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another approach for final decoder

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neulus
2025-09-29 23:20:27 +09:00
parent 12a165e461
commit 0ccf1ff42d
14 changed files with 88 additions and 24 deletions
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8
quick_eval.py
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@@ -41,13 +41,13 @@ with torch.no_grad():
batch = test_dataset[i : i + batch_size]
images = rf.sample(batch["x0"].to(device))
image = denormalize(images[-1]).clamp(0, 1) * 255
original = denormalize(batch["x1"]).clamp(0, 1) * 255
image = denormalize(images[-1]).clamp(0, 1)
original = denormalize(batch["x1"]).clamp(0, 1)
if saved_count < max_save:
for j in range(min(image.shape[0], max_save - saved_count)):
save_image(image[j] / 255, f"{save_dir}/pred_{saved_count}.png")
save_image(original[j] / 255, f"{save_dir}/gt_{saved_count}.png")
save_image(image[j], f"{save_dir}/pred_{saved_count}.png")
save_image(original[j], f"{save_dir}/gt_{saved_count}.png")
save_image(
denormalize(batch["x0"][j]).clamp(0, 1),
f"{save_dir}/input_{saved_count}.png",