import matplotlib.pyplot as plt
-import torch
+import torch, torchvision
from torch import nn
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def sample_gaussian_mixture(nb):
p, std = 0.3, 0.2
- result = torch.empty(nb, 1, device = device).normal_(0, std)
- result = result + torch.sign(torch.rand(result.size(), device = device) - p) / 2
+ result = torch.empty(nb, 1).normal_(0, std)
+ result = result + torch.sign(torch.rand(result.size()) - p) / 2
return result
-def sample_arc(nb):
- theta = torch.rand(nb, device = device) * math.pi
- rho = torch.rand(nb, device = device) * 0.1 + 0.7
- result = torch.empty(nb, 2, device = device)
- result[:, 0] = theta.cos() * rho
- result[:, 1] = theta.sin() * rho
+def sample_two_discs(nb):
+ a = torch.rand(nb) * math.pi * 2
+ b = torch.rand(nb).sqrt()
+ q = (torch.rand(nb) <= 0.5).long()
+ b = b * (0.3 + 0.2 * q)
+ result = torch.empty(nb, 2)
+ result[:, 0] = a.cos() * b - 0.5 + q
+ result[:, 1] = a.sin() * b - 0.5 + q
+ return result
+
+def sample_disc_grid(nb):
+ a = torch.rand(nb) * math.pi * 2
+ b = torch.rand(nb).sqrt()
+ q = torch.randint(5, (nb,)) / 2.5 - 2 / 2.5
+ r = torch.randint(5, (nb,)) / 2.5 - 2 / 2.5
+ b = b * 0.1
+ result = torch.empty(nb, 2)
+ result[:, 0] = a.cos() * b + q
+ result[:, 1] = a.sin() * b + r
return result
def sample_spiral(nb):
- u = torch.rand(nb, device = device)
- rho = u * 0.65 + 0.25 + torch.rand(nb, device = device) * 0.15
+ u = torch.rand(nb)
+ rho = u * 0.65 + 0.25 + torch.rand(nb) * 0.15
theta = u * math.pi * 3
- result = torch.empty(nb, 2, device = device)
+ result = torch.empty(nb, 2)
result[:, 0] = theta.cos() * rho
result[:, 1] = theta.sin() * rho
return result
+def sample_mnist(nb):
+ train_set = torchvision.datasets.MNIST(root = './data/', train = True, download = True)
+ result = train_set.data[:nb].to(device).view(-1, 1, 28, 28).float()
+ return result
+
samplers = {
'gaussian_mixture': sample_gaussian_mixture,
- 'arc': sample_arc,
+ 'two_discs': sample_two_discs,
+ 'disc_grid': sample_disc_grid,
'spiral': sample_spiral,
+ 'mnist': sample_mnist,
}
######################################################################
p.copy_(self.ema[p])
######################################################################
-# Train
+
+class ConvNet(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+
+ ks, nc = 5, 64
+
+ self.core = nn.Sequential(
+ nn.Conv2d(in_channels, nc, ks, padding = ks//2),
+ nn.ReLU(),
+ nn.Conv2d(nc, nc, ks, padding = ks//2),
+ nn.ReLU(),
+ nn.Conv2d(nc, nc, ks, padding = ks//2),
+ nn.ReLU(),
+ nn.Conv2d(nc, nc, ks, padding = ks//2),
+ nn.ReLU(),
+ nn.Conv2d(nc, nc, ks, padding = ks//2),
+ nn.ReLU(),
+ nn.Conv2d(nc, out_channels, ks, padding = ks//2),
+ )
+
+ def forward(self, x):
+ return self.core(x)
+
+######################################################################
+# Data
try:
- train_input = samplers[args.data](args.nb_samples)
+ train_input = samplers[args.data](args.nb_samples).to(device)
except KeyError:
print(f'unknown data {args.data}')
exit(1)
+train_mean, train_std = train_input.mean(), train_input.std()
+
######################################################################
+# Model
+
+if train_input.dim() == 2:
+ nh = 64
+
+ model = nn.Sequential(
+ nn.Linear(train_input.size(1) + 1, nh),
+ nn.ReLU(),
+ nn.Linear(nh, nh),
+ nn.ReLU(),
+ nn.Linear(nh, nh),
+ nn.ReLU(),
+ nn.Linear(nh, train_input.size(1)),
+ )
+
+elif train_input.dim() == 4:
-nh = 64
+ model = ConvNet(train_input.size(1) + 1, train_input.size(1))
-model = nn.Sequential(
- nn.Linear(train_input.size(1) + 1, nh),
- nn.ReLU(),
- nn.Linear(nh, nh),
- nn.ReLU(),
- nn.Linear(nh, nh),
- nn.ReLU(),
- nn.Linear(nh, train_input.size(1)),
-).to(device)
+model.to(device)
+
+######################################################################
+# Train
T = 1000
beta = torch.linspace(1e-4, 0.02, T, device = device)
optimizer = torch.optim.Adam(model.parameters(), lr = args.learning_rate)
for x0 in train_input.split(args.batch_size):
- t = torch.randint(T, (x0.size(0), 1), device = device)
- eps = torch.randn(x0.size(), device = device)
- input = alpha_bar[t].sqrt() * x0 + (1 - alpha_bar[t]).sqrt() * eps
- input = torch.cat((input, 2 * t / T - 1), 1)
