我已经定义了
zclass 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(128,2)
self.fin = torch.nn.Linear(128, 64)
self.fin_inten = torch.nn.Linear(128,6)
self.fin_e1 = torch.nn.Linear(128,2)
self.fin_e2 = torch.nn.Linear(128,2)
self.fin_e3 = torch.nn.Linear(128,2)
self.fin_e4 = torch.nn.Linear(128,2)
self.fin_e5 = torch.nn.Linear(128,2)
self.fin_e6 = torch.nn.Linear(128,2)
self.fin_e7 = torch.nn.Linear(128,2)
self.fin_e8 = torch.nn.Linear(128,2)
self.fin_e9 = torch.nn.Linear(128,2)
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=2)
# z_new = torch.squeeze(z_newe, dim=2)
z_newe = torch.cat((z, z_rag), dim=2) # Assuming the concatenation should be along the last dimension
# Flatten the concatenated tensor to a 2D shape if necessary
z_new = z_newe.view(z_newe.size(0), -1)
print("z_new.shape after view:", z_new.shape)
# Ensure the input to the Linear layers match their expected input dimensions
c_inten = self.fin_inten(z_new)
print("c_inten.shape:", c_inten.shape)
c_e1 = self.fin_e1(z_new)
print("c_e1.shape:", c_e1.shape)
c_e2 = self.fin_e2(z_new)
print("c_e2.shape:", c_e2.shape)
c_e3 = self.fin_e3(z_new)
print("c_e3.shape:", c_e3.shape)
c_e4 = self.fin_e4(z_new)
print("c_e4.shape:", c_e4.shape)
c_e5 = self.fin_e5(z_new)
print("c_e5.shape:", c_e5.shape)
c_e6 = self.fin_e6(z_new)
print("c_e6.shape:", c_e6.shape)
c_e7 = self.fin_e7(z_new)
print("c_e7.shape:", c_e7.shape)
c_e8 = self.fin_e8(z_new)
print("c_e8.shape:", c_e8.shape)
c_e9 = self.fin_e9(z_new)
print("c_e9.shape:", c_e9.shape)
c = self.fin_old(z_new)
print("c.shape:", c.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):
print(f"logits.shape: {logits.shape}, labels.shape: {labels.shape}")
return F.nll_loss(logits, labels)
def contrastive_loss(self, z1, z2, 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)
NameError Traceback (most recent call last)
<ipython-input-52-53dcd8ce73ec> in <cell line: 296>()
294 #if torch.cuda.is_available():gpus=0
295 trainer = pl.Trainer(deterministic=True,max_epochs=20,precision=16,callbacks=all_callbacks)
--> 296 trainer.fit(hm_model, data_module)
27 frames
<ipython-input-52-53dcd8ce73ec> in training_step(self, train_batch, batch_idx)
132
133 contrastive_labels = (lab == 1).float()
--> 134 loss_contrastive = self.contrastive_loss(z, z_rag, contrastive_labels)
135
136 loss = loss1 + loss4 + loss5 + loss6 + loss7 + loss8 +loss9 + loss10 + loss11 + loss12 + loss17 + loss_contrastive
NameError: name 'z' is not defined
如何解决这个错误？
您已在
zforward()zforward()forward()解决此问题的一种方法是将
zz def forward(self, x,y,rag):
x_,y_,rag_ = x,y,rag
print("x.shape", x.shape)
self.z = self.MFB(torch.unsqueeze(y, axis=1), torch.unsqueeze(x, axis=1))
稍后，为了引用此实例属性，您再次使用
self.z contrastive_labels = (lab == 1).float()
loss_contrastive = self.contrastive_loss(self.z, z_rag, contrastive_labels)
为了使您的代码正常工作，其他变量可能也应该定义为实例属性。