File size: 3,953 Bytes
3e1334e 241abc0 337c718 3e1334e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
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())
print("0",outputs)
print("1",torch.argmax(outputs, dim=1))
print("2",torch.argmax(outputs, dim=1).cpu())
print("3",torch.argmax(outputs, dim=1).cpu().numpy())
fin_outputs.extend(torch.argmax(
outputs, dim=1).cpu().detach().numpy().tolist())
return fin_outputs, extracted_features
|