def main():
model_type = 'dl'
input_type = 'image'
config = Config(model_type)
model = DeepLabModel(config).prepare_model(input_type)
model.run()
model_names = config.SEQ_MODELS
else:
model_names = get_model_list(MODELS_DIR)
# Sequential testing
for model_name in model_names:
print("> testing model: {}".format(model_name))
# conditionals
optimized = False
single_class = False
# Test Model
if 'hands' in model_name or 'person' in model_name:
single_class = True
if 'deeplab' in model_name:
config = create_test_config('dl', model_name, optimized, single_class)
model = DeepLabModel(config).prepare_model(INPUT_TYPE)
else:
config = create_test_config('od', model_name, optimized, single_class)
model = ObjectDetectionModel(config).prepare_model(INPUT_TYPE)
# Check if there is an optimized graph
model_dir = os.path.join(os.getcwd(), 'models', model_name)
optimized = check_if_optimized_model(model_dir)
# Again for the optimized graph
if optimized:
if 'deeplab' in model_name:
config = create_test_config('dl', model_name, optimized,
single_class)
model = DeepLabModel(config).prepare_model(INPUT_TYPE)
else: