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car-detection-bayes
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burnin merged with prebias

This commit is contained in:
Glenn Jocher 2020-04-02 14:08:21 -07:00
parent 27c7334e81
commit 9155ef3f4f
1 changed files with 14 additions and 28 deletions
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train.py
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@ -137,7 +137,8 @@ def train():
model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
# Scheduler https://github.com/ultralytics/yolov3/issues/238
lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.95 + 0.05 # cosine https://arxiv.org/pdf/1812.01187.pdf
lf = lambda x: (((1 + math.cos(
x * math.pi / epochs)) / 2) ** 1.0) * 0.95 + 0.05 # cosine https://arxiv.org/pdf/1812.01187.pdf
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
# scheduler = lr_scheduler.MultiStepLR(optimizer, [round(epochs * x) for x in [0.8, 0.9]], 0.1, start_epoch - 1)
@ -193,7 +194,7 @@ def train():
# Model parameters
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 0.0 # giou loss ratio (obj_loss = 1.0 or giou)
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
# Model EMA
@ -201,7 +202,7 @@ def train():
# Start training
nb = len(dataloader) # number of batches
prebias = start_epoch == 0
n_burn = max(3 * nb, 300) # burn-in iterations, max(3 epochs, 300 iterations)
maps = np.zeros(nc) # mAP per class
# torch.autograd.set_detect_anomaly(True)
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
@ -211,21 +212,6 @@ def train():
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
# Prebias
if prebias:
ne = 3 # number of prebias epochs
ps = 0.1, 0.9 # prebias settings (lr=0.1, momentum=0.9)
if epoch == ne:
ps = hyp['lr0'], hyp['momentum'] # normal training settings
model.gr = 1.0 # giou loss ratio (obj_loss = giou)
print_model_biases(model)
prebias = False
# Bias optimizer settings
optimizer.param_groups[2]['lr'] = ps[0]
if optimizer.param_groups[2].get('momentum') is not None: # for SGD but not Adam
optimizer.param_groups[2]['momentum'] = ps[1]
# Update image weights (optional)
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
@ -240,17 +226,17 @@ def train():
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
# Hyperparameter Burn-in
n_burn = 300 # number of burn-in batches
# Burn-in
if ni <= n_burn:
g = (ni / n_burn) ** 2 # gain
for x in model.named_modules(): # initial stats may be poor, wait to track
if x[0].endswith('BatchNorm2d'):
x[1].track_running_stats = ni == n_burn
for x in optimizer.param_groups:
x['lr'] = x['initial_lr'] * lf(epoch) * g # gain rises from 0 - 1
model.gr = np.interp(ni, [0, n_burn], [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
if ni == n_burn: # burnin complete
print_model_biases(model)
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, [0, n_burn], [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = hyp['momentum'] * g
x['momentum'] = np.interp(ni, [0, n_burn], [0.9, hyp['momentum']])
# Multi-Scale training
if opt.multi_scale:
@ -353,7 +339,7 @@ def train():
torch.save(chkpt, last)
# Save best checkpoint
if best_fitness == fi:
if (best_fitness == fi) and not final_epoch:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)