "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
 501         ]:
 502             primer += [primer_descr + " <img>"] * nb_per_primer
 503
 504         result = self.tensorize(primer)
 505         fill = result.new_full(
 506             result.size()[:-1] + (self.height * self.width + 1,), self.t_nul
 507         )
 508         result = torch.cat((result, fill), 1)
 509         ar_mask = (result == self.t_nul).long()
 510         masked_inplace_autoregression(
 511             model,
 512             self.batch_size,
 513             result,
 514             ar_mask,
 515             deterministic_synthesis,
 516             device=self.device,
 517         )
 518         result_descr = self.detensorize(result)
 519
 520         np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
 521
 522         acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
 523         acc_nb_results = len(result_descr)
 524
 525         nb_requested_properties = sum(acc_nb_requested_properties)
 526         nb_missing_properties = sum(acc_nb_missing_properties)
 527
 528         prefix = "demo_"
 529         logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
 530         logger(
 531             f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
 532         )
 533         logger(
 534             f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
 535         )
 536
 537         img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
 538
 539         if img.dim() == 5:
 540             if img.size(1) == 1:
 541                 img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
 542             else:
 543                 img = torch.cat(
 544                     [
 545                         torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
 546                         for x in img
 547                     ],
 548                     0,
 549                 )
 550
 551         image_name = os.path.join(result_dir, f"picoclvr_result_{n_epoch:04d}.png")
 552         torchvision.utils.save_image(
 553             img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0
 554         )
 555         logger(f"wrote {image_name}")
 556
 557
 558 ######################################################################
 559
 560
 561 class MNIST(Task):
 562     def __init__(
 563         self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
 564     ):
 565         super().__init__()
 566
 567         self.nb_train_samples = (nb_train_samples,)
 568         self.nb_test_samples = (nb_test_samples,)
 569         self.batch_size = batch_size
 570         self.device = device
 571         data_set = torchvision.datasets.MNIST(root="./data", train=True, download=True)
 572         self.train_input = data_set.data[:nb_train_samples].view(-1, 28 * 28).long()
 573         data_set = torchvision.datasets.MNIST(root="./data", train=False, download=True)
 574         self.test_input = data_set.data[:nb_test_samples].view(-1, 28 * 28).long()
 575
 576     def batches(self, split="train", nb_to_use=-1, desc=None):
 577         assert split in {"train", "test"}
 578         input = self.train_input if split == "train" else self.test_input
 579         if nb_to_use > 0:
 580             input = input[:nb_to_use]
 581         if desc is None:
 582             desc = f"epoch-{split}"
 583         for batch in tqdm.tqdm(
 584             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 585         ):
 586             yield batch
 587
 588     def vocabulary_size(self):
 589         return 256
 590
 591     def produce_results(
 592         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 593     ):
 594         results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
 595         ar_mask = torch.full_like(results, 1)
 596         masked_inplace_autoregression(
 597             model,
 598             self.batch_size,
 599             results,
 600             ar_mask,
 601             deterministic_synthesis,
 602             device=self.device,
 603         )
 604         image_name = os.path.join(result_dir, f"mnist_result_{n_epoch:04d}.png")
 605         torchvision.utils.save_image(
 606             1 - results.reshape(-1, 1, 28, 28) / 255.0,
 607             image_name,
 608             nrow=16,
 609             pad_value=0.8,
 610         )
 611         logger(f"wrote {image_name}")
 612
 613
 614 ######################################################################
 615
 616 import maze
 617
 618
 619 class Maze(Task):
 620     def map2seq(self, *m):
 621         return torch.cat([x.flatten(1) for x in m], 1)
 622
 623     def seq2map(self, s):
 624         s = s.reshape(s.size(0), -1, self.height, self.width)
 625         return (s[:, k] for k in range(s.size(1)))
 626
 627     def __init__(
 628         self,
 629         nb_train_samples,
 630         nb_test_samples,
 631         batch_size,
 632         height,
 633         width,
