def test_against_unquantized(self):
kernel_size = 3
test_input = torch.randn(16, _NUM_IN_CHANNELS, 32, 32, 32).cuda()
torch.manual_seed(1234)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(1234)
fake_quant_conv3d = quant_conv.QuantConvTranspose3d(
_NUM_IN_CHANNELS,
_NUM_OUT_CHANNELS,
kernel_size,
bias=True,
quant_desc_input=QuantDescriptor(num_bits=16),
quant_desc_weight=QuantDescriptor(num_bits=16, axis=(1)))
# Reset seed. Make sure weight and bias are the same
torch.manual_seed(1234)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(1234)
conv3d = nn.ConvTranspose3d(_NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=True)
fake_quant_output = fake_quant_conv3d(test_input)
output = conv3d(test_input)
test_utils.compare(fake_quant_output, output, rtol=1e-5, atol=2e-4)
def test_fake_quant_per_channel_other_prec(self):
kernel_size = 3
quant_desc_input = QuantDescriptor(num_bits=4)
quant_desc_weight = QuantDescriptor(num_bits=3, axis=(1))
quant_conv_object = quant_conv.QuantConvTranspose3d(
_NUM_IN_CHANNELS,
_NUM_OUT_CHANNELS,
kernel_size,
bias=False,
quant_desc_input=quant_desc_input,
quant_desc_weight=quant_desc_weight)
test_input = torch.randn(16, _NUM_IN_CHANNELS, 16, 16, 16)
test_input_quantizer = TensorQuantizer(quant_desc_input)
weight_quantizer = TensorQuantizer(quant_desc_weight)
quant_input = test_input_quantizer(test_input)
weight_copy = quant_conv_object.weight.clone()
quant_weight = weight_quantizer(weight_copy)
out1 = F.conv_transpose3d(quant_input, quant_weight)
out2 = quant_conv_object(test_input)
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())
def test_fake_quant_per_channel_bias(self):
kernel_size = 3
quant_conv_object = quant_conv.QuantConvTranspose3d(
_NUM_IN_CHANNELS,
_NUM_OUT_CHANNELS,
kernel_size,
bias=True,
quant_desc_weight=tensor_quant.
QUANT_DESC_8BIT_CONVTRANSPOSE3D_WEIGHT_PER_CHANNEL)
test_input = torch.randn(2, _NUM_IN_CHANNELS, 2, 2, 2)
quant_input = tensor_quant.fake_tensor_quant(
test_input, torch.max(torch.abs(test_input)))
weight_copy = quant_conv_object.weight.clone()
amax = quant_utils.reduce_amax(weight_copy, axis=(0, 2, 3, 4))
quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax)
out1 = F.conv_transpose3d(quant_input,
quant_weight,
bias=quant_conv_object.bias)
out2 = quant_conv_object(test_input)
np.testing.assert_array_equal(out1.detach().cpu().numpy(),
out2.detach().cpu().numpy())
def test_no_quant(self):
kernel_size = 3
quant_conv_object = quant_conv.QuantConvTranspose3d(
_NUM_IN_CHANNELS,
_NUM_OUT_CHANNELS,
kernel_size,
bias=False)
quant_conv_object.input_quantizer.disable()
quant_conv_object.weight_quantizer.disable()
test_input = torch.randn(16, _NUM_IN_CHANNELS, 32, 32, 32)
weight_copy = quant_conv_object.weight.clone()
quant_weight = weight_copy
out1 = F.conv_transpose3d(test_input, quant_weight)
out2 = quant_conv_object(test_input)
np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy())