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fixed scaler

pull/13483/head
Bala-Vignesh-Reddy 2025-01-07 18:57:49 +05:30
parent c510954d17
commit 1b82b72ed5
2 changed files with 3 additions and 3 deletions
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2
segment/train.py
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@ -320,7 +320,7 @@ def train(hyp, opt, device, callbacks):
maps = np.zeros(nc) # mAP per class maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move scheduler.last_epoch = start_epoch - 1 # do not move
scaler = torch.amp.GradScaler(enabled=amp) scaler = amp.GradScaler(enabled=device.type != "cpu")
stopper, stop = EarlyStopping(patience=opt.patience), False stopper, stop = EarlyStopping(patience=opt.patience), False
compute_loss = ComputeLoss(model, overlap=overlap) # init loss class compute_loss = ComputeLoss(model, overlap=overlap) # init loss class
# callbacks.run('on_train_start') # callbacks.run('on_train_start')

4
train.py
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@ -352,7 +352,7 @@ def train(hyp, opt, device, callbacks):
maps = np.zeros(nc) # mAP per class maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda) scaler = amp.GradScaler(enabled=device.type != "cpu")
stopper, stop = EarlyStopping(patience=opt.patience), False stopper, stop = EarlyStopping(patience=opt.patience), False
compute_loss = ComputeLoss(model) # init loss class compute_loss = ComputeLoss(model) # init loss class
callbacks.run("on_train_start") callbacks.run("on_train_start")
@ -409,7 +409,7 @@ def train(hyp, opt, device, callbacks):
imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False) imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
# Forward # Forward
with torch.amp.autocast("cuda", enabled=amp): with torch.amp.autocast("cuda", enabled=device.type != "cpu"):
pred = model(imgs) # forward pred = model(imgs) # forward
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
if RANK != -1: if RANK != -1: