def test_per_tensor_dynamic_quant_observers(self, X, reduce_range):
X, (scale, zero_point, torch_type) = X
x = torch.from_numpy(X)
obs = MinMaxDynamicQuantObserver(dtype=torch.quint8, reduce_range=reduce_range)
result = obs(x)
qparams = obs.calculate_qparams()
ref = torch._choose_qparams_per_tensor(x, reduce_range)
self.assertEqual(ref[0], qparams[0])
self.assertEqual(ref[1], qparams[1])
def test_observer_scriptable(self):
obs_list = [
MinMaxObserver(),
MovingAverageMinMaxObserver(),
MinMaxDynamicQuantObserver()
]
for obs in obs_list:
scripted = torch.jit.script(obs)
x = torch.rand(3, 4)
obs(x)
scripted(x)
self.assertEqual(obs.calculate_qparams(),
scripted.calculate_qparams())
buf = io.BytesIO()
torch.jit.save(scripted, buf)
buf.seek(0)
loaded = torch.jit.load(buf)
self.assertEqual(obs.calculate_qparams(),
loaded.calculate_qparams())
# Check TensorListObserver
from torch.quantization.observer import _MinMaxTensorListObserver
obs = _MinMaxTensorListObserver()
scripted = torch.jit.script(obs)
x = [torch.rand(3, 4), torch.rand(4, 5)]
obs(x)
scripted(x)
self.assertEqual(obs.calculate_qparams(), scripted.calculate_qparams())
def test_observer_scriptable(self):
obs_list = [MinMaxObserver(), MovingAverageMinMaxObserver(), MinMaxDynamicQuantObserver()]
for obs in obs_list:
scripted = torch.jit.script(obs)
x = torch.rand(3, 4)
obs(x)
scripted(x)
self.assertEqual(obs.calculate_qparams(), scripted.calculate_qparams())
buf = io.BytesIO()
torch.jit.save(scripted, buf)
buf.seek(0)
loaded = torch.jit.load(buf)
self.assertEqual(obs.calculate_qparams(), loaded.calculate_qparams())