X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=minidiffusion.py;h=27842d9e0d785f4b1dac433702f8a7782db69444;hb=989834728791abe50c14aad4294e1ef10d9bbf35;hp=075eb8208455045bc5df1d869e26de639419eee1;hpb=142b09825bec53a432795cb34c2cc325b0e994c2;p=pytorch.git

diff --git a/minidiffusion.py b/minidiffusion.py
index 075eb82..27842d9 100755
--- a/minidiffusion.py
+++ b/minidiffusion.py
@@ -9,40 +9,68 @@ import math, argparse
 
 import matplotlib.pyplot as plt
 
-import torch
+import torch, torchvision
 from torch import nn
 
 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, device = device).normal_(0, std)
-    result = result + torch.sign(torch.rand(result.size(), device = device) - p) / 2
+    result = torch.randn(nb, 1) * std
+    result = result + torch.sign(torch.rand(result.size()) - p) / 2
     return result
 
-def sample_arc(nb):
-    theta = torch.rand(nb, device = device) * math.pi
-    rho = torch.rand(nb, device = device) * 0.1 + 0.7
-    result = torch.empty(nb, 2, device = device)
-    result[:, 0] = theta.cos() * rho
-    result[:, 1] = theta.sin() * rho
+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()
+    q = (torch.rand(nb) <= 0.5).long()
+    b = b * (0.3 + 0.2 * q)
+    result = torch.empty(nb, 2)
+    result[:, 0] = a.cos() * b - 0.5 + q
+    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()
+    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
+    result[:, 1] = a.sin() * b + r
     return result
 
 def sample_spiral(nb):
-    u = torch.rand(nb, device = device)
-    rho = u * 0.65 + 0.25 + torch.rand(nb, device = device) * 0.15
+    u = torch.rand(nb)
+    rho = u * 0.65 + 0.25 + torch.rand(nb) * 0.15
     theta = u * math.pi * 3
-    result = torch.empty(nb, 2, device = device)
+    result = torch.empty(nb, 2)
     result[:, 0] = theta.cos() * rho
     result[:, 1] = theta.sin() * rho
     return result
 
+def sample_mnist(nb):
+    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,
-    'arc': sample_arc,
+    'ramp': sample_ramp,
+    'two_discs': sample_two_discs,
+    'disc_grid': sample_disc_grid,
     'spiral': sample_spiral,
+    'mnist': sample_mnist,
 }
 
 ######################################################################
@@ -77,7 +105,7 @@ parser.add_argument('--learning_rate',
 
 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 ])
 
@@ -101,46 +129,119 @@ class EMA:
     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])
 
 ######################################################################
-# Train
+
+# 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(
+            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, nc, ks, padding = ks//2),
+            nn.ReLU(),
+            nn.Conv2d(nc, 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, u):
+        return self.core(u)
+
+######################################################################
+# Data
 
 try:
-    train_input = samplers[args.data](args.nb_samples)
+    train_input = samplers[args.data](args.nb_samples).to(device)
 except KeyError:
     print(f'unknown data {args.data}')
     exit(1)
 
+train_mean, train_std = train_input.mean(), train_input.std()
+
+######################################################################
+# Model
+
+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),
+        nn.ReLU(),
+        nn.Linear(nh, nh),
+        nn.ReLU(),
+        nn.Linear(nh, train_input.size(1)),
+    )
+
+elif train_input.dim() == 4:
+
+    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() ])}')
+
 ######################################################################
+# 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):
+            output = model((x, t / (T - 1) - 0.5))
+            z = torch.zeros_like(x) if t == 0 else torch.randn_like(x)
+            x = 1/torch.sqrt(alpha[t]) \
+                * (x - (1-alpha[t]) / torch.sqrt(1-alpha_bar[t]) * output) \
+                + sigma[t] * z
 
-nh = 64
+        x = x * train_std + train_mean
 
-model = nn.Sequential(
-    nn.Linear(train_input.size(1) + 1, nh),
-    nn.ReLU(),
-    nn.Linear(nh, nh),
-    nn.ReLU(),
-    nn.Linear(nh, nh),
-    nn.ReLU(),
-    nn.Linear(nh, train_input.size(1)),
-).to(device)
+        return x
+
+######################################################################
+# Train
 
