RuntimeError: Expected target size [20, 2], got [20]

username: Harshal_Dharpure
cooked: <pre><code class="lang-auto">
class Classifier(pl.LightningModule):

  def __init__(self):
    super().__init__()
    self.MFB = MFB(512,768,True,256,64,0.1)
    self.fin_y_shape = torch.nn.Linear(768,512)
    self.fin_old = torch.nn.Linear(64,2)
    self.fin = torch.nn.Linear(16 * 768, 64)
    self.fin_inten = torch.nn.Linear(64,6)
    self.fin_e1 = torch.nn.Linear(64,2)
    self.fin_e2 = torch.nn.Linear(64,2)
    self.fin_e3 = torch.nn.Linear(64,2)
    self.fin_e4 = torch.nn.Linear(64,2)
    self.fin_e5 = torch.nn.Linear(64,2)
    self.fin_e6 = torch.nn.Linear(64,2)
    self.fin_e7 = torch.nn.Linear(64,2)
    self.fin_e8 = torch.nn.Linear(64,2)
    self.fin_e9 = torch.nn.Linear(64,2)



    self.validation_step_outputs = []
    self.test_step_outputs = []

  def forward(self, x,y,rag):
      x_,y_,rag_ = x,y,rag
      print("x.shape", x.shape)
      z = self.MFB(torch.unsqueeze(y, axis=1), torch.unsqueeze(x, axis=1))
      z_rag = self.MFB(torch.unsqueeze(y, axis=1), torch.unsqueeze(rag, axis=1))
      z_newe = torch.cat((z, z_rag), dim=1)

      z_new = torch.squeeze(z_newe, dim=1)
      c_inten = self.fin_inten(z_new)
      c_e1 = self.fin_e1(z_new)
      c_e2 = self.fin_e2(z_new)
      c_e3 = self.fin_e3(z_new)
      c_e4 = self.fin_e4(z_new)
      c_e5 = self.fin_e5(z_new)
      c_e6 = self.fin_e6(z_new)
      c_e7 = self.fin_e7(z_new)
      c_e8 = self.fin_e8(z_new)
      c_e9 = self.fin_e9(z_new)
      c = self.fin_old(z_new)


      output = torch.log_softmax(c, dim=1)
      c_inten = torch.log_softmax(c_inten, dim=1)
      c_e1 = torch.log_softmax(c_e1, dim=1)
      c_e2 = torch.log_softmax(c_e2, dim=1)
      c_e3 = torch.log_softmax(c_e3, dim=1)
      c_e4 = torch.log_softmax(c_e4, dim=1)
      c_e5 = torch.log_softmax(c_e5, dim=1)
      c_e6 = torch.log_softmax(c_e6, dim=1)
      c_e7 = torch.log_softmax(c_e7, dim=1)
      c_e8 = torch.log_softmax(c_e8, dim=1)
      c_e9 = torch.log_softmax(c_e9, dim=1)
      return output,c_inten,c_e1,c_e2,c_e3,c_e4,c_e5,c_e6,c_e7,c_e8,c_e9



  def cross_entropy_loss(self, logits, labels):
    return F.nll_loss(logits, labels)

  def contrastive_loss(self, z, z_rag, label, margin=1.0):
    euclidean_distance = F.pairwise_distance(z1, z2)
    loss_contrastive = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) + (label) * torch.pow(torch.clamp(margin - euclidean_distance, min=0.0), 2))
    return loss_contrastive

  def training_step(self, train_batch, batch_idx):
      
      lab,txt,rag,img,name,intensity,e1,e2,e3,e4,e5,e6,e7,e8,e9 = train_batch
      lab = train_batch[lab]
      #print(lab)
      txt = train_batch[txt]
      rag = train_batch[rag]
      img = train_batch[img]
      name= train_batch[name]
      intensity = train_batch[intensity]
      e1 = train_batch[e1]
      e2 = train_batch[e2]
      e3 = train_batch[e3]
      e4 = train_batch[e4]
      e5 = train_batch[e5]
      e6 = train_batch[e6]
      e7 = train_batch[e7]
      e8 = train_batch[e8]
      e9 = train_batch[e9]

      logit_offen,logit_inten_target,a,b,c,d,e,f,g,h,i= self.forward(txt,img,rag)

      loss1 = self.cross_entropy_loss(logit_offen, lab)
      loss4 = self.cross_entropy_loss(a, e1)
      loss5 = self.cross_entropy_loss(b, e2)
      loss6 = self.cross_entropy_loss(c, e3)
      loss7 = self.cross_entropy_loss(d, e4)
      loss8 = self.cross_entropy_loss(e, e5)
      loss9 = self.cross_entropy_loss(f, e6)
      loss10 = self.cross_entropy_loss(g, e7)
      loss11 = self.cross_entropy_loss(h, e8)
      loss12 = self.cross_entropy_loss(i, e9)
      loss17 = self.cross_entropy_loss(logit_inten_target, intensity)

      contrastive_labels = (lab == 1).float()
      loss_contrastive = self.contrastive_loss(z, z_rag, contrastive_labels)

      loss = loss1 + loss4 + loss5 + loss6 + loss7 + loss8 +loss9 + loss10 + loss11 + loss12 + loss17 + loss_contrastive

      self.log('train_loss', loss)
      return loss

  def validation_step(self, val_batch, batch_idx):
      #lab,txt,rag,img,name,per,iro,alli,ana,inv,meta,puns,sat,hyp = val_batch
      lab,txt,rag,img,name,intensity,e1,e2,e3,e4,e5,e6,e7,e8,e9= val_batch
      lab = val_batch[lab]
      #print(lab)
      txt = val_batch[txt]
      rag = val_batch[rag]
      img = val_batch[img]
      name = val_batch[name]
      intensity = val_batch[intensity]
      e1 = val_batch[e1]
      e2 = val_batch[e2]
      e3 = val_batch[e3]
      e4 = val_batch[e4]
      e5 = val_batch[e5]
      e6 = val_batch[e6]
      e7 = val_batch[e7]
      e8 = val_batch[e8]
      e9 = val_batch[e9]

      logits,inten,a,b,c,d,e,f,g,h,i = self.forward(txt,img,rag)


      logits=logits.float()


      tmp = np.argmax(logits.detach().cpu().numpy(),axis=1)
      loss = self.cross_entropy_loss(logits, lab)
      lab = lab.detach().cpu().numpy()
      self.log('val_acc', accuracy_score(lab,tmp))
      self.log('val_roc_auc',roc_auc_score(lab,tmp))
      self.log('val_loss', loss)
      tqdm_dict = {'val_acc': accuracy_score(lab,tmp)}
      self.validation_step_outputs.append({'progress_bar': tqdm_dict,'val_f1 offensive': f1_score(lab,tmp,average='macro')})

      return {
                'progress_bar': tqdm_dict,
      'val_f1 offensive': f1_score(lab,tmp,average='macro')
      }

  def on_validation_epoch_end(self):
    outs = []
    outs14=[]
    for out in self.validation_step_outputs:
       outs.append(out['progress_bar']['val_acc'])
       outs14.append(out['val_f1 offensive'])
    self.log('val_acc_all_offn', sum(outs)/len(outs))
    self.log('val_f1 offensive', sum(outs14)/len(outs14))
    print(f'***val_acc_all_offn at epoch end {sum(outs)/len(outs)}****')
    print(f'***val_f1 offensive at epoch end {sum(outs14)/len(outs14)}****')
    self.validation_step_outputs.clear()

  def test_step(self, batch, batch_idx):
      lab,txt,rag,img,name,intensity,e1,e2,e3,e4,e5,e6,e7,e8,e9= batch
      #lab,txt,rag,img,name,per,iro,alli,ana,inv,meta,puns,sat,hyp= batch

      lab = batch[lab]
      #print(lab)


      txt = batch[txt]
      rag = batch[rag]
      img = batch[img]
      name = batch[name]
      intensity = batch[intensity]
      e1 = batch[e1]
      e2 = batch[e2]
      e3 = batch[e3]
      e4 = batch[e4]
      e5 = batch[e5]
      e6 = batch[e6]
      e7 = batch[e7]
      e8 = batch[e8]
      e9 = batch[e9]