 634         nb_walls,
 635         device=torch.device("cpu"),
 636     ):
 637         super().__init__()
 638
 639         self.batch_size = batch_size
 640         self.height = height
 641         self.width = width
 642         self.device = device
 643
 644         train_mazes, train_paths, _ = maze.create_maze_data(
 645             nb_train_samples,
 646             height=height,
 647             width=width,
 648             nb_walls=nb_walls,
 649             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
 650         )
 651         self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
 652
 653         test_mazes, test_paths, _ = maze.create_maze_data(
 654             nb_test_samples,
 655             height=height,
 656             width=width,
 657             nb_walls=nb_walls,
 658             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
 659         )
 660         self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
 661
 662         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 663
 664     def batches(self, split="train", nb_to_use=-1, desc=None):
 665         assert split in {"train", "test"}
 666         input = self.train_input if split == "train" else self.test_input
 667         if nb_to_use > 0:
 668             input = input[:nb_to_use]
 669         if desc is None:
 670             desc = f"epoch-{split}"
 671         for batch in tqdm.tqdm(
 672             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 673         ):
 674             yield batch
 675
 676     def vocabulary_size(self):
 677         return self.nb_codes
 678
 679     def compute_error(
 680         self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
 681     ):
 682         nb_total, nb_correct = 0, 0
 683         count = torch.zeros(
 684             self.width * self.height,
 685             self.width * self.height,
 686             device=self.device,
 687             dtype=torch.int64,
 688         )
 689
 690         for input in self.batches(split, nb_to_use):
 691             result = input.clone()
 692             ar_mask = result.new_zeros(result.size())
 693             ar_mask[:, self.height * self.width :] = 1
 694             result *= 1 - ar_mask
 695             masked_inplace_autoregression(
 696                 model,
 697                 self.batch_size,
 698                 result,
 699                 ar_mask,
 700                 deterministic_synthesis,
 701                 progress_bar_desc=None,
 702                 device=self.device,
 703             )
 704             mazes, paths = self.seq2map(result)
 705             path_correctness = maze.path_correctness(mazes, paths)
 706             nb_correct += path_correctness.long().sum()
 707             nb_total += mazes.size(0)
 708
 709             optimal_path_lengths = (
 710                 (input[:, self.height * self.width :] == maze.v_path).long().sum(1)
 711             )
 712             predicted_path_lengths = (
 713                 (result[:, self.height * self.width :] == maze.v_path).long().sum(1)
 714             )
 715             optimal_path_lengths = optimal_path_lengths[path_correctness]
 716             predicted_path_lengths = predicted_path_lengths[path_correctness]
 717             count[optimal_path_lengths, predicted_path_lengths] += 1
 718
 719         if count.max() == 0:
 720             count = None
 721         else:
 722             count = count[
 723                 : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
 724             ]
 725
 726         return nb_total, nb_correct, count
 727
 728     def produce_results(
 729         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 730     ):
 731         train_nb_total, train_nb_correct, count = self.compute_error(
 732             model,
 733             "train",
 734             nb_to_use=1000,
 735             deterministic_synthesis=deterministic_synthesis,
 736         )
 737         logger(
 738             f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
 739         )
 740
 741         test_nb_total, test_nb_correct, count = self.compute_error(
 742             model,
 743             "test",
 744             nb_to_use=1000,
 745             deterministic_synthesis=deterministic_synthesis,
 746         )
 747         logger(
 748             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 749         )
 750
 751         if count is not None:
 752             proportion_optimal = count.diagonal().sum().float() / count.sum()
 753             logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
 754             with open(
 755                 os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
 756             ) as f:
 757                 for i in range(count.size(0)):
 758                     for j in range(count.size(1)):
 759                         eol = " " if j < count.size(1) - 1 else "\n"
 760                         f.write(f"{count[i,j]}{eol}")
