def shift_concat_input(module, grad_input, grad_output):
if isinstance(module, nn.Conv2d) and first_element(grad_output[0]):
shift = first_element(grad_output[0])
print('shifted module {} by {} bits'.format(module.__class__.__name__, shift))
module.norm += shift
module.bias = nn.Parameter(roundnorm_reg(module.bias, shift))
grad_input = tuple(torch.zeros_like(tensor) for tensor in grad_input)
module.out_frac_bits[0] -= shift
module.b_frac_bits[0] -= shift
elif grad_output[0].sum() != 0:
tmp = []
for tensor in grad_input:
if tensor is not None:
tmp.append(torch.empty_like(tensor).fill_(first_element(grad_output[0])))
else:
tmp.append(None)
grad_input = tuple(tmp)
print('propagated through {}'.format(module.__class__.__name__))
elif isinstance(module, Concat):
print(module.norm)
tmp = []
for curr_norm, tensor in zip(module.norm, grad_input):
tmp.append(torch.empty_like(tensor).fill_(curr_norm))
module.norm = nn.Parameter(torch.Tensor([0 for _ in module.norm]))
grad_input = tuple(tmp)
elif module.__class__ in module_classes(nn) \
and not isinstance(module, (nn.Sequential, nn.ModuleList)) \
or module.__class__ in module_classes(gap_quantization.layers):
tmp = []
for tensor in grad_input:
if tensor is not None:
tmp.append(torch.zeros_like(tensor))
else:
tmp.append(None) # for convolutions without biases
return grad_input
def forward(self, inputs):
inputs = F.avg_pool2d(inputs, self.kernel_size, self.stride, self.padding, self.ceil_mode,
self.count_include_pad)
inputs = torch.floor_(inputs * self.kernel_size * self.kernel_size + 0.1)
pool_factor = math.pow(2, 16) // math.pow(self.kernel_size, 2)
bound = math.pow(2.0, self.bits - 1)
min_val = -bound
max_val = bound - 1
return torch.clamp(roundnorm_reg(inputs * pool_factor, self.bits), min_val, max_val)
def forward(self, inputs):
inputs = F.adaptive_avg_pool2d(inputs, self.output_size)
mult = inputs.shape[2] * inputs.shape[3] // self.output_size[
0] // self.output_size[1]
inputs = torch.floor_(inputs * mult + 0.1)
pool_factor = math.pow(2, 16) // mult
bound = math.pow(2.0, self.bits - 1)
min_val = -bound
max_val = bound - 1
return torch.clamp(roundnorm_reg(inputs * pool_factor, self.bits),
min_val, max_val)
def forward(self, inputs):
self.weights = nn.ParameterList( # pylint: disable=attribute-defined-outside-init
[nn.Parameter(self.weight.data[:, i, :, :].unsqueeze_(1)) for i in range(self.weight.shape[1])])
out = None
for i in range(inputs.shape[1]):
conv_res = F.conv2d(inputs[:, i, :, :].unsqueeze_(1), self.weights[i], None, self.stride,
self.padding, self.dilation, self.groups)
if out is None:
out = conv_res
else:
out += conv_res
out += (self.bias * math.pow(2, self.norm)).view(1, -1, 1, 1).expand_as(out)
out = gap8_clip(roundnorm_reg(out, self.norm), self.bits)
return out
def forward(self, inp1, inp2):
inputs = [inp1, inp2]
for idx, _ in enumerate([inp1, inp2]):
inputs[idx] = roundnorm_reg(inputs[idx], self.norm[idx])
return torch.stack(inputs, dim=0).sum(dim=0)
def forward(self, inputs):
for idx, _ in enumerate(inputs):
inputs[idx] = roundnorm_reg(inputs[idx], self.norm[idx])
return torch.cat(inputs, self.dim)
