Adjustments

endovit_online.py

@@ -1,43 +0,0 @@
-import torch
-from pathlib import Path
-from timm.models.vision_transformer import VisionTransformer
-from functools import partial
-from torch import nn
-from huggingface_hub import snapshot_download
-
-def load_model_from_huggingface(repo_id, model_filename):
-    # Download model files
-    model_path = snapshot_download(repo_id=repo_id, revision="main")
-    model_weights_path = Path(model_path) / model_filename
-
-    # Load model weights
-    model_weights = torch.load(model_weights_path)['model']
-
-    # Define the model (ensure this matches your model's architecture)
-    model = VisionTransformer(patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6)).eval()
-
-    # Load the weights into the model
-    loading = model.load_state_dict(model_weights, strict=False)
-
-    return model, loading
-def process_single_image(image_path, input_size=224, dataset_mean=[0.3464, 0.2280, 0.2228], dataset_std=[0.2520, 0.2128, 0.2093]):
-    # Define the transformations
-    transform = T.Compose([
-        T.Resize((input_size, input_size)),
-        T.ToTensor(),
-        T.Normalize(mean=dataset_mean, std=dataset_std)
-    ])
-
-    # Open the image
-    image = Image.open(image_path).convert('RGB')
-
-    # Apply the transformations
-    processed_image = transform(image)
-
-    return processed_image
-
-device = "cuda"
-dtype = torch.float16
-model, loading_info = load_model_from_huggingface("egeozsoy/EndoViT", "endovit.pth")
-model = model.to(device, dtype)
-print(loading_info)