X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=minidiffusion.py;h=a3ffda008ef7672ebbe5d98246a21113cd2030b1;hb=HEAD;hp=65ca94737443bcf8bc179aef884ceb30f6897886;hpb=a810bbe6c5bc84f66e4fdb85dca41a39bd71afac;p=pytorch.git

diff --git a/minidiffusion.py b/minidiffusion.py
index 65ca947..a3ffda0 100755
--- a/minidiffusion.py
+++ b/minidiffusion.py
@@ -11,23 +11,27 @@ import matplotlib.pyplot as plt
 
 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()
@@ -38,6 +42,7 @@ def sample_two_discs(nb):
     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()
@@ -50,6 +55,7 @@ def sample_disc_grid(nb):
     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
@@ -59,59 +65,62 @@ def sample_spiral(nb):
     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()
 
@@ -125,11 +134,12 @@ if args.seed >= 0:
 
 ######################################################################
 
+
 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()
@@ -144,8 +154,25 @@ class EMA:
             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__()
@@ -153,21 +180,23 @@ class ConvNet(nn.Module):
         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
@@ -175,7 +204,7 @@ class ConvNet(nn.Module):
 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()
@@ -187,6 +216,7 @@ if train_input.dim() == 2:
     nh = 256
 
     model = nn.Sequential(
+        TimeAppender(),
         nn.Linear(train_input.size(1) + 1, nh),
         nn.ReLU(),
         nn.Linear(nh, nh),
@@ -197,136 +227,180 @@ if train_input.dim() == 2:
     )
 
 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, train_mean, train_std):
 
+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)
 
-    # Nx1 -> histogram
-    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, train_mean, train_std)
+    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)
 
-    # Nx2 -> scatter plot
-    elif train_input.size(1) == 2:
+    ax = fig.add_subplot(1, 1, 1)
 
-        x = generate((1000, 2), alpha, alpha_bar, sigma,
-                     model, train_mean, train_std)
+    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, 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)
+else:
+    print(f"cannot plot result of size {train_input.size()}")
 
 ######################################################################