This is the Updated Code which I have change the Dimension, I have also got Some suggestion on this code about the error which i have posted above Now , but I didn't understand Properly, can you please explain it where I have to update in the code.
From Pytorch community --
This error points to the target used in nn.CrossEntropyLoss or nn.NLLLoss having an invalid shape.
Your code is unfortunately not executable, so itโs not trivial to copy/paste it to fix other issues.
From stackoverflow--
You need to focus, I previously told you to update the shape of fin_inten which you did. Now: you are throwing z_new into fin_inten which is a nn.Linear(2048,6) and right after into fin_e1 which is a nn.Linear(64,2). Your code is not consistent, you can't just throw random tensors into functions and hope for the best!
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(2048,2)
self.fin = torch.nn.Linear(16 * 768, 64)
self.fin_inten = torch.nn.Linear(2048,6)
self.fin_e1 = torch.nn.Linear(2048,2)
self.fin_e2 = torch.nn.Linear(2048,2)
self.fin_e3 = torch.nn.Linear(2048,2)
self.fin_e4 = torch.nn.Linear(2048,2)
self.fin_e5 = torch.nn.Linear(2048,2)
self.fin_e6 = torch.nn.Linear(2048,2)
self.fin_e7 = torch.nn.Linear(2048,2)
self.fin_e8 = torch.nn.Linear(2048,2)
self.fin_e9 = torch.nn.Linear(2048,2)
# self.reduce_x = torch.nn.Linear(768, 512)
# self.reduce_rag = torch.nn.Linear(768, 512)
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)
print("y.shape",y.shape)
# print("rag.shape",rag.shape)
# x = self.reduce_x(x)
# rag = self.reduce_rag(rag)
# print("x.shape", x.shape)
# print("y.shape",y.shape)
# print("rag.shape",rag.shape)
# z = self.MFB(torch.unsqueeze(y, axis=1), torch.unsqueeze(rag, axis=1))
# z_rag = self.MFB(torch.unsqueeze(y, axis=1),torch.unsqueeze(rag, axis=1))
# z_con = torch.cat((z, z_rag), dim=1)
# Concatenate x with y and then with rag
# z= torch.cat((torch.cat((x, y), dim=1), rag), dim=1)
# # Pass concatenated x with y and x with rag through your network
# z_new = torch.squeeze(z,dim=1)
# print("z_new shape",z_new)
z = torch.cat((x, y, rag), dim=1)
z_new = torch.squeeze(z, 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)
# print("z.shape",z.shape)
# print("z_new shape",z_new.shape)
# print("intensity error:", c_inten.shape)
# print("output:", c.shape)
# print("c_e1:", c_e1.shape)
# print("c_e2:", c_e2.shape)
# print("c_e3:", c_e3.shape)
# print("c_e4:", c_e4.shape)
# print("c_e5:", c_e5.shape)
# print("c_e6:", c_e6.shape)
# print("c_e7:", c_e7.shape)
# print("c_e8:", c_e8.shape)
# print("c_e9:", c_e9.shape)
# print("logits.shape",logits.shape)
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 training_step(self, train_batch, batch_idx):
#lab,txt,rag,img,name,per,iro,alli,ana,inv,meta,puns,sat,hyp= train_batch
lab,txt,rag,img,name,intensity,e1,e2,e3,e4,e5,e6,e7,e8,e9= train_batch
#logit_offen,a,b,c,d,e,f,g,h,i,logit_inten_target= self.forward(txt,img,rag)
lab = train_batch[lab].unsqueeze(1)
#print(lab)
txt = train_batch[txt]
rag = train_batch[rag]
img = train_batch[img]
name= train_batch[name]
intensity = train_batch[intensity].unsqueeze(1)
e1 = train_batch[e1].unsqueeze(1)
e2 = train_batch[e2].unsqueeze(1)
e3 = train_batch[e3].unsqueeze(1)
e4 = train_batch[e4].unsqueeze(1)
e5 = train_batch[e5].unsqueeze(1)
e6 = train_batch[e6].unsqueeze(1)
e7 = train_batch[e7].unsqueeze(1)
e8 = train_batch[e8].unsqueeze(1)
e9 = train_batch[e9].unsqueeze(1)
lab = F.one_hot(lab, num_classes=2)
intensity = torch.abs(intensity)
intensity = F.one_hot(intensity, num_classes=6) # Assuming you have 6 classes
e1 = F.one_hot(e1,num_classes = 2)
e2 = F.one_hot(e2,num_classes = 2)
e3 = F.one_hot(e3,num_classes = 2)
e4 = F.one_hot(e4,num_classes = 2)
e5 = F.one_hot(e5,num_classes = 2)
e6 = F.one_hot(e6,num_classes = 2)
e7 = F.one_hot(e7,num_classes = 2)
e8 = F.one_hot(e8,num_classes = 2)
e9 = F.one_hot(e9,num_classes = 2)
lab = lab.squeeze(dim=1)
intensity = intensity.squeeze(dim=1)
e1 = e1.squeeze(dim=1)
e2 = e2.squeeze(dim=1)
e3 = e3.squeeze(dim=1)
e4 = e4.squeeze(dim=1)
e5 = e5.squeeze(dim=1)
e6 = e6.squeeze(dim=1)
e7 = e7.squeeze(dim=1)
e8 = e8.squeeze(dim=1)
e9 = e9.squeeze(dim=1)
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)
loss17 = self.cross_entropy_loss(logit_inten_target, intensity)