 761
 762         input = self.test_input[:48]
 763         result = input.clone()
 764         ar_mask = result.new_zeros(result.size())
 765         ar_mask[:, self.height * self.width :] = 1
 766         result *= 1 - ar_mask
 767         masked_inplace_autoregression(
 768             model,
 769             self.batch_size,
 770             result,
 771             ar_mask,
 772             deterministic_synthesis,
 773             device=self.device,
 774         )
 775
 776         mazes, paths = self.seq2map(input)
 777         _, predicted_paths = self.seq2map(result)
 778
 779         filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
 780         maze.save_image(
 781             filename,
 782             mazes=mazes,
 783             target_paths=paths,
 784             predicted_paths=predicted_paths,
 785             path_correct=maze.path_correctness(mazes, predicted_paths),
 786             path_optimal=maze.path_optimality(paths, predicted_paths),
 787         )
 788         logger(f"wrote {filename}")
 789
 790
 791 ######################################################################
 792
 793
 794 import snake
 795
 796
 797 class Snake(Task):
 798     def __init__(
 799         self,
 800         nb_train_samples,
 801         nb_test_samples,
 802         batch_size,
 803         height,
 804         width,
 805         nb_colors,
 806         length,
 807         prompt_length,
 808         device=torch.device("cpu"),
 809     ):
 810         super().__init__()
 811
 812         self.batch_size = batch_size
 813         self.height = height
 814         self.width = width
 815         self.device = device
 816         self.prompt_length = prompt_length
 817
 818         self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences(
 819             nb_train_samples,
 820             height,
 821             width,
 822             nb_colors,
 823             length,
 824             prompt_length,
 825             self.device,
 826         )
 827         self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
 828             nb_test_samples,
 829             height,
 830             width,
 831             nb_colors,
 832             length,
 833             prompt_length,
 834             self.device,
 835         )
 836
 837         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 838
 839     def batches(self, split="train", nb_to_use=-1, desc=None):
 840         assert split in {"train", "test"}
 841         input = self.train_input if split == "train" else self.test_input
 842         if nb_to_use > 0:
 843             input = input[:nb_to_use]
 844         if desc is None:
 845             desc = f"epoch-{split}"
 846         for batch in tqdm.tqdm(
 847             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 848         ):
 849             yield batch
 850
 851     def vocabulary_size(self):
 852         return self.nb_codes
 853
 854     def produce_results(
 855         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 856     ):
 857         def compute_nb_correct(input, prior_visits):
 858             result = input.clone()
 859             i = torch.arange(result.size(1), device=result.device)[None, :]
 860             ar_mask = (
 861                 torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
 862                 .long()
 863                 .expand_as(result)
 864             )
 865             result *= 1 - ar_mask
 866
 867             masked_inplace_autoregression(
 868                 model,
 869                 self.batch_size,
 870                 result,
 871                 ar_mask,
 872                 deterministic_synthesis,
 873                 device=self.device,
 874             )
 875
 876             nb_total = ((prior_visits > 0) * ar_mask).sum()
 877
 878             nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
 879
 880             return nb_total, nb_correct
 881
 882         test_nb_total, test_nb_correct = compute_nb_correct(
 883             self.test_input[:1000], self.test_prior_visits[:1000]
 884         )
 885
 886         logger(
 887             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 888         )
 889
 890
 891 ######################################################################
 892
 893
 894 import stack
 895
 896
 897 class Stack(Task):
 898     def __init__(
 899         self,
 900         nb_train_samples,
 901         nb_test_samples,
 902         batch_size,
 903         logger,
 904         nb_steps,
 905         nb_stacks,
 906         nb_digits,
 907         fraction_values_for_train=None,
 908         device=torch.device("cpu"),
 909     ):
 910         super().__init__()
 911
 912         self.batch_size = batch_size
 913         self.nb_steps = nb_steps
 914         self.nb_stacks = nb_stacks
 915         self.nb_digits = nb_digits
 916         self.device = device
 917
