def test_raise(self):
with pytest.raises(ValueError) as excinfo:
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(
output_size=3,
quant_desc_input=tensor_quant.QuantDescriptor(
fake_quant=False))
assert "Only fake quantization is supported" in str(excinfo.value)
def test_input_fake_quant_disable(self):
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(
output_size=3)
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
quant_pooling_object.input_quantizer.disable()
out1 = F.adaptive_avg_pool2d(test_input, 3)
out2 = quant_pooling_object(test_input)
np.testing.assert_array_equal(out1.detach().cpu().numpy(),
out2.detach().cpu().numpy())
def test_input_fake_quant(self):
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(
output_size=3)
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
quant_input = tensor_quant.fake_tensor_quant(
test_input, torch.max(torch.abs(test_input)))
out1 = F.adaptive_avg_pool2d(quant_input, 3)
out2 = quant_pooling_object(test_input)
np.testing.assert_array_equal(out1.detach().cpu().numpy(),
out2.detach().cpu().numpy())
def test_input_variable_bits(self):
# Repeat checking the output for variable number of bits to QuantDescriptor
for bits in [2, 4, 6]:
quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits)
quant_pooling.QuantAdaptiveAvgPool2d.set_default_quant_desc_input(
quant_desc_input)
quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(
output_size=3)
test_input = torch.randn(1, 5, 5, 5, dtype=torch.double)
quant_input = tensor_quant.fake_tensor_quant(
test_input, torch.max(torch.abs(test_input)), bits)
out1 = F.adaptive_avg_pool2d(quant_input, 3)
out2 = quant_pooling_object(test_input)
np.testing.assert_array_equal(out1.detach().cpu().numpy(),
out2.detach().cpu().numpy())