# Written by Francois Fleuret <francois@fleuret.org>
-import math, os, tqdm
+import math, os, tqdm, warnings
import torch, torchvision
from mygpt import BracketedSequence
-try:
- from graph import save_attention_image
-except ImportError:
- save_attention_image = None
-
######################################################################
batch_size,
input,
ar_mask,
+ temperature,
deterministic_synthesis,
forbidden_tokens=None,
+ logit_biases=None,
progress_bar_desc="autoregression",
device=torch.device("cpu"),
):
t = model.training
model.eval()
+ sum_logits = 0
+
for input, ar_mask in batches:
- model.masked_inplace_autoregression(
- input, ar_mask, forbidden_tokens, deterministic_synthesis
+ sum_logits += model.masked_inplace_autoregression(
+ input=input,
+ ar_mask=ar_mask,
+ temperature=temperature,
+ deterministic_synthesis=deterministic_synthesis,
+ forbidden_tokens=forbidden_tokens,
+ forced_biases=logit_biases,
)
model.train(t)
+ return sum_logits
+
######################################################################
class Task:
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
pass
def vocabulary_size(self):
pass
-####################
-
-import problems
-
-
-class SandBox(Task):
- def __init__(
- self,
- problem,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- logger=None,
- device=torch.device("cpu"),
- max_nb_codes=1024,
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.device = device
- self.problem = problem
-
- self.train_input, self.train_ar_mask = self.problem.generate_sequences(
- nb_train_samples
- )
- self.test_input, self.test_ar_mask = self.problem.generate_sequences(
- nb_test_samples
- )
-
- self.train_input, self.train_ar_mask = self.train_input.to(
- device
- ), self.train_ar_mask.to(device)
- self.test_input, self.test_ar_mask = self.test_input.to(
- device
- ), self.test_ar_mask.to(device)
-
- self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
- # A bit of paranoia never hurts
- assert (
- self.nb_codes <= max_nb_codes
- and self.train_input.min() >= 0
- and self.test_input.min() >= 0
- and tuple(self.train_ar_mask.unique()) == (0, 1)
- and tuple(self.test_ar_mask.unique()) == (0, 1)
- )
-
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
- ):
- def compute_accuracy(input, ar_mask, logger=None):
- input, ar_mask = input[:nmax], ar_mask[:nmax]
- result = input.clone() * (1 - ar_mask)
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- progress_bar_desc=None,
- device=self.device,
- )
-
- if logger is not None:
- for sp, st in zip(result[:10], input[:10]):
- logger(
- f"test_sequences {n_epoch} prediction {self.problem.seq2str(sp)}"
- )
- logger(
- f" {n_epoch} ground truth {self.problem.seq2str(st)}"
- )
-
- nb_total = ar_mask.sum().item()
- nb_correct = ((result == input).long() * ar_mask).sum().item()
-
- return nb_total, nb_correct
-
- train_nb_total, train_nb_correct = compute_accuracy(
- self.train_input, self.train_ar_mask
- )
-
- logger(
- 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}%"
- )
-
- test_nb_total, test_nb_correct = compute_accuracy(
- self.test_input, self.test_ar_mask, logger
- )
-
- logger(
- 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}%"
- )
+######################################################################
+import world
-######################################################################
-import picoclvr
-
-
-class PicoCLVR(Task):
- # Make a tensor from a list of strings
- def tensorize(self, descr):
- token_descr = [s.strip().split(" ") for s in descr]
- l = max([len(s) for s in token_descr])
- token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
- id_descr = [[self.token2id[u] for u in s] for s in token_descr]
- return torch.tensor(id_descr, device=self.device)
-
- # Make a list of strings from a tensor
- def detensorize(self, x):
- return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
-
- # trim all the tensors in the tuple z to remove as much token from
- # left and right in the first tensor. If z is a tuple, all its
- # elements are trimed according to the triming for the first
- def trim(self, z, token="<nul>"):
- n = self.token2id[token]
- if type(z) == tuple:
- x = z[0]
- i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return tuple([t[:, a:b] for t in z])
- else:
- i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return z[:, a:b]
+class World(Task):
+ def save_image(self, input, result_dir, filename, logger):
+ img = world.seq2img(input.to("cpu"), self.height, self.width)
+ image_name = os.path.join(result_dir, filename)
+ torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=6, padding=4)
+ logger(f"wrote {image_name}")
- ######################
+ def make_ar_mask(self, input):
+ b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
+ return b.long()[None, :].expand_as(input)
