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TEST: use autocast for mixed-precision training

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Zhengyi Chen 2024-03-04 18:36:40 +00:00
parent 94867bd8bf
commit 2d31162c58
1 changed files with 22 additions and 21 deletions
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43
train.py
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@ -243,32 +243,36 @@ def train_one_epoch(
kpoint = kpoint.type(torch.FloatTensor)
gt_count_whole = gt_count_whole.type(torch.FloatTensor).unsqueeze(1)
batch_size = img.size(0)
# fpass
# send to device
if device is not None:
img = img.to(device)
kpoint = kpoint.to(device)
gt_count_whole = gt_count_whole.to(device)
device_type = device.type
elif torch.cuda.is_available():
img = img.cuda()
kpoint = kpoint.cuda()
gt_count_whole = gt_count_whole.cuda()
out, gt_count = model(img, kpoint)
device_type = "cuda"
# loss
loss = criterion(out, gt_count) # wrt. transformer
loss += (
F.mse_loss( # stn: info retainment
gt_count.view(batch_size, -1).sum(axis=1, keepdim=True),
gt_count_whole)
+ F.threshold( # stn: perspective correction
gt_count.view(batch_size, -1).var(dim=1).mean(),
threshold=loss.item(),
value=loss.item()
with torch.autocast(device_type):
# fpass
out, gt_count = model(img, kpoint)
# loss
loss = criterion(out, gt_count) # wrt. transformer
loss += (
F.mse_loss( # stn: info retainment
gt_count.view(batch_size, -1).sum(axis=1, keepdim=True),
gt_count_whole)
+ F.threshold( # stn: perspective correction
gt_count.view(batch_size, -1).var(dim=1).mean(),
threshold=loss.item(),
value=loss.item()
)
)
)
# free grad from mem
optimizer.zero_grad()
optimizer.zero_grad(set_to_none=True)
# bpass
loss.backward()
@ -276,10 +280,6 @@ def train_one_epoch(
# optimizer
optimizer.step()
# periodic message
# if i % args.print_freq == 0:
# print("Epoch {}: {}/{}".format(epoch, i, len(train_loader)))
if args.debug:
break
@ -324,9 +324,10 @@ def valid_one_epoch(test_loader, model, device, args):
mse += diff ** 2
if i % 5 == 0:
print("[valid_one_epoch] {} | Gt {:.2f} Pred {:.4f} | mae {:.4f} mse {:.4f} |".format(
fname[0], torch.sum(gt_count_whole).item(), torch.sum(pred_count).item(),
mae, mse
print("[valid_one_epoch] {} | Gt {:.2f} Pred {:.4f} |".format(
fname[0],
torch.sum(gt_count_whole).item(),
torch.sum(pred_count).item()
))
mae = mae * 1.0 / (len(test_loader) * batch_size)