vit_model_test.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from transformers import ViTForImageClassification
from PIL import Image
import os
import pandas as pd




class CustomDataset(Dataset):
    def __init__(self, dataframe, transform=None):
        self.dataframe = dataframe
        self.transform = transform

    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, idx):
        image_path = self.dataframe.iloc[idx, 0]  # Image path is in the first column
        image = Image.open(image_path).convert('RGB')  # Convert to RGB format

        if self.transform:
            image = self.transform(image)

        return image
    
      
if __name__ == "__main__":
    # Check for GPU availability
    device = torch.device('cuda')

    # Load the pre-trained ViT model and move it to GPU
    model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224').to(device)



    model.classifier = nn.Linear(model.config.hidden_size, 2).to(device)
    # Define the image preprocessing pipeline
    preprocess = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor()
    ])



    

    # Load the test dataset
    
    
    ### need to recive image from gratio/streamlit
    
    test_set = 'datasets/'
    
    image_paths = []
    for filename in os.listdir(test_set):
        image_paths.append(os.path.join(test_set, filename))
        dataset = pd.DataFrame({'image_path': image_paths})
        
    
    
    test_dataset = CustomDataset(dataset, transform=preprocess)
    test_loader = DataLoader(test_dataset, batch_size=32)

    # Load the trained model
    model.load_state_dict(torch.load('trained_model.pth'))

    # Evaluate the model
    model.eval()
    confidences = []
    predicted_labels = []
    

    with torch.no_grad():
        for images in test_loader:
            images = images.to(device)
            outputs = model(images)
            logits = outputs.logits  # Extract logits from the output
            probabilities = F.softmax(logits, dim=1)
            confidences_per_image, predicted = torch.max(probabilities, 1)
            predicted_labels.extend(predicted.cpu().numpy())
            confidences.extend(confidences_per_image.cpu().numpy())


    print(predicted_labels)
    print(confidences)
    
    confidence_percentages = [confidence * 100 for confidence in confidences]
for label, confidence in zip(predicted_labels, confidence_percentages):
   print(f"Predicted label: {label}, Confidence: {confidence:.2f}%")