#!/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),
- (0.60, 0.60, 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),
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
)
-def train_encoder(input, device=torch.device("cpu")):
- class SomeLeNet(nn.Module):
- def __init__(self):
- super().__init__()
- self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
- self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
- self.fc1 = nn.Linear(256, 200)
- self.fc2 = nn.Linear(200, 10)
+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)
+
- def forward(self, x):
- x = F.relu(F.max_pool2d(self.conv1(x), kernel_size=3))
- x = F.relu(F.max_pool2d(self.conv2(x), kernel_size=2))
- x = x.view(x.size(0), -1)
- x = F.relu(self.fc1(x))
- x = self.fc2(x)
- return x
+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 = SomeLeNet()
+ model = nn.Sequential(encoder, decoder)
nb_parameters = sum(p.numel() for p in model.parameters())
print(f"nb_parameters {nb_parameters}")
- optimizer = torch.optim.SGD(model.parameters(), lr=lr)
- criterion = nn.CrossEntropyLoss()
-
model.to(device)
- criterion.to(device)
- train_input, train_targets = train_input.to(device), train_targets.to(device)
- test_input, test_targets = test_input.to(device), test_targets.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)
- mu, std = train_input.mean(), train_input.std()
- train_input.sub_(mu).div_(std)
- test_input.sub_(mu).div_(std)
+ acc_train_loss = 0.0
- start_time = time.perf_counter()
+ for input in train_input.split(batch_size):
+ z = encoder(input)
+ zq = z if k < 1 else quantizer(z)
+ output = decoder(zq)
- for k in range(nb_epochs):
- acc_loss = 0.0
+ output = output.reshape(
+ output.size(0), -1, 3, output.size(2), output.size(3)
+ )
- for input, targets in zip(
- train_input.split(batch_size), train_targets.split(batch_size)
- ):
- output = model(input)
- loss = criterion(output, targets)
- acc_loss += loss.item()
+ train_loss = F.cross_entropy(output, input)
+
+ acc_train_loss += train_loss.item() * input.size(0)
optimizer.zero_grad()
- loss.backward()
+ train_loss.backward()
optimizer.step()
- nb_test_errors = 0
- for input, targets in zip(
- test_input.split(batch_size), test_targets.split(batch_size)
- ):
- wta = model(input).argmax(1)
- nb_test_errors += (wta != targets).long().sum()
- test_error = nb_test_errors / test_input.size(0)
- duration = time.perf_counter() - start_time
+ 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)
- print(f"loss {k} {duration:.02f}s {acc_loss:.02f} {test_error*100:.02f}%")
+ 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__":
- import time
+ create_data_and_processors(250,100)
- all_frames = []
- nb = 1000
- start_time = time.perf_counter()
- for n in range(nb):
- frames, actions = sequence(nb_steps=31)
- all_frames += frames
- end_time = time.perf_counter()
- print(f"{nb / (end_time - start_time):.02f} samples per second")
-
- input = torch.cat(all_frames, 0)
-
- # x = patchify(input, 8)
- # y = x.reshape(x.size(0), -1)
- # print(f"{x.size()=} {y.size()=}")
- # centroids, t = kmeans(y, 4096)
- # results = centroids[t]
- # results = results.reshape(x.size())
- # results = patchify(results, 8, input.size())
-
- print(f"{input.size()=} {results.size()=}")
-
- torchvision.utils.save_image(input[:64], "orig.png", nrow=8)
- torchvision.utils.save_image(results[:64], "qtiz.png", nrow=8)
-
- # frames, actions = sequence(nb_steps=31, all_frames=True)
- # frames = torch.cat(frames, 0)
- # torchvision.utils.save_image(frames, "seq.png", nrow=8)
+ # 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