import torch, torchvision
from torch import nn
+from torch.nn import functional as F
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f'device {device}')
+print(f"device {device}")
######################################################################
+
def sample_gaussian_mixture(nb):
p, std = 0.3, 0.2
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()
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()
result[:, 1] = a.sin() * b + r
return result
+
def sample_spiral(nb):
u = torch.rand(nb)
rho = u * 0.65 + 0.25 + torch.rand(nb) * 0.15
result[:, 1] = theta.sin() * rho
return result
+
def sample_mnist(nb):
- train_set = torchvision.datasets.MNIST(root = './data/', train = True, download = True)
+ 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,
- 'ramp': sample_ramp,
- 'two_discs': sample_two_discs,
- 'disc_grid': sample_disc_grid,
- 'spiral': sample_spiral,
- 'mnist': sample_mnist,
+ f.__name__.removeprefix("sample_"): f
+ for f in [
+ sample_gaussian_mixture,
+ sample_ramp,
+ sample_two_discs,
+ sample_disc_grid,
+ sample_spiral,
+ sample_mnist,
+ ]
}
######################################################################
parser = argparse.ArgumentParser(
- description = '''A minimal implementation of Jonathan Ho, Ajay Jain, Pieter Abbeel
+ description="""A minimal implementation of Jonathan Ho, Ajay Jain, Pieter Abbeel
"Denoising Diffusion Probabilistic Models" (2020)
-https://arxiv.org/abs/2006.11239''',
+https://arxiv.org/abs/2006.11239""",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+)
- formatter_class = argparse.ArgumentDefaultsHelpFormatter
+parser.add_argument(
+ "--seed", type=int, default=0, help="Random seed, < 0 is no seeding"
)
-parser.add_argument('--seed',
- type = int, default = 0,
- help = 'Random seed, < 0 is no seeding')
+parser.add_argument("--nb_epochs", type=int, default=100, help="How many epochs")
-parser.add_argument('--nb_epochs',
- type = int, default = 100,
- help = 'How many epochs')
+parser.add_argument("--batch_size", type=int, default=25, help="Batch size")
-parser.add_argument('--batch_size',
- type = int, default = 25,
- help = 'Batch size')
+parser.add_argument(
+ "--nb_samples", type=int, default=25000, help="Number of training examples"
+)
-parser.add_argument('--nb_samples',
- type = int, default = 25000,
- help = 'Number of training examples')
+parser.add_argument("--learning_rate", type=float, default=1e-3, help="Learning rate")
-parser.add_argument('--learning_rate',
- type = float, default = 1e-3,
- help = 'Learning rate')
+parser.add_argument(
+ "--ema_decay", type=float, default=0.9999, help="EMA decay, <= 0 is no EMA"
+)
-parser.add_argument('--ema_decay',
- type = float, default = 0.9999,
- help = 'EMA decay, <= 0 is no EMA')
+data_list = ", ".join([str(k) for k in samplers])
-data_list = ', '.join( [ str(k) for k in samplers ])
+parser.add_argument(
+ "--data",
+ type=str,
+ default="gaussian_mixture",
+ help=f"Toy data-set to use: {data_list}",
+)
-parser.add_argument('--data',
- type = str, default = 'gaussian_mixture',
- help = f'Toy data-set to use: {data_list}')
+parser.add_argument("--no_window", action="store_true", default=False)
args = parser.parse_args()
######################################################################
+
class EMA:
def __init__(self, model, decay):
self.model = model
self.decay = decay
- self.mem = { }
+ self.mem = {}
with torch.no_grad():
for p in model.parameters():
self.mem[p] = p.clone()
for p in self.model.parameters():
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.Conv2d(nc, nc, ks, padding=ks // 2),
nn.ReLU(),
- nn.Conv2d(nc, nc, ks, padding = ks//2),
+ nn.Conv2d(nc, nc, ks, padding=ks // 2),
nn.ReLU(),
- nn.Conv2d(nc, nc, ks, padding = ks//2),
+ nn.Conv2d(nc, nc, ks, padding=ks // 2),
nn.ReLU(),
- nn.Conv2d(nc, nc, ks, padding = ks//2),
+ nn.Conv2d(nc, nc, ks, padding=ks // 2),
nn.ReLU(),
- nn.Conv2d(nc, out_channels, ks, padding = ks//2),
+ 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
try:
train_input = samplers[args.data](args.nb_samples).to(device)
except KeyError:
- print(f'unknown data {args.data}')
+ print(f"unknown data {args.data}")
exit(1)
train_mean, train_std = train_input.mean(), train_input.std()
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)
-print(f'nb_parameters {sum([ p.numel() for p in model.parameters() ])}')
+print(f"nb_parameters {sum([ p.numel() for p in model.parameters() ])}")
######################################################################
# Generate
-def generate(size, alpha, alpha_bar, sigma, model):
+
+def generate(size, T, alpha, alpha_bar, sigma, model, train_mean, train_std):
with torch.no_grad():
- x = torch.randn(size, device = device)
+ x = torch.randn(size, device=device)
- for t in range(T-1, -1, -1):
+ 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
+ / torch.sqrt(alpha[t])
+ * (x - (1 - alpha[t]) / torch.sqrt(1 - alpha_bar[t]) * output)
+ sigma[t] * z
