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-rw-r--r--main.py164
1 files changed, 85 insertions, 79 deletions
diff --git a/main.py b/main.py
index aa07c3d..81f832d 100644
--- a/main.py
+++ b/main.py
@@ -3,6 +3,7 @@ import torch.nn as nn
import torch.nn.functional as F
import torch.nn.attention as attention
from torch.utils.data import DataLoader, Dataset
+from rich.progress import track
from torchvision import datasets, transforms
@@ -14,7 +15,7 @@ import cv2
from trainer import Trainer
from autoencoder import Autoencoder
-from utils import fid
+from utils import *
class SelfAttention(nn.Module):
@@ -303,104 +304,104 @@ if __name__ == '__main__':
##############
with torch.no_grad():
- # # Forward diffusion
- # img, label = dataset[np.random.randint(0, len(dataset))]
- # img = img.to(DEVICE)
- # if autoencoder is not None:
- # img = autoencoder.encode(img.unsqueeze(0)).squeeze(0)
- # img = img * 2 - 1
+ # Forward diffusion
+ img, label = dataset[np.random.randint(0, len(dataset))]
+ img = img.to(DEVICE)
+ if autoencoder is not None:
+ img = autoencoder.encode(img.unsqueeze(0)).squeeze(0)
+ img = img * 2 - 1
- # nb_plots = 10
- # plots_id = [i for i in np.linspace(1, DIFFU_STEPS, nb_plots, dtype=int)]
+ nb_plots = 10
+ plots_id = [i for i in np.linspace(1, DIFFU_STEPS, nb_plots, dtype=int)]
- # xs = forward_diffusion(img)
+ xs = forward_diffusion(img)
- # plt.figure(figsize=(nb_plots, 2.5))
- # for plot_i, t in enumerate(plots_id):
- # x = xs[t]
- # plt.subplot(2, nb_plots + 1, plot_i + 2)
- # plt.title(f"t={t}")
- # plt.imshow(tensor_to_image(x), interpolation='none')
- # plt.axis("off")
+ plt.figure(figsize=(nb_plots, 2.5))
+ for plot_i, t in enumerate(plots_id):
+ x = xs[t]
+ plt.subplot(2, nb_plots + 1, plot_i + 2)
+ plt.title(f"t={t}")
+ plt.imshow(tensor_to_image(x), interpolation='none')
+ plt.axis("off")
- # for plot_i, t in enumerate(plots_id):
- # x = q_xt_x0(img, t)[0]
- # plt.subplot(2, nb_plots + 1, nb_plots + plot_i + 3)
- # plt.imshow(tensor_to_image(x), interpolation='none')
- # plt.axis("off")
+ for plot_i, t in enumerate(plots_id):
+ x = q_xt_x0(img, t)[0]
+ plt.subplot(2, nb_plots + 1, nb_plots + plot_i + 3)
+ plt.imshow(tensor_to_image(x), interpolation='none')
+ plt.axis("off")
- # plt.subplot(2, nb_plots + 1, 1)
- # plt.text(0, 0.5, "Implicit", fontsize=12)
- # plt.axis("off")
- # plt.subplot(2, nb_plots + 1, nb_plots + 2)
- # plt.text(0, 0.5, "Explicit", fontsize=12)
- # plt.axis("off")
+ plt.subplot(2, nb_plots + 1, 1)
+ plt.text(0, 0.5, "Implicit", fontsize=12)
+ plt.axis("off")
+ plt.subplot(2, nb_plots + 1, nb_plots + 2)
+ plt.text(0, 0.5, "Explicit", fontsize=12)
+ plt.axis("off")
- # plt.suptitle("Forward diffusion")
- # plt.tight_layout()
- # plt.savefig("forward_diffusion.tmp.png")
+ plt.suptitle("Forward diffusion")
+ plt.tight_layout()
+ plt.savefig("forward_diffusion.tmp.png")
- # # Backward diffusion
- # t_plots = np.linspace(1, DIFFU_STEPS, nb_plots, dtype=int)
+ # Backward diffusion
+ t_plots = np.linspace(1, DIFFU_STEPS, nb_plots, dtype=int)
- # n_classes = 10
+ n_classes = 10
- # x = torch.randn(n_classes, NB_CHANNEL, IMG_SIZE, IMG_SIZE, device=DEVICE)
- # vec = torch.tensor([[min(i, NB_LABEL-1)] for i in range(n_classes)], dtype=torch.int64)
- # vec = torch.nn.functional.one_hot(vec, num_classes=NB_LABEL).to(device=DEVICE, dtype=torch.float32)
+ x = torch.randn(n_classes, NB_CHANNEL, IMG_SIZE, IMG_SIZE, device=DEVICE)
+ vec = torch.tensor([[min(i, NB_LABEL-1)] for i in range(n_classes)], dtype=torch.int64)
+ vec = torch.nn.functional.one_hot(vec, num_classes=NB_LABEL).to(device=DEVICE, dtype=torch.float32)
- # plt.figure(figsize=(nb_plots, n_classes))
- # plt.suptitle("Backward diffusion")
- # for t in range(DIFFU_STEPS, 0, -1):
- # t_tensor = torch.tensor([[t]] * n_classes, device=DEVICE, dtype=torch.float32)
- # x = p_xt_1_xt(model, x, t_tensor, vec)
- # if t in t_plots:
- # t_plot_i = nb_plots - t_plots.tolist().index(t) - 1
- # for class_i in range(n_classes):
- # plt.subplot(n_classes, nb_plots, t_plot_i + nb_plots * class_i + 1)
- # if class_i == 0:
- # plt.title(f"t={t}")
- # plt.imshow(tensor_to_image(x[class_i]))
- # plt.axis("off")
- # plt.tight_layout()
- # plt.savefig("backward_diffusion.tmp.png")
+ plt.figure(figsize=(nb_plots, n_classes))
+ plt.suptitle("Backward diffusion")
+ for t in track(range(DIFFU_STEPS, 0, -1), description='Diffusing...'):
+ t_tensor = torch.tensor([[t]] * n_classes, device=DEVICE, dtype=torch.float32)
+ x = p_xt_1_xt(model, x, t_tensor, vec)
+ if t in t_plots:
+ t_plot_i = nb_plots - t_plots.tolist().index(t) - 1
+ for class_i in range(n_classes):
+ plt.subplot(n_classes, nb_plots, t_plot_i + nb_plots * class_i + 1)
+ if class_i == 0:
+ plt.title(f"t={t}")
+ plt.imshow(tensor_to_image(x[class_i]))
+ plt.axis("off")
+ plt.tight_layout()
+ plt.savefig("backward_diffusion.tmp.png")
- # # Benchmark
- # x = torch.randn(nb_plots * n_classes, NB_CHANNEL, IMG_SIZE, IMG_SIZE).to(DEVICE)
- # vec = sum([[[min(i, NB_LABEL-1)]] * nb_plots for i in range(n_classes)], [])
- # vec = torch.tensor(vec, device=DEVICE, dtype=torch.int64)
- # vec = torch.nn.functional.one_hot(vec, num_classes=NB_LABEL).to(device=DEVICE, dtype=torch.float32)
+ # Benchmark
+ x = torch.randn(nb_plots * n_classes, NB_CHANNEL, IMG_SIZE, IMG_SIZE).to(DEVICE)
+ vec = sum([[[min(i, NB_LABEL-1)]] * nb_plots for i in range(n_classes)], [])
+ vec = torch.tensor(vec, device=DEVICE, dtype=torch.int64)
+ vec = torch.nn.functional.one_hot(vec, num_classes=NB_LABEL).to(device=DEVICE, dtype=torch.float32)
- # for ti in range(DIFFU_STEPS, 0, -1):
- # t = torch.tensor([[ti]] * n_classes * nb_plots, device=DEVICE, dtype=torch.float32)
- # x = p_xt_1_xt(model, x, t, vec)
+ for ti in track(range(DIFFU_STEPS, 0, -1), description='Benchmarking...'):
+ t = torch.tensor([[ti]] * n_classes * nb_plots, device=DEVICE, dtype=torch.float32)
+ x = p_xt_1_xt(model, x, t, vec)
- # x = x * 0.5 + 0.5
- # x = x.clamp(0, 1)
+ x = x * 0.5 + 0.5
+ x = x.clamp(0, 1)
- # plt.figure(figsize=(nb_plots, n_classes))
- # for i in range(nb_plots):
- # for j in range(n_classes):
- # id = i * n_classes + j
- # img = x[id]
- # if autoencoder is not None:
- # img = train_dataset.autoencoder.decode(img.unsqueeze(0)).squeeze(0)
- # plt.subplot(n_classes, nb_plots, id + 1)
- # plt.imshow(tensor_to_image(img))
- # plt.axis("off")
- # plt.tight_layout()
- # plt.savefig("benchmark.tmp.png")
+ plt.figure(figsize=(nb_plots, n_classes))
+ for i in range(nb_plots):
+ for j in range(n_classes):
+ id = i * n_classes + j
+ img = x[id]
+ if autoencoder is not None:
+ img = train_dataset.autoencoder.decode(img.unsqueeze(0)).squeeze(0)
+ plt.subplot(n_classes, nb_plots, id + 1)
+ plt.imshow(tensor_to_image(img))
+ plt.axis("off")
+ plt.tight_layout()
+ plt.savefig("benchmark.tmp.png")
- # FID
- n_samples = 10
- batch_size = 4
- n_batches = n_samples // batch_size
+ # Metrics
+ batch_size = 64
+ n_batches = 16
+ n_samples = batch_size * n_batches
fakes = np.zeros((0, NB_CHANNEL, IMG_SIZE, IMG_SIZE))
- for _ in range(n_batches):
+ for _ in track(range(n_batches), description=f'Sampling {n_samples} images...'):
x = torch.randn(batch_size, NB_CHANNEL, IMG_SIZE, IMG_SIZE).to(DEVICE)
vec = torch.randint(0, NB_LABEL, (batch_size,)).to(DEVICE)
vec = torch.nn.functional.one_hot(vec, num_classes=NB_LABEL).to(device=DEVICE, dtype=torch.float32)
@@ -414,8 +415,13 @@ if __name__ == '__main__':
reals = torch.stack([dataset[i][0] for i in range(n_samples)]).cpu().numpy()
reals = reals * 2 - 1
+
fid_score = fid(reals, fakes)
print(f"FID score: {fid_score}")
+ kl_score = kl(reals, fakes)
+ print(f"KL divergence: {kl_score}")
+
+
# plt.show()
generated by cgit v1.3.1 (git 2.54.0) at 2026-09-21 05:28:07 +0000