I know there are many posts related to this error, but I still can’t seem to get my model to run. The error, Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for argument #2 'other' always occurs it seems no matter where I put .cuda(). I have inserted the code below, and I would really appreciate if someone could point out where the type mismatch is happening. Thank you so much in advance for any help. Here is what I believe to be the relevant code.
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 = []
# clearing the layers for next vgg model
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)).cuda()
self.state_to_state = torch.nn.Conv2d(self.ch,4*self.ch,kernel_size=(1,3),padding=(0,1)).cuda() # error is here: hidPrev is an array - not a valid number of input channel
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
Note: the code tries to run through the try block, but then the error is thrown.