+ )
x = x * train_std + train_mean
return x
+
######################################################################
# Train
T = 1000
-beta = torch.linspace(1e-4, 0.02, T, device = device)
+beta = torch.linspace(1e-4, 0.02, T, device=device)
alpha = 1 - beta
alpha_bar = alpha.log().cumsum(0).exp()
sigma = beta.sqrt()
-ema = EMA(model, decay = args.ema_decay) if args.ema_decay > 0 else None
+ema = EMA(model, decay=args.ema_decay) if args.ema_decay > 0 else None
for k in range(args.nb_epochs):
-
acc_loss = 0
- optimizer = torch.optim.Adam(model.parameters(), lr = args.learning_rate)
+ optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
for x0 in train_input.split(args.batch_size):
x0 = (x0 - train_mean) / train_std
- t = torch.randint(T, (x0.size(0),) + (1,) * (x0.dim() - 1), device = x0.device)
+ t = torch.randint(T, (x0.size(0),) + (1,) * (x0.dim() - 1), device=x0.device)
eps = torch.randn_like(x0)
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()
+ 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()
- if ema is not None: ema.step()
+ if ema is not None:
+ ema.step()
- print(f'{k} {acc_loss / train_input.size(0)}')
+ print(f"{k} {acc_loss / train_input.size(0)}")
-if ema is not None: ema.copy_to_model()
+if ema is not None:
+ ema.copy_to_model()
######################################################################
# Plot
model.eval()
-if train_input.dim() == 2:
-
+########################################
+# Nx1 -> histogram
+if train_input.dim() == 2 and train_input.size(1) == 1:
fig = plt.figure()
+ fig.set_figheight(5)
+ fig.set_figwidth(8)
+
ax = fig.add_subplot(1, 1, 1)
- if train_input.size(1) == 1:
+ x = generate((10000, 1), T, 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),
+ density=True,
+ histtype="bar",
+ edgecolor="white",
+ color="lightblue",
+ label="Train",
+ )
- x = generate((10000, 1), alpha, alpha_bar, sigma, model)
+ d = x.flatten().detach().to("cpu").numpy()
+ ax.hist(
+ d, 25, (-1, 1), density=True, histtype="step", color="red", label="Synthesis"
+ )
- ax.set_xlim(-1.25, 1.25)
- ax.spines.right.set_visible(False)
- ax.spines.top.set_visible(False)
+ ax.legend(frameon=False, loc=2)
- d = train_input.flatten().detach().to('cpu').numpy()
- ax.hist(d, 25, (-1, 1),
- density = True,
- histtype = 'stepfilled', color = 'lightblue', label = 'Train')
+ filename = f"minidiffusion_{args.data}.pdf"
+ print(f"saving {filename}")
+ fig.savefig(filename, bbox_inches="tight")
- d = x.flatten().detach().to('cpu').numpy()
- ax.hist(d, 25, (-1, 1),
- density = True,
- histtype = 'step', color = 'red', label = 'Synthesis')
+ if not args.no_window and hasattr(plt.get_current_fig_manager(), "window"):
+ plt.get_current_fig_manager().window.setGeometry(2, 2, 1024, 768)
+ plt.show()
- ax.legend(frameon = False, loc = 2)
+########################################
+# Nx2 -> scatter plot
+elif train_input.dim() == 2 and train_input.size(1) == 2:
+ fig = plt.figure()
+ fig.set_figheight(6)
+ fig.set_figwidth(6)
- elif train_input.size(1) == 2:
+ ax = fig.add_subplot(1, 1, 1)
- x = generate((1000, 2), alpha, alpha_bar, sigma, model)
+ x = generate((1000, 2), T, alpha, alpha_bar, sigma, model, train_mean, train_std)
- 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)
+ 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 = x.detach().to('cpu').numpy()
- ax.scatter(d[:, 0], d[:, 1],
- s = 2.0, color = 'red', label = 'Synthesis')
+ d = train_input[: x.size(0)].detach().to("cpu").numpy()
+ ax.scatter(d[:, 0], d[:, 1], s=2.5, color="gray", label="Train")
- d = train_input[:x.size(0)].detach().to('cpu').numpy()
- ax.scatter(d[:, 0], d[:, 1],
- s = 2.0, color = 'gray', label = 'Train')
+ d = x.detach().to("cpu").numpy()
+ ax.scatter(d[:, 0], d[:, 1], s=2.0, 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"minidiffusion_{args.data}.pdf"
+ print(f"saving {filename}")
+ fig.savefig(filename, bbox_inches="tight")
- if hasattr(plt.get_current_fig_manager(), 'window'):
+ if not args.no_window and hasattr(plt.get_current_fig_manager(), "window"):
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:],
+ T,
+ alpha,
+ alpha_bar,
+ sigma,
+ model,
+ train_mean,
+ train_std,
+ )
+
+ x = torchvision.utils.make_grid(
+ x.clamp(min=0, max=255), nrow=16, padding=1, pad_value=64
+ )
+ x = F.pad(x, pad=(2, 2, 2, 2), value=64)[None]
+
+ t = torchvision.utils.make_grid(train_input[:128], nrow=16, padding=1, pad_value=64)
+ t = F.pad(t, pad=(2, 2, 2, 2), value=64)[None]
+
+ result = 1 - torch.cat((t, x), 2) / 255
+
+ filename = f"minidiffusion_{args.data}.png"
+ print(f"saving {filename}")
+ torchvision.utils.save_image(result, filename)
- x = generate((128,) + train_input.size()[1:], alpha, alpha_bar, sigma, 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)
+else:
+ print(f"cannot plot result of size {train_input.size()}")
######################################################################
projects / pytorch.git / blobdiff