def test_int(self):
shape = (BATCH, IN_CHANNEL, HEIGHT)
input_quant_int, input_quant = generate_quant_input(
shape, BIT, SCALE, True, True)
mod = QuantConv1d(in_channels=IN_CHANNEL,
out_channels=OUT_CHANNEL,
kernel_size=KERNEL,
stride=STRIDE,
weight_quant_type=QuantType.INT,
weight_bit_width=BIT,
bias=False)
results_float_quantized = mod(input_quant)
weight_int = mod.int_weight()
bias = mod.bias
results_int_quantized = mod.conv1d_zeros_pad(input_quant_int,
weight_int.float(), bias)
total_scale = SCALE * mod.quant_weight_scale()
result_rescaled = torch.round(results_float_quantized / total_scale)
assert (torch.allclose(results_int_quantized,
result_rescaled,
atol=ATOL,
rtol=RTOL))
def quant_weight_scale(module: QuantConv1d):
quant_weight_scale = module.quant_weight_scale().type(
torch.FloatTensor).detach()
if len(quant_weight_scale.shape) == 3:
quant_weight_scale = quant_weight_scale.view(1, -1, 1)
return quant_weight_scale