device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f'device {device}')
+
######################################################################
def sample_gaussian_mixture(nb):
p, std = 0.3, 0.2
- result = torch.empty(nb, 1).normal_(0, std)
+ result = torch.randn(nb, 1) * std
result = result + torch.sign(torch.rand(result.size()) - p) / 2
return result
+def sample_ramp(nb):
+ result = torch.min(torch.rand(nb, 1), torch.rand(nb, 1))
+ return result
+
def sample_two_discs(nb):
a = torch.rand(nb) * math.pi * 2
b = torch.rand(nb).sqrt()
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
+ N = 4
+ q = (torch.randint(N, (nb,)) - (N - 1) / 2) / ((N - 1) / 2)
+ r = (torch.randint(N, (nb,)) - (N - 1) / 2) / ((N - 1) / 2)
b = b * 0.1
result = torch.empty(nb, 2)
result[:, 0] = a.cos() * b + q
samplers = {
'gaussian_mixture': sample_gaussian_mixture,
+ 'ramp': sample_ramp,
'two_discs': sample_two_discs,
'disc_grid': sample_disc_grid,
'spiral': sample_spiral,
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])
######################################################################
# Model
if train_input.dim() == 2:
- nh = 64
+ nh = 256
model = nn.Sequential(
nn.Linear(train_input.size(1) + 1, nh),
model.to(device)
+print(f'nb_parameters {sum([ p.numel() for p in model.parameters() ])}')
+
+######################################################################
+# Generate
+
+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):
+ 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
+
######################################################################
# Train
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()
- if k%10 == 0: print(f'{k} {acc_loss / train_input.size(0)}')
+ print(f'{k} {acc_loss / train_input.size(0)}')
-ema.copy()
-
-######################################################################
-# Generate
-
-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_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
+if ema is not None: ema.copy_to_model()
######################################################################
# Plot
model.eval()
if train_input.dim() == 2:
+
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
+ # Nx1 -> histogram
if train_input.size(1) == 1:
- x = generate((10000, 1), 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.spines.top.set_visible(False)
d = train_input.flatten().detach().to('cpu').numpy()
ax.hist(d, 25, (-1, 1),
ax.legend(frameon = False, loc = 2)
+ # Nx2 -> scatter plot
elif train_input.size(1) == 2:
- x = generate((1000, 2), model)
+ x = generate((1000, 2), alpha, alpha_bar, sigma,
+ model, train_mean, train_std)
- ax.set_xlim(-1.25, 1.25)
- ax.set_ylim(-1.25, 1.25)
+ ax.set_xlim(-1.5, 1.5)
+ ax.set_ylim(-1.5, 1.5)
ax.set(aspect = 1)
+ ax.spines.right.set_visible(False)
+ ax.spines.top.set_visible(False)
- d = train_input[:x.size(0)].detach().to('cpu').numpy()
+ d = x.detach().to('cpu').numpy()
ax.scatter(d[:, 0], d[:, 1],
- color = 'lightblue', label = 'Train')
+ s = 2.0, color = 'red', label = 'Synthesis')
- d = x.detach().to('cpu').numpy()
+ d = train_input[:x.size(0)].detach().to('cpu').numpy()
ax.scatter(d[:, 0], d[:, 1],
- facecolors = 'none', color = 'red', label = 'Synthesis')
+ s = 2.0, color = 'gray', label = 'Train')
ax.legend(frameon = False, loc = 2)
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:], 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)
projects / pytorch.git / blobdiff