task,
nb_for_train=1000,
nb_for_test=100,
+ desired_average_logits=None,
):
kept = []
+ nb_generated_tokens, sum_logits = 0, 0
while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
- new_quizzes, nb_correct = task.create_new_quizzes(
+ nb_to_generate = 4 * (nb_for_train + nb_for_test)
+ new_quizzes, nb_correct, average_logits = task.create_new_quizzes(
n_epoch=n_epoch,
result_dir=args.result_dir,
logger=log_string,
- nb=4 * (nb_for_train + nb_for_test),
+ nb=nb_to_generate,
model=model,
other_models=other_models,
+ desired_average_logits=desired_average_logits,
)
- print(nb_correct)
+ nb_generated_tokens += new_quizzes.numel()
+ sum_logits += average_logits * new_quizzes.numel()
to_keep = new_quizzes[nb_correct == len(other_models) - 1]
- log_string(f"keep {to_keep.size(0)} quizzes")
+ log_string(
+ f"keep {to_keep.size(0)}/{new_quizzes.size(0)} quizzes ({to_keep.size(0)*100/new_quizzes.size(0):.02f}%)"
+ )
kept.append(to_keep)
new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
log_string,
)
+ return sum_logits / nb_generated_tokens
+
######################################################################
nb_new_quizzes_for_train = 10
nb_new_quizzes_for_test = 10
+desired_average_logits = None
+
for n_epoch in range(args.nb_epochs):
a = [(model.id, float(model.main_test_accuracy)) for model in models]
a.sort(key=lambda p: p[0])
# test it
run_tests(model, task, deterministic_synthesis=False)
+ log_string(
+ f"test_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
+ )
+
if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_quizzes:
other_models = models.copy()
other_models.remove(model)
- create_quizzes(
+ average_logits = create_quizzes(
model,
other_models,
task,
nb_for_train=nb_new_quizzes_for_train,
nb_for_test=nb_new_quizzes_for_test,
+ desired_average_logits=desired_average_logits,
)
+ # We keep the first average logits as a reference
+ if desired_average_logits is None:
+ desired_average_logits = average_logits
+ else:
+ log_string(
+ f"desired_average_logits {desired_average_logits} average_logits {average_logits}"
+ )
+
# We update everyone
for model in models:
run_tests(model, task, deterministic_synthesis=False)
batch_size,
input,
ar_mask,
+ temperature,
deterministic_synthesis,
forbidden_tokens=None,
logit_biases=None,
t = model.training
model.eval()
+ sum_logits = 0
+
for input, ar_mask in batches:
- model.masked_inplace_autoregression(
- input,
- ar_mask,
- deterministic_synthesis,
- forbidden_tokens,
- logit_biases,
+ 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 World(Task):
def save_image(self, input, result_dir, filename, logger):
- img = world.sample2img(input.to("cpu"), self.height, self.width)
+ 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}")
result = input.clone() * (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,
)
result = input.clone() * (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,
)
nb,
model,
other_models,
+ desired_average_logits=None,
):
###############################################################
# Generate quizzes with model
)
ar_mask = torch.full(quizzes.size(), 1, device=self.device)
- masked_inplace_autoregression(
- model,
- self.batch_size,
- quizzes,
- ar_mask,
+ 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,
)
+ average_logits = sum_logits / quizzes.numel()
+
+ if desired_average_logits is not None:
+ temperature = average_logits / desired_average_logits
+ masked_inplace_autoregression(
+ 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,
+ )
+
###############################################################
# Create the reverse quizzes
result = quizzes.clone()
masked_inplace_autoregression(
- m,
- self.batch_size,
- result,
- ar_mask,
+ 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,
reverse_result = reverse_quizzes.clone()
masked_inplace_autoregression(
- m,
- self.batch_size,
- reverse_result,
- ar_mask,
+ 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,
for k in nb_correct:
f.write(f"{k}\n")
- return quizzes, nb_correct.sum(dim=0)
+ return quizzes, nb_correct.sum(dim=0), average_logits
def generate_seq(
- nb,
- height,
