# logger(f"wrote {filename}")
+######################################################################
+
+import world
+
+
+class World(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ logger=None,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+ self.height = 6
+ self.width = 8
+
+ self.train_input = world.generate(
+ nb_train_samples, height=self.height, width=self.width
+ )
+ self.train_ar_mask = (
+ (torch.arange(self.train_input.size(1)) > self.train_input.size(1) // 2)
+ .long()[None, :]
+ .expand_as(self.train_input)
+ )
+
+ self.test_input = world.generate(
+ nb_test_samples, height=self.height, width=self.width
+ )
+ self.test_ar_mask = (
+ (torch.arange(self.test_input.size(1)) > self.test_input.size(1) // 2)
+ .long()[None, :]
+ .expand_as(self.test_input)
+ )
+
+ 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
+
+ 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,
+ )
+
+ nb_total, nb_correct = (
+ input.size(0),
+ (input == result).long().min(dim=1).values.sum(),
+ )
+
+ 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}%"
+ )
+
+ logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
+
+ if save_attention_image is not None:
+ for k in range(10):
+ ns = torch.randint(self.test_input.size(0), (1,)).item()
+ input = self.test_input[ns : ns + 1].clone()
+
+ 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 = [c for c in self.problem.seq2str(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"sandbox_attention_{k}_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}")
+
+
######################################################################
import picoclvr
from torch import nn
from torch.nn import functional as F
-import cairo
######################################################################
-class Box:
- nb_rgb_levels = 10
-
- def __init__(self, x, y, w, h, r, g, b):
- self.x = x
- self.y = y
- self.w = w
- self.h = h
- self.r = r
- self.g = g
- self.b = b
-
- def collision(self, scene):
- for c in scene:
- if (
- self is not c
- and max(self.x, c.x) <= min(self.x + self.w, c.x + c.w)
- and max(self.y, c.y) <= min(self.y + self.h, c.y + c.h)
- ):
- return True
- return False
-
-
-######################################################################
-
-
-class Normalizer(nn.Module):
- def __init__(self, mu, std):
- super().__init__()
- self.register_buffer("mu", mu)
- self.register_buffer("log_var", 2 * torch.log(std))
-
- def forward(self, x):
- return (x - self.mu) / torch.exp(self.log_var / 2.0)
-
-
-class SignSTE(nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, x):
- # torch.sign() takes three values
- s = (x >= 0).float() * 2 - 1
-
- if self.training:
- u = torch.tanh(x)
- return s + u - u.detach()
- else:
- return s
-
-
-class DiscreteSampler2d(nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, x):
- s = (x >= x.max(-3, keepdim=True).values).float()
-
- if self.training:
- u = x.softmax(dim=-3)
- return s + u - u.detach()
- else:
- return s
-
-
-def loss_H(binary_logits, h_threshold=1):
- p = binary_logits.sigmoid().mean(0)
- h = (-p.xlogy(p) - (1 - p).xlogy(1 - p)) / math.log(2)
- h.clamp_(max=h_threshold)
- return h_threshold - h.mean()
-
-
-def train_encoder(
- train_input,
- test_input,
- depth,
- nb_bits_per_token,
- dim_hidden=48,
- lambda_entropy=0.0,
- lr_start=1e-3,
- lr_end=1e-4,
- nb_epochs=10,
- batch_size=25,
- logger=None,
- device=torch.device("cpu"),
-):
- mu, std = train_input.float().mean(), train_input.float().std()
-
- def encoder_core(depth, dim):
- l = [
- [
- nn.Conv2d(
- dim * 2**k, dim * 2**k, kernel_size=5, stride=1, padding=2
- ),
- nn.ReLU(),
