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car-detection-bayes
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simplify train.py

This commit is contained in:
Glenn Jocher 2018-09-19 04:21:46 +02:00
parent 68de92f1a1
commit 29fbcb059f
1 changed files with 33 additions and 40 deletions
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train.py
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@ -90,7 +90,7 @@ def main(opt):
modelinfo(model)
t0, t1 = time.time(), time.time()
print('%10s' * 16 % (
'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nGT', 'TP', 'FP', 'FN', 'time'))
'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nTargets', 'TP', 'FP', 'FN', 'time'))
for epoch in range(opt.epochs):
epoch += start_epoch
@ -115,56 +115,49 @@ def main(opt):
metrics = torch.zeros(4, num_classes)
for i, (imgs, targets) in enumerate(dataloader):
n = opt.batch_size # number of pictures at a time
for j in range(int(len(imgs) / n)):
targets_j = targets[j * n:j * n + n]
nGT = sum([len(x) for x in targets_j])
if nGT < 1:
continue
if sum([len(x) for x in targets]) < 1: # if no targets continue
continue
loss = model(imgs[j * n:j * n + n].to(device), targets_j, requestPrecision=True, epoch=epoch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss = model(imgs.to(device), targets, requestPrecision=True, epoch=epoch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
ui += 1
metrics += model.losses['metrics']
for key, val in model.losses.items():
rloss[key] = (rloss[key] * ui + val) / (ui + 1)
ui += 1
metrics += model.losses['metrics']
for key, val in model.losses.items():
rloss[key] = (rloss[key] * ui + val) / (ui + 1)
# Precision
precision = metrics[0] / (metrics[0] + metrics[1] + 1e-16)
k = (metrics[0] + metrics[1]) > 0
if k.sum() > 0:
mean_precision = precision[k].mean()
else:
mean_precision = 0
# Precision
precision = metrics[0] / (metrics[0] + metrics[1] + 1e-16)
k = (metrics[0] + metrics[1]) > 0
if k.sum() > 0:
mean_precision = precision[k].mean()
else:
mean_precision = 0
# Recall
recall = metrics[0] / (metrics[0] + metrics[2] + 1e-16)
k = (metrics[0] + metrics[2]) > 0
if k.sum() > 0:
mean_recall = recall[k].mean()
else:
mean_recall = 0
# Recall
recall = metrics[0] / (metrics[0] + metrics[2] + 1e-16)
k = (metrics[0] + metrics[2]) > 0
if k.sum() > 0:
mean_recall = recall[k].mean()
else:
mean_recall = 0
s = ('%10s%10s' + '%10.3g' * 14) % (
'%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'],
rloss['loss'], mean_precision, mean_recall, model.losses['nGT'], model.losses['TP'],
model.losses['FP'], model.losses['FN'], time.time() - t1)
t1 = time.time()
print(s)
# if i == 1:
# return
s = ('%10s%10s' + '%10.3g' * 14) % (
'%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'],
rloss['loss'], mean_precision, mean_recall, model.losses['nT'], model.losses['TP'],
model.losses['FP'], model.losses['FN'], time.time() - t1)
t1 = time.time()
print(s)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '\n')
# Update best loss
loss_per_target = rloss['loss'] / rloss['nGT']
loss_per_target = rloss['loss'] / rloss['nT']
if loss_per_target < best_loss:
best_loss = loss_per_target