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import torch
import torch.nn as nn
from tqdm import tqdm
from utils import categorical_accuracy
def loss_fn(outputs, targets):
return nn.CrossEntropyLoss()(outputs, targets)
def train_fn(data_loader, model, optimizer, device, scheduler):
model.train()
train_loss, train_acc = 0.0, 0.0
for bi, d in tqdm(enumerate(data_loader), total=len(data_loader)):
ids = d["ids"]
token_type_ids = d["token_type_ids"]
mask = d["mask"]
targets = d["targets"]
ids = ids.to(device, dtype=torch.long)
token_type_ids = token_type_ids.to(device, dtype=torch.long)
mask = mask.to(device, dtype=torch.long)
targets = targets.to(device, dtype=torch.long)
optimizer.zero_grad()
outputs = model(
ids=ids,
mask=mask,
token_type_ids=token_type_ids
)
loss = loss_fn(outputs, targets)
loss.backward()
optimizer.step()
scheduler.step()
train_loss += loss.item()
pred_labels = torch.argmax(outputs, dim=1)
# (pred_labels == targets).sum().item()
train_acc += categorical_accuracy(outputs, targets).item()
train_loss /= len(data_loader)
train_acc /= len(data_loader)
return train_loss, train_acc
def eval_fn(data_loader, model, device):
model.eval()
eval_loss, eval_acc = 0.0, 0.0
fin_targets = []
fin_outputs = []
with torch.no_grad():
for bi, d in tqdm(enumerate(data_loader), total=len(data_loader)):
ids = d["ids"]
token_type_ids = d["token_type_ids"]
mask = d["mask"]
targets = d["targets"]
ids = ids.to(device, dtype=torch.long)
token_type_ids = token_type_ids.to(device, dtype=torch.long)
mask = mask.to(device, dtype=torch.long)
targets = targets.to(device, dtype=torch.long)
outputs = model(
ids=ids,
mask=mask,
token_type_ids=token_type_ids
)
loss = loss_fn(outputs, targets)
eval_loss += loss.item()
pred_labels = torch.argmax(outputs, axis=1)
# (pred_labels == targets).sum().item()
eval_acc += categorical_accuracy(outputs, targets).item()
fin_targets.extend(targets.cpu().detach().numpy().tolist())
fin_outputs.extend(torch.argmax(
outputs, dim=1).cpu().detach().numpy().tolist())
eval_loss /= len(data_loader)
eval_acc /= len(data_loader)
return fin_outputs, fin_targets, eval_loss, eval_acc
def predict_fn(data_loader, model, device, extract_features=False):
model.eval()
fin_outputs = []
extracted_features =[]
with torch.no_grad():
for bi, d in tqdm(enumerate(data_loader), total=len(data_loader)):
ids = d["ids"]
token_type_ids = d["token_type_ids"]
mask = d["mask"]
# targets = d["targets"]
ids = ids.to(device, dtype=torch.long)
token_type_ids = token_type_ids.to(device, dtype=torch.long)
mask = mask.to(device, dtype=torch.long)
outputs = model(
ids=ids,
mask=mask,
token_type_ids=token_type_ids
)
if extract_features:
extracted_features.extend( model.extract_features(
ids=ids,
mask=mask,
token_type_ids=token_type_ids
).cpu().detach().numpy().tolist())
fin_outputs.extend(torch.argmax(
outputs, dim=1).cpu().detach().numpy().tolist())
return fin_outputs, extracted_features
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