- output = model(input)
- loss = (eps - output).pow(2).mean()
+ x0 = (x0 - train_mean) / train_std
+ t = torch.randint(T, (x0.size(0),) + (1,) * (x0.dim() - 1), device = x0.device)
+ eps = torch.randn_like(x0)
+ input = torch.sqrt(alpha_bar[t]) * x0 + torch.sqrt(1 - alpha_bar[t]) * eps
+ input = torch.cat((input, t.expand_as(x0[:,:1]) / (T - 1) - 0.5), 1)
+ loss = (eps - model(input)).pow(2).mean()
+ acc_loss += loss.item() * x0.size(0)
+
optimizer.zero_grad()
loss.backward()
optimizer.step()
- acc_loss += loss.item() * x0.size(0)
-
ema.step()
if k%10 == 0: print(f'{k} {acc_loss / train_input.size(0)}')
######################################################################
# Generate
-x = torch.randn(10000, train_input.size(1), device = device)
+def generate(size, model):
+ with torch.no_grad():
+ x = torch.randn(size, device = device)
-for t in range(T-1, -1, -1):
- z = torch.zeros(x.size(), device = device) if t == 0 else torch.randn(x.size(), device = device)
- input = torch.cat((x, torch.ones(x.size(0), 1, device = device) * 2 * t / T - 1), 1)
- x = 1 / alpha[t].sqrt() * (x - (1 - alpha[t])/(1 - alpha_bar[t]).sqrt() * model(input)) \
- + sigma[t] * z
+ for t in range(T-1, -1, -1):
+ z = torch.zeros_like(x) if t == 0 else torch.randn_like(x)
+ input = torch.cat((x, torch.full_like(x[:,:1], t / (T - 1) - 0.5)), 1)
+ x = 1/torch.sqrt(alpha[t]) \
+ * (x - (1-alpha[t]) / torch.sqrt(1-alpha_bar[t]) * model(input)) \
+ + sigma[t] * z
+
+ x = x * train_std + train_mean
+
+ return x
######################################################################
# Plot
-fig = plt.figure()
-ax = fig.add_subplot(1, 1, 1)
+model.eval()
+
+if train_input.dim() == 2:
+ fig = plt.figure()
+ ax = fig.add_subplot(1, 1, 1)
+
+ if train_input.size(1) == 1:
+
+ x = generate((10000, 1), model)
+
+ ax.set_xlim(-1.25, 1.25)
-if train_input.size(1) == 1:
+ d = train_input.flatten().detach().to('cpu').numpy()
+ ax.hist(d, 25, (-1, 1),
+ density = True,
+ histtype = 'stepfilled', color = 'lightblue', label = 'Train')
- ax.set_xlim(-1.25, 1.25)
+ d = x.flatten().detach().to('cpu').numpy()
+ ax.hist(d, 25, (-1, 1),
+ density = True,
+ histtype = 'step', color = 'red', label = 'Synthesis')
- d = train_input.flatten().detach().to('cpu').numpy()
- ax.hist(d, 25, (-1, 1),
- density = True,
- histtype = 'stepfilled', color = 'lightblue', label = 'Train')
+ ax.legend(frameon = False, loc = 2)
- d = x.flatten().detach().to('cpu').numpy()
- ax.hist(d, 25, (-1, 1),
- density = True,
- histtype = 'step', color = 'red', label = 'Synthesis')
+ elif train_input.size(1) == 2:
- ax.legend(frameon = False, loc = 2)
+ x = generate((1000, 2), model)
-elif train_input.size(1) == 2:
+ ax.set_xlim(-1.25, 1.25)
+ ax.set_ylim(-1.25, 1.25)
+ ax.set(aspect = 1)
- ax.set_xlim(-1.25, 1.25)
- ax.set_ylim(-1.25, 1.25)
- ax.set(aspect = 1)
+ d = train_input[:x.size(0)].detach().to('cpu').numpy()
+ ax.scatter(d[:, 0], d[:, 1],
+ color = 'lightblue', label = 'Train')
- d = train_input[:200].detach().to('cpu').numpy()
- ax.scatter(d[:, 0], d[:, 1],
- color = 'lightblue', label = 'Train')
+ d = x.detach().to('cpu').numpy()
+ ax.scatter(d[:, 0], d[:, 1],
+ facecolors = 'none', color = 'red', label = 'Synthesis')
- d = x[:200].detach().to('cpu').numpy()
- ax.scatter(d[:, 0], d[:, 1],
- color = 'red', label = 'Synthesis')
+ ax.legend(frameon = False, loc = 2)
- ax.legend(frameon = False, loc = 2)
+ filename = f'diffusion_{args.data}.pdf'
+ print(f'saving {filename}')
+ fig.savefig(filename, bbox_inches='tight')
-filename = f'diffusion_{args.data}.pdf'
-print(f'saving {filename}')
-fig.savefig(filename, bbox_inches='tight')
+ if hasattr(plt.get_current_fig_manager(), 'window'):
+ plt.get_current_fig_manager().window.setGeometry(2, 2, 1024, 768)
+ plt.show()
-if hasattr(plt.get_current_fig_manager(), 'window'):
- plt.get_current_fig_manager().window.setGeometry(2, 2, 1024, 768)
- plt.show()
+elif train_input.dim() == 4:
+ x = generate((128,) + train_input.size()[1:], model)
+ x = 1 - x.clamp(min = 0, max = 255) / 255
+ torchvision.utils.save_image(x, f'diffusion_{args.data}.png', nrow = 16, pad_value = 0.8)
######################################################################
projects / pytorch.git / commitdiff