Update.

diff --git a/minidiffusion.py b/minidiffusion.py
index 27842d9..7327522 100755 (executable)
--- a/minidiffusion.py
+++ b/minidiffusion.py
@@ -11,6 +11,7 @@ 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')
 
@@ -113,6 +114,9 @@ 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()
 
 if args.seed >= 0:
@@ -223,7 +227,7 @@ 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():
 
@@ -280,72 +284,97 @@ if ema is not None: ema.copy_to_model()
 
 model.eval()
 
-if train_input.dim() == 2:
+########################################
+# Nx1 -> histogram
+if train_input.dim() == 2 and train_input.size(1) == 1:
 
     fig = plt.figure()
     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)
 
-        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)
 
-        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')
 
-        d = train_input.flatten().detach().to('cpu').numpy()
-        ax.hist(d, 25, (-1, 1),
-                density = True,
-                histtype = 'stepfilled', color = 'lightblue', label = 'Train')
+    d = x.flatten().detach().to('cpu').numpy()
+    ax.hist(d, 25, (-1, 1),
+            density = True,
+            histtype = 'step', color = 'red', label = 'Synthesis')
 
-        d = x.flatten().detach().to('cpu').numpy()
-        ax.hist(d, 25, (-1, 1),
-                density = True,
-                histtype = 'step', 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')
 
-    # Nx2 -> scatter plot
-    elif train_input.size(1) == 2:
+    if not args.no_window and hasattr(plt.get_current_fig_manager(), 'window'):
+        plt.get_current_fig_manager().window.setGeometry(2, 2, 2048, 768)
+        plt.show()
 
-        x = generate((1000, 2), alpha, alpha_bar, sigma,
-                     model, train_mean, train_std)
+########################################
+# Nx2 -> scatter plot
+elif train_input.dim() == 2 and train_input.size(1) == 2:
 
-        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)
+    fig = plt.figure()
+    ax = fig.add_subplot(1, 1, 1)
 
-        d = x.detach().to('cpu').numpy()
-        ax.scatter(d[:, 0], d[:, 1],
-                   s = 2.0, color = 'red', label = 'Synthesis')
+    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)
 
-        d = train_input[:x.size(0)].detach().to('cpu').numpy()
-        ax.scatter(d[:, 0], d[:, 1],
-                   s = 2.0, color = 'gray', label = 'Train')
+    d = train_input[:x.size(0)].detach().to('cpu').numpy()
+    ax.scatter(d[:, 0], d[:, 1],
+               s = 2.5, color = 'gray', label = 'Train')
 
-        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')
+
+    ax.legend(frameon = False, loc = 2)
 
     filename = f'diffusion_{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:], alpha, alpha_bar, sigma,
+    x = generate((128,) + train_input.size()[1:], T, alpha, alpha_bar, sigma,
                  model, train_mean, train_std)
-    x = 1 - x.clamp(min = 0, max = 255) / 255
+
+    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'diffusion_{args.data}.png'
     print(f'saving {filename}')
-    torchvision.utils.save_image(x, filename, nrow = 16, pad_value = 0.8)
+    torchvision.utils.save_image(result, filename)
+
+else:
+
+    print(f'cannot plot result of size {train_input.size()}')
 
 ######################################################################