def forward(ctx,
input,
shift,
sign,
bias=None,
conc_weight=None,
stride=1,
padding=0,
dilation=1,
groups=1,
use_kernel=False,
use_cuda=False,
rounding='deterministic',
shift_range=(-14, 0)):
fraction_bits = 16
integer_bits = 16
# start_time = time.time()
if use_kernel:
input_fixed_point = (input * (2**fraction_bits)).int()
if bias is not None:
bias_fixed_point = (bias * (2**fraction_bits)).int()
else:
bias_fixed_point = None
out = deepshift.kernels.conv2d(input_fixed_point, shift, sign,
bias_fixed_point, conc_weight,
stride, padding, dilation, groups,
use_cuda)
out = out.float()
out = out / (2**fraction_bits)
else:
shift = shift.clamp(*shift_range)
sign = sign.clamp(-1, 1)
input.data = utils.round_to_fixed(input.data, fraction_bits,
integer_bits)
if bias is not None:
bias.data = utils.round_to_fixed(bias.data, fraction_bits,
integer_bits)
# shift_rounded = utils.round(self.shift, stochastic=False)
# sign_rounded_signed = torch.sign(utils.round(self.sign, stochastic=False))
shift_rounded = utils.round(shift, stochastic=False)
sign_rounded_signed = torch.sign(
utils.round(sign, stochastic=False))
v = 2**shift_rounded * sign_rounded_signed
out = F.conv2d(input, v, bias, stride, padding, dilation, groups)
ctx.save_for_backward(input, shift, sign, bias, v)
ctx.stride = stride
ctx.padding = padding
ctx.dilation = dilation
ctx.groups = groups
return out
def forward(ctx, input, rounding='deterministic'):
return utils.round(input, rounding)