def forward(self, input):
weight_q = ste.round_power_of_2(self.weight)
input_fixed_point = ste.round_fixed_point(input)
if self.bias is not None:
bias_fixed_point = ste.round_fixed_point(self.bias)
else:
bias_fixed_point = None
if self.padding_mode == 'circular':
expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2,
(self.padding[0] + 1) // 2, self.padding[0] // 2)
input_padded = F.pad(input_fixed_point, expanded_padding, mode='circular')
padding = _pair(0)
else:
input_padded = input_fixed_point
padding = self.padding
if self.use_kernel:
return Conv2dShiftQFunction.apply(input_padded, weight_q, bias_fixed_point, self.conc_weight,
self.stride, padding, self.dilation, self.groups,
self.use_kernel, self.use_cuda)
else:
return torch.nn.functional.conv2d(input_padded, weight_q, bias_fixed_point,
self.stride, padding, self.dilation, self.groups)
def forward(self, input):
if self.shift_or_full == 'shift':
self.weight.data = ste.clampabs(self.weight.data,
2**self.shift_range[0],
2**self.shift_range[1])
weight_q = ste.round_power_of_2(self.weight, self.rounding)
# print(weight_q)
input_fixed_point = ste.round_fixed_point(input)
if self.bias is not None:
bias_fixed_point = ste.round_fixed_point(self.bias)
else:
bias_fixed_point = None
elif self.shift_or_full == 'full': # full precision training
weight_q = self.weight
input_fixed_point = ste.round_fixed_point(input)
if self.bias is not None:
bias_fixed_point = self.bias
else:
bias_fixed_point = None
else:
raise NotImplementedError('No such type!')
if self.padding_mode == 'circular':
expanded_padding = ((self.padding[1] + 1) // 2,
self.padding[1] // 2,
(self.padding[0] + 1) // 2,
self.padding[0] // 2)
input_padded = F.pad(input_fixed_point,
expanded_padding,
mode='circular')
padding = _pair(0)
else:
input_padded = input_fixed_point
padding = self.padding
if self.use_kernel:
return Conv2dShiftQFunction.apply(input_padded, weight_q,
bias_fixed_point,
self.conc_weight, self.stride,
padding, self.dilation,
self.groups, self.use_kernel,
self.use_cuda)
else:
return torch.nn.functional.conv2d(input_padded, weight_q,
bias_fixed_point, self.stride,
padding, self.dilation,
self.groups)
def forward(self, input):
weight_q = ste.round_power_of_2(self.weight)
input_fixed_point = ste.round_fixed_point(input)
if self.bias is not None:
bias_fixed_point = ste.round_fixed_point(self.bias)
else:
bias_fixed_point = None
if self.use_kernel:
return LinearShiftQFunction.apply(input_fixed_point, weight_q, bias_fixed_point, self.conc_weight, self.use_kernel, self.use_cuda)
else:
out = input_fixed_point.mm(weight_q.t())
if self.bias is not None:
out += self.bias.unsqueeze(0).expand_as(out)
return out
def forward(self, input):
self.weight.data = ste.clampabs(self.weight.data, self.base**self.shift_range[0], self.base**self.shift_range[1])
weight_q = ste.round_power_of_2(self.weight, self.base, self.rounding)
input_fixed_point = ste.round_fixed_point(input)
if self.bias is not None:
bias_fixed_point = ste.round_fixed_point(self.bias)
else:
bias_fixed_point = None
if self.use_kernel:
return LinearShiftQFunction.apply(input_fixed_point, weight_q, self.base, bias_fixed_point, self.conc_weight, self.use_kernel, self.use_cuda)
else:
out = input_fixed_point.mm(weight_q.t())
if self.bias is not None:
out += self.bias.unsqueeze(0).expand_as(out)
return out