X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=world.py;h=a43eff9f3787d216daada4b20da5904c19c1dffa;hb=c03e968adc7bf73df07a0fad89a835b98f4e76df;hp=fb5d5c796990ef476ff79fb9ead66615a17fd7fd;hpb=a8f039a9b491b1b4b47f6b9f8123c7261e758661;p=picoclvr.git diff --git a/world.py b/world.py index fb5d5c7..a43eff9 100755 --- a/world.py +++ b/world.py @@ -1,6 +1,6 @@ #!/usr/bin/env python -import math +import math, sys import torch, torchvision @@ -30,7 +30,7 @@ class Box: 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() @@ -85,7 +85,7 @@ def random_scene(): return scene -def sequence(nb_steps=10, all_frames=False): +def generate_sequence(nb_steps=10, all_frames=False, size=64): delta = 0.1 effects = [ (False, 0, 0), @@ -105,7 +105,7 @@ def sequence(nb_steps=10, all_frames=False): 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 @@ -138,10 +138,10 @@ def sequence(nb_steps=10, all_frames=False): xh, yh = x, y if all_frames: - frames.append(scene2tensor(xh, yh, scene)) + frames.append(scene2tensor(xh, yh, scene, size=size)) if not all_frames: - frames.append(scene2tensor(xh, yh, scene)) + frames.append(scene2tensor(xh, yh, scene, size=size)) if change: break @@ -203,12 +203,12 @@ 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 + 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) @@ -216,44 +216,157 @@ def patchify(x, factor, invert_size=None): 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 + 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.mu = nn.Parameter(mu) + self.log_var = nn.Parameter(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, + dim_hidden=64, + block_size=16, + nb_bits_per_block=10, + lr_start=1e-3, lr_end=1e-5, + nb_epochs=50, + batch_size=25, + device=torch.device("cpu"), +): + mu, std = train_input.mean(), train_input.std() + + encoder = nn.Sequential( + Normalizer(mu, std), + nn.Conv2d(3, dim_hidden, kernel_size=5, stride=1, padding=2), + nn.ReLU(), + nn.Conv2d(dim_hidden, dim_hidden, kernel_size=5, stride=1, padding=2), + nn.ReLU(), + nn.Conv2d(dim_hidden, dim_hidden, kernel_size=5, stride=1, padding=2), + nn.ReLU(), + nn.Conv2d(dim_hidden, dim_hidden, kernel_size=5, stride=1, padding=2), + nn.ReLU(), + nn.Conv2d(dim_hidden, dim_hidden, kernel_size=5, stride=1, padding=2), + nn.ReLU(), + nn.Conv2d( + dim_hidden, + nb_bits_per_block, + kernel_size=block_size, + stride=block_size, + padding=0, + ), + SignSTE(), + ) + + decoder = nn.Sequential( + nn.ConvTranspose2d( + nb_bits_per_block, + dim_hidden, + kernel_size=block_size, + stride=block_size, + padding=0, + ), + nn.ReLU(), + nn.Conv2d(dim_hidden, dim_hidden, kernel_size=5, stride=1, padding=2), + nn.ReLU(), + nn.Conv2d(dim_hidden, dim_hidden, kernel_size=5, stride=1, padding=2), + nn.ReLU(), + nn.Conv2d(dim_hidden, dim_hidden, kernel_size=5, stride=1, padding=2), + nn.ReLU(), + nn.Conv2d(dim_hidden, 3, kernel_size=5, stride=1, padding=2), + ) + + 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) + print(f"lr {lr}") + optimizer = torch.optim.Adam(model.parameters(), lr=lr) + acc_loss, nb_samples = 0.0, 0 + + for input in train_input.split(batch_size): + output = model(input) + loss = F.mse_loss(output, input) + acc_loss += loss.item() * input.size(0) + nb_samples += input.size(0) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + print(f"loss {k} {acc_loss/nb_samples}") + sys.stdout.flush() + + return encoder, decoder + + +###################################################################### + if __name__ == "__main__": import time all_frames = [] - nb = 1000 + nb = 25000 start_time = time.perf_counter() for n in range(nb): - frames, actions = sequence(nb_steps=31) + frames, actions = generate_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()) + encoder, decoder = train_encoder(input) + + # 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()=}") + z = encoder(input) + results = decoder(z) + + print(f"{input.size()=} {z.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, actions = generate_sequence(nb_steps=31, all_frames=True) # frames = torch.cat(frames, 0) # torchvision.utils.save_image(frames, "seq.png", nrow=8)