- nn.Conv2d(dim * 2**k, dim * 2 ** (k + 1), kernel_size=2, stride=2),
- nn.ReLU(),
- ]
- for k in range(depth)
- ]
-
- return nn.Sequential(*[x for m in l for x in m])
-
- def decoder_core(depth, dim):
- l = [
- [
- nn.ConvTranspose2d(
- dim * 2 ** (k + 1), dim * 2**k, kernel_size=2, stride=2
- ),
- nn.ReLU(),
- nn.ConvTranspose2d(
- dim * 2**k, dim * 2**k, kernel_size=5, stride=1, padding=2
- ),
- nn.ReLU(),
- ]
- for k in range(depth - 1, -1, -1)
- ]
-
- return nn.Sequential(*[x for m in l for x in m])
-
- encoder = nn.Sequential(
- Normalizer(mu, std),
- nn.Conv2d(3, dim_hidden, kernel_size=1, stride=1),
- nn.ReLU(),
- # 64x64
- encoder_core(depth=depth, dim=dim_hidden),
- # 8x8
- nn.Conv2d(dim_hidden * 2**depth, nb_bits_per_token, kernel_size=1, stride=1),
- )
-
- quantizer = SignSTE()
-
- decoder = nn.Sequential(
- nn.Conv2d(nb_bits_per_token, dim_hidden * 2**depth, kernel_size=1, stride=1),
- # 8x8
- decoder_core(depth=depth, dim=dim_hidden),
- # 64x64
- nn.ConvTranspose2d(dim_hidden, 3 * Box.nb_rgb_levels, kernel_size=1, stride=1),
- )
-
- model = nn.Sequential(encoder, decoder)
-
- nb_parameters = sum(p.numel() for p in model.parameters())
-
- logger(f"vqae nb_parameters {nb_parameters}")
-
- model.to(device)
-
- for k in range(nb_epochs):
- lr = math.exp(
- math.log(lr_start) + math.log(lr_end / lr_start) / (nb_epochs - 1) * k
- )
- optimizer = torch.optim.Adam(model.parameters(), lr=lr)
-
- acc_train_loss = 0.0
-
- for input in tqdm.tqdm(train_input.split(batch_size), desc="vqae-train"):
- input = input.to(device)
- z = encoder(input)
- zq = quantizer(z)
- output = decoder(zq)
-
- output = output.reshape(
- output.size(0), -1, 3, output.size(2), output.size(3)
- )
-
- train_loss = F.cross_entropy(output, input)
-
- if lambda_entropy > 0:
- train_loss = train_loss + lambda_entropy * loss_H(z, h_threshold=0.5)
-
- acc_train_loss += train_loss.item() * input.size(0)
-
- optimizer.zero_grad()
- train_loss.backward()
- optimizer.step()
-
- acc_test_loss = 0.0
-
- for input in tqdm.tqdm(test_input.split(batch_size), desc="vqae-test"):
- input = input.to(device)
- z = encoder(input)
- zq = quantizer(z)
- output = decoder(zq)
-
- output = output.reshape(
- output.size(0), -1, 3, output.size(2), output.size(3)
- )
-
- test_loss = F.cross_entropy(output, input)
-
- acc_test_loss += test_loss.item() * input.size(0)
-
- train_loss = acc_train_loss / train_input.size(0)
- test_loss = acc_test_loss / test_input.size(0)
-
- logger(f"vqae train {k} lr {lr} train_loss {train_loss} test_loss {test_loss}")
- sys.stdout.flush()
-
- return encoder, quantizer, decoder
-
-
-######################################################################
-
-
-def scene2tensor(xh, yh, scene, size):
- width, height = size, size
- pixel_map = torch.ByteTensor(width, height, 4).fill_(255)
- data = pixel_map.numpy()
- surface = cairo.ImageSurface.create_for_data(
- data, cairo.FORMAT_ARGB32, width, height
- )
-
- ctx = cairo.Context(surface)
- ctx.set_fill_rule(cairo.FILL_RULE_EVEN_ODD)
-
- for b in scene:
- ctx.move_to(b.x * size, b.y * size)
- ctx.rel_line_to(b.w * size, 0)
- ctx.rel_line_to(0, b.h * size)
- ctx.rel_line_to(-b.w * size, 0)
- ctx.close_path()
- ctx.set_source_rgba(
- b.r / (Box.nb_rgb_levels - 1),
- b.g / (Box.nb_rgb_levels - 1),
- b.b / (Box.nb_rgb_levels - 1),
- 1.0,
- )
- ctx.fill()
-
- hs = size * 0.1
- ctx.set_source_rgba(0.0, 0.0, 0.0, 1.0)
- ctx.move_to(xh * size - hs / 2, yh * size - hs / 2)
- ctx.rel_line_to(hs, 0)
- ctx.rel_line_to(0, hs)
- ctx.rel_line_to(-hs, 0)
- ctx.close_path()
- ctx.fill()
-
- return (
- pixel_map[None, :, :, :3]
- .flip(-1)
- .permute(0, 3, 1, 2)
