def forward(self, x):
residual = x
out = self.conv1(x)
#out = self.bn1(out)
out = BFPActivation.transform_activation_online(
out, self.exp_bit, self.mantisa_bit, -1)
out = self.relu(out)
out = self.conv2(out)
#out = self.bn2(out)
out = BFPActivation.transform_activation_online(
out, self.exp_bit, self.mantisa_bit, -1)
out = self.relu(out)
out = self.conv3(out)
#out = self.bn3(out)
out = BFPActivation.transform_activation_online(
out, self.exp_bit, self.mantisa_bit, -1)
if self.downsample is not None:
# Get a max of two list
#max_exp_act_list = np.maximum.reduce([self.opt_exp_act_list[self.start_exp_ind+2], self.opt_exp_act_list[self.start_exp_ind+3]]).tolist()
residual = self.downsample(x)
# bfp quantize both tensor for shortcut using the max exponent list
# since they have the same exp list, no need for realignment
# residual = BFPActivation.transform_activation_online(residual, self.exp_bit,
# self.mantisa_bit, self.opt_exp_act_list[self.start_exp_ind+3])
#out = BFPActivation.transform_activation_offline(out, self.exp_bit, self.mantisa_bit, max_exp_act_list)
# else:
# bfp quantize both tensor for shortcut using the third exponent list
# residual = BFPActivation.transform_activation_online(residual, self.exp_bit, self.mantisa_bit, self.opt_exp_act_list[self.start_exp_ind+2])
# Get the exponent from out
out_exp = Utils.find_exponent(out, self.exp_bit)
out_exp = Utils.find_max_exponent(out_exp,
quant_dim=len(out.shape) - 1)
out_exp = Utils.find_max_exponent(out_exp,
quant_dim=len(out.shape) - 2)
out_exp = Utils.find_max_exponent(out_exp, quant_dim=0)
out_exp = out_exp.int().cpu().data.tolist()
# Get the exponent from input
in_exp = Utils.find_exponent(residual, self.exp_bit)
in_exp = Utils.find_max_exponent(in_exp,
quant_dim=len(residual.shape) - 1)
in_exp = Utils.find_max_exponent(in_exp,
quant_dim=len(residual.shape) - 2)
in_exp = Utils.find_max_exponent(in_exp, quant_dim=0)
in_exp = in_exp.int().cpu().data.tolist()
# Quantize accordint to the max
max_exp = np.maximum.reduce([out_exp, in_exp]).tolist()
residual = BFPActivation.transform_activation_offline(
residual, self.exp_bit, self.mantisa_bit, max_exp)
out = BFPActivation.transform_activation_offline(
out, self.exp_bit, self.mantisa_bit, max_exp)
out += residual
out = self.relu(out)
return out
def forward(self, x):
if self.use_res_connect:
'''
max_exp_act_list = np.maximum.reduce([self.opt_exp_act_list[self.start_exp_ind+2], self.opt_exp_act_list[self.start_exp_ind-1]]).tolist()
out = self.conv(x)
x = BFPActivation.transform_activation_offline(x, self.exp_bit, self.mantisa_bit, max_exp_act_list)
out = BFPActivation.transform_activation_offline(out, self.exp_bit, self.mantisa_bit, max_exp_act_list)
return x + out
'''
out = self.conv(x)
# Get the exponent from out
out_exp = Utils.find_exponent(out, self.exp_bit)
out_exp = Utils.find_max_exponent(out_exp,
quant_dim=len(out.shape) - 1)
out_exp = Utils.find_max_exponent(out_exp,
quant_dim=len(out.shape) - 2)
out_exp = Utils.find_max_exponent(out_exp, quant_dim=0)
out_exp = out_exp.int().cpu().data.tolist()
# Get the exponent from input
in_exp = Utils.find_exponent(x, self.exp_bit)
in_exp = Utils.find_max_exponent(in_exp,
quant_dim=len(x.shape) - 1)
in_exp = Utils.find_max_exponent(in_exp,
quant_dim=len(x.shape) - 2)
in_exp = Utils.find_max_exponent(in_exp, quant_dim=0)
in_exp = in_exp.int().cpu().data.tolist()
# Quantize accordint to the max
max_exp = np.maximum.reduce([out_exp, in_exp]).tolist()
x = BFPActivation.transform_activation_offline(
x, self.exp_bit, self.mantisa_bit, max_exp)
out = BFPActivation.transform_activation_offline(
out, self.exp_bit, self.mantisa_bit, max_exp)
return x + out
else:
return self.conv(x)
