from torch.nn import functional as F
import cairo
+######################################################################
+
class Box:
nb_rgb_levels = 10
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
+
+
+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"),
+):
+ 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 < 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 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)
+ )
+
+ 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)
return scene
-def generate_episode(nb_steps=10, size=64):
+def generate_episode(steps, size=64):
delta = 0.1
effects = [
(False, 0, 0),
scene = random_scene()
xh, yh = tuple(x.item() for x in torch.rand(2))
- frames.append(scene2tensor(xh, yh, scene, size=size))
-
- actions = torch.randint(len(effects), (nb_steps,))
+ actions = torch.randint(len(effects), (len(steps),))
change = False
- for a in actions:
+ 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 xh < 0 or xh > 1 or yh < 0 or yh > 1:
xh, yh = x, y
- frames.append(scene2tensor(xh, yh, scene, size=size))
-
if change:
break
######################################################################
-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)
- ]
+def generate_episodes(nb, steps):
+ all_frames = []
+ for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world-data"):
+ frames, actions = generate_episode(steps)
+ all_frames += frames
+ return torch.cat(all_frames, 0).contiguous()
- 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)
- ]
+def create_data_and_processors(nb_train_samples, nb_test_samples, nb_epochs=10):
+ steps = [True] + [False] * 30 + [True]
+ train_input = generate_episodes(nb_train_samples, steps)
+ test_input = generate_episodes(nb_test_samples, steps)
- return nn.Sequential(*[x for m in l for x in m])
+ print(f"{train_input.size()=} {test_input.size()=}")
- 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),
+ 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(x):
+ z = encoder(x)
+ ze_bool = (quantizer(z) >= 0).long()
+ seq = (
+ ze_bool.permute(0, 2, 3, 1).reshape(ze_bool.size(0), -1, ze_bool.size(1))
+ * pow2
+ ).sum(-1)
+ return seq
+
+ def seq2frame(seq, T=1e-2):
+ 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)
+ results = torch.distributions.categorical.Categorical(
+ logits=logits / T
+ ).sample()
+ return results
+
+ return train_input, test_input, frame2seq, seq2frame
- 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)
+if __name__ == "__main__":
+ train_input, test_input, frame2seq, seq2frame = create_data_and_processors(
+ 10000, 1000
+ )
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())
+ seq = frame2seq(input)
- 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()
+ print(f"{seq.size()=} {seq.dtype=} {seq.min()=} {seq.max()=}")
+ output = seq2frame(seq)
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
+ output.float() / (Box.nb_rgb_levels - 1), "qtiz.png", nrow=8
)
-
-
-######################################################################
-
-if __name__ == "__main__":
- 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 / commitdiff