def __init__(
self,
nb_train_samples,
nb_test_samples,
batch_size,
- height,
- width,
- nb_colors=5,
+ result_dir=None,
logger=None,
device=torch.device("cpu"),
- pruner_train=None,
- pruner_eval=None,
):
super().__init__()
- def generate_descr(nb, cache_suffix, pruner):
- return picoclvr.generate(
- nb,
- height=self.height,
- width=self.width,
- nb_colors=nb_colors,
- pruner=pruner,
- )
-
- self.height = height
- self.width = width
self.batch_size = batch_size
self.device = device
- self.pruner_train = pruner_train
- self.pruner_eval = pruner_eval
-
- if logger is not None:
- logger(
- f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
- )
+ self.height = 6
+ self.width = 8
- self.train_descr = generate_descr(
- nb_train_samples, "train", pruner=self.pruner_train
- )
- self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
-
- # Build the tokenizer
- tokens = {"<nul>", "<img>"}
- for d in [self.train_descr, self.test_descr]:
- for s in d:
- for t in s.strip().split(" "):
- tokens.add(t)
- # make this set a sorted list to get the same tensors given
- # the same descr
- tokens = list(tokens)
- tokens.sort()
- self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
- self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
- self.t_img, self.t_nul = self.token2id["<img>"], self.token2id["<nul>"]
-
- # Tokenize the train and test sets
- self.train_input = self.tensorize(self.train_descr)
- self.test_input = self.tensorize(self.test_descr)
-
- def batches(self, split="train"):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
- ):
- yield self.trim(batch)
+ self.train_input = world.generate_seq(
+ nb_train_samples, height=self.height, width=self.width
+ ).to(device)
- def vocabulary_size(self):
- return len(self.token2id)
+ self.test_input = world.generate_seq(
+ nb_test_samples, height=self.height, width=self.width
+ ).to(device)
- def compute_missing_properties(
- self, n_epoch, model, logger, deterministic_synthesis, pruner=None
- ):
- acc_nb_requested_properties = []
- acc_nb_missing_properties = []
- acc_nb_results = 0
+ self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
- for input in tqdm.tqdm(
- self.test_input.split(self.batch_size),
- dynamic_ncols=True,
- desc=f"test-properties",
- ):
- result = input.clone()
- ar_mask = (result == self.t_img).long().cumsum(dim=1).clamp(max=1)
- result = (1 - ar_mask) * result + ar_mask * self.t_nul
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- progress_bar_desc=None,
- device=self.device,
- )
+ self.train_quizzes = []
+ self.test_quizzes = []
- result_descr = self.detensorize(result)
- np = picoclvr.nb_properties(
- result_descr,
- height=self.height,
- width=self.width,
- pruner=pruner,
+ if result_dir is not None:
+ self.save_image(
+ self.train_input[:72], result_dir, f"world_train.png", logger
)
- nb_requested_properties, _, nb_missing_properties = zip(*np)
- acc_nb_requested_properties += nb_requested_properties
- acc_nb_missing_properties += nb_missing_properties
- acc_nb_results += len(result_descr)
-
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
-
- prefix = "" if pruner is None else "pruned_"
- logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
- logger(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
- )
- logger(
- f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
- )
-
- ######################################################################
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- self.compute_missing_properties(n_epoch, model, logger, deterministic_synthesis)
-
- if self.pruner_eval is not None:
- self.compute_missing_properties(n_epoch, model, self.pruner_eval)
-
- nb_tokens_to_generate = self.height * self.width + 3
- result_descr = []
- nb_per_primer = 8
- primer = []
-
- for primer_descr in [
- "red above green <sep> green top <sep> blue right of red",
- "there is red <sep> there is yellow <sep> there is blue",
- "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
- "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
- ]:
- primer += [primer_descr + " <img>"] * nb_per_primer
-
- result = self.tensorize(primer)
- fill = result.new_full(
- result.size()[:-1] + (self.height * self.width + 1,), self.t_nul
- )
- result = torch.cat((result, fill), 1)
- ar_mask = (result == self.t_nul).long()
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
- result_descr = self.detensorize(result)
-
- np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
-
- acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
- acc_nb_results = len(result_descr)
-
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
-
- prefix = "demo_"
- logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
- logger(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
- )
- logger(
- f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
