Not needed; deleted visualizer file

ModelVisualizer.py

@@ -1,89 +0,0 @@
-import torch
-import numpy as np
-import torchvision.transforms as transforms
-
-import matplotlib.pyplot as plt
-from PIL import Image
-from models.maevit import MAEViT
-
-
-def visualize(model_path, img_path, figure_name):
-    
-    model = MAEViT(
-        image_size=224,
-        patch_size=16,
-        embed_dim=128,
-        encoder_layers=2,
-        encoder_heads=4,
-        mlp_ratio=2.0,
-        mask_ratio=0.75,
-        decoder_embed_dim=64,
-        decoder_layers=2,
-        decoder_heads=4,
-        dropout=0.1
-    )
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    model.to(device)
-    checkpoint = torch.load(model_path, map_location=device)
-    model.load_state_dict(checkpoint)
-    model.eval()
-
-    
-    to_tensor = transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize(
-            mean=[0.485, 0.456, 0.406],
-            std =[0.229, 0.224, 0.225]
-        )
-    ])
-
-    
-    img = Image.open(img_path).convert('RGB')
-    x = to_tensor(img).unsqueeze(0).to(device)  # [1,3,224,224]
-
-    
-    with torch.no_grad():
-        
-        x_enc, mask, ids_restore = model.forward_encoder(x)
-        
-        x_rec_patches = model.forward_decoder(x_enc, ids_restore)
-
-    
-    img_rec = model.unpatchify(x_rec_patches[:, 1:, :]) # exclude CLS    # [1,3,224,224]
-    img_patches = model.patchify(x)              # [1, num_patches, patch_dim]
-
-    masked_patches = img_patches.clone()
-    mask = mask.unsqueeze(-1).to(torch.bool)     # [1, num_patches, 1]
-    # masked_patches[mask] = 0
-    masked_patches = masked_patches.masked_fill(mask, 0)
-
-    img_masked = model.unpatchify(masked_patches) # [1,3,224,224]
-
-    inv_normalize = transforms.Normalize(
-        mean=[-m/s for m, s in zip((0.485,0.456,0.406),(0.229,0.224,0.225))],
-        std =[1/s for s in       (0.229,0.224,0.225)]
-    )
-    def to_img(tensor):
-        img = tensor.squeeze(0).cpu()
-        img = inv_normalize(img)
-        img = img.permute(1,2,0).clamp(0,1).numpy()
-        return img
-
-    orig_np     = to_img(x)
-    masked_np   = to_img(img_masked)
-    recon_np    = to_img(img_rec)
-
-    # 8. Plot
-    fig, axes = plt.subplots(1, 3, figsize=(15,5))
-    for ax, im, title in zip(axes,
-                             [orig_np, masked_np, recon_np],
-                             ['Original', 'Masked Input', 'Reconstruction']):
-        ax.imshow(im)
-        ax.set_title(title)
-        ax.axis('off')
-    plt.tight_layout()
-    plt.show()
-    plt.savefig(figure_name)
-
-visualize('MAE1.bin', img_path='guineapig.jpg', figure_name='figures/MAE_visualization1.png')