def forward(self, input):
if self.config["quantization"] == "BNN":
Wb = quant.bnn_sign(self.weight/self.H)*self.H
return quant.bnn_sign(F.linear(input, Wb, self.bias))
elif self.config["quantization"] == "INT":
Wb = quant.int_nn(self.weight, self.config["weight_i_width"], self.config["weight_f_width"])
return quant.int_nn(F.linear(input, Wb, self.bias), self.config["activation_i_width"], self.config["activation_f_width"])
else:
Wb = self.weight
return F.linear(input, Wb, self.bias)
def forward(self, x):
# self.cnt += 1
# if self.cnt==1000:
#import ipdb as pdb; pdb.set_trace()
if self.config["quantization"].lower() == "bnn":
Wb = quant.bnn_sign(self.weight/self.H)*self.H
return quant.bnn_sign(F.conv2d(x, Wb, self.bias, self.stride, self.padding, self.dilation, self.groups))
elif self.config["quantization"].lower() == "int":
Wb = quant.int_nn(self.weight, self.config["weight_i_width"])
return quant.int_nn(F.conv2d(x, Wb, self.bias, self.stride, self.padding, self.dilation, self.groups), self.config["activation_i_width"])
elif self.config["quantization"].lower() == "fixed":
Wb = quant.fixed_nn(self.weight, self.config["weight_i_width"], self.config["weight_f_width"], "weight")
return quant.fixed_nn(F.conv2d(x, Wb, self.bias, self.stride, self.padding, self.dilation, self.groups), self.config["activation_i_width"], self.config["activation_f_width"], "activation")
else:
Wb = self.weight
return F.conv2d(x, Wb, self.bias, self.stride, self.padding, self.dilation, self.groups)
def forward(self, x):
# import ipdb as pdb; pdb.set_trace()
if self.config["quantization"] == "BNN":
Wb = q.bnn_sign(self.weight / self.H) * self.H
return q.bnn_sign(
F.conv2d(x, Wb, self.bias, self.stride, self.padding,
self.dilation, self.groups))
elif self.config["quantization"] == "INT":
Wb = q.int_nn(self.weight, self.config["weight_width"])
return q.int_nn(
F.conv2d(x, Wb, self.bias, self.stride, self.padding,
self.dilation, self.groups),
self.config["activation_width"])
else:
Wb = self.weight
return F.conv2d(x, Wb, self.bias, self.stride, self.padding,
self.dilation, self.groups)
def forward(self, x):
#import ipdb as pdb; pdb.set_trace()
if self.config["quantization"].lower() == "int":
return quant.int_nn(x, self.config["activation_i_width"])
elif self.config["quantization"].lower() == "fixed":
return quant.fixed_nn(x, self.config["activation_i_width"],
self.config["activation_f_width"])
elif self.config["quantization"].lower() == "bnn":
# import ipdb as pdb; pdb.set_trace()
return quant.bnn_sign(x)
else:
return x
