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_tensor_list_observer(self):
from torch.quantization.observer import _MinMaxTensorListObserver
x = [torch.tensor([1.0, 2.5, 3.5]),
torch.tensor([2.0, 4.5, 3.5]),
torch.tensor([4.0, 2.5, 3.5]), ]
obs = _MinMaxTensorListObserver()
obs(x)
qparams = obs.calculate_qparams()
ref_min_val = []
ref_max_val = []
ref_qparams = []
for i in x:
obs_ref = MinMaxObserver()
obs_ref(i)
ref_min_val.append(obs_ref.min_val)
ref_max_val.append(obs_ref.max_val)
ref_qparams.append(obs_ref.calculate_qparams())
for i in range(len(x)):
self.assertEqual(obs.min_val[i], ref_min_val[i])
self.assertEqual(obs.max_val[i], ref_max_val[i])
self.assertEqual(qparams[0][i], ref_qparams[i][0])
self.assertEqual(qparams[1][i], ref_qparams[i][1])