#!/usr/bin/env python
-import math
+import math, sys, tqdm
import torch, torchvision
class Box:
+ nb_rgb_levels = 10
+
def __init__(self, x, y, w, h, r, g, b):
self.x = x
self.y = y
return False
-def scene2tensor(xh, yh, scene, size=64):
+def scene2tensor(xh, yh, scene, size):
width, height = size, size
pixel_map = torch.ByteTensor(width, height, 4).fill_(255)
data = pixel_map.numpy()
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, b.g, b.b, 1.0)
+ 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.close_path()
ctx.fill()
- return pixel_map[None, :, :, :3].flip(-1).permute(0, 3, 1, 2).float() / 255
+ return (
+ pixel_map[None, :, :, :3]
+ .flip(-1)
+ .permute(0, 3, 1, 2)
+ .long()
+ .mul(Box.nb_rgb_levels)
+ .floor_divide(256)
+ )
def random_scene():
scene = []
colors = [
- (1.00, 0.00, 0.00),
- (0.00, 1.00, 0.00),
- (0.00, 0.00, 1.00),
- (1.00, 1.00, 0.00),
- (0.75, 0.75, 0.75),
+ ((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):
return scene
-def sequence(nb_steps=10, all_frames=False):
+def generate_episode(nb_steps=10, size=64):
delta = 0.1
effects = [
(False, 0, 0),
]
while True:
-
- frames =[]
+ frames = []
scene = random_scene()
xh, yh = tuple(x.item() for x in torch.rand(2))
- frames.append(scene2tensor(xh, yh, scene))
+ frames.append(scene2tensor(xh, yh, scene, size=size))
actions = torch.randint(len(effects), (nb_steps,))
change = False
if xh < 0 or xh > 1 or yh < 0 or yh > 1:
xh, yh = x, y
- if all_frames:
- frames.append(scene2tensor(xh, yh, scene))
-
- if not all_frames:
- frames.append(scene2tensor(xh, yh, scene))
+ frames.append(scene2tensor(xh, yh, scene, size=size))
if change:
break
return frames, actions
+######################################################################
+
+
+# ||x_i - c_j||^2 = ||x_i||^2 + ||c_j||^2 - 2<x_i, c_j>
+def sq2matrix(x, c):
+ nx = x.pow(2).sum(1)
+ nc = c.pow(2).sum(1)
+ return nx[:, None] + nc[None, :] - 2 * x @ c.t()
+
+
+def update_centroids(x, c, nb_min=1):
+ _, b = sq2matrix(x, c).min(1)
+ b.squeeze_()
+ nb_resets = 0
+
+ for k in range(0, c.size(0)):
+ i = b.eq(k).nonzero(as_tuple=False).squeeze()
+ if i.numel() >= nb_min:
+ c[k] = x.index_select(0, i).mean(0)
+ else:
+ n = torch.randint(x.size(0), (1,))
+ nb_resets += 1
+ c[k] = x[n]
+
+ return c, b, nb_resets
+
+
+def kmeans(x, nb_centroids, nb_min=1):
+ if x.size(0) < nb_centroids * nb_min:
+ print("Not enough points!")