 T = 1000
 beta = torch.linspace(1e-4, 0.02, T, device = device)
@@ -148,7 +249,7 @@ alpha = 1 - beta
 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):
 
@@ -156,79 +257,95 @@ for k in range(args.nb_epochs):
     optimizer = torch.optim.Adam(model.parameters(), lr = args.learning_rate)
 
     for x0 in train_input.split(args.batch_size):
-        t = torch.randint(T, (x0.size(0), 1), device = device)
-        eps = torch.randn(x0.size(), device = device)
-        input = alpha_bar[t].sqrt() * x0 + (1 - alpha_bar[t]).sqrt() * eps
-        input = torch.cat((input, 2 * t / T - 1), 1)
-        output = model(input)
+        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)
+        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()
 
-        acc_loss += loss.item() * x0.size(0)
-
-        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()
+if ema is not None: ema.copy_to_model()
 
 ######################################################################
-# Generate
+# Plot
 
-x = torch.randn(10000, train_input.size(1), device = device)
+model.eval()
 
-for t in range(T-1, -1, -1):
-    z = torch.zeros(x.size(), device = device) if t == 0 else torch.randn(x.size(), device = device)
-    input = torch.cat((x, torch.ones(x.size(0), 1, device = device) * 2 * t / T - 1), 1)
-    x = 1 / alpha[t].sqrt() * (x - (1 - alpha[t])/(1 - alpha_bar[t]).sqrt() * model(input)) \
-        + sigma[t] * z
+if train_input.dim() == 2:
 
-######################################################################
-# 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, 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 = 'stepfilled', color = 'lightblue', label = 'Train')
 
-fig = plt.figure()
-ax = fig.add_subplot(1, 1, 1)
+        d = x.flatten().detach().to('cpu').numpy()
+        ax.hist(d, 25, (-1, 1),
+                density = True,
+                histtype = 'step', color = 'red', label = 'Synthesis')
 
-if train_input.size(1) == 1:
+        ax.legend(frameon = False, loc = 2)
 
-    ax.set_xlim(-1.25, 1.25)
+    # Nx2 -> scatter plot
+    elif train_input.size(1) == 2:
 
-    d = train_input.flatten().detach().to('cpu').numpy()
-    ax.hist(d, 25, (-1, 1),
-            density = True,
-            histtype = 'stepfilled', color = 'lightblue', label = 'Train')
+        x = generate((1000, 2), 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(-1.5, 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.legend(frameon = False, loc = 2)
+        d = x.detach().to('cpu').numpy()
+        ax.scatter(d[:, 0], d[:, 1],
+                   s = 2.0, color = 'red', label = 'Synthesis')
 
-elif train_input.size(1) == 2:
+        d = train_input[:x.size(0)].detach().to('cpu').numpy()
+        ax.scatter(d[:, 0], d[:, 1],
+                   s = 2.0, color = 'gray', label = 'Train')
 
-    ax.set_xlim(-1.25, 1.25)
-    ax.set_ylim(-1.25, 1.25)
-    ax.set(aspect = 1)
+        ax.legend(frameon = False, loc = 2)
 
-    d = train_input[:200].detach().to('cpu').numpy()
-    ax.scatter(d[:, 0], d[:, 1],
-               color = 'lightblue', label = 'Train')
+    filename = f'diffusion_{args.data}.pdf'
+    print(f'saving {filename}')
+    fig.savefig(filename, bbox_inches='tight')
 
-    d = x[:200].detach().to('cpu').numpy()
-    ax.scatter(d[:, 0], d[:, 1],
-               color = 'red', label = 'Synthesis')
+    if 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)
+# NxCxHxW -> image
+elif train_input.dim() == 4:
 
-filename = f'diffusion_{args.data}.pdf'
-print(f'saving {filename}')
-fig.savefig(filename, bbox_inches='tight')
+    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
 
-if hasattr(plt.get_current_fig_manager(), 'window'):
-    plt.get_current_fig_manager().window.setGeometry(2, 2, 1024, 768)
-    plt.show()
+    filename = f'diffusion_{args.data}.png'
+    print(f'saving {filename}')
+    torchvision.utils.save_image(x, filename, nrow = 16, pad_value = 0.8)
 
 ######################################################################