models/convnext.py

import torch
from torchvision.models import convnext_base, ConvNeXt_Base_Weights
from PIL import Image
import torch.nn.functional as F

class ConvNextBase:
    def __init__(self, weights_path: str):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.model = convnext_base()
        self.model.classifier[2] = torch.nn.Linear(self.model.classifier[2].in_features, 7)

        state_dict = torch.load(weights_path, map_location=self.device)
        self.model.load_state_dict(state_dict)
        self.model.eval()

        self.transform = ConvNeXt_Base_Weights.IMAGENET1K_V1.transforms()

    def make_prediction(self, image: Image):
        image = self.transform(image).unsqueeze(0)
        with torch.no_grad():
            pred = F.softmax(self.model(image), dim=1)
        return pred