def test_manual(self):
model = ManualLinearQATModel()
model.qconfig = default_qat_qconfig
model = prepare_qat(model)
self.checkObservers(model)
test_only_train_fn(model, self.train_data)
convert(model)
def checkQuantized(model):
self.assertEqual(type(model.fc1), nnq.Linear)
self.assertEqual(type(model.fc2), nnq.Linear)
test_only_eval_fn(model, self.calib_data)
model = ManualLinearQATModel()
model.qconfig = default_qat_qconfig
model = quantize_qat(model, test_only_train_fn, self.train_data)
checkQuantized(model)
def test_eval_only_fake_quant(self):
r"""Using FakeQuant in evaluation only mode,
this is useful for estimating accuracy loss when we quantize the
network
"""
model = ManualLinearQATModel()
model.qconfig = default_qat_qconfig
model = prepare_qat(model)
self.checkObservers(model)
model.eval()
test_only_eval_fn(model, self.calib_data)