- )
-
- img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
-
- if img.dim() == 5:
- if img.size(1) == 1:
- img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
- else:
- img = torch.cat(
- [
- torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
- for x in img
- ],
- 0,
- )
-
- image_name = os.path.join(result_dir, f"picoclvr_result_{n_epoch:04d}.png")
- torchvision.utils.save_image(
- img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0
- )
- logger(f"wrote {image_name}")
-
-
-######################################################################
-
-
-class MNIST(Task):
- def __init__(
- self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
- ):
- super().__init__()
-
- self.nb_train_samples = (nb_train_samples,)
- self.nb_test_samples = (nb_test_samples,)
- self.batch_size = batch_size
- self.device = device
- data_set = torchvision.datasets.MNIST(root="./data", train=True, download=True)
- self.train_input = data_set.data[:nb_train_samples].view(-1, 28 * 28).long()
- data_set = torchvision.datasets.MNIST(root="./data", train=False, download=True)
- self.test_input = data_set.data[:nb_test_samples].view(-1, 28 * 28).long()
- def batches(self, split="train", nb_to_use=-1, desc=None):
+ def batches(self, split="train", desc=None):
assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- yield batch
-
- def vocabulary_size(self):
- return 256
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
- ar_mask = torch.full_like(results, 1)
- masked_inplace_autoregression(
- model,
- self.batch_size,
- results,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
- image_name = os.path.join(result_dir, f"mnist_result_{n_epoch:04d}.png")
- torchvision.utils.save_image(
- 1 - results.reshape(-1, 1, 28, 28) / 255.0,
- image_name,
- nrow=16,
- pad_value=0.8,
- )
- logger(f"wrote {image_name}")
-
-
-######################################################################
-
-import maze
-
-
-class Maze(Task):
- def map2seq(self, *m):
- return torch.cat([x.flatten(1) for x in m], 1)
-
- def seq2map(self, s):
- s = s.reshape(s.size(0), -1, self.height, self.width)
- return (s[:, k] for k in range(s.size(1)))
+ if split == "train":
+ input = self.train_input
+ quizzes = self.train_quizzes
+ else:
+ input = self.test_input
+ quizzes = self.test_quizzes
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_walls,
- device=torch.device("cpu"),
- ):
- super().__init__()
+ if len(quizzes) > 0:
+ quizzes = torch.cat(quizzes, dim=0)
+ if quizzes.size(0) > input.size(0) // 2:
+ i = torch.randperm(input.size(0))[: input.size(0) // 2]
+ quizzes = quizzes[i]
- self.batch_size = batch_size
- self.height = height
- self.width = width
- self.device = device
+ i = torch.randperm(input.size(0))[: input.size(0) - quizzes.size(0)]
+ input = input[i]
- train_mazes, train_paths, _ = maze.create_maze_data(
- nb_train_samples,
- height=height,
- width=width,
- nb_walls=nb_walls,
- progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
- )
- self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
-
- test_mazes, test_paths, _ = maze.create_maze_data(
- nb_test_samples,
- height=height,
- width=width,
- nb_walls=nb_walls,
- progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
- )
- self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
+ self.nb_batch_samples_world = input.size(0)
+ self.nb_batch_samples_quizzes = quizzes.size(0)
- self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+ input = torch.cat([input, quizzes], dim=0)
+ else:
+ self.nb_batch_samples_world = input.size(0)
+ self.nb_batch_samples_quizzes = 0
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
if desc is None:
desc = f"epoch-{split}"
for batch in tqdm.tqdm(
def vocabulary_size(self):
return self.nb_codes
- def compute_error(
- self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
):
- nb_total, nb_correct = 0, 0
- count = torch.zeros(
- self.width * self.height,
- self.width * self.height,
- device=self.device,
- dtype=torch.int64,
- )
+ def compute_accuracy(input, logger=None):
+ input = input[:nmax]
+ ar_mask = self.make_ar_mask(input)
+ result = input.clone() * (1 - ar_mask)
- for input in self.batches(split, nb_to_use):
- result = input.clone()
- ar_mask = result.new_zeros(result.size())
- ar_mask[:, self.height * self.width :] = 1
- result *= 1 - ar_mask
masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
+ model=model,
+ batch_size=self.batch_size,
+ input=result,
+ ar_mask=ar_mask,
+ temperature=1.0,
+ deterministic_synthesis=deterministic_synthesis,
progress_bar_desc=None,
device=self.device,
)
- mazes, paths = self.seq2map(result)
- path_correctness = maze.path_correctness(mazes, paths)
- nb_correct += path_correctness.long().sum()
- nb_total += mazes.size(0)
- optimal_path_lengths = (