 918         if fraction_values_for_train is None:
 919             values_for_train = None
 920             values_for_test = None
 921         else:
 922             all = torch.randperm(10**nb_digits)
 923             nb_for_train = int(all.size(0) * fraction_values_for_train)
 924             values_for_train = all[:nb_for_train]
 925             values_for_test = all[nb_for_train:]
 926
 927         self.train_input, self.train_stack_counts = stack.generate_sequences(
 928             nb_train_samples,
 929             nb_steps,
 930             nb_stacks,
 931             nb_digits,
 932             values_for_train,
 933             self.device,
 934         )
 935
 936         self.test_input, self.test_stack_counts = stack.generate_sequences(
 937             nb_test_samples,
 938             nb_steps,
 939             nb_stacks,
 940             nb_digits,
 941             values_for_test,
 942             self.device,
 943         )
 944
 945         i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks)
 946         counts = self.test_stack_counts.flatten()[i.flatten()]
 947         counts = F.one_hot(counts).sum(0)
 948         logger(f"test_pop_stack_counts {counts}")
 949
 950         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 951
 952     def batches(self, split="train", nb_to_use=-1, desc=None):
 953         assert split in {"train", "test"}
 954         input = self.train_input if split == "train" else self.test_input
 955         if nb_to_use > 0:
 956             input = input[:nb_to_use]
 957         if desc is None:
 958             desc = f"epoch-{split}"
 959         for batch in tqdm.tqdm(
 960             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 961         ):
 962             yield batch
 963
 964     def vocabulary_size(self):
 965         return self.nb_codes
 966
 967     def produce_results(
 968         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 969     ):
 970         def compute_nb_correct(input):
 971             result = input.clone()
 972             stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
 973             ar_mask = (result != input).long()
 974             masked_inplace_autoregression(
 975                 model,
 976                 self.batch_size,
 977                 result,
 978                 ar_mask,
 979                 deterministic_synthesis,
 980                 device=self.device,
 981             )
 982
 983             errors = ((result != input).long() * ar_mask).reshape(
 984                 -1, 1 + self.nb_digits
 985             )
 986             ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
 987
 988             nb_total = ar_mask.max(1).values.sum()
 989             nb_correct = nb_total - errors.max(1).values.sum()
 990
 991             return nb_total, nb_correct
 992
 993         test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
 994
 995         logger(
 996             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 997         )
 998
 999         ##############################################################
1000         # Log a few generated sequences
1001         input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
1002         result = input.clone()
1003         stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
1004         ar_mask = (result != input).long()
1005
1006         # for n in range(result.size(0)):
1007         # logger(
1008         # f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
1009         # )
1010
1011         masked_inplace_autoregression(
1012             model,
1013             self.batch_size,
1014             result,
1015             ar_mask,
1016             deterministic_synthesis,
1017             device=self.device,
1018         )
1019
1020         for n in range(result.size(0)):
1021             logger(
1022                 f"test_after  {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
1023             )
1024         ##############################################################
1025
1026
1027 ######################################################################
1028
1029 import rpl
1030
1031
1032 class RPL(Task):
1033     def tensorize(self, sequences):
1034         len_max = max([len(x) for x in sequences])
1035         return torch.cat(
1036             [
1037                 torch.tensor(
1038                     [
1039                         [
1040                             self.token2id[str(c)]
1041                             for c in s + ["<nul>"] * (len_max - len(s))
1042                         ]
1043                         for s in sequences
1044                     ]
1045                 )
1046             ],
1047             0,
1048         )
1049
1050     def seq2str(self, seq):
1051         return " ".join([self.id2token[i] for i in seq])
1052
1053     def __init__(
1054         self,
1055         nb_train_samples,
1056         nb_test_samples,
1057         batch_size,