- width,
- nb_birds=3,
- nb_iterations=2,
+ nb, height, width, nb_birds=3, nb_iterations=2, return_iterations=False
):
pairs = []
+ kept_iterations = []
for _ in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
while True:
+ iterations = []
+
f_start = torch.zeros(height, width, dtype=torch.int64)
i, j, vi, vj = (
f_end = f_start.clone()
for l in range(nb_iterations):
+ iterations.append(f_end.clone())
f_end[...] = 0
nb_collisions = 0
for n in range(nb_birds):
f_end[i[n] - vi[n], j[n]] = c
f_end[i[n], j[n] - vj[n]] = c
+ iterations.append(f_end.clone())
+
if nb_collisions == 0:
break
+ kept_iterations.append(iterations)
pairs.append((f_start, f_end))
result = []
)[None, :]
)
- return torch.cat(result, dim=0)
+ if return_iterations:
+ # iterations = torch.cat([ torch.cat([ x[None, None] for x in l], dim = 1) for l in kept_iterations ], dim=0)
+ return torch.cat(result, dim=0), kept_iterations
+ else:
+ return torch.cat(result, dim=0)
######################################################################
return torch.cat(result, dim=0)
-def sample2img(seq, height, width, upscale=15):
- f_first = seq[:, : height * width].reshape(-1, height, width)
- f_second = seq[:, height * width + 1 :].reshape(-1, height, width)
- direction = seq[:, height * width]
+def frame2img(x, height, width, upscale=15):
+ x = x.reshape(-1, height, width)
+ m = torch.logical_and(x >= 0, x < first_bird_token + nb_bird_tokens).long()
+ x = colors[x * m].permute(0, 3, 1, 2)
+ s = x.shape
+ x = x[:, :, :, None, :, None].expand(-1, -1, -1, upscale, -1, upscale)
+ x = x.reshape(s[0], s[1], s[2] * upscale, s[3] * upscale)
- def mosaic(x, upscale):
- x = x.reshape(-1, height, width)
- m = torch.logical_and(x >= 0, x < first_bird_token + nb_bird_tokens).long()
- x = colors[x * m].permute(0, 3, 1, 2)
- s = x.shape
- x = x[:, :, :, None, :, None].expand(-1, -1, -1, upscale, -1, upscale)
- x = x.reshape(s[0], s[1], s[2] * upscale, s[3] * upscale)
+ x[:, :, :, torch.arange(0, x.size(3), upscale)] = 0
+ x[:, :, torch.arange(0, x.size(2), upscale), :] = 0
+ x = x[:, :, 1:, 1:]
- x[:, :, :, torch.arange(0, x.size(3), upscale)] = 0
- x[:, :, torch.arange(0, x.size(2), upscale), :] = 0
- x = x[:, :, 1:, 1:]
+ for n in range(m.size(0)):
+ for i in range(m.size(1)):
+ for j in range(m.size(2)):
+ if m[n, i, j] == 0:
+ for k in range(2, upscale - 2):
+ x[n, :, i * upscale + k, j * upscale + k] = 0
+ x[n, :, i * upscale + upscale - 1 - k, j * upscale + k] = 0
- for n in range(m.size(0)):
- for i in range(m.size(1)):
- for j in range(m.size(2)):
- if m[n, i, j] == 0:
- for k in range(2, upscale - 2):
- x[n, :, i * upscale + k, j * upscale + k] = 0
- x[n, :, i * upscale + upscale - 1 - k, j * upscale + k] = 0
+ return x
- return x
+
+def seq2img(seq, height, width, upscale=15):
+ f_first = seq[:, : height * width].reshape(-1, height, width)
+ f_second = seq[:, height * width + 1 :].reshape(-1, height, width)
+ direction = seq[:, height * width]
direction_symbol = torch.full((direction.size(0), height * upscale - 1, upscale), 0)
direction_symbol = colors[direction_symbol].permute(0, 3, 1, 2)
return torch.cat(
[
- mosaic(f_first, upscale),
+ frame2img(f_first, height, width, upscale),
separator,
direction_symbol,
separator,
- mosaic(f_second, upscale),
+ frame2img(f_second, height, width, upscale),
],
dim=3,
)
height, width = 6, 8
start_time = time.perf_counter()
- seq = generate_seq(nb=90, height=height, width=width)
+ seq, it = generate_seq(
+ nb=64, height=height, width=width, nb_iterations=100, return_iterations=True
+ )
delay = time.perf_counter() - start_time
print(f"{seq.size(0)/delay:02f} samples/s")
print(seq2str(seq[:4]))
+ for t in range(len(it[0])):
+ img = torch.cat([frame2img(f[t], height, width) for f in it], dim=0)
+ torchvision.utils.save_image(
+ img.float() / 255.0,
+ f"/tmp/frame_{t:03d}.png",
+ nrow=8,
+ padding=6,
+ pad_value=0,
+ )
+
# m = (torch.rand(seq.size()) < 0.05).long()
# seq = (1 - m) * seq + m * 23
- img = sample2img(seq, height, width)
+ img = seq2img(seq, height, width)
print(img.size())
torchvision.utils.save_image(
projects / culture.git / commitdiff