      logits,inten,a,b,c,d,e,f,g,h,i= self.forward(txt,img,rag)
      logits = logits.float()
      tmp = np.argmax(logits.detach().cpu().numpy(force=True),axis=-1)
      loss = self.cross_entropy_loss(logits, lab)
      lab = lab.detach().cpu().numpy()
      self.log('test_acc', accuracy_score(lab,tmp))
      self.log('test_roc_auc',roc_auc_score(lab,tmp))
      self.log('test_loss', loss)
      tqdm_dict = {'test_acc': accuracy_score(lab,tmp)}
      self.test_step_outputs.append({'progress_bar': tqdm_dict,'test_acc': accuracy_score(lab,tmp), 'test_f1_score': f1_score(lab,tmp,average='macro')})
      return {
                'progress_bar': tqdm_dict,
                'test_acc': accuracy_score(lab,tmp),
                'test_f1_score': f1_score(lab,tmp,average='macro')
      }
  def on_test_epoch_end(self):
      # OPTIONAL
      outs = []
      outs1,outs2,outs3,outs4,outs5,outs6,outs7,outs8,outs9,outs10,outs11,outs12,outs13,outs14 = \
      [],[],[],[],[],[],[],[],[],[],[],[],[],[]
      for out in self.test_step_outputs:
        outs.append(out['test_acc'])
        outs2.append(out['test_f1_score'])
      self.log('test_acc', sum(outs)/len(outs))
      self.log('test_f1_score', sum(outs2)/len(outs2))
      self.test_step_outputs.clear()

  def configure_optimizers(self):
    # optimizer = torch.optim.Adam(self.parameters(), lr=3e-2)
    optimizer = torch.optim.Adam(self.parameters(), lr=1e-5)

    return optimizer


"""
Main Model:
Initialize
Forward Pass
Training Step
Validation Step
Testing Step

Pp
"""

class HmDataModule(pl.LightningDataModule):

  def setup(self, stage):
    self.hm_train = t_p
    self.hm_val = v_p
    # self.hm_test = test
    self.hm_test = te_p

  def train_dataloader(self):
    return DataLoader(self.hm_train, batch_size=20, drop_last=True)

  def val_dataloader(self):
    return DataLoader(self.hm_val, batch_size=20, drop_last=True)

  def test_dataloader(self):
    return DataLoader(self.hm_test, batch_size=20, drop_last=True)

data_module = HmDataModule()
checkpoint_callback = ModelCheckpoint(
     monitor='val_acc_all_offn',
     dirpath='mrinal/',
     filename='epoch{epoch:02d}-val_f1_all_offn{val_acc_all_offn:.2f}',
     auto_insert_metric_name=False,
     save_top_k=1,
    mode="max",
 )
all_callbacks = []
all_callbacks.append(checkpoint_callback)
# train
from pytorch_lightning import seed_everything
seed_everything(42, workers=True)
hm_model = Classifier()
gpus=1
#if torch.cuda.is_available():gpus=0
trainer = pl.Trainer(deterministic=True,max_epochs=20,precision=16,callbacks=all_callbacks)
trainer.fit(hm_model, data_module)
</code></pre>
<pre><code class="lang-auto">INFO:lightning_fabric.utilities.seed:Seed set to 42
INFO:pytorch_lightning.utilities.rank_zero:Using bfloat16 Automatic Mixed Precision (AMP)
INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False
INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores
INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs
INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs
INFO:pytorch_lightning.callbacks.model_summary:
   | Name        | Type   | Params
----------------------------------------
0  | MFB         | MFB    | 21.0 M
1  | fin_y_shape | Linear | 393 K 
2  | fin_old     | Linear | 130   
3  | fin         | Linear | 786 K 
4  | fin_inten   | Linear | 390   
5  | fin_e1      | Linear | 130   
6  | fin_e2      | Linear | 130   
7  | fin_e3      | Linear | 130   
8  | fin_e4      | Linear | 130   
9  | fin_e5      | Linear | 130   
10 | fin_e6      | Linear | 130   
11 | fin_e7      | Linear | 130   
12 | fin_e8      | Linear | 130   
13 | fin_e9      | Linear | 130   
----------------------------------------
22.2 M    Trainable params
0         Non-trainable params
22.2 M    Total params
88.745    Total estimated model params size (MB)
x.shape torch.Size([20, 768])
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-43-c76eb94fa135&gt; in &lt;cell line: 266&gt;()
    264 #if torch.cuda.is_available():gpus=0
    265 trainer = pl.Trainer(deterministic=True,max_epochs=20,precision=16,callbacks=all_callbacks)
--&gt; 266 trainer.fit(hm_model, data_module)

