pythonmachine-learningpytorchlstm

Pytorch GRU error RuntimeError : size mismatch, m1: [1600 x 3], m2: [50 x 20]


I'm trying to make the training model for an LSTM and GRU. The LSTM is working perfectly, but once I switched into GRU training, errors pop-out such as size mismatch error.

This is my code

path = "new_z_axis"
device = "cuda:0"
in_size = 3
h_size = 50
n_layers = 3
fc = 20
out = 1
batch_size = 16
seq = 100
epoch = 100
learning_rate = 1e-3
ratio = 0.8
checkpoint = os.path.join("checkpoints","model_"+path+"_"+str(in_size)+".pth")

class GRUNet(nn.Module):
    def __init__(self,in_size,h_size,n_layers,fc_out,out_size,dropout=0.5):
        super(GRUNet, self).__init__()   
        self.gru = nn.GRU(input_size=in_size,hidden_size=h_size,num_layers=n_layers,dropout=dropout,bias=False)
        self.fc = nn.Linear(in_features=h_size,out_features=fc_out,bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.out = nn.Linear(in_features=fc_out,out_features=out_size,bias=False)
        self.tanh = nn.Tanh()        
    def forward(self, x, hidden):
        out, hidden = self.gru(x, hidden)
        x = self.fc(x)
        x = self.relu(x)
        x = self.out(x)
        x = self.tanh(x)
        return x, hidden

class MyLstm(nn.Module):
    def __init__(self,in_size,h_size,n_layers,fc_out,out_size,dropout=0.5):
        super(MyLstm, self).__init__()
        self.lstm = nn.LSTM(input_size=in_size,hidden_size=h_size,num_layers=n_layers,dropout=dropout,bias=False)
        self.fc = nn.Linear(in_features=h_size,out_features=fc_out,bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.out = nn.Linear(in_features=fc_out,out_features=out_size,bias=False)
        self.tanh = nn.Tanh()
    def forward(self,x,hidden):
        x, hidden = self.lstm(x,hidden)
#         x = x[-1:]
        x = self.fc(x)
        x = self.relu(x)
        x = self.out(x)
        x = self.tanh(x)
        return x, hidden

def train(model,train_list,val_list,path,seq,epoch,batch_size,criterion,optimizer,model_type):
    for e in range(epoch):
        train_data = load_data(train_list,batch_size)
        a_loss = 0
        a_size = 0
        model.train()
        for x,y in train_data:
            x,y = x.to(device),y.to(device)
            bs = x.size()[1]
            
#             hidden = (hidden[0].detach(),hidden[1].detach())
#             print(x.size(),hidden[0].size())
            if model_type == "GRU":
                h1 = torch.zeros((n_layers,bs,h_size)).to("cuda:0")
                hidden = h1
                hidden = hidden.data
            else:
                h1 = torch.zeros((n_layers,bs,h_size)).to("cuda:0")
                h2 = torch.zeros((n_layers,bs,h_size)).to("cuda:0")
                hidden = (h1,h2)
                hidden = tuple([e.data for e in hidden])
                
            model.zero_grad()
            print (len(hidden))
            pred,hidden = model(x,hidden)
            loss = criterion(pred,y)
            loss.backward()
            nn.utils.clip_grad_norm_(model.parameters(),5)
            optimizer.step()
            a_loss += loss.detach()
            a_size += bs
#         print(e,a_loss/a_size*1e+6)
        model.eval()
        with torch.no_grad():
            val_data = load_data(val_list,batch_size)
            b_loss = 0
            b_size = 0
            for x,y in val_data:
                x,y = x.to(device),y.to(device)
                bs = x.size()[1]
                if model_type == "GRU":
                    h1 = torch.zeros((n_layers,bs,h_size)).to("cuda:0")
                    hidden = h1
                    hidden = hidden.data
                else:
                    h1 = torch.zeros((n_layers,bs,h_size)).to("cuda:0")
                    h2 = torch.zeros((n_layers,bs,h_size)).to("cuda:0")
                    hidden = (h1,h2)
                    hidden = tuple([e.data for e in hidden])
                pred,hidden = model(x,hidden)
                loss = criterion(pred,y)
                b_loss += loss.detach()
                b_size += bs
        print("epoch: {} - train_loss: {} - val_loss: {}".format(e+1,float(a_loss.item()/a_size*1e+6),b_loss.item()/b_size*1e+6))


train(modelGRU,train_list,val_list,path,seq,epoch,batch_size,criterionGRU,optimizerGRU,model_type="GRU")

This is the error i got

RuntimeError                               
Traceback (most recent call last)  <ipython-input-9-a382a9688da2> in <module> 
---- > 1  train ( modelGRU , train_list , val_list , path , seq , epoch , batch_size , criterionGRU , optimizerGRU , model_type = "GRU" )

<ipython-input-6-4565cf358824> in train (model, train_list, val_list, path, seq, epoch, batch_size, criterion, optimizer, model_type) 
     61              model . zero_grad ( ) 
     62              print ( len ( hidden ) ) 
---> 63              pred , hidden = model ( x , hidden ) 
     64              loss = criterion ( pred , y ) 
     65              loss .backward ( )

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__ (self, *input, **kwargs) 
    539              result = self . _slow_forward ( * input ,  ** kwargs ) 
    540          else : 
--> 541              result = self . forward ( * input ,  ** kwargs ) 
    542          for hook in self . _forward_hooks . values ( ) :
    543              hook_result = hook ( self , input , result )

<ipython-input-5-4ecae472cc96> in forward (self, x, hidden) 
     11      def forward ( self , x , hidden ) : 
     12          out , hidden = self . gru ( x , hidden ) 
---> 13          x = self . fc ( x ) 
     14          x = self . relu ( x ) 
     15          x =self . out ( x )

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__ (self, *input, **kwargs) 
    539              result = self . _slow_forward ( * input ,  ** kwargs ) 
    540          else : 
--> 541              result = self . forward ( * input ,  ** kwargs ) 
    542          for hook in self . _forward_hooks . values ( ) :
    543              hook_result = hook ( self , input , result )

~ \ Anaconda3 \ lib \ site-packages \ torch \ nn \ modules \ linear.py in forward (self, input) 
     85  
     86      def forward ( self , input ) : 
---> 87 return F . Linear ( the Input , Self . Weight , Self . negative bias )      88      89 def extra_repr ( Self ) :         
 
     

~\Anaconda3\lib\site-packages\torch\nn\functional.py in linear (input, weight, bias)     1370          ret = torch . addmm ( bias , input , weight . t ( ) )     1371      else : 
-> 1372          output = input . matmul ( weight . t ( ) )     1373          if bias is  not  None :     1374              output += bias

RuntimeError : size mismatch, m1: [1600 x 3], m2: [50 x 20] at C:/w/1/s/tmp_conda_3.7_104508/conda/conda-bld/pytorch_1572950778684/work/aten/src\THC/ generic/THCTensorMathBlas.cu:290

Any advice?


Solution

  • This might have to do with the fact that you are not passing the output of your nn.GRU to the first linear layer in GRUNet's forward function:

        def forward(self, x, hidden):
            out, hidden = self.gru(x, hidden)
            x = self.fc(out)