Skip to content

Fix device mismatch issue in #1071 #1073

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Sep 30, 2022
Merged

Fix device mismatch issue in #1071 #1073

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
c83a8eb
fix device mismatch issue #1071
hudeven Sep 29, 2022
c6f817a
fix device mismatch issue #1071
hudeven Sep 29, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
25 changes: 10 additions & 15 deletions imagenet/main.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,18 +7,18 @@
from enum import Enum

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
from torch.optim.lr_scheduler import StepLR
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import torchvision.transforms as transforms
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import Subset

model_names = sorted(name for name in models.__dict__
Expand Down Expand Up @@ -275,13 +275,12 @@ def main_worker(gpu, ngpus_per_node, args):
train_sampler.set_epoch(epoch)

# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args)
train(train_loader, model, criterion, optimizer, epoch, device, args)

# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args)

scheduler.step()


# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
Expand All @@ -299,7 +298,7 @@ def main_worker(gpu, ngpus_per_node, args):
}, is_best)


def train(train_loader, model, criterion, optimizer, epoch, args):
def train(train_loader, model, criterion, optimizer, epoch, device, args):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
Expand All @@ -318,13 +317,9 @@ def train(train_loader, model, criterion, optimizer, epoch, args):
# measure data loading time
data_time.update(time.time() - end)

if args.gpu is not None and torch.cuda.is_available():
images = images.cuda(args.gpu, non_blocking=True)
elif not args.gpu and torch.cuda.is_available():
target = target.cuda(args.gpu, non_blocking=True)
elif torch.backends.mps.is_available():
images = images.to('mps')
target = target.to('mps')
# move data to the same device as model
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)

# compute output
output = model(images)
Expand Down