- (input[:, self.height * self.width :] == maze.v_path).long().sum(1)
- )
- predicted_path_lengths = (
- (result[:, self.height * self.width :] == maze.v_path).long().sum(1)
+ nb_total, nb_correct = (
+ input.size(0),
+ (input == result).long().min(dim=1).values.sum(),
)
- optimal_path_lengths = optimal_path_lengths[path_correctness]
- predicted_path_lengths = predicted_path_lengths[path_correctness]
- count[optimal_path_lengths, predicted_path_lengths] += 1
- if count.max() == 0:
- count = None
- else:
- count = count[
- : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
- ]
+ return nb_total, nb_correct
- return nb_total, nb_correct, count
+ train_nb_total, train_nb_correct = compute_accuracy(self.train_input)
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- train_nb_total, train_nb_correct, count = self.compute_error(
- model,
- "train",
- nb_to_use=1000,
- deterministic_synthesis=deterministic_synthesis,
- )
logger(
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}%"
)
- test_nb_total, test_nb_correct, count = self.compute_error(
- model,
- "test",
- nb_to_use=1000,
- deterministic_synthesis=deterministic_synthesis,
- )
+ test_nb_total, test_nb_correct = compute_accuracy(self.test_input, logger)
+
logger(
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}%"
)
- if count is not None:
- proportion_optimal = count.diagonal().sum().float() / count.sum()
- logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
- with open(
- os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
- ) as f:
- for i in range(count.size(0)):
- for j in range(count.size(1)):
- eol = " " if j < count.size(1) - 1 else "\n"
- f.write(f"{count[i,j]}{eol}")
-
- input = self.test_input[:48]
- result = input.clone()
- ar_mask = result.new_zeros(result.size())
- ar_mask[:, self.height * self.width :] = 1
- result *= 1 - ar_mask
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- mazes, paths = self.seq2map(input)
- _, predicted_paths = self.seq2map(result)
-
- filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
- maze.save_image(
- filename,
- mazes=mazes,
- target_paths=paths,
- predicted_paths=predicted_paths,
- path_correct=maze.path_correctness(mazes, predicted_paths),
- path_optimal=maze.path_optimality(paths, predicted_paths),
- )
- logger(f"wrote {filename}")
-
-
-######################################################################
-
-
-import snake
-
-
-class Snake(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.height = height
- self.width = width
- self.device = device
- self.prompt_length = prompt_length
-
- self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences(
- nb_train_samples,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- self.device,
- )
- self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
- nb_test_samples,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- self.device,
- )
-
- self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- def compute_nb_correct(input, prior_visits):
- result = input.clone()
- i = torch.arange(result.size(1), device=result.device)[None, :]
- ar_mask = (
- torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
- .long()
- .expand_as(result)
- )
- result *= 1 - ar_mask
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- nb_total = ((prior_visits > 0) * ar_mask).sum()
+ main_test_accuracy = test_nb_correct / test_nb_total
+ logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
- nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
+ ##############################
- return nb_total, nb_correct
+ input = self.test_input[:96]
+ ar_mask = self.make_ar_mask(input)
+ result = input.clone() * (1 - ar_mask)
- test_nb_total, test_nb_correct = compute_nb_correct(
- self.test_input[:1000], self.test_prior_visits[:1000]
+ masked_inplace_autoregression(
+ model=model,
+ batch_size=self.batch_size,
+ input=result,
+ ar_mask=ar_mask,
+ temperature=1.0,
+ deterministic_synthesis=deterministic_synthesis,
+ progress_bar_desc=None,
+ device=self.device,
)
- logger(
- 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}%"
+ self.save_image(
+ result[:72],
+ result_dir,
+ f"world_prediction_{n_epoch:04d}_{model.id:02d}.png",
+ logger,
)
+ return main_test_accuracy
-######################################################################
-
-
-import stack
+ def renew_samples(self, nb, for_train=True):
+ input = self.train_input if for_train else self.test_input
+ nb = min(nb, input.size(0))
+ input[:-nb] = input[nb:].clone()
+ input[-nb:] = world.generate_seq(nb, height=self.height, width=self.width).to(
+ self.device
+ )
+ def store_new_quizzes(self, new_quizzes, for_train=True):
+ if for_train:
+ self.train_quizzes.append(new_quizzes)
+ else:
+ self.test_quizzes.append(new_quizzes)
-class Stack(Task):
- def __init__(
+ def create_new_quizzes(
