Changed directory structure to accomodate examples as apposed to everything being a part of the core library. May need to rethink this in the future. Added some boilerplate for pip packaging to the .gitignore.

examples/variational_autoencoder/autoencoder_models/generate_interpolation.py

@@ -0,0 +1,49 @@
+import torch
+from variational_autoencoder import VAE, load_dataset
+import matplotlib.pyplot as plt
+from torchvision import datasets
+from torchvision import transforms
+from tqdm import tqdm
+import numpy as np
+import pickle
+
+# Load model
+vae = VAE(latent_dim=16)
+vae.load_state_dict(torch.load("saved_models/model.pth"))
+dataset = load_dataset()
+# Generate reconstructions
+num_images = 50
+image_pairs = []
+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)
+z_a, _, _, _ = vae.forward(image_a)
+z_a = z_a.detach().cpu().numpy()
+z_b, _, _, _ = vae.forward(image_b)
+z_b = z_b.detach().cpu().numpy()
+interpolation_path = np.linspace(z_a, z_b, num=num_images)
+# interpolation_path[:, 4] = np.linspace(-3, 3, num=num_images)
+save_object["interpolation_path"] = interpolation_path
+
+for i in range(num_images):
+    # 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
+    save_object["interpolation_images"].append(gen_image)
+
+fig, axs = plt.subplots(num_images, 1, figsize=(1, num_images))
+image_pairs = []
+for i in range(num_images):
+    recon_image = save_object["interpolation_images"][i]
+    # Add to plot
+    axs[i].imshow(recon_image)
+
+# Perform intrpolations
+with open("interpolations.pkl", "wb") as f:
+    pickle.dump(save_object, f)
+
+plt.show()