1058         nb_starting_values=3,
1059         max_input=9,
1060         prog_len=6,
1061         nb_runs=5,
1062         logger=None,
1063         device=torch.device("cpu"),
1064     ):
1065         super().__init__()
1066
1067         self.batch_size = batch_size
1068         self.device = device
1069
1070         train_sequences = [
1071             rpl.generate(
1072                 nb_starting_values=nb_starting_values,
1073                 max_input=max_input,
1074                 prog_len=prog_len,
1075                 nb_runs=nb_runs,
1076             )
1077             for _ in tqdm.tqdm(range(nb_train_samples), desc="train-data")
1078         ]
1079
1080         test_sequences = [
1081             rpl.generate(
1082                 nb_starting_values=nb_starting_values,
1083                 max_input=max_input,
1084                 prog_len=prog_len,
1085                 nb_runs=nb_runs,
1086             )
1087             for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data")
1088         ]
1089
1090         symbols = list(
1091             set(["<nul>"] + [x for l in train_sequences + test_sequences for x in l])
1092         )
1093         val_max = max([x if type(x) is int else 0 for x in symbols])
1094         symbols = list(filter(lambda x: type(x) is str, symbols))
1095         symbols.sort()
1096         symbols += [str(n) for n in range(val_max + 1)]
1097         self.token2id = dict([(c, n) for n, c in enumerate(symbols)])
1098         self.id2token = dict([(n, c) for c, n in self.token2id.items()])
1099
1100         self.t_nul = self.token2id["<nul>"]
1101         self.t_prog = self.token2id["<prog>"]
1102         self.t_input = self.token2id["<input>"]
1103         self.t_output = self.token2id["<output>"]
1104
1105         self.train_input = self.tensorize(train_sequences)
1106         self.test_input = self.tensorize(test_sequences)
1107
1108         if logger is not None:
1109             logger(f"value_max {val_max}")
1110             for x in self.train_input[:25]:
1111                 end = (x != self.t_nul).nonzero().max().item() + 1
1112                 seq = [self.id2token[i.item()] for i in x[:end]]
1113                 s = " ".join(seq)
1114                 logger(f"example_seq {s}")
1115
1116         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
1117
1118     def batches(self, split="train", nb_to_use=-1, desc=None):
1119         assert split in {"train", "test"}
1120         input = self.train_input if split == "train" else self.test_input
1121         if nb_to_use > 0:
1122             input = input[:nb_to_use]
1123         if desc is None:
1124             desc = f"epoch-{split}"
1125         for batch in tqdm.tqdm(
1126             input.split(self.batch_size), dynamic_ncols=True, desc=desc
1127         ):
1128             last = (batch != self.t_nul).max(0).values.nonzero().max() + 3
1129             batch = batch[:, :last].to(self.device)
1130             yield batch
1131
1132     def vocabulary_size(self):
1133         return self.nb_codes
1134
1135     def produce_results(
1136         self, n_epoch, model, result_dir, logger, deterministic_synthesis
1137     ):
1138         # --------------------------------------------------------------------
1139         def compute_nb_errors_prog(input, nb_to_log=0):
1140             result = input.clone()
1141             s = (result == self.t_prog).long()
1142             ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
1143             result = (1 - ar_mask) * result + ar_mask * self.t_nul
1144
1145             masked_inplace_autoregression(
1146                 model,
1147                 self.batch_size,
1148                 result,
1149                 ar_mask,
1150                 deterministic_synthesis,
1151                 device=self.device,
1152             )
1153
1154             sum_nb_total, sum_nb_errors = 0, 0
1155             for x, y in zip(input, result):
1156                 seq = [self.id2token[i.item()] for i in y]
1157                 nb_total, nb_errors, prog, stacks = rpl.compute_nb_errors(seq)
1158                 sum_nb_total += 1
1159                 sum_nb_errors += 0 if nb_errors == 0 else 1
1160                 if nb_to_log > 0:
1161                     gt_seq = [self.id2token[i.item()] for i in x]
1162                     _, _, gt_prog, _ = rpl.compute_nb_errors(gt_seq)
1163                     gt_prog = " ".join([str(x) for x in gt_prog])
1164                     prog = " ".join([str(x) for x in prog])
1165                     comment = "*" if nb_errors == 0 else "-"
1166                     logger(f"{comment} PROG [{gt_prog}] PREDICTED [{prog}]")
1167                     for start_stack, target_stack, result_stack, correct in stacks:
1168                         comment = "*" if correct else "-"
1169                         start_stack = " ".join([str(x) for x in start_stack])
1170                         target_stack = " ".join([str(x) for x in target_stack])
