Use torch.accelerator in DCGAN example (#1344)

Signed-off-by: dggaytan <diana.gaytan.munoz@intel.com>

Author: dggaytan

dcgan/README.md

@@ -20,31 +20,35 @@ python download.py -c bedroom
 ## Usage
 
 ```
-usage: main.py [-h] --dataset DATASET --dataroot DATAROOT [--workers WORKERS]
- [--batchSize BATCHSIZE] [--imageSize IMAGESIZE] [--nz NZ]
- [--ngf NGF] [--ndf NDF] [--niter NITER] [--lr LR]
- [--beta1 BETA1] [--cuda] [--ngpu NGPU] [--netG NETG]
- [--netD NETD] [--mps]
-
-optional arguments:
+usage: main.py [-h] --dataset DATASET [--dataroot DATAROOT]
+ [--workers WORKERS] [--batchSize BATCHSIZE]
+ [--imageSize IMAGESIZE] [--nz NZ] [--ngf NGF] [--ndf NDF]
+ [--niter NITER] [--lr LR] [--beta1 BETA1] [--dry-run]
+ [--ngpu NGPU] [--netG NETG] [--netD NETD] [--outf OUTF]
+ [--manualSeed MANUALSEED] [--classes CLASSES] [--accel]
+
+options:
  -h, --help show this help message and exit
  --dataset DATASET cifar10 | lsun | mnist |imagenet | folder | lfw | fake
  --dataroot DATAROOT path to dataset
  --workers WORKERS number of data loading workers
- --batchSize BATCHSIZE input batch size
- --imageSize IMAGESIZE the height / width of the input image to network
+ --batchSize BATCHSIZE
+ input batch size
+ --imageSize IMAGESIZE
+ the height / width of the input image to network
  --nz NZ size of the latent z vector
- --ngf NGF number of filters in the generator
- --ndf NDF number of filters in the discriminator
+ --ngf NGF number of generator filters
+ --ndf NDF number of discriminator filters
  --niter NITER number of epochs to train for
  --lr LR learning rate, default=0.0002
  --beta1 BETA1 beta1 for adam. default=0.5
- --cuda enables cuda
- --mps enables macOS GPU
+ --dry-run check a single training cycle works
  --ngpu NGPU number of GPUs to use
  --netG NETG path to netG (to continue training)
  --netD NETD path to netD (to continue training)
  --outf OUTF folder to output images and model checkpoints
- --manualSeed SEED manual seed
+ --manualSeed MANUALSEED
+ manual seed
  --classes CLASSES comma separated list of classes for the lsun data set
+ --accel enables accelerator
 ```

dcgan/main.py

@@ -20,20 +20,19 @@
 parser.add_argument('--batchSize', type=int, default=64, help='input batch size')
 parser.add_argument('--imageSize', type=int, default=64, help='the height / width of the input image to network')
 parser.add_argument('--nz', type=int, default=100, help='size of the latent z vector')
-parser.add_argument('--ngf', type=int, default=64)
-parser.add_argument('--ndf', type=int, default=64)
+parser.add_argument('--ngf', type=int, default=64, help='number of generator filters')
+parser.add_argument('--ndf', type=int, default=64, help='number of discriminator filters')
 parser.add_argument('--niter', type=int, default=25, help='number of epochs to train for')
 parser.add_argument('--lr', type=float, default=0.0002, help='learning rate, default=0.0002')
 parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
-parser.add_argument('--cuda', action='store_true', default=False, help='enables cuda')
 parser.add_argument('--dry-run', action='store_true', help='check a single training cycle works')
 parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')
 parser.add_argument('--netG', default='', help="path to netG (to continue training)")
 parser.add_argument('--netD', default='', help="path to netD (to continue training)")
 parser.add_argument('--outf', default='.', help='folder to output images and model checkpoints')
 parser.add_argument('--manualSeed', type=int, help='manual seed')
 parser.add_argument('--classes', default='bedroom', help='comma separated list of classes for the lsun data set')
-parser.add_argument('--mps', action='store_true', default=False, help='enables macOS GPU training')
+parser.add_argument('--accel', action='store_true', default=False, help='enables accelerator')
 
 opt = parser.parse_args()
 print(opt)
@@ -51,12 +50,13 @@
 
 cudnn.benchmark = True
 
-if torch.cuda.is_available() and not opt.cuda:
- print("WARNING: You have a CUDA device, so you should probably run with --cuda")
+if opt.accel and torch.accelerator.is_available():
+ device = torch.accelerator.current_accelerator()
+else:
+ device = torch.device("cpu")
+
+print(f"Using device: {device}")
 
-if torch.backends.mps.is_available() and not opt.mps:
- print("WARNING: You have mps device, to enable macOS GPU run with --mps")
- 
 if opt.dataroot is None and str(opt.dataset).lower() != 'fake':
  raise ValueError("`dataroot` parameter is required for dataset \"%s\"" % opt.dataset)
 
@@ -106,13 +106,6 @@
 assert dataset
 dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,
  shuffle=True, num_workers=int(opt.workers))
-use_mps = opt.mps and torch.backends.mps.is_available()
-if opt.cuda:
- device = torch.device("cuda:0")
-elif use_mps:
- device = torch.device("mps")
-else:
- device = torch.device("cpu")
 
 ngpu = int(opt.ngpu)
 nz = int(opt.nz)
@@ -158,7 +151,8 @@ def __init__(self, ngpu):
  )
 
  def forward(self, input):
- if input.is_cuda and self.ngpu > 1:
+ 
+ if (input.is_cuda or input.is_xpu) and self.ngpu > 1:
  output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
  else:
  output = self.main(input)
@@ -198,7 +192,7 @@ def __init__(self, ngpu):
  )
 
  def forward(self, input):
- if input.is_cuda and self.ngpu > 1:
+ if (input.is_cuda or input.is_xpu) and self.ngpu > 1:
  output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
  else:
  output = self.main(input)

dcgan/requirements.txt

@@ -1,3 +1,3 @@
 torch
-torchvision==0.20.0
+torchvision
 lmdb

run_python_examples.sh

@@ -58,7 +58,7 @@ case $USE_CUDA in
 esac
 
 function dcgan() {
- uv run main.py --dataset fake $CUDA_FLAG --mps --dry-run || error "dcgan failed"
+ uv run main.py --dataset fake $ACCEL_FLAG --dry-run || error "dcgan failed"
 }
 
 function fast_neural_style() {