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)

NFO: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)
[<ipython-input-43-c76eb94fa135>](https://localhost:8080/#) in <cell line: 266>()
 264 #if torch.cuda.is_available():gpus=0
 265 trainer = pl.Trainer(deterministic=True,max_epochs=20,precision=16,callbacks=all_callbacks)
--> 266 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: Expected target size [20, 2], got [20]
Sanity Checking DataLoader 0: