#!/usr/bin/env python
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
import math, sys, tqdm
import torch, torchvision
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
+colors = torch.tensor(
+ [
+ [255, 255, 255],
+ [0, 0, 0],
+ [255, 0, 0],
+ [0, 128, 0],
+ [0, 0, 255],
+ [255, 255, 0],
+ [192, 192, 192],
+ ]
+)
- if self.training:
- u = torch.tanh(x)
- return s + u - u.detach()
- else:
- return s
+token2char = "_X" + "".join([str(n) for n in range(len(colors) - 2)]) + ">"
-def train_encoder(
- train_input,
- test_input,
- depth=3,
- dim_hidden=48,
- nb_bits_per_token=8,
- lr_start=1e-3,
- lr_end=1e-4,
- nb_epochs=10,
- batch_size=25,
- device=torch.device("cpu"),
+def generate(
+ nb,
+ height,
+ width,
+ max_nb_obj=2,
+ nb_iterations=2,
):
- 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())
-
- print(f"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"):
- z = encoder(input)
- zq = z if k < 2 else 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)
-
- 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"):
- z = encoder(input)
- zq = z if k < 1 else quantizer(z)
- output = decoder(zq)
-
- output = output.reshape(
- output.size(0), -1, 3, output.size(2), output.size(3)
+ f_start = torch.zeros(nb, height, width, dtype=torch.int64)
+ f_end = torch.zeros(nb, height, width, dtype=torch.int64)
+ n = torch.arange(f_start.size(0))
+
+ for n in range(nb):
+ nb_fish = torch.randint(max_nb_obj, (1,)).item() + 1
+ for c in torch.randperm(colors.size(0) - 2)[:nb_fish].sort().values:
+ i, j = (
+ torch.randint(height - 2, (1,))[0] + 1,
+ torch.randint(width - 2, (1,))[0] + 1,
)
-
- 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)
-
- print(f"train_ae {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
+ vm = torch.randint(4, (1,))[0]
+ vi, vj = (vm // 2) * (2 * (vm % 2) - 1), (1 - vm // 2) * (2 * (vm % 2) - 1)
+
+ f_start[n, i, j] = c + 2
+ f_start[n, i - vi, j - vj] = c + 2
+ f_start[n, i + vj, j - vi] = c + 2
+ f_start[n, i - vj, j + vi] = c + 2
+
+ for l in range(nb_iterations):
+ i += vi
+ j += vj
+ if i < 0 or i >= height or j < 0 or j >= width:
+ i -= vi
+ j -= vj
+ vi, vj = -vi, -vj
+ i += vi
+ j += vj
+
+ f_end[n, i, j] = c + 2
+ f_end[n, i - vi, j - vj] = c + 2
+ f_end[n, i + vj, j - vi] = c + 2
+ f_end[n, i - vj, j + vi] = c + 2
+
+ return torch.cat(
+ [
+ f_end.flatten(1),
+ torch.full((f_end.size(0), 1), len(colors)),
+ f_start.flatten(1),
+ ],
+ dim=1,
)
- 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()
+def sample2img(seq, height, width):
+ f_start = seq[:, : height * width].reshape(-1, height, width)
+ f_start = (f_start >= len(colors)).long() + (f_start < len(colors)).long() * f_start
+ f_end = seq[:, height * width + 1 :].reshape(-1, height, width)
+ f_end = (f_end >= len(colors)).long() + (f_end < len(colors)).long() * f_end
- 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()
+ img_f_start, img_f_end = colors[f_start], colors[f_end]
- return (
- pixel_map[None, :, :, :3]
- .flip(-1)
- .permute(0, 3, 1, 2)
- .long()
- .mul(Box.nb_rgb_levels)
- .floor_divide(256)
+ img = torch.cat(
+ [
+ img_f_start,
+ torch.full(
+ (img_f_start.size(0), img_f_start.size(1), 1, img_f_start.size(3)), 1
+ ),
+ img_f_end,
+ ],
+ dim=2,
)
+ return img.permute(0, 3, 1, 2)
-def random_scene():
- 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(10):
- 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),
- ]
-
- while True:
- frames = []
-
- scene = random_scene()
- xh, yh = tuple(x.item() for x in torch.rand(2))
-
- actions = torch.randint(len(effects), (len(steps),))
- change = False
-
- for s, a in zip(steps, actions):
- if s:
- frames.append(scene2tensor(xh, yh, scene, size=size))
- g, dx, dy = effects[a]
- if g:
- 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
- change = True
- 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 change:
- 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]
- 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, nb_epochs=10
-):
- assert mode in ["first_last"]
-
- if mode == "first_last":
- steps = [True] + [False] * (nb_steps + 1) + [True]
-
- train_input, train_actions = generate_episodes(nb_train_samples, steps)
- test_input, test_actions = generate_episodes(nb_test_samples, steps)
-
- encoder, quantizer, decoder = train_encoder(
- train_input, test_input, nb_epochs=nb_epochs
- )
- encoder.train(False)
- quantizer.train(False)
- decoder.train(False)
-
- z = encoder(train_input[:1])
- pow2 = (2 ** torch.arange(z.size(1), device=z.device))[None, None, :]
- z_h, z_w = z.size(2), z.size(3)
-
- def frame2seq(input, batch_size=25):
- seq = []
-
- for x in input.split(batch_size):
- 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)
- )
- * pow2
- ).sum(-1)
-
- seq.append(output)
-
- return torch.cat(seq, dim=0)
-
- def seq2frame(input, batch_size=25, T=1e-2):
- frames = []
-
- for seq in input.split(batch_size):
- zd_bool = (seq[:, :, None] // pow2) % 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()
-
- frames.append(output)
-
- 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(
- # 10000, 1000,
- 100,
- 100,
- nb_epochs=2,
- mode="first_last",
- nb_steps=20,
- )
-
- input = test_input[:64]
+ import time
- seq = frame2seq(input)
+ height, width = 6, 8
+ start_time = time.perf_counter()
+ seq = generate(nb=64, height=height, width=width, max_nb_obj=3)
+ delay = time.perf_counter() - start_time
+ print(f"{seq.size(0)/delay:02f} samples/s")
- print(f"{seq.size()=} {seq.dtype=} {seq.min()=} {seq.max()=}")
+ print(seq2str(seq[:4]))
- output = seq2frame(seq)
+ img = sample2img(seq, height, width)
+ print(img.size())
- torchvision.utils.save_image(
- input.float() / (Box.nb_rgb_levels - 1), "orig.png", nrow=8
- )
-
- torchvision.utils.save_image(
- output.float() / (Box.nb_rgb_levels - 1), "qtiz.png", nrow=8
- )
+ torchvision.utils.save_image(img.float() / 255.0, "world.png", nrow=8, padding=2)