extract features

# extract features from each photo in the directory
def extract_features(directory):
    # load the model
    model = Encoder()
    # model.to(device)
    model.eval()
    # extract features from each photo
    features = dict()
    for i, name in enumerate(listdir(directory)):
        # load an image from file
        filename = directory + '/' + name
        image = load_image(filename, size=224)
        # convert the image pixels to a numpy array
        image = transforms.ToTensor()(image)
        # reshape data for the model
        image = image.unsqueeze(0)
        # prepare the image for the VGG model
        image = normalize_batch(image)
        # get features
        feature = model(image)
        # get image id
        image_id = name.split('.')[0]
        # store feature
        features[image_id] = feature
#         print('>%s' % name)
        if i%50==0:
            print("{} image done.".format(i))
    return features