source/predict_sample.py

from torchvision import transforms
import torch
import torch.utils.data
from PIL import Image
from source.model import CNN


def classify_eye(image: torch.Tensor,
                 model: CNN) -> str:
    """
    Generate caption of a single image of size (3, 224, 224).
    Generating of caption starts with <sos>, and each next predicted word ID
    is appended for the next LSTM input until the sentence reaches MAX_LENGTH or <eos>.

    Returns:
        list[str]: caption for given image
    """

    # image: (3, 32, 32)
    image = image.unsqueeze(0)
    # image: (1, 3, 32, 32)

    output = model.forward(image)
    _, prediction = torch.max(output, dim=1)

    if prediction == 0:
        output = 'Normal'
    elif prediction == 1:
        output = 'Red'

    return output

def main_classification(image):

    image = Image.fromarray(image.astype('uint8'), 'RGB')

    transform = transforms.Compose([
        transforms.Resize((32, 32)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
        ])
    image = transform(image)
    image = image.to(torch.device("cpu"))

    cnn = CNN().to(torch.device("cpu"))
    cnn.eval()

    cnn.load_state_dict(torch.load(f='source/weights/CNN-B8-LR-0.01-E30.pt', map_location=torch.device("cpu")))

    prediction_outcome = classify_eye(image, cnn)

    return prediction_outcome