def test(net, data_loader):
net.eval()
ds = data_loader.dataset
sum = 0
for i, (seq1, seq2, mask1, mask2) in enumerate(tqdm(data_loader)):
input_ids1, attention_mask1 = split_seq(seq1.to(device)), split_seq(
mask1.to(device))
input_ids2, attention_mask2 = split_seq(seq2.to(device)), split_seq(
mask2.to(device))
with torch.no_grad():
for i in range(len(input_ids1)):
input, label = ds.DataCollatorForLanguageModeling(
input_ids1[i])
output = net(input_ids=input,
attention_mask=attention_mask1[i],
labels=label)
sum += output.loss.cpu().numpy()
for i in range(len(input_ids2)):
input, label = ds.DataCollatorForLanguageModeling(
input_ids2[i])
output = net(input_ids=input,
attention_mask=attention_mask2[i],
labels=label)
sum += output.loss.cpu().numpy()
print("eval loss ", sum)
def finetune_eval(net, data_loader, epoch):
net.eval()
ds = data_loader.dataset
with torch.no_grad():
avg = []
gt = []
cons = []
for i, (seq1, seq2, seq3, mask1, mask2,
mask3) in enumerate(tqdm(data_loader)):
input_ids1, attention_mask1 = split_seq(
seq1.to(device)), split_seq(mask1.to(device))
input_ids2, attention_mask2 = split_seq(
seq2.to(device)), split_seq(mask2.to(device))
anchor, pos = 0, 0
for i in range(len(input_ids1)):
output = net.bert(input_ids=input_ids1[i],
attention_mask=attention_mask1[i])
embeddings1 = output.last_hidden_state[:, 0:1, :]
anchor = anchor + embeddings1
anchor = anchor / len(input_ids1)
anchor = F.normalize(anchor, dim=1)
for i in range(len(input_ids2)):
output = net.bert(input_ids=input_ids2[i],
attention_mask=attention_mask2[i])
embeddings2 = output.last_hidden_state[:, 0:1, :]
pos = pos + embeddings2
pos = pos / len(input_ids2)
pos = F.normalize(pos, dim=1)
ans = 0
for i in range(len(anchor)): # check every vector of (vA,vB)
vA = anchor[i].cpu().numpy()
sim = []
vA = vA[0]
for j in range(len(pos)):
vB = pos[j].cpu().numpy()
vB = vB[0]
sim.append(np.linalg.norm(vA - vB))
if j != i:
cons.append(sim[-1])
sim = np.array(sim)
#print(sim)
y = np.argsort(sim)
posi = 0
for j in range(len(pos)):
if y[j] == i:
posi = j + 1
#print(i,": ",y)
gt.append(sim[i])
print(posi, len(anchor))
ans += 1 / posi
ans = ans / len(anchor)
avg.append(ans)
print("MRR ", np.mean(np.array(avg)))
print("random similarity: ", np.mean(np.array(cons)))
print("gt similarity: ", np.mean(np.array(gt)))
def train(net, data_loader, train_optimizer, epoch):
net.train()
ds=data_loader.dataset
for i, (seq1,seq2,mask1,mask2) in enumerate(tqdm(data_loader)):
input_ids1, attention_mask1= split_seq(seq1.to(device)),split_seq(mask1.to(device))
input_ids2, attention_mask2= split_seq(seq2.to(device)),split_seq(mask2.to(device))
for i in range(len(input_ids1)):
input,label=ds.DataCollatorForLanguageModeling(input_ids1[i])
output=net(input_ids=input,attention_mask=attention_mask1[i],labels=label)
train_optimizer.zero_grad()
output.loss.backward()
train_optimizer.step()
for i in range(len(input_ids2)):
input,label=ds.DataCollatorForLanguageModeling(input_ids2[i])
output=net(input_ids=input,attention_mask=attention_mask2[i],labels=label)
train_optimizer.zero_grad()
output.loss.backward()
train_optimizer.step()
def finetune_train(net, data_loader, train_optimizer, epoch):
net.train()
ds=data_loader.dataset
trainloss=0
triplet_loss = nn.TripletMarginLoss(margin=1.0, p=2)
for i, (seq1,seq2,seq3,mask1,mask2,mask3) in enumerate(tqdm(data_loader)):
input_ids1, attention_mask1= split_seq(seq1.to(device)),split_seq(mask1.to(device))
input_ids2, attention_mask2= split_seq(seq2.to(device)),split_seq(mask2.to(device))
input_ids3, attention_mask3= split_seq(seq3.to(device)),split_seq(mask3.to(device))
train_optimizer.zero_grad()
anchor,pos,neg=0,0,0
for i in range(len(input_ids1)):
output=net.bert(input_ids=input_ids1[i],attention_mask=attention_mask1[i])
embeddings1=output.last_hidden_state[:,0:1,:]
anchor=anchor+embeddings1
for i in range(len(input_ids2)):
output=net.bert(input_ids=input_ids2[i],attention_mask=attention_mask2[i])
embeddings2=output.last_hidden_state[:,0:1,:]
pos=pos+embeddings2
for i in range(len(input_ids3)):
output=net.bert(input_ids=input_ids3[i],attention_mask=attention_mask3[i])
embeddings3=output.last_hidden_state[:,0:1,:]
neg=neg+embeddings3
loss = triplet_loss(anchor, pos, neg)
loss.backward()
train_optimizer.step()
print(trainloss)