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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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21
train.py
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@ -243,17 +243,21 @@ def train_one_epoch(
kpoint = kpoint.type(torch.FloatTensor) kpoint = kpoint.type(torch.FloatTensor)
gt_count_whole = gt_count_whole.type(torch.FloatTensor).unsqueeze(1) gt_count_whole = gt_count_whole.type(torch.FloatTensor).unsqueeze(1)
batch_size = img.size(0) batch_size = img.size(0)
# fpass # send to device
if device is not None: if device is not None:
img = img.to(device) img = img.to(device)
kpoint = kpoint.to(device) kpoint = kpoint.to(device)
gt_count_whole = gt_count_whole.to(device) gt_count_whole = gt_count_whole.to(device)
device_type = device.type
elif torch.cuda.is_available(): elif torch.cuda.is_available():
img = img.cuda() img = img.cuda()
kpoint = kpoint.cuda() kpoint = kpoint.cuda()
gt_count_whole = gt_count_whole.cuda() gt_count_whole = gt_count_whole.cuda()
out, gt_count = model(img, kpoint) device_type = "cuda"
with torch.autocast(device_type):
# fpass
out, gt_count = model(img, kpoint)
# loss # loss
loss = criterion(out, gt_count) # wrt. transformer loss = criterion(out, gt_count) # wrt. transformer
loss += ( loss += (
@ -268,7 +272,7 @@ def train_one_epoch(
) )
# free grad from mem # free grad from mem
optimizer.zero_grad() optimizer.zero_grad(set_to_none=True)
# bpass # bpass
loss.backward() loss.backward()
@ -276,10 +280,6 @@ def train_one_epoch(
# optimizer # optimizer
optimizer.step() optimizer.step()
# periodic message
# if i % args.print_freq == 0:
# print("Epoch {}: {}/{}".format(epoch, i, len(train_loader)))
if args.debug: if args.debug:
break break
@ -324,9 +324,10 @@ def valid_one_epoch(test_loader, model, device, args):
mse += diff ** 2 mse += diff ** 2
if i % 5 == 0: if i % 5 == 0:
print("[valid_one_epoch] {} | Gt {:.2f} Pred {:.4f} | mae {:.4f} mse {:.4f} |".format( print("[valid_one_epoch] {} | Gt {:.2f} Pred {:.4f} |".format(
fname[0], torch.sum(gt_count_whole).item(), torch.sum(pred_count).item(), fname[0],
mae, mse torch.sum(gt_count_whole).item(),
torch.sum(pred_count).item()
)) ))
mae = mae * 1.0 / (len(test_loader) * batch_size) mae = mae * 1.0 / (len(test_loader) * batch_size)