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())