- .long()
- .mul(Box.nb_rgb_levels)
- .floor_divide(256)
- )
-
-
-def random_scene(nb_insert_attempts=3):
- scene = []
- colors = [
- ((Box.nb_rgb_levels - 1), 0, 0),
- (0, (Box.nb_rgb_levels - 1), 0),
- (0, 0, (Box.nb_rgb_levels - 1)),
- ((Box.nb_rgb_levels - 1), (Box.nb_rgb_levels - 1), 0),
- (
- (Box.nb_rgb_levels * 2) // 3,
- (Box.nb_rgb_levels * 2) // 3,
- (Box.nb_rgb_levels * 2) // 3,
- ),
- ]
-
- for k in range(nb_insert_attempts):
- wh = torch.rand(2) * 0.2 + 0.2
- xy = torch.rand(2) * (1 - wh)
- c = colors[torch.randint(len(colors), (1,))]
- b = Box(
- xy[0].item(), xy[1].item(), wh[0].item(), wh[1].item(), c[0], c[1], c[2]
- )
- if not b.collision(scene):
- scene.append(b)
-
- return scene
-
-
-def generate_episode(steps, size=64):
- delta = 0.1
- effects = [
- (False, 0, 0),
- (False, delta, 0),
- (False, 0, delta),
- (False, -delta, 0),
- (False, 0, -delta),
- (True, delta, 0),
- (True, 0, delta),
- (True, -delta, 0),
- (True, 0, -delta),
+colors = torch.tensor(
+ [
+ [255, 255, 255],
+ [0, 0, 0],
+ [255, 0, 0],
+ [0, 128, 0],
+ [0, 0, 255],
+ [255, 255, 0],
+ [192, 192, 192],
]
+)
- while True:
- frames = []
-
- scene = random_scene()
- xh, yh = tuple(x.item() for x in torch.rand(2))
-
- actions = torch.randint(len(effects), (len(steps),))
- nb_changes = 0
-
- for s, a in zip(steps, actions):
- if s:
- frames.append(scene2tensor(xh, yh, scene, size=size))
+token2char = "_X01234>"
- grasp, dx, dy = effects[a]
- if grasp:
- for b in scene:
- if b.x <= xh and b.x + b.w >= xh and b.y <= yh and b.y + b.h >= yh:
- x, y = b.x, b.y
- b.x += dx
- b.y += dy
- if (
- b.x < 0
- or b.y < 0
- or b.x + b.w > 1
- or b.y + b.h > 1
- or b.collision(scene)
- ):
- b.x, b.y = x, y
- else:
- xh += dx
- yh += dy
- nb_changes += 1
- else:
- x, y = xh, yh
- xh += dx
- yh += dy
- if xh < 0 or xh > 1 or yh < 0 or yh > 1:
- xh, yh = x, y
-
- if nb_changes > len(steps) // 3:
- break
-
- return frames, actions
-
-
-######################################################################
-
-
-def generate_episodes(nb, steps):
- all_frames, all_actions = [], []
- for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world-data"):
- frames, actions = generate_episode(steps)
- all_frames += frames
- all_actions += [actions[None, :]]
- return torch.cat(all_frames, 0).contiguous(), torch.cat(all_actions, 0)
-
-
-def create_data_and_processors(
- nb_train_samples,
- nb_test_samples,
- mode,
- nb_steps,
- depth=3,
- nb_bits_per_token=8,
- nb_epochs=10,
- device=torch.device("cpu"),
- device_storage=torch.device("cpu"),
- logger=None,
+def generate(
+ nb,
+ height,
+ width,
+ obj_length=6,
+ mask_height=3,
+ mask_width=3,
+ nb_obj=3,
):
- assert mode in ["first_last"]
-
- if mode == "first_last":
- steps = [True] + [False] * (nb_steps + 1) + [True]
-
- if logger is None:
- logger = lambda s: print(s)
-
- train_input, train_actions = generate_episodes(nb_train_samples, steps)
- train_input, train_actions = train_input.to(device_storage), train_actions.to(
- device_storage
+ intact = torch.zeros(nb, height, width, dtype=torch.int64)
+ n = torch.arange(intact.size(0))
+
+ for n in range(nb):
+ for c in torch.randperm(colors.size(0) - 2)[:nb_obj] + 2:
+ z = intact[n].flatten()
+ m = (torch.rand(z.size()) * (z == 0)).argmax(dim=0)
+ i, j = m // width, m % width
+ vm = torch.randint(4, (1,))[0]
+ vi, vj = (vm // 2) * (2 * (vm % 2) - 1), (1 - vm // 2) * (2 * (vm % 2) - 1)
+ for l in range(obj_length):
+ intact[n, i, j] = c
+ i += vi
+ j += vj
+ if i < 0 or i >= height or j < 0 or j >= width or intact[n, i, j] != 0:
+ i -= vi
+ j -= vj
+ vi, vj = -vj, vi
+ i += vi
+ j += vj
+ if (
+ i < 0