self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
+ n_epoch,
+ result_dir,
logger,
- nb_steps,
- nb_stacks,
- nb_digits,
- fraction_values_for_train=None,
- device=torch.device("cpu"),
+ nb,
+ model,
+ other_models,
+ desired_average_logits=None,
):
- super().__init__()
-
- self.batch_size = batch_size
- self.nb_steps = nb_steps
- self.nb_stacks = nb_stacks
- self.nb_digits = nb_digits
- self.device = device
-
- if fraction_values_for_train is None:
- values_for_train = None
- values_for_test = None
- else:
- all = torch.randperm(10**nb_digits)
- nb_for_train = int(all.size(0) * fraction_values_for_train)
- values_for_train = all[:nb_for_train]
- values_for_test = all[nb_for_train:]
-
- self.train_input, self.train_stack_counts = stack.generate_sequences(
- nb_train_samples,
- nb_steps,
- nb_stacks,
- nb_digits,
- values_for_train,
- self.device,
- )
-
- self.test_input, self.test_stack_counts = stack.generate_sequences(
- nb_test_samples,
- nb_steps,
- nb_stacks,
- nb_digits,
- values_for_test,
- self.device,
+ ###############################################################
+ # Generate quizzes with model
+
+ quizzes = torch.empty(
+ nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64
+ )
+ ar_mask = torch.full(quizzes.size(), 1, device=self.device)
+
+ sum_logits = masked_inplace_autoregression(
+ model=model,
+ batch_size=self.batch_size,
+ input=quizzes,
+ ar_mask=ar_mask,
+ temperature=1.0,
+ deterministic_synthesis=False,
+ progress_bar_desc="creating quizzes",
+ device=self.device,
)
- i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks)
- counts = self.test_stack_counts.flatten()[i.flatten()]
- counts = F.one_hot(counts).sum(0)
- logger(f"test_pop_stack_counts {counts}")
-
- self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
+ average_logits = sum_logits / quizzes.numel()
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- def compute_nb_correct(input):
- result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
+ if desired_average_logits is not None:
+ temperature = average_logits / desired_average_logits
masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
+ model=model,
+ batch_size=self.batch_size,
+ input=quizzes,
+ ar_mask=ar_mask,
+ temperature=temperature,
+ deterministic_synthesis=False,
+ progress_bar_desc="creating quizzes",
device=self.device,
)
- errors = ((result != input).long() * ar_mask).reshape(
- -1, 1 + self.nb_digits
- )
- ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
-
- nb_total = ar_mask.max(1).values.sum()
- nb_correct = nb_total - errors.max(1).values.sum()
-
- return nb_total, nb_correct
-
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
-
- logger(
- 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}%"
- )
-
- ##############################################################
- # Log a few generated sequences
- input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
- result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
-
- # for n in range(result.size(0)):
- # logger(
- # f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- # )
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- for n in range(result.size(0)):
- logger(
- f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- )
- ##############################################################
-
-
-######################################################################
-
-import rpl
-
-
-class RPL(Task):
- def tensorize(self, sequences):
- len_max = max([len(x) for x in sequences])
- return torch.cat(
- [
- torch.tensor(
- [
- [
- self.token2id[str(c)]
- for c in s + ["<nul>"] * (len_max - len(s))
- ]
- for s in sequences
- ]
- )
- ],
- 0,
- )
-
- def seq2str(self, seq):
- return " ".join([self.id2token[i] for i in seq])
+ ###############################################################
+ # Create the reverse quizzes
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- nb_starting_values=3,
- max_input=9,
- prog_len=6,
- nb_runs=5,
- no_prog=False,
- logger=None,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.device = device
- self.no_prog = no_prog
-
- train_sequences = [
- rpl.generate(
- nb_starting_values=nb_starting_values,
- nb_result_values_max=4 * nb_starting_values,
- max_input=max_input,
- prog_len=prog_len,
- nb_runs=nb_runs,
- )
- for _ in tqdm.tqdm(range(nb_train_samples), desc="train-data")
- ]
-
- test_sequences = [
- rpl.generate(
- nb_starting_values=nb_starting_values,
- nb_result_values_max=4 * nb_starting_values,
- max_input=max_input,
- prog_len=prog_len,
- nb_runs=nb_runs,
- )
- for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data")
- ]
-
- symbols = list(
- set(["<nul>"] + [x for l in train_sequences + test_sequences for x in l])
+ l = self.height * self.width
+ direction = quizzes[:, l : l + 1]
+ direction = world.token_forward * (
