Datasets:
jblitzar
/
github-python

Dataset card Data Studio Files
xet
Community
text
stringlengths
0
93.6k
outputs, features = model(imgs)
CE_loss = CE_criterion(outputs, targets)
lsce_loss = lsce_criterion(outputs, targets)
loss = 2 * lsce_loss + CE_loss
loss.backward() # make sure to do a full forward pass
optimizer.second_step(zero_grad=True)
train_loss += loss
_, predicts = torch.max(outputs, 1)
correct_num = torch.eq(predicts, targets).sum()
correct_sum += correct_num
train_acc = correct_sum.float() / float(train_dataset.__len__())
train_loss = train_loss / iter_cnt
elapsed = (time() - start_time) / 60
print('[Epoch %d] Train time:%.2f, Training accuracy:%.4f. Loss: %.3f LR:%.6f' %
(i, elapsed, train_acc, train_loss, optimizer.param_groups[0]["lr"]))
scheduler.step()
pre_labels = []
gt_labels = []
with torch.no_grad():
val_loss = 0.0
iter_cnt = 0
bingo_cnt = 0
model.eval()
for batch_i, (imgs, targets) in enumerate(val_loader):
outputs, features = model(imgs.cuda())
targets = targets.cuda()
CE_loss = CE_criterion(outputs, targets)
loss = CE_loss
val_loss += loss
iter_cnt += 1
_, predicts = torch.max(outputs, 1)
correct_or_not = torch.eq(predicts, targets)
bingo_cnt += correct_or_not.sum().cpu()
pre_labels += predicts.cpu().tolist()
gt_labels += targets.cpu().tolist()
val_loss = val_loss / iter_cnt
val_acc = bingo_cnt.float() / float(val_num)
val_acc = np.around(val_acc.numpy(), 4)
f1 = f1_score(pre_labels, gt_labels, average='macro')
total_socre = 0.67 * f1 + 0.33 * val_acc
print("[Epoch %d] Validation accuracy:%.4f, Loss:%.3f, f1 %4f, score %4f" % (
i, val_acc, val_loss, f1, total_socre))
if val_acc > 0.907 and val_acc > best_acc:
torch.save({'iter': i,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(), },
os.path.join('./checkpoint', "epoch" + str(i) + "_acc" + str(val_acc) + ".pth"))
print('Model saved.')
if val_acc > best_acc:
best_acc = val_acc
print("best_acc:" + str(best_acc))
if __name__ == "__main__":
run_training()
# <FILESEP>
#!/usr/bin/env python3
# This file is covered by the LICENSE file in the root of this project.
import argparse
import os
import yaml
from auxiliary.laserscan import LaserScan, SemLaserScan
from auxiliary.laserscancomp import LaserScanComp
if __name__ == '__main__':
parser = argparse.ArgumentParser("./compare.py")
parser.add_argument(
'--dataset', '-d',
type=str,
required=True,
help='Dataset to visualize. No Default',
)
parser.add_argument(
'--labels',
required=True,
nargs='+',
help='Labels A to visualize. No Default',
)
parser.add_argument(
'--config', '-c',
type=str,
required=False,
default="config/semantic-kitti.yaml",
help='Dataset config file. Defaults to %(default)s',
)
parser.add_argument(