load trained model.py

from glob import glob
model_paths = glob('snapshots/resnet50_csv_0*.h5')
latest_path = sorted(model_paths)[-1]
print("path:", latest_path)

from keras_retinanet import models

model = models.load_model(latest_path, backbone_name='resnet50')
model = models.convert_model(model)

label_map = {}
for line in open('../maskDetectorClasses.csv'):
  row = line.rstrip().split(',')
  label_map[int(row[1])] = row[0]