- ax.set_ylim(-1.25, 1.25)
- ax.set(aspect = 1)
-
- d = train_input[:200].detach().to('cpu').numpy()
- ax.scatter(d[:, 0], d[:, 1],
- color = 'lightblue', label = 'Train')
-
- d = x[:200].detach().to('cpu').numpy()
- ax.scatter(d[:, 0], d[:, 1],
- 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'):
- plt.get_current_fig_manager().window.setGeometry(2, 2, 1024, 768)
- plt.show()
+ 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 = 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)
+
+ filename = f"minidiffusion_{args.data}.pdf"
+ print(f"saving {filename}")
+ fig.savefig(filename, bbox_inches="tight")
+
+ 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()
+
+########################################
+# 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)
+
+ ax = fig.add_subplot(1, 1, 1)
+
+ 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=2.5, 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)
+
+ filename = f"minidiffusion_{args.data}.pdf"
+ print(f"saving {filename}")
+ fig.savefig(filename, bbox_inches="tight")
+
+ 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)
+
+else:
+ print(f"cannot plot result of size {train_input.size()}")