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This commit is contained in:
Alec Helbling
2023-01-01 23:24:59 -05:00
parent 334662e8c8
commit 3d6e8072e1
71 changed files with 1701 additions and 1135 deletions
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10
examples/variational_autoencoder/autoencoder_models/generate_interpolation.py
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@ -14,12 +14,12 @@ dataset = load_dataset()
# Generate reconstructions
num_images = 50
image_pairs = []
save_object = {"interpolation_path":[], "interpolation_images":[]}
save_object = {"interpolation_path": [], "interpolation_images": []}
# Make interpolation path
image_a, image_b = dataset[0][0], dataset[1][0]
image_a = image_a.view(32*32)
image_b = image_b.view(32*32)
image_a = image_a.view(32 * 32)
image_b = image_b.view(32 * 32)
z_a, _, _, _ = vae.forward(image_a)
z_a = z_a.detach().cpu().numpy()
z_b, _, _, _ = vae.forward(image_b)
@ -29,7 +29,7 @@ interpolation_path = np.linspace(z_a, z_b, num=num_images)
save_object["interpolation_path"] = interpolation_path
for i in range(num_images):
# Generate
# Generate
z = torch.Tensor(interpolation_path[i]).unsqueeze(0)
gen_image = vae.decode(z).detach().numpy()
gen_image = np.reshape(gen_image, (32, 32)) * 255
@ -46,4 +46,4 @@ for i in range(num_images):
with open("interpolations.pkl", "wb") as f:
pickle.dump(save_object, f)
plt.show()
plt.show()