+ exit(1)
+
+ c = x[torch.randperm(x.size(0))[:nb_centroids]]
+ t = torch.full((x.size(0),), -1)
+ n = 0
+
+ while True:
+ c, u, nb_resets = update_centroids(x, c, nb_min)
+ n = n + 1
+ nb_changes = (u - t).sign().abs().sum() + nb_resets
+ t = u
+ if nb_changes == 0:
+ break
+
+ return c, t
+
+
+######################################################################
+
+
+def patchify(x, factor, invert_size=None):
+ if invert_size is None:
+ return (
+ x.reshape(
+ x.size(0), # 0
+ x.size(1), # 1
+ factor, # 2
+ x.size(2) // factor, # 3
+ factor, # 4
+ x.size(3) // factor, # 5
+ )
+ .permute(0, 2, 4, 1, 3, 5)
+ .reshape(-1, x.size(1), x.size(2) // factor, x.size(3) // factor)
+ )
+ else:
+ return (
+ x.reshape(
+ invert_size[0], # 0
+ factor, # 1
+ factor, # 2
+ invert_size[1], # 3
+ invert_size[2] // factor, # 4
+ invert_size[3] // factor, # 5
+ )
+ .permute(0, 3, 1, 4, 2, 5)
+ .reshape(invert_size)
+ )
+
+
+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
+
+
+def train_encoder(
+ train_input,
+ test_input,
+ depth=2,
+ dim_hidden=48,
+ nb_bits_per_token=10,
+ lr_start=1e-3,
+ lr_end=1e-4,
+ nb_epochs=10,
+ batch_size=25,
+ 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())
+
+ print(f"nb_parameters {nb_parameters}")
+
+ model.to(device)
+
+ g5x5 = torch.exp(-torch.tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]]) ** 2 / 2)
+ g5x5 = (g5x5.t() @ g5x5).view(1, 1, 5, 5)
+ g5x5 = g5x5 / g5x5.sum()
+
+ 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 train_input.split(batch_size):
+ 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)
+ )
+
+ 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 test_input.split(batch_size):
+ 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)
+ )
+
+ 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 generate_episodes(nb):
+ all_frames = []
+ for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world-data"):
+ frames, actions = generate_episode(nb_steps=31)
+ all_frames += [ frames[0], frames[-1] ]
+ return torch.cat(all_frames, 0).contiguous()
+
+def create_data_and_processors(nb_train_samples, nb_test_samples):
+ train_input = generate_episodes(nb_train_samples)
+ test_input = generate_episodes(nb_test_samples)
+ encoder, quantizer, decoder = train_encoder(train_input, test_input, nb_epochs=2)
+
+ input = test_input[:64]
+
+ z = encoder(input.float())
+ height, width = z.size(2), z.size(3)
+ zq = quantizer(z).long()
+ pow2=(2**torch.arange(zq.size(1), device=zq.device))[None,None,:]
+ seq = (zq.permute(0,2,3,1).clamp(min=0).reshape(zq.size(0),-1,zq.size(1)) * pow2).sum(-1)
+ print(f"{seq.size()=}")
+
+ ZZ=zq
+
+ zq = ((seq[:,:,None] // pow2)%2)*2-1
+ zq = zq.reshape(zq.size(0), height, width, -1).permute(0,3,1,2)
+
+ print(ZZ[0])
+ print(zq[0])
+
+ print("CHECK", (ZZ-zq).abs().sum())
+
+ results = decoder(zq.float())
+ T = 0.1
+ results = results.reshape(
+ results.size(0), -1, 3, results.size(2), results.size(3)
+ ).permute(0, 2, 3, 4, 1)
+ results = torch.distributions.categorical.Categorical(logits=results / T).sample()
+
+
+ torchvision.utils.save_image(
+ input.float() / (Box.nb_rgb_levels - 1), "orig.png", nrow=8
+ )
+
+ torchvision.utils.save_image(
+ results.float() / (Box.nb_rgb_levels - 1), "qtiz.png", nrow=8
+ )
+
+
+######################################################################
+
if __name__ == "__main__":
- frames, actions = sequence(nb_steps=31,all_frames=True)
- frames = torch.cat(frames,0)
- print(f"{frames.size()=}")
- torchvision.utils.save_image(frames, "seq.png", nrow=8)
+ create_data_and_processors(250,100)
+
+ # train_input = generate_episodes(2500)
+ # test_input = generate_episodes(1000)
+
+ # encoder, quantizer, decoder = train_encoder(train_input, test_input)
+
+ # input = test_input[torch.randperm(test_input.size(0))[:64]]
+ # z = encoder(input.float())
+ # zq = quantizer(z)
+ # results = decoder(zq)
+
+ # T = 0.1
+ # results = torch.distributions.categorical.Categorical(logits=results / T).sample()
projects / picoclvr.git / blobdiff