1171                         result_stack = " ".join([str(x) for x in result_stack])
1172                         logger(
1173                             f"  {comment} [{start_stack}] -> [{target_stack}] PREDICTED [{result_stack}]"
1174                         )
1175                     nb_to_log -= 1
1176
1177             return sum_nb_total, sum_nb_errors
1178
1179         # --------------------------------------------------------------------
1180         def compute_nb_errors_output(input, nb_to_log=0):
1181             result = input.clone()
1182             k = torch.arange(result.size(1), device=result.device)[None, :]
1183             last_output_idx = ((result == self.t_output) * k).max(dim=1, keep_dim=True)
1184             first_prog_idx = ((result == self.t_prog) * k).min(dim=1, keep_dim=True)
1185             ar_mask = (k > last_output_idx).long() * (k < first_prog_idx)
1186             result = (1 - ar_mask) * result + ar_mask * self.t_nul
1187
1188             masked_inplace_autoregression(
1189                 model,
1190                 self.batch_size,
1191                 result,
1192                 ar_mask,
1193                 deterministic_synthesis,
1194                 device=self.device,
1195             )
1196
1197             sum_nb_total, sum_nb_errors = 0, 0
1198             for x, y in zip(input, result):
1199                 seq = [self.id2token[i.item()] for i in y]
1200                 sum_nb_total += 1
1201                 sum_nb_errors += 0 if (x - y).abs().max() == 0 else 1
1202                 if nb_to_log > 0:
1203                     gt_seq = [self.id2token[i.item()] for i in x]
1204                     _, _, gt_prog, _ = rpl.compute_nb_errors(gt_seq)
1205                     gt_prog = " ".join([str(x) for x in gt_prog])
1206                     prog = " ".join([str(x) for x in prog])
1207                     comment = "*" if nb_errors == 0 else "-"
1208                     logger(f"{comment} PROG [{gt_prog}] PREDICTED [{prog}]")
1209                     for start_stack, target_stack, result_stack, correct in stacks:
1210                         comment = "*" if correct else "-"
1211                         start_stack = " ".join([str(x) for x in start_stack])
1212                         target_stack = " ".join([str(x) for x in target_stack])
1213                         result_stack = " ".join([str(x) for x in result_stack])
1214                         logger(
1215                             f"  {comment} [{start_stack}] -> [{target_stack}] PREDICTED [{result_stack}]"
1216                         )
1217                     nb_to_log -= 1
1218
1219             return sum_nb_total, sum_nb_errors
1220
1221         # --------------------------------------------------------------------
1222
1223         test_nb_total, test_nb_errors = compute_nb_errors_prog(
1224             self.test_input[:1000].to(self.device), nb_to_log=10
1225         )
1226
1227         logger(
1228             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
1229         )
1230
1231
1232 ######################################################################
1233
1234
1235 import expr
1236
1237
1238 class Expr(Task):
1239     def tensorize(self, sequences):
1240         len_max = max([len(x) for x in sequences])
1241         return torch.cat(
1242             [
1243                 torch.tensor(
1244                     [
1245                         [self.char2id[c] for c in s + "#" * (len_max - len(s))]
1246                         for s in sequences
1247                     ]
1248                 )
1249             ],
1250             0,
1251         ).to(self.device)
1252
1253     def __init__(
1254         self,
1255         nb_train_samples,
1256         nb_test_samples,
1257         nb_variables,
1258         sequence_length,
1259         operand_max,
1260         result_max,
1261         batch_size,
1262         device=torch.device("cpu"),
1263     ):
1264         super().__init__()
1265
1266         self.batch_size = batch_size
1267         self.device = device
1268
1269         train_sequences = expr.generate_sequences(
1270             nb_train_samples,
1271             nb_variables=nb_variables,
1272             length=sequence_length,
1273             operand_max=operand_max,
1274             result_max=result_max,
1275         )
1276
1277         test_sequences = expr.generate_sequences(
1278             nb_test_samples,
1279             nb_variables=nb_variables,
1280             length=sequence_length,
1281             operand_max=operand_max,
1282             result_max=result_max,
1283         )
1284
1285         symbols = list(set("#" + "".join(train_sequences + test_sequences)))
1286         symbols.sort()
1287
1288         self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
1289         self.id2char = dict([(n, c) for c, n in self.char2id.items()])
1290
