Delete traininginVIT

traininginVIT

@@ -1,79 +0,0 @@
-from torch.utils.data import DataLoader, Dataset
-import torch
-from transformers import ViTForImageClassification, AdamW
-import os
-import numpy as np
-import torch
-import streamlit as st
-from transformers import ViTForImageClassification, ViTImageProcessor
-
-# Custom dataset class for loading images
-class MRIDataset(Dataset):
-    def __init__(self, image_paths, labels):
-        self.image_paths = image_paths
-        self.labels = labels
-
-    def __len__(self):
-        return len(self.image_paths)
-
-    def __getitem__(self, idx):
-        image = preprocess_image(self.image_paths[idx])
-        label = torch.tensor(self.labels[idx])
-        return image, label
-
-# Load your ViT model and processor
-model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224-in21k", num_labels=3)
-processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
-
-# Move the model to the device (GPU if available)
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model.to(device)
-
-# Define optimizer and loss function
-optimizer = AdamW(model.parameters(), lr=1e-4)
-criterion = torch.nn.CrossEntropyLoss()
-
-# Load your dataset
-image_paths = ["path_to_image1.npy", "path_to_image2.npy"]  # Update with actual image paths
-labels = [0, 1]  # Corresponding labels
-dataset = MRIDataset(image_paths, labels)
-data_loader = DataLoader(dataset, batch_size=16, shuffle=True)
-
-# Fine-tuning loop
-num_epochs = 10
-for epoch in range(num_epochs):
-    model.train()
-    total_loss = 0
-    for images, labels in data_loader:
-        images, labels = images.to(device), labels.to(device)
-
-        optimizer.zero_grad()
-        outputs = model(pixel_values=images).logits
-        loss = criterion(outputs, labels)
-        loss.backward()
-        optimizer.step()
-
-        total_loss += loss.item()
-
-    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {total_loss/len(data_loader)}')
-
-# Save the fine-tuned model
-torch.save(model.state_dict(), 'vit_finetuned.pth')
-
-def fine_tune_model():
-    # Your fine-tuning logic goes here (using the ViT model)
-    num_epochs = 10
-    running_loss = 0.0
-    for epoch in range(num_epochs):
-        # Fine-tuning loop (train the model)
-        # ...
-        running_loss += 0.5  # Just a placeholder for demo purposes
-    return running_loss  # Return the final loss after training
-
-# Streamlit UI to trigger fine-tuning and display results
-st.title("MRI Image Fine-Tuning with ViT")
-
-if st.button("Start Training"):
-    # Run the fine-tuning loop when the button is clicked
-    final_loss = fine_tune_model()  # Call the function where your fine-tuning loop is
-    st.write(f"Training complete with final loss: {final_loss}")