def test_record_observer(self):
model = SingleLayerLinearModel()
model.qconfig = default_debug_qconfig
prepare(model)
# run the evaluation and dump all tensors
test_only_eval_fn(model, self.calib_data)
test_only_eval_fn(model, self.calib_data)
observer_dict = {}
get_observer_dict(model, observer_dict)
self.assertTrue('fc1.module.observer' in observer_dict.keys(),
'observer is not recorded in the dict')
self.assertEqual(len(observer_dict['fc1.module.observer'].get_tensor_value()), 2 * len(self.calib_data))
self.assertEqual(observer_dict['fc1.module.observer'].get_tensor_value()[0], model(self.calib_data[0][0]))
def test_tensor_observer(self):
model = SingleLayerLinearModel()
model.qconfig = default_debug_qconfig
prepare(model)
# run the evaluation and dump all tensors
test_only_eval_fn(model, self.calib_data)
test_only_eval_fn(model, self.calib_data)
tensor_dict = {}
dump_tensor(model, tensor_dict)
# we can torch,save() and torch_load() in bento for further analysis
self.assertTrue('fc1.module.activation' in tensor_dict.keys(),
'activation is not recorded in the dict')
self.assertEqual(len(tensor_dict['fc1.module.activation']), 2 * len(self.calib_data))
