This is my model:
def make_residual_fc(input,R=8,k=2):
in_channel=input.shape[1]
out_channel=int(in_channel/R)
fc1=nn.Linear(in_channel,out_channel)
fc_list=[]
for i in range(k):
fc=nn.Linear(out_channel,2*in_channel)
fc_list.append(fc)
fc2=nn.ModuleList(fc_list)
return fc1,fc2
class residual(nn.Module):
def __init__(self,R=8,k=2):
super(residual, self).__init__()
self.avg=nn.AdaptiveAvgPool2d((1,1))
self.relu=nn.ReLU(inplace=True)
self.R=R
self.k=k
def forward(self,x):
x=self.avg(x)
fc1,fc2=make_residual_fc(x,self.R,self.k)
x=torch.squeeze(x)
x=fc1(x)
x=self.relu(x)
result_list=[]
for i in range(self.k):
result=fc2[i](x)
result=2*torch.sigmoid(result)-1
result_list.append(result)
return result_list
class Dynamic_relu_b(nn.Module):
def __init__(self,R=8,k=2):
super(Dynamic_relu_b, self).__init__()
self.lambda_alpha=1
self.lambda_beta=0.5
self.R=R
self.k=k
self.init_alpha=torch.zeros(self.k)
self.init_beta=torch.zeros(self.k)
self.init_alpha[0]=1
self.init_beta[0]=1
for i in range(1,k):
self.init_alpha[i]=0
self.init_beta[i]=0
self.residual=residual(self.R,self.k)
def forward(self,input):
delta=self.residual(input)
in_channel=input.shape[1]
bs=input.shape[0]
alpha=torch.zeros((self.k,bs,in_channel))
beta=torch.zeros((self.k,bs,in_channel))
for i in range(self.k):
for j,c in enumerate(range(0,in_channel*2,2)):
alpha[i,:,j]=delta[i][:,c]
beta[i,:,j]=delta[i][:,c+1]
alpha1=alpha[0]
beta1=beta[0]
max_result=self.dynamic_function(alpha1,beta1,input,0)
for i in range(1,self.k):
alphai=alpha[i]
betai=beta[i]
result=self.dynamic_function(alphai,betai,input,i)
max_result=torch.max(max_result,result)
return max_result
def dynamic_function(self,alpha,beta,x,k):
init_alpha=self.init_alpha[k]
init_beta=self.init_beta[k]
alpha=init_alpha+self.lambda_alpha*alpha
beta=init_beta+self.lambda_beta*beta
bs=x.shape[0]
channel=x.shape[1]
results=torch.zeros_like(x)
for i in range(bs):
for c in range(channel):
results[i,c,:,:]=x[i,c]*alpha[i,c]+beta[i,c]
return results
image=torch.rand((4,20,100,100))
image=image.cuda()
act=Dynamic_relu_b()
act(image)
This is the error
RuntimeError Traceback (most recent call last)
<ipython-input-7-436962e32e77> in <module>
3 image=image.cuda()
4 act=Dynamic_relu_b()
----> 5 act(image)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
<ipython-input-6-77bcacd35271> in forward(self, input)
56
57 def forward(self,input):
---> 58 delta=self.residual(input)
59 in_channel=input.shape[1]
60 bs=input.shape[0]
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
<ipython-input-6-77bcacd35271> in forward(self, x)
24 fc1,fc2=make_residual_fc(x,self.R,self.k)
25 x=torch.squeeze(x)
---> 26 x=fc1(x)
27 x=self.relu(x)
28 result_list=[]
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input)
85
86 def forward(self, input):
---> 87 return F.linear(input, self.weight, self.bias)
88
89 def extra_repr(self):
/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1368 if input.dim() == 2 and bias is not None:
1369 # fused op is marginally faster
-> 1370 ret = torch.addmm(bias, input, weight.t())
1371 else:
1372 output = input.matmul(weight.t())
RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_addmm
I print the X device before fc1(X) code
It was on cuda！
So why this error happen?
