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27 changes: 15 additions & 12 deletions dcgan/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -20,31 +20,34 @@ 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]
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] [--xpu] [--dry-run]
[--ngpu NGPU] [--netG NETG] [--netD NETD] [--outf OUTF] [--manualSeed MANUALSEED] [--classes CLASSES] [--mps]

optional arguments:
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
--ndf NDF
--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
--xpu enables XPU training
--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
--mps enables macOS GPU training
```
20 changes: 20 additions & 0 deletions dcgan/main.py
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Expand Up @@ -26,6 +26,7 @@
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('--xpu', action='store_true', default=False, help='enables XPU training')
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)")
Expand Down Expand Up @@ -57,6 +58,9 @@
if torch.backends.mps.is_available() and not opt.mps:
print("WARNING: You have mps device, to enable macOS GPU run with --mps")

if torch.xpu.is_available() and not opt.xpu:
print("WARNING: You have XPU device, to enable XPU training run with --xpu")

if opt.dataroot is None and str(opt.dataset).lower() != 'fake':
raise ValueError("`dataroot` parameter is required for dataset \"%s\"" % opt.dataset)

Expand Down Expand Up @@ -107,19 +111,29 @@
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()
use_xpu = opt.xpu and torch.xpu.is_available()

##xpu support
if opt.cuda:
device = torch.device("cuda:0")
elif use_mps:
device = torch.device("mps")
elif use_xpu:
device = torch.device("xpu")
print("Number of devices: ", torch.xpu.device_count())
else:
device = torch.device("cpu")


ngpu = int(opt.ngpu)
nz = int(opt.nz)
ngf = int(opt.ngf)
ndf = int(opt.ndf)


print("DEVICE TO USE: ", device)
print(ngpu)

# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
Expand Down Expand Up @@ -160,6 +174,8 @@ def __init__(self, ngpu):
def forward(self, input):
if input.is_cuda and self.ngpu > 1:
output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
if input.is_xpu and self.ngpu > 1:
output = nn.DataParallel(self.main, input, range(self.ngpu))
else:
output = self.main(input)
return output
Expand Down Expand Up @@ -200,6 +216,8 @@ def __init__(self, ngpu):
def forward(self, input):
if input.is_cuda and self.ngpu > 1:
output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
if input.is_xpu and self.ngpu > 1:
output = nn.DataParallel(self.main, input, range(self.ngpu))
else:
output = self.main(input)

Expand Down Expand Up @@ -281,3 +299,5 @@ def forward(self, input):
# do checkpointing
torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' % (opt.outf, epoch))
torch.save(netD.state_dict(), '%s/netD_epoch_%d.pth' % (opt.outf, epoch))