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)
loss = loss1 + loss4 + loss5 + loss6 + loss7 + loss8 +loss9 + loss10 +loss11 +loss12 + loss17
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].unsqueeze(1)
#print(lab)
txt = val_batch[txt]
rag = val_batch[rag]
img = val_batch[img]
name = val_batch[name]
intensity = val_batch[intensity].unsqueeze(1)
e1 = val_batch[e1].unsqueeze(1)
e2 = val_batch[e2].unsqueeze(1)
e3 = val_batch[e3].unsqueeze(1)
e4 = val_batch[e4].unsqueeze(1)
e5 = val_batch[e5].unsqueeze(1)
e6 = val_batch[e6].unsqueeze(1)
e7 = val_batch[e7].unsqueeze(1)
e8 = val_batch[e8].unsqueeze(1)
e9 = val_batch[e9].unsqueeze(1)
lab = F.one_hot(lab, num_classes=2)
intensity = torch.abs(intensity)
intensity = F.one_hot(intensity, num_classes=6)
e1 = F.one_hot(e1,num_classes = 2)
e2 = F.one_hot(e2,num_classes = 2)
e3 = F.one_hot(e3,num_classes = 2)
e4 = F.one_hot(e4,num_classes = 2)
e5 = F.one_hot(e5,num_classes = 2)
e6 = F.one_hot(e6,num_classes = 2)
e7 = F.one_hot(e7,num_classes = 2)
e8 = F.one_hot(e8,num_classes = 2)
e9 = F.one_hot(e9,num_classes = 2)
lab = lab.squeeze(dim=1)
intensity = intensity.squeeze(dim = 1)
e1 = e1.squeeze(dim=1)
e2 = e2.squeeze(dim=1)
e3 = e3.squeeze(dim=1)
e4 = e4.squeeze(dim=1)
e5 = e5.squeeze(dim=1)
e6 = e6.squeeze(dim=1)
e7 = e7.squeeze(dim=1)
e8 = e8.squeeze(dim=1)
e9 = e9.squeeze(dim=1)
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 = batch[lab].unsqueeze(1)
#print(lab)
txt = batch[txt]
rag = batch[rag]
img = batch[img]
name = batch[name]
intensity = batch[intensity].unsqueeze(1)
e1 = batch[e1].unsqueeze(1)
e2 = batch[e2].unsqueeze(1)
e3 = batch[e3].unsqueeze(1)
e4 = batch[e4].unsqueeze(1)
e5 = batch[e5].unsqueeze(1)
e6 = batch[e6].unsqueeze(1)
e7 = batch[e7].unsqueeze(1)
e8 = batch[e8].unsqueeze(1)
e9 = batch[e9].unsqueeze(1)
lab = F.one_hot(lab, num_classes=2)
intensity = F.one_hot(intensity, num_classes=6)
e1 = F.one_hot(e1,num_classes = 2)
e2 = F.one_hot(e2,num_classes = 2)
e3 = F.one_hot(e3,num_classes = 2)
e4 = F.one_hot(e4,num_classes = 2)
e5 = F.one_hot(e5,num_classes = 2)
e6 = F.one_hot(e6,num_classes = 2)
e7 = F.one_hot(e7,num_classes = 2)
e8 = F.one_hot(e8,num_classes = 2)
e9 = F.one_hot(e9,num_classes = 2)
lab = lab.squeeze(dim=1)
intensity = intensity.squeeze(dim=1)
e1 = e1.squeeze(dim=1)
e2 = e2.squeeze(dim=1)
e3 = e3.squeeze(dim=1)
e4 = e4.squeeze(dim=1)
e5 = e5.squeeze(dim=1)
e6 = e6.squeeze(dim=1)
e7 = e7.squeeze(dim=1)
e8 = e8.squeeze(dim=1)
e9 = e9.squeeze(dim=1)
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=10, drop_last=True)
def val_dataloader(self):
return DataLoader(self.hm_val, batch_size=10, drop_last=True)
def test_dataloader(self):
return DataLoader(self.hm_test, batch_size=10, 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)
Here is the Full Traceback Error
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 | 4.1 K
3 | fin | Linear | 786 K
4 | fin_inten | Linear | 12.3 K
5 | fin_e1 | Linear | 4.1 K
6 | fin_e2 | Linear | 4.1 K
7 | fin_e3 | Linear | 4.1 K
8 | fin_e4 | Linear | 4.1 K
9 | fin_e5 | Linear | 4.1 K
10 | fin_e6 | Linear | 4.1 K
11 | fin_e7 | Linear | 4.1 K
12 | fin_e8 | Linear | 4.1 K
13 | fin_e9 | Linear | 4.1 K
----------------------------------------
22.2 M Trainable params
0 Non-trainable params
22.2 M Total params
88.951 Total estimated model params size (MB)
SanityโCheckingโDataLoaderโ0:โโโ0%
โ0/2โ[00:00<?,โ?it/s]
x.shape torch.Size([10, 768])
y.shape torch.Size([10, 512])
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-34-4b5757f5c04a>](https://localhost:8080/#) in <cell line: 375>()
373 #if torch.cuda.is_available():gpus=0
374 trainer = pl.Trainer(deterministic=True,max_epochs=20,precision=16,callbacks=all_callbacks)
--> 375 trainer.fit(hm_model, data_module)
13 frames
[/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py](https://localhost:8080/#) 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)
-> 2704 return torch._C._nn.nll_loss_nd(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
2705
2706
RuntimeError: 0D or 1D target tensor expected, multi-target not supported
I am getting the error on RuntimeError: 0D or 1D target tensor expected, multi-target not supported I need to solve this can Anyone help me to figure out How i can Solve this issue.
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