我有一些代码，当我运行它时，出现以下错误：预期类型为torch.cuda.FloatTensor的对象，但为参数＃2'other'发现了torch.FloatTensor类型
根据此错误消息，我认为将模型推入GPU时出现问题。但是，我不确定问题出在哪里。
我将代码放置在我认为问题可能在该问题的结尾处的地方。有人可以说明错误的确切含义以及如何解决吗？任何帮助深表感谢。
class VGG(nn.Module):
'''
VGG model
'''
def __init__(self, features): # features represents the layers array
super(VGG, self).__init__()
self.features = features
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(512,512),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(512, 512),
nn.ReLU(True),
nn.Linear(512, 10),
)
# Initialize weights
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
m.bias.data.zero_()
def forward(self, x): # x is the image, we run x through the layers
print("Running through features")
x = self.features(x) # runs through all features, where each feature is a function
print("Finsihed features, going to classifier")
x = x.view(x.size(0), -1)
# after running through features, does sequential steps to finally classify
x = self.classifier(x)
return x
def make_layers(cfg, batch_norm=False):
# print("Making layers!")
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
rlstm =RLSTM(v)
rlstm.input_to_state = torch.nn.DataParallel(rlstm.input_to_state)
rlstm.state_to_state = torch.nn.DataParallel(rlstm.state_to_state)
rlstm=rlstm.cuda()
layers+=[rlstm]
return nn.Sequential(*layers)
class RLSTM(nn.Module):
def __init__(self,ch):
# torch.set_default_tensor_type('torch.cuda.FloatTensor')
super(RLSTM,self).__init__()
self.ch=ch
self.input_to_state = torch.nn.Conv2d(self.ch,4*self.ch,kernel_size=(1,3),padding=(0,1))
self.state_to_state = torch.nn.Conv2d(self.ch,4*self.ch,kernel_size=(1,3),padding=(0,1)) # error is here: hidPrev is an array - not a valid number of input channel
# self.input_to_state = self.input_to_state.cuda()
#self.state_to_state = self.state_to_state.cuda()
def forward(self, image):
# print("going in row forward")
global current
global _layer
global isgates
size = image.size()
print("size: "+str(size))
b = size[0]
indvs = list(image.split(1,0)) # split up the batch into individual images
#print(indvs[0].size())
tensor_array = []
for i in range(b):
current = 0
_layer = []
isgates = []
print(len(tensor_array))
tensor_array.append(self.RowLSTM(indvs[i]))
seq=tuple(tensor_array)
trans = torch.cat(seq,0)
print(trans.size())
return trans.cuda() # trying to make floattensor error go away
def RowLSTM(self, image):
# print("going in rowlstm")
global current
global _layer
global isgates
# input-to-state (K_is * x_i) : 3x1 convolution. generate 4h x n x n tensor. 4hxnxn tensor contains all i -> s info
# the input to state convolution should only be computed one time
if current==0:
n = image.size()[2]
ch=image.size()[1]
# input_to_state = torch.nn.Conv2d(ch,4*ch,kernel_size=(1,3),padding=(0,1))
# print("about to do convolution")
isgates = self.splitIS(self.input_to_state(image)) # convolve, then split into gates (4 per row)
cell=RowLSTMCell(0,torch.randn(ch,n,1),torch.randn(ch,n,1),torch.randn(ch,n,1),torch.randn(ch,n,1),torch.randn(ch,n,1),torch.randn(ch,n,1))
# now have dummy, learnable variables for first row
_layer.append(cell)
print("layeres: "+str(len(_layer)))
else:
Cell_prev = _layer[current-1] # access previous row
hidPrev = Cell_prev.getHiddenState()
ch = image.size()[1]
# print("about to apply conv1d")
# state_to_state = torch.nn.Conv2d(ch,4*ch,kernel_size=(1,3),padding=(0,1)) # error is here: hidPrev is an array - not a valid number of input channel
# print("applied conv1d")
prevHid=Cell_prev.getHiddenState()
ssgates = self.splitSS(self.state_to_state(prevHid.unsqueeze(0))) #need to unsqueeze (Ex: currently 16x5, need to make 1x16x5)
gates = self.addGates(isgates,ssgates,current)
# split gates
ig, og, fg, gg = gates[0], gates[1], gates[2], gates[3] # into four, ADD SIGMOID!
cell = RowLSTMCell(Cell_prev,ig,og,fg,gg,0,0)
cell.compute()
_layer.append(cell)
# attempting to eliminate requirement of getting size
#print(current)
try:
print("adding one to current")
current+=1
y=(isgates[0][0][1][current])
return self.RowLSTM(image) #expecting floattensor, but gets cuda floattensor
except Exception as error:
print(error)
concats=[]
print(len(_layer))
for cell in _layer:
tensor=torch.unsqueeze(cell.h,0)
concats.append(tensor)
seq=tuple(concats)
print("non catted tensor: "+str(tensor.size()))
tense=torch.cat(seq,3)
print("catted lstm tensor "+str(tense.size()))
return tensor
代码运行，但是当尝试通过try / except块时，将引发错误。我猜错误在这里某处？
编辑：使用打印语句查看程序在何处正确终止，似乎我已记下的代码中有错误！现在，我将其发布，因为错误“ finished computing”永远不会被打印出来，因此看起来像是compute（）函数中的错误。
class RowLSTMCell(): #inherit torch.nn.LSTM?
def __init__(self,prev_row, i, o, f, g, c, h):
#super(RowLSTMCell,self).__init__()
self.c=c
#self.c = self.c.cuda()
self.h=h
# self.h = self.h.cuda()
self.i=i
self.i = self.i.cuda()
self.o=o
self.o = self.o.cuda()
self.g=g
self.g = self.g.cuda()
self.f=f
self.f = self.f.cuda()
self.prev_row=prev_row
def getStateSize(self):
return self._state_size
def getOutputSize(self):
return self._output_size
def compute(self):
print("computing")
c_prev = self.prev_row.getCellState()
h_prev = self.prev_row.getHiddenState()
self.c = self.f * c_prev + self.i * self.g
self.h = torch.tanh(self.c) * self.o
print("finished computing")
def getHiddenState(self):
return self.h
def getCellState(self):
return self.c
答案 0 :(得分：0)
self.c和self.h不是CUDA！我猜您真的必须确保每个张量都使用cuda。我只是将.cuda（）放在self.c的末尾，而self.h的计算放在了execute（）方法中。