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])
######################################################################
+# Gets a pair (x, t) and appends t (scalar or 1d tensor) to x as an
+# additional dimension / channel
+
+class TimeAppender(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, u):
+ x, t = u
+ if not torch.is_tensor(t):
+ t = x.new_full((x.size(0),), t)
+ t = t.view((-1,) + (1,) * (x.dim() - 1)).expand_as(x[:,:1])
+ return torch.cat((x, t), 1)
+
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),
+ TimeAppender(),
+ nn.Conv2d(in_channels + 1, 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)
+ def forward(self, u):
+ return self.core(u)
######################################################################
# Data
nh = 256
model = nn.Sequential(
+ TimeAppender(),
nn.Linear(train_input.size(1) + 1, nh),
nn.ReLU(),
nn.Linear(nh, nh),
elif train_input.dim() == 4:
- model = ConvNet(train_input.size(1) + 1, train_input.size(1))
+ model = ConvNet(train_input.size(1), train_input.size(1))
model.to(device)
######################################################################
# 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):
+ output = model((x, t / (T - 1) - 0.5))
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)) \
+ * (x - (1-alpha[t]) / torch.sqrt(1-alpha_bar[t]) * output) \
+ sigma[t] * z
x = x * train_std + train_mean
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)
- loss = (eps - model(input)).pow(2).mean()
+ xt = torch.sqrt(alpha_bar[t]) * x0 + torch.sqrt(1 - alpha_bar[t]) * eps
+ output = model((xt, t / (T - 1) - 0.5))
+ loss = (eps - output).pow(2).mean()
acc_loss += loss.item() * x0.size(0)
optimizer.zero_grad()
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)
+
+ filename = f'diffusion_{args.data}.png'
+ print(f'saving {filename}')
+ torchvision.utils.save_image(x, filename, nrow = 16, pad_value = 0.8)
######################################################################
projects / pytorch.git / blobdiff