parser.add_argument('--ema_decay',
type = float, default = 0.9999,
- help = 'EMA decay, < 0 is no EMA')
+ help = 'EMA decay, <= 0 is no EMA')
data_list = ', '.join( [ str(k) for k in samplers ])
def __init__(self, model, decay):
self.model = model
self.decay = decay
- if self.decay < 0: return
- self.ema = { }
+ self.mem = { }
with torch.no_grad():
for p in model.parameters():
- self.ema[p] = p.clone()
+ self.mem[p] = p.clone()
def step(self):
- if self.decay < 0: return
with torch.no_grad():
for p in self.model.parameters():
- self.ema[p].copy_(self.decay * self.ema[p] + (1 - self.decay) * p)
+ self.mem[p].copy_(self.decay * self.mem[p] + (1 - self.decay) * p)
- def copy(self):
- if self.decay < 0: return
+ def copy_to_model(self):
with torch.no_grad():
for p in self.model.parameters():
- p.copy_(self.ema[p])
+ p.copy_(self.mem[p])
######################################################################
######################################################################
# Generate
-def generate(size, alpha, alpha_bar, sigma, model):
+def generate(size, alpha, alpha_bar, sigma, model, train_mean, train_std):
+
with torch.no_grad():
+
x = torch.randn(size, device = device)
for t in range(T-1, -1, -1):
alpha_bar = alpha.log().cumsum(0).exp()
sigma = beta.sqrt()
-ema = EMA(model, decay = args.ema_decay)
+ema = EMA(model, decay = args.ema_decay) if args.ema_decay > 0 else None
for k in range(args.nb_epochs):
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)
+ xt = torch.sqrt(alpha_bar[t]) * x0 + torch.sqrt(1 - alpha_bar[t]) * eps
+ input = torch.cat((xt, t.expand_as(x0[:,:1]) / (T - 1) - 0.5), 1)
loss = (eps - model(input)).pow(2).mean()
acc_loss += loss.item() * x0.size(0)
loss.backward()
optimizer.step()
- ema.step()
+ if ema is not None: ema.step()
print(f'{k} {acc_loss / train_input.size(0)}')
-ema.copy()
+if ema is not None: ema.copy_to_model()
######################################################################
# Plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
+ # Nx1 -> histogram
if train_input.size(1) == 1:
- x = generate((10000, 1), alpha, alpha_bar, sigma, model)
+ x = generate((10000, 1), alpha, alpha_bar, sigma,
+ model, train_mean, train_std)
ax.set_xlim(-1.25, 1.25)
ax.spines.right.set_visible(False)
ax.legend(frameon = False, loc = 2)
+ # Nx2 -> scatter plot
elif train_input.size(1) == 2:
- x = generate((1000, 2), alpha, alpha_bar, sigma, model)
+ x = generate((1000, 2), alpha, alpha_bar, sigma,
+ model, train_mean, train_std)
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
plt.get_current_fig_manager().window.setGeometry(2, 2, 1024, 768)
plt.show()
+# NxCxHxW -> image
elif train_input.dim() == 4:
- x = generate((128,) + train_input.size()[1:], alpha, alpha_bar, sigma, model)
+ x = generate((128,) + train_input.size()[1:], alpha, alpha_bar, sigma,
+ model, train_mean, train_std)
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)