+ or i >= height
+ or j < 0
+ or j >= width
+ or intact[n, i, j] != 0
+ ):
+ break
+
+ masked = intact.clone()
+
+ for n in range(nb):
+ i = torch.randint(height - mask_height + 1, (1,))[0]
+ j = torch.randint(width - mask_width + 1, (1,))[0]
+ masked[n, i : i + mask_height, j : j + mask_width] = 1
+
+ return torch.cat(
+ [
+ masked.flatten(1),
+ torch.full((masked.size(0), 1), len(colors)),
+ intact.flatten(1),
+ ],
+ dim=1,
)
- test_input, test_actions = generate_episodes(nb_test_samples, steps)
- test_input, test_actions = test_input.to(device_storage), test_actions.to(
- device_storage
- )
-
- encoder, quantizer, decoder = train_encoder(
- train_input,
- test_input,
- depth=depth,
- nb_bits_per_token=nb_bits_per_token,
- lambda_entropy=1.0,
- nb_epochs=nb_epochs,
- logger=logger,
- device=device,
- )
- encoder.train(False)
- quantizer.train(False)
- decoder.train(False)
-
- z = encoder(train_input[:1].to(device))
- pow2 = (2 ** torch.arange(z.size(1), device=device))[None, None, :]
- z_h, z_w = z.size(2), z.size(3)
-
- logger(f"vqae input {train_input[0].size()} output {z[0].size()}")
-
- def frame2seq(input, batch_size=25):
- seq = []
- p = pow2.to(device)
- for x in input.split(batch_size):
- x = x.to(device)
- z = encoder(x)
- ze_bool = (quantizer(z) >= 0).long()
- output = (
- ze_bool.permute(0, 2, 3, 1).reshape(
- ze_bool.size(0), -1, ze_bool.size(1)
- )
- * p
- ).sum(-1)
- seq.append(output)
- return torch.cat(seq, dim=0)
-
- def seq2frame(input, batch_size=25, T=1e-2):
- frames = []
- p = pow2.to(device)
- for seq in input.split(batch_size):
- seq = seq.to(device)
- zd_bool = (seq[:, :, None] // p) % 2
- zd_bool = zd_bool.reshape(zd_bool.size(0), z_h, z_w, -1).permute(0, 3, 1, 2)
- logits = decoder(zd_bool * 2.0 - 1.0)
- logits = logits.reshape(
- logits.size(0), -1, 3, logits.size(2), logits.size(3)
- ).permute(0, 2, 3, 4, 1)
- output = torch.distributions.categorical.Categorical(
- logits=logits / T
- ).sample()
+def sample2img(seq, height, width):
+ intact = seq[:, : height * width].reshape(-1, height, width)
+ masked = seq[:, height * width + 1 :].reshape(-1, height, width)
+ img_intact, img_masked = colors[intact], colors[masked]
+
+ img = torch.cat(
+ [
+ img_intact,
+ torch.full(
+ (img_intact.size(0), img_intact.size(1), 1, img_intact.size(3)), 1
+ ),
+ img_masked,
+ ],
+ dim=2,
+ )
- frames.append(output)
+ return img.permute(0, 3, 1, 2)
- return torch.cat(frames, dim=0)
- return train_input, train_actions, test_input, test_actions, frame2seq, seq2frame
+def seq2str(seq):
+ result = []
+ for s in seq:
+ result.append("".join([token2char[v] for v in s]))
+ return result
######################################################################
if __name__ == "__main__":
- (
- train_input,
- train_actions,
- test_input,
- test_actions,
- frame2seq,
- seq2frame,
- ) = create_data_and_processors(
- 250,
- 1000,
- nb_epochs=5,
- mode="first_last",
- nb_steps=20,
- )
+ import time
- input = test_input[:256]
+ height, width = 6, 8
+ start_time = time.perf_counter()
+ seq = generate(nb=64, height=height, width=width)
+ delay = time.perf_counter() - start_time
+ print(f"{seq.size(0)/delay:02f} samples/s")
- seq = frame2seq(input)
- output = seq2frame(seq)
+ print(seq2str(seq[:4]))
- torchvision.utils.save_image(
- input.float() / (Box.nb_rgb_levels - 1), "orig.png", nrow=16
- )
+ img = sample2img(seq, height, width)
+ print(img.size())
- torchvision.utils.save_image(
- output.float() / (Box.nb_rgb_levels - 1), "qtiz.png", nrow=16
- )
+ torchvision.utils.save_image(img.float() / 255.0, "world.png", nrow=8, padding=2)
projects / culture.git / commitdiff