class Grid(Task):
# Make a tensor from a list of strings
- def tensorize(self, descr):
+ def str2tensor(self, descr):
token_descr = [s.strip().split(" ") for s in descr]
l = max([len(s) for s in token_descr])
token_descr = [s + ["#"] * (l - len(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):
+ def tensor2str(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
self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
self.t_nul = self.token2id["#"]
- self.t_true = self.token2id["<true>"]
- self.t_false = self.token2id["<false>"]
+ self.t_true = self.token2id["true"]
+ self.t_false = self.token2id["false"]
# Tokenize the train and test sets
- self.train_input = self.tensorize(self.train_descr)
- self.test_input = self.tensorize(self.test_descr)
+ self.train_input = self.str2tensor(self.train_descr)
+ self.test_input = self.str2tensor(self.test_descr)
def batches(self, split="train"):
assert split in {"train", "test"}
correct = self.test_input[:1000]
result = correct.clone()
ar_mask = torch.logical_or(result == self.t_true, result == self.t_false).long()
- result *= 1 - ar_mask
+ result *= 1 - ar_mask # paraaaaanoiaaaaaaa
- for e in self.detensorize(result[:10]):
+ logger(f"----------------------------------------------------------")
+
+ for e in self.tensor2str(result[:10]):
logger(f"test_before {e}")
masked_inplace_autoregression(
device=self.device,
)
- for e in self.detensorize(result[:10]):
- logger(f"test_after {e}")
+ logger(f"----------------------------------------------------------")
+
+ for e in self.tensor2str(result[:10]):
+ logger(f"test_after {e}")
+
+ logger(f"----------------------------------------------------------")
nb_total = ar_mask.sum().item()
nb_correct = ((correct == result).long() * ar_mask).sum().item()
- logger(f"test_performance {nb_total=} {nb_correct=}")
- logger(f"main_test_accuracy {nb_correct / nb_total}")
+ logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
+ logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
######################################################################
-import world
+import qmlp
-class World(Task):
+class QMLP(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
+ self.batch_size = batch_size
- (
- 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)
+ if logger is not None:
+ logger(
+ f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
+ )
- train_action_seq += nb_frame_codes
- self.train_input = torch.cat(
- (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
+ self.train_descr = self.grid_factory.generate_samples(
+ nb_train_samples, lambda r: tqdm.tqdm(r)
)
-
- 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
+ self.test_descr = self.grid_factory.generate_samples(
+ nb_test_samples, lambda r: tqdm.tqdm(r)
)
- def batches(self, split="train", nb_to_use=-1, desc=None):
+ # Build the tokenizer
+ tokens = set()
+ 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()
+ tokens = ["#"] + tokens
+ self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
+ self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
+ self.t_nul = self.token2id["#"]
+ self.t_true = self.token2id["true"]
+ self.t_false = self.token2id["false"]
+
+ # Tokenize the train and test sets
+ self.train_input = self.str2tensor(self.train_descr)
+ self.test_input = self.str2tensor(self.test_descr)
+
+ def batches(self, split="train"):
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
+ input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
):
- yield batch.to(self.device)
+ yield self.trim(batch)
def vocabulary_size(self):
- return self.nb_codes
+ return len(self.token2id)
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, :]
+ correct = self.test_input[:1000]
+ result = correct.clone()
+ ar_mask = torch.logical_or(result == self.t_true, result == self.t_false).long()
+ result *= 1 - ar_mask # paraaaaanoiaaaaaaa
- input = self.test_input[:64].to(self.device)
- result = input.clone()
+ logger(f"----------------------------------------------------------")
- ar_mask = (
- (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result)
- )
- result *= 1 - ar_mask
+ for e in self.tensor2str(result[:10]):
+ logger(f"test_before {e}")
masked_inplace_autoregression(
model,
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 :]
+ logger(f"----------------------------------------------------------")
- result = torch.cat(
- (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
- )
- result = result.reshape(-1, result.size(-1))
+ for e in self.tensor2str(result[:10]):
+ logger(f"test_after {e}")
- 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}")
+ logger(f"----------------------------------------------------------")
+
+ nb_total = ar_mask.sum().item()
+ nb_correct = ((correct == result).long() * ar_mask).sum().item()
+
+ logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
+ logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
######################################################################
projects / picoclvr.git / blobdiff