+ direction == world.token_backward
+ ) + world.token_backward * (direction == world.token_forward)
+ reverse_quizzes = torch.cat(
+ [quizzes[:, l + 1 :], direction, quizzes[:, :l]], dim=1
)
- val_max = max([x if type(x) is int else 0 for x in symbols])
- symbols = list(filter(lambda x: type(x) is str, symbols))
- symbols.sort()
- symbols += [str(n) for n in range(val_max + 1)]
- self.token2id = dict([(c, n) for n, c in enumerate(symbols)])
- self.id2token = dict([(n, c) for c, n in self.token2id.items()])
-
- self.t_nul = self.token2id["<nul>"]
- self.t_input = self.token2id["<in>"]
- self.t_output = self.token2id["<out>"]
- self.t_prog = self.token2id["<prg>"]
- self.t_end = self.token2id["<end>"]
-
- self.train_input = self.tensorize(train_sequences)
- self.test_input = self.tensorize(test_sequences)
-
- if no_prog:
- # Excise the program from every train and test example
- k = torch.arange(self.train_input.size(1), device=self.train_input.device)[
- None, :
- ]
- p = (
- ((self.train_input == self.t_prog).long() * k)
- .max(1, keepdim=True)
- .values
- )
- self.train_input = (
- self.train_input * (k <= p).long()
- + self.t_end * (k == p + 1).long()
- + self.t_nul * (k > p + 1).long()
- )
- k = torch.arange(self.test_input.size(1), device=self.test_input.device)[
- None, :
- ]
- p = (
- ((self.test_input == self.t_prog).long() * k)
- .max(1, keepdim=True)
- .values
- )
- self.test_input = (
- self.test_input * (k <= p).long()
- + self.t_end * (k == p + 1).long()
- + self.t_nul * (k > p + 1).long()
- )
-
- if logger is not None:
- logger(f"value_max {val_max}")
- for x in self.train_input[:25]:
- end = (x != self.t_nul).nonzero().max().item() + 1
- seq = [self.id2token[i.item()] for i in x[:end]]
- s = " ".join(seq)
- logger(f"example_seq {s}")
-
- self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- last = (batch != self.t_nul).max(0).values.nonzero().max() + 3
- batch = batch[:, :last].to(self.device)
- yield batch
- def vocabulary_size(self):
- return self.nb_codes
+ ar_mask = self.make_ar_mask(quizzes)
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- # --------------------------------------------------------------------
- def compute_nb_errors_prog(input, nb_to_log=0):
- result = input.clone()
- s = (result == self.t_prog).long()
- ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
- result = (1 - ar_mask) * result + ar_mask * self.t_nul
+ ###############################################################
+ # Check how many of the other models can solve them in both
+ # directions
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
+ nb_correct = []
- sum_nb_total, sum_nb_errors = 0, 0
- for one_input, one_result in zip(input, result):
- seq = [self.id2token[i.item()] for i in one_result]
- nb_total, nb_errors, prog, stacks = rpl.compute_nb_errors(seq)
- sum_nb_total += 1
- sum_nb_errors += 0 if nb_errors == 0 else 1
- if nb_to_log > 0:
- gt_seq = [self.id2token[i.item()] for i in one_input]
- _, _, gt_prog, _ = rpl.compute_nb_errors(gt_seq)
- gt_prog = " ".join([str(x) for x in gt_prog])
- prog = " ".join([str(x) for x in prog])
- comment = "*" if nb_errors == 0 else "-"
- logger(f"{comment} PROG [{gt_prog}] PREDICTED [{prog}]")
- for start_stack, target_stack, result_stack, correct in stacks:
- comment = "*" if correct else "-"
- start_stack = " ".join([str(x) for x in start_stack])
- target_stack = " ".join([str(x) for x in target_stack])
- result_stack = " ".join([str(x) for x in result_stack])
- logger(
- f" {comment} [{start_stack}] -> [{target_stack}] PREDICTED [{result_stack}]"
- )
- nb_to_log -= 1
-
- return sum_nb_total, sum_nb_errors
-
- # --------------------------------------------------------------------
- def compute_nb_errors_output(input, nb_to_log=0):
- result = input.clone()
- k = torch.arange(result.size(1), device=result.device)[None, :]
- last_output_idx = (
- ((result == self.t_output) * k).max(dim=1, keepdim=True).values
- )
- first_prog_idx = (
- ((result == self.t_prog) * k).max(dim=1, keepdim=True).values
- )
- ar_mask = (k > last_output_idx).long() * (k < first_prog_idx).long()
- result = (1 - ar_mask) * result + ar_mask * self.t_nul
+ for m in other_models:
+ result = quizzes.clone()
masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
+ model=m,
+ batch_size=self.batch_size,
+ input=result,
+ ar_mask=ar_mask,
+ temperature=1.0,
+ deterministic_synthesis=True,
+ progress_bar_desc="solving quizzes",
device=self.device,
)
- sum_nb_total, sum_nb_errors = 0, 0
- for one_input, one_result, i, j in zip(
- input, result, last_output_idx, first_prog_idx
- ):
- seq = [self.id2token[i.item()] for i in one_result]
- sum_nb_total += 1
- correct = (one_input - one_result).abs().max() == 0
- sum_nb_errors += 0 if correct else 1
- if nb_to_log > 0:
- result_stack = [