1291         self.filler, self.space = self.char2id["#"], self.char2id[" "]
1292
1293         self.train_input = self.tensorize(train_sequences)
1294         self.test_input = self.tensorize(test_sequences)
1295
1296         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
1297
1298     def batches(self, split="train", nb_to_use=-1, desc=None):
1299         assert split in {"train", "test"}
1300         input = self.train_input if split == "train" else self.test_input
1301         if nb_to_use > 0:
1302             input = input[:nb_to_use]
1303         if desc is None:
1304             desc = f"epoch-{split}"
1305         for batch in tqdm.tqdm(
1306             input.split(self.batch_size), dynamic_ncols=True, desc=desc
1307         ):
1308             last = (batch != self.filler).max(0).values.nonzero().max() + 3
1309             batch = batch[:, :last]
1310             yield batch
1311
1312     def vocabulary_size(self):
1313         return self.nb_codes
1314
1315     def seq2str(self, s):
1316         return "".join([self.id2char[k.item()] for k in s])
1317
1318     def produce_results(
1319         self,
1320         n_epoch,
1321         model,
1322         result_dir,
1323         logger,
1324         deterministic_synthesis,
1325         input_file=None,
1326     ):
1327         def compute_nb_correct(input):
1328             result = input.clone()
1329             s = (result == self.space).long()
1330             ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
1331             result = (1 - ar_mask) * result + ar_mask * self.filler
1332             masked_inplace_autoregression(
1333                 model,
1334                 self.batch_size,
1335                 result,
1336                 ar_mask,
1337                 deterministic_synthesis,
1338                 device=self.device,
1339             )
1340
1341             nb_total = input.size(0)
1342             nb_correct = (input == result).long().min(1).values.sum()
1343
1344             #######################################################################
1345             # Comput predicted vs. true variable values
1346
1347             nb_delta = torch.zeros(5, dtype=torch.int64)
1348             nb_missed = 0
1349
1350             values_input = expr.extract_results([self.seq2str(s) for s in input])
1351             values_result = expr.extract_results([self.seq2str(s) for s in result])
1352
1353             filename = os.path.join(result_dir, f"expr_result_{n_epoch:04d}.txt")
1354
1355             with open(filename, "w") as f:
1356                 for i, r in zip(values_input, values_result):
1357                     for n, vi in i.items():
1358                         vr = r.get(n)
1359                         f.write(f"{vi} {-1 if vr is None else vr}\n")
1360
1361                         if vr is None or vr < 0:
1362                             nb_missed += 1
1363                         else:
1364                             d = abs(vr - vi)
1365                             if d >= nb_delta.size(0):
1366                                 nb_missed += 1
1367                             else:
1368                                 nb_delta[d] += 1
1369
1370             ######################################################################
1371
1372             return nb_total, nb_correct, nb_delta, nb_missed
1373
1374         (
1375             test_nb_total,
1376             test_nb_correct,
1377             test_nb_delta,
1378             test_nb_missed,
1379         ) = compute_nb_correct(self.test_input[:10000])
1380
1381         logger(
1382             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
1383         )
1384
1385         nb_total = test_nb_delta.sum() + test_nb_missed
1386         for d in range(test_nb_delta.size(0)):
1387             logger(
1388                 f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
1389             )
1390         logger(
1391             f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
1392         )
1393
1394         ##############################################################
1395         # Log a few generated sequences
1396         if input_file is None:
1397             input = self.test_input[:10]
1398         else:
1399             with open(input_file, "r") as f:
1400                 sequences = [e.strip() for e in f.readlines()]
1401                 sequences = [s + " " + "#" * 50 for s in sequences]
1402                 input = self.tensorize(sequences)
1403
1404         result = input.clone()
1405         s = (result == self.space).long()
1406         ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
1407         result = (1 - ar_mask) * result + ar_mask * self.filler
1408
1409         for n in range(result.size(0)):
1410             logger(f"test_before {self.seq2str(result[n])}")
1411
1412         masked_inplace_autoregression(
1413             model,
1414             self.batch_size,
1415             result,
1416             ar_mask,