13 frames
/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
   2702     if size_average is not None or reduce is not None:
   2703         reduction = _Reduction.legacy_get_string(size_average, reduce)
-&gt; 2704     return torch._C._nn.nll_loss_nd(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
   2705 
   2706 

RuntimeError: Expected target size [20, 2], got [20]
Sanity Checking DataLoader 0:   0%
 0/2 [00:00&lt;?, ?it/s]
</code></pre>

username: KFrank
cooked: <p>Hi Harshal!</p>
<aside class="quote no-group" data-username="Harshal_Dharpure" data-post="1" data-topic="203003" data-full="true">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/harshal_dharpure/48/68664_2.png" class="avatar"> Harshal_Dharpure:</div>
<blockquote>
<pre><code class="lang-auto">      z_newe = torch.cat((z, z_rag), dim=1)

      z_new = torch.squeeze(z_newe, dim=1)
</code></pre>
</blockquote>
</aside>
<p>I haven’t traced through your code in detail, but here you are trying (unsuccessfully)<br>
to <code>squeeze()</code> away a non-singleton dimension.  I think that this extra dimension<br>
persists and ultimately leads to your error.</p>
<p>Because <code>z_newe</code> is the result of <code>cat()</code>, it has <code>z_newe.size (1) &gt; 1</code> (that is, <code>dim = 1</code><br>
is a nontrivial “non-singleton” dimension).  Therefore the <code>squeeze()</code> operation doesn’t<br>
actually do anything (as <code>squeeze()</code> only affects singleton dimensions).</p>
<p>Here is an illustration based loosely on your code:</p>
<pre><code class="lang-plaintext">&gt;&gt;&gt; import torch
&gt;&gt;&gt; print (torch.__version__)
2.3.0
&gt;&gt;&gt;
&gt;&gt;&gt; _ = torch.manual_seed (2024)
&gt;&gt;&gt;
&gt;&gt;&gt; x = torch.randn (20, 2)
&gt;&gt;&gt; targ = torch.randint (2, (20,))
&gt;&gt;&gt;
&gt;&gt;&gt; torch.nn.functional.nll_loss (torch.log_softmax (x, dim = 1), targ)       # okay
tensor(1.2112)
&gt;&gt;&gt;
&gt;&gt;&gt; z = x.unsqueeze (dim = 1)
&gt;&gt;&gt; z_cat = torch.cat ((z, z), dim = 1)
&gt;&gt;&gt; z_cat.shape
torch.Size([20, 2, 2])
&gt;&gt;&gt;
&gt;&gt;&gt; z_new = z_cat.squeeze (dim = 1)                                           # nothing gets squeezed away
&gt;&gt;&gt; z_new.shape
torch.Size([20, 2, 2])
&gt;&gt;&gt;
&gt;&gt;&gt; torch.nn.functional.nll_loss (torch.log_softmax (z_new, dim = 1), targ)   # size-mismatch error
Traceback (most recent call last):
  File "&lt;stdin&gt;", line 1, in &lt;module&gt;
  File "&lt;path_to_pytorch_install&gt;torch\nn\functional.py", line 2760, in nll_loss
    return torch._C._nn.nll_loss_nd(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Expected target size [20, 2], got [20]
</code></pre>
<p>Try adding <code>print (logits.shape, labels.shape)</code> to your <code>cross_entropy_loss()</code><br>
function to check if this is what is going on.</p>
<p>Best.</p>
<p>K. Frank</p>