- self.id2token[i.item()] for i in one_result[i : j + 1]
- ]
- target_stack = [
- self.id2token[i.item()] for i in one_input[i : j + 1]
- ]
- comment = "*" if correct else "-"
- result_stack = " ".join([str(x) for x in result_stack])
- target_stack = " ".join([str(x) for x in target_stack])
- logger(
- f"output_test {comment} [{target_stack}] PREDICTED [{result_stack}]"
- )
- nb_to_log -= 1
-
- return sum_nb_total, sum_nb_errors
-
- # --------------------------------------------------------------------
-
- if not self.no_prog:
- test_nb_total, test_nb_errors = compute_nb_errors_prog(
- self.test_input[:1000].to(self.device), nb_to_log=10
- )
-
- logger(
- f"accuracy_prog_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
- )
-
- test_nb_total, test_nb_errors = compute_nb_errors_output(
- self.test_input[:1000].to(self.device), nb_to_log=10
- )
-
- logger(
- f"accuracy_output_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
- )
-
- if save_attention_image is not None:
- ns = torch.randint(self.test_input.size(0), (1,)).item()
- input = self.test_input[ns : ns + 1].clone()
- last = (input != self.t_nul).max(0).values.nonzero().max() + 3
- input = input[:, :last].to(self.device)
-
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
- model.record_attention(True)
- model(BracketedSequence(input))
- model.train(t)
- ram = model.retrieve_attention()
- model.record_attention(False)
-
- tokens_output = [self.id2token[i.item()] for i in input[0]]
- tokens_input = ["n/a"] + tokens_output[:-1]
- for n_head in range(ram[0].size(1)):
- filename = os.path.join(
- result_dir, f"rpl_attention_{n_epoch}_h{n_head}.pdf"
- )
- attention_matrices = [m[0, n_head] for m in ram]
- save_attention_image(
- filename,
- tokens_input,
- tokens_output,
- attention_matrices,
- k_top=10,
- # min_total_attention=0.9,
- token_gap=12,
- layer_gap=50,
- )
- logger(f"wrote {filename}")
-
-
-######################################################################
+ correct = (quizzes == result).long().min(dim=-1).values
+ reverse_result = reverse_quizzes.clone()
-import expr
-
-
-class Expr(Task):
- def tensorize(self, sequences):
- len_max = max([len(x) for x in sequences])
- return torch.cat(
- [
- torch.tensor(
- [
- [self.char2id[c] for c in s + "#" * (len_max - len(s))]
- for s in sequences
- ]
- )
- ],
- 0,
- ).to(self.device)
-
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- nb_variables,
- sequence_length,
- operand_max,
- result_max,
- batch_size,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.device = device
-
- train_sequences = expr.generate_sequences(
- nb_train_samples,
- nb_variables=nb_variables,
- length=sequence_length,
- operand_max=operand_max,
- result_max=result_max,
- )
-
- test_sequences = expr.generate_sequences(
- nb_test_samples,
- nb_variables=nb_variables,
- length=sequence_length,
- operand_max=operand_max,
- result_max=result_max,
- )
-
- symbols = list(set("#" + "".join(train_sequences + test_sequences)))
- symbols.sort()
-
- self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
- self.id2char = dict([(n, c) for c, n in self.char2id.items()])
-
- self.filler, self.space = self.char2id["#"], self.char2id[" "]
-
- self.train_input = self.tensorize(train_sequences)
- self.test_input = self.tensorize(test_sequences)
-
- self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- last = (batch != self.filler).max(0).values.nonzero().max() + 3
- batch = batch[:, :last]
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def seq2str(self, s):
- return "".join([self.id2char[k.item()] for k in s])
-
- def produce_results(
- self,
- n_epoch,
- model,
- result_dir,
- logger,
- deterministic_synthesis,
- input_file=None,
- ):
- def compute_nb_correct(input):
- result = input.clone()
- s = (result == self.space).long()
- ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
+ model=m,
+ batch_size=self.batch_size,
+ input=reverse_result,
+ ar_mask=ar_mask,
+ temperature=1.0,
+ deterministic_synthesis=True,
+ progress_bar_desc="solving reversed quizzes",
device=self.device,
)
- nb_total = input.size(0)
- nb_correct = (input == result).long().min(1).values.sum()
-
- #######################################################################
- # Comput predicted vs. true variable values
-
- nb_delta = torch.zeros(5, dtype=torch.int64)
- nb_missed = 0
-
- values_input = expr.extract_results([self.seq2str(s) for s in input])
- values_result = expr.extract_results([self.seq2str(s) for s in result])
-
- filename = os.path.join(result_dir, f"expr_result_{n_epoch:04d}.txt")
-
- with open(filename, "w") as f:
- for i, r in zip(values_input, values_result):
- for n, vi in i.items():
- vr = r.get(n)
- f.write(f"{vi} {-1 if vr is None else vr}\n")
-
- if vr is None or vr < 0:
- nb_missed += 1
- else:
- d = abs(vr - vi)
- if d >= nb_delta.size(0):