1417             deterministic_synthesis,
1418             device=self.device,
1419         )
1420
1421         correct = (1 - ar_mask) * self.space + ar_mask * input
1422         for n in range(result.size(0)):
1423             comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
1424             logger(f"test_after  {self.seq2str(result[n])} {comment}")
1425             logger(f"truth       {self.seq2str(correct[n])}")
1426         ##############################################################
1427
1428
1429 ######################################################################
1430
1431 import world
1432
1433
1434 class World(Task):
1435     def __init__(
1436         self,
1437         nb_train_samples,
1438         nb_test_samples,
1439         batch_size,
1440         vqae_nb_epochs,
1441         logger=None,
1442         device=torch.device("cpu"),
1443         device_storage=torch.device("cpu"),
1444     ):
1445         super().__init__()
1446
1447         self.batch_size = batch_size
1448         self.device = device
1449
1450         (
1451             train_frames,
1452             train_action_seq,
1453             test_frames,
1454             test_action_seq,
1455             self.frame2seq,
1456             self.seq2frame,
1457         ) = world.create_data_and_processors(
1458             nb_train_samples,
1459             nb_test_samples,
1460             mode="first_last",
1461             nb_steps=30,
1462             nb_epochs=vqae_nb_epochs,
1463             logger=logger,
1464             device=device,
1465             device_storage=device_storage,
1466         )
1467
1468         train_frame_seq = self.frame2seq(train_frames).to(device_storage)
1469         test_frame_seq = self.frame2seq(test_frames).to(device_storage)
1470
1471         nb_frame_codes = max(train_frame_seq.max(), test_frame_seq.max()) + 1
1472         nb_action_codes = max(train_action_seq.max(), test_action_seq.max()) + 1
1473
1474         self.len_frame_seq = train_frame_seq.size(1)
1475         self.len_action_seq = train_action_seq.size(1)
1476         self.nb_codes = nb_frame_codes + nb_action_codes
1477
1478         train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
1479
1480         train_action_seq += nb_frame_codes
1481         self.train_input = torch.cat(
1482             (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
1483         )
1484
1485         test_frame_seq = test_frame_seq.reshape(test_frame_seq.size(0) // 2, 2, -1)
1486         test_action_seq += nb_frame_codes
1487         self.test_input = torch.cat(
1488             (test_frame_seq[:, 0, :], test_action_seq, test_frame_seq[:, 1, :]), 1
1489         )
1490
1491     def batches(self, split="train", nb_to_use=-1, desc=None):
1492         assert split in {"train", "test"}
1493         input = self.train_input if split == "train" else self.test_input
1494         if nb_to_use > 0:
1495             input = input[:nb_to_use]
1496         if desc is None:
1497             desc = f"epoch-{split}"
1498         for batch in tqdm.tqdm(
1499             input.split(self.batch_size), dynamic_ncols=True, desc=desc
1500         ):
1501             yield batch.to(self.device)
1502
1503     def vocabulary_size(self):
1504         return self.nb_codes
1505
1506     def produce_results(
1507         self, n_epoch, model, result_dir, logger, deterministic_synthesis
1508     ):
1509         k = torch.arange(
1510             2 * self.len_frame_seq + self.len_action_seq, device=self.device
1511         )[None, :]
1512
1513         input = self.test_input[:64].to(self.device)
1514         result = input.clone()
1515
1516         ar_mask = (
1517             (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result)
1518         )
1519         result *= 1 - ar_mask
1520
1521         masked_inplace_autoregression(
1522             model,
1523             self.batch_size,
1524             result,
1525             ar_mask,
1526             deterministic_synthesis,
1527             device=self.device,
1528         )
1529
1530         seq_start = input[:, : self.len_frame_seq]
1531         seq_end = input[:, self.len_frame_seq + self.len_action_seq :]
1532         seq_predicted = result[:, self.len_frame_seq + self.len_action_seq :]
1533
1534         result = torch.cat(
1535             (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
1536         )
1537         result = result.reshape(-1, result.size(-1))
1538
1539         frames = self.seq2frame(result)
1540         image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
1541         torchvision.utils.save_image(
1542             frames.float() / (world.Box.nb_rgb_levels - 1),
1543             image_name,
1544             nrow=12,
1545             padding=1,
1546             pad_value=0.0,
1547         )
1548         logger(f"wrote {image_name}")
1549
1550
1551 ######################################################################