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)
- )
+ assert self.nb_codes <= max_nb_codes
+ assert self.train_input.min() >= 0
+ assert self.test_input.min() >= 0
+ assert tuple(x.item() for x in self.train_ar_mask.unique()) in {
+ (0,),
+ (1,),
+ (0, 1),
+ }
+ assert tuple(x.item() for x in self.test_ar_mask.unique()) in {
+ (0,),
+ (1,),
+ (0, 1),
+ }
def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
device=self.device,
)
+ log_ground_truth = ar_mask.min() == 0
+
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)}"
- )
+ if log_ground_truth:
+ logger(
+ f" {n_epoch} ground truth {self.problem.seq2str(st)}"
+ )
+
+ nb_total, nb_correct = self.problem.compute_nb_correct(
+ input, ar_mask, result
+ )
- nb_total = ar_mask.sum().item()
- nb_correct = ((result == input).long() * ar_mask).sum().item()
+ # nb_total = ar_mask.sum().item()
+ # nb_correct = ((result == input).long() * ar_mask).sum().item()
return nb_total, nb_correct
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
+
+ logger(f"----------------------------------------------------------")
- for e in self.detensorize(result[:10]):
+ 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 QMLP(Task):
+ ######################
-class World(Task):
def __init__(
self,
nb_train_samples,
nb_test_samples,
batch_size,
- vqae_nb_epochs,
+ result_dir,
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
+ self.nb_samples_per_mlp = 256
- (
- 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)
+ if logger is not None:
+ logger(
+ f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
+ )
- 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
+ seq, q_test_set, test_error = qmlp.generate_sequence_and_test_set(
+ nb_mlps=nb_train_samples + nb_test_samples,
+ nb_samples=self.nb_samples_per_mlp,
+ device=self.device,
+ batch_size=64,
+ nb_epochs=250,
+ nb_mlps_per_batch=1024,
+ )
- 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
+ self.train_input = seq[:nb_train_samples]
+ self.train_q_test_set = q_test_set[:nb_train_samples]
+ self.train_ref_test_errors = test_error[:nb_train_samples]
+ self.test_input = seq[nb_train_samples:]
+ self.test_q_test_set = q_test_set[nb_train_samples:]
+ self.test_ref_test_errors = test_error[nb_train_samples:]
- train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
+ filename = os.path.join(result_dir, f"train_errors_ref.dat")
+ with open(filename, "w") as f:
+ for e in self.train_ref_test_errors:
+ f.write(f"{e}\n")
- train_action_seq += nb_frame_codes
- self.train_input = torch.cat(
- (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
- )
+ filename = os.path.join(result_dir, f"test_errors_ref.dat")
+ with open(filename, "w") as f:
+ for e in self.test_ref_test_errors:
+ f.write(f"{e}\n")
- 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.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
- def batches(self, split="train", nb_to_use=-1, desc=None):
+ 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 batch
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()
-
+ correct = self.test_input[:1000]
+ result = correct.clone()
ar_mask = (
- (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result)
- )
- result *= 1 - ar_mask
+ torch.arange(result.size(1), device=result.device)
+ > self.nb_samples_per_mlp * 3 + 1
+ ).long()[None, :]
+ ar_mask = ar_mask.expand_as(result)
+ result *= 1 - ar_mask # paraaaaanoiaaaaaaa
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 :]
-
- result = torch.cat(
- (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
- )
- result = result.reshape(-1, result.size(-1))
+ q_train_set = result[:, : self.nb_samples_per_mlp * 3]
+ q_params = result[:, self.nb_samples_per_mlp * 3 + 1 :]
+ error_test = qmlp.evaluate_q_params(q_params, self.test_q_test_set)
- 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}")
+ filename = os.path.join(result_dir, f"test_errors_{n_epoch:04d}.dat")
+ with open(filename, "w") as f:
+ for e in error_test:
+ f.write(f"{e}\n")
######################################################################
projects / picoclvr.git / blobdiff