- nb_missed += 1
- else:
- nb_delta[d] += 1
-
- ######################################################################
-
- return nb_total, nb_correct, nb_delta, nb_missed
-
- (
- test_nb_total,
- test_nb_correct,
- test_nb_delta,
- test_nb_missed,
- ) = compute_nb_correct(self.test_input[:10000])
-
- logger(
- 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}%"
- )
-
- nb_total = test_nb_delta.sum() + test_nb_missed
- for d in range(test_nb_delta.size(0)):
- logger(
- f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
+ reverse_correct = (
+ (reverse_quizzes == reverse_result).long().min(dim=-1).values
)
- logger(
- f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
- )
-
- ##############################################################
- # Log a few generated sequences
- if input_file is None:
- input = self.test_input[:10]
- else:
- with open(input_file, "r") as f:
- sequences = [e.strip() for e in f.readlines()]
- sequences = [s + " " + "#" * 50 for s in sequences]
- input = self.tensorize(sequences)
-
- result = input.clone()
- s = (result == self.space).long()
- ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
- for n in range(result.size(0)):
- logger(f"test_before {self.seq2str(result[n])}")
+ nb_correct.append((correct * reverse_correct)[None, :])
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
+ nb_correct = torch.cat(nb_correct, dim=0)
- correct = (1 - ar_mask) * self.space + ar_mask * input
- for n in range(result.size(0)):
- comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
- logger(f"test_after {self.seq2str(result[n])} {comment}")
- logger(f"truth {self.seq2str(correct[n])}")
- ##############################################################
+ filename = os.path.join(result_dir, "correct_{n_epoch:04d}.dat")
+ with open(filename, "w") as f:
+ for k in nb_correct:
+ f.write(f"{k}\n")
-
-######################################################################
-
-import world
-
-
-class World(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- vqae_nb_epochs,
- logger=None,
- device=torch.device("cpu"),
- device_storage=torch.device("cpu"),
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.device = device
-
- (
- train_frames,
- train_action_seq,
- test_frames,
- test_action_seq,
- self.frame2seq,
- self.seq2frame,
- ) = world.create_data_and_processors(
- nb_train_samples,
- nb_test_samples,
- mode="first_last",
- nb_steps=30,
- nb_epochs=vqae_nb_epochs,
- logger=logger,
- device=device,
- device_storage=device_storage,
- )
-
- train_frame_seq = self.frame2seq(train_frames).to(device_storage)
- test_frame_seq = self.frame2seq(test_frames).to(device_storage)
-
- nb_frame_codes = max(train_frame_seq.max(), test_frame_seq.max()) + 1
- nb_action_codes = max(train_action_seq.max(), test_action_seq.max()) + 1
-
- self.len_frame_seq = train_frame_seq.size(1)
- self.len_action_seq = train_action_seq.size(1)
- self.nb_codes = nb_frame_codes + nb_action_codes
-
- train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
-
- train_action_seq += nb_frame_codes
- self.train_input = torch.cat(
- (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
- )
-
- test_frame_seq = test_frame_seq.reshape(test_frame_seq.size(0) // 2, 2, -1)
- test_action_seq += nb_frame_codes
- self.test_input = torch.cat(
- (test_frame_seq[:, 0, :], test_action_seq, test_frame_seq[:, 1, :]), 1
- )
-
- def batches(self, split="train", nb_to_use=-1, desc=None):
- assert split in {"train", "test"}
- input = self.train_input if split == "train" else self.test_input
- if nb_to_use > 0:
- input = input[:nb_to_use]
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- yield batch.to(self.device)
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- k = torch.arange(
- 2 * self.len_frame_seq + self.len_action_seq, device=self.device
- )[None, :]
-
- input = self.test_input[:64].to(self.device)
- result = input.clone()
-
- ar_mask = (
- (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result)
- )
- result *= 1 - ar_mask
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- seq_start = input[:, : self.len_frame_seq]
- seq_end = input[:, self.len_frame_seq + self.len_action_seq :]
- seq_predicted = result[:, self.len_frame_seq + self.len_action_seq :]
-
- result = torch.cat(
- (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
- )
- result = result.reshape(-1, result.size(-1))
-
- frames = self.seq2frame(result)
- image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
- torchvision.utils.save_image(
- frames.float() / (world.Box.nb_rgb_levels - 1),
- image_name,
- nrow=12,
- padding=1,
- pad_value=0.0,
- )
- logger(f"wrote {image_name}")
-
-
-######################################################################
+ return quizzes, nb_correct.sum(dim=0), average_logits
projects / culture.git / blobdiff