Help with an error

gistfile1.txt

Hello everyone i run the following code:
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from os import listdir
from xml.etree import ElementTree
from numpy import zeros
from numpy import asarray
from numpy import expand_dims
from numpy import mean
from mrcnn.config import Config
from mrcnn.model import MaskRCNN
from mrcnn.utils import Dataset
from mrcnn.utils import compute_ap
from mrcnn.model import load_image_gt
from mrcnn.model import mold_image


class BSTLD(Dataset):
    def load_dataset(self, dataset_dir, is_train=True):
        self.add_class("dataset", 1, "dataset1")
        image_dir = dataset_dir + "/train/"
        annotation_dir = dataset_dir + "/save/"
        img_cnt = 0
        for image in listdir(image_dir):
            img_cnt += 1
            image_id = image[:-4]
            if is_train and img_cnt < 41:
                continue
            if not is_train and img_cnt > 41:
                continue
            img_path = image_dir + image
            ann_path = annotation_dir + image_id + ".xml"
            self.add_image('dataset', image_id=image_id, path=img_path, annotation=ann_path)
    # extract bboxes
    def extract_boxes(self, filename):
        tree = ElementTree.parse(filename)
        root = tree.getroot()
        boxes = list()

        for box in root.findall('.//bndbox'):
            xmin = int(float(box.find('xmin').text))
            ymin = int(float(box.find('ymin').text))
            xmax = int(float(box.find('xmax').text))
            ymax = int(float(box.find('ymax').text))
            coors = [xmin, ymin, xmax, ymax]
            boxes.append(coors)
        width = int(root.find('.//size/width').text)
        height = int(root.find('.//size/height').text)
        return boxes, width, height

    def load_mask(self, image_id):
        info = self.image_info[image_id]
        path = info['annotation']
        boxes, w, h = self.extract_boxes(path)
        masks = zeros([h, w, len(boxes)], dtype='uint8')
        class_ids = list()
        for i in range(len(boxes)):
            box = boxes[i]
            row_s, row_e = box[1], box[3]
            col_s, col_e = box[0], box[2]
            masks[row_s:row_e, col_s:col_e, i] = 1
            class_ids.append(self.class_names.index('dataset1'))

        return masks, asarray(class_ids, dtype='int32')

    def image_reference(self, image_id):
        info = self.image_info[image_id]
        return info['path']

class PredictionConfig(Config):
    NAME = "BSTLD_cfg"
    NUM_CLASSES = 1 + 1
    STEPS_PER_EPOCH = 174
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1

def evaluate_model(dataset, model, cfg):
    APs = list()
    for image_id in dataset.image_ids:
        # load image, bboxes and masks
        image, image_meta, gt_class_id, gt_box, gt_mask = load_image_gt(dataset, cfg, image_id, use_mini_mask=False)
        # convert pixel values
        scaled_image = mold_image(image, cfg)
        # convert image into one sample
        sample = expand_dims(scaled_image, 0)
        # make prediction
        yhat = model.detect(sample, verbose=0)
        # extract results for first sample
        r = yhat[0]
        # calculate statistics, including AP
        AP, _, _, _ = compute_ap(gt_box, gt_class_id, gt_mask, r["rois"], r["class_ids"], r["scores"], r["masks"])
        # store
        APs.append(AP)
    # calculate the mean AP across all images
    mAP = mean(APs)
    return mAP

# load the train dataset
train_set = BSTLD()
train_set.load_dataset('dataset1', is_train=True)
train_set.prepare()
print('Train: %d' % len(train_set.image_ids))

# load test dataset
test_set = BSTLD()
test_set.load_dataset('dataset1', is_train=False)
test_set.prepare()
print('Test: %d' % len(test_set.image_ids))

# create config
cfg = PredictionConfig()

# define the model
model = MaskRCNN(mode='inference', model_dir='./', config=cfg)

# load model weights
model.load_weights(r'C:\Users\Nickos_Kal\PycharmProjects\Traffic_Lights\bstld_cfg20210516T0208\mask_rcnn_bstld_cfg_0005.h5', by_name=True)

# evaluate model on training dataset
train_mAP = evaluate_model(train_set, model, cfg)
print("Train mAP: %.3f" % train_mAP)

# evaluate model on test dataset
test_mAP = evaluate_model(test_set, model, cfg)
print("Test mAP: %.3f" % test_mAP)
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And I get the following error:
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Traceback (most recent call last):
  File "C:/Users/Nickos_Kal/PycharmProjects/Traffic_Lights/Evaluation.py", line 117, in <module>
    train_mAP = evaluate_model(train_set, model, cfg)
  File "C:/Users/Nickos_Kal/PycharmProjects/Traffic_Lights/Evaluation.py", line 88, in evaluate_model
    AP, _, _, _ = compute_ap(gt_box, gt_class_id, gt_mask, r["rois"], r["class_ids"], r["scores"], r["masks"])
  File "C:\Users\Nickos_Kal\anaconda3\envs\tensorflow_env\lib\site-packages\mrcnn\utils.py", line 739, in compute_ap
    iou_threshold)
  File "C:\Users\Nickos_Kal\anaconda3\envs\tensorflow_env\lib\site-packages\mrcnn\utils.py", line 691, in compute_matches
    overlaps = compute_overlaps_masks(pred_masks, gt_masks)
  File "C:\Users\Nickos_Kal\anaconda3\envs\tensorflow_env\lib\site-packages\mrcnn\utils.py", line 108, in compute_overlaps_masks
    masks2 = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32)
  File "C:\Users\Nickos_Kal\anaconda3\envs\tensorflow_env\lib\site-packages\numpy\core\fromnumeric.py", line 292, in reshape
    return _wrapfunc(a, 'reshape', newshape, order=order)
  File "C:\Users\Nickos_Kal\anaconda3\envs\tensorflow_env\lib\site-packages\numpy\core\fromnumeric.py", line 56, in _wrapfunc
    return getattr(obj, method)(*args, **kwds)
ValueError: cannot reshape array of size 0 into shape (0)
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Any help would be appreciated thanks in advance