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Update classify.py #60

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69d04fd
Update classify.py
Njoom Nov 19, 2025
c4796a0
Update classify.py
Njoom Nov 19, 2025
f5a3aa8
Update classify.py
Njoom Nov 19, 2025
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66 changes: 42 additions & 24 deletions classify.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,6 +81,18 @@ def run_training(args):
model = models.__dict__[args.arch](args.pretrained)

model = torch.nn.DataParallel(model).cuda()
# ===== NEWEdit: adjust classifier to 2 classes (real/fake) =====
NUM_CLASSES = 2
base_model = model.module # unwrap DataParallel
# DRN uses a Conv2d "fc" with out_dim -> num_classes, then avgpoo
in_channels = base_model.out_dim # this is channels[-1]

base_model.fc = nn.Conv2d(in_channels, NUM_CLASSES, kernel_size=1, stride=1, padding=0, bias=True)
nn.init.kaiming_normal_(base_model.fc.weight, mode='fan_out', nonlinearity='relu')
if base_model.fc.bias is not None:
nn.init.constant_(base_model.fc.bias, 0.)
# ===========================================================


best_prec1 = 0

Expand All @@ -100,14 +112,14 @@ def run_training(args):
cudnn.benchmark = True

# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
traindir = os.path.join(args.data, 'training')
valdir = os.path.join(args.data, 'validation')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])

train_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(traindir, transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
Expand All @@ -117,7 +129,7 @@ def run_training(args):

val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Scale(256),
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
Expand Down Expand Up @@ -162,6 +174,17 @@ def test_model(args):
model = models.__dict__[args.arch](args.pretrained)

model = torch.nn.DataParallel(model).cuda()
# ===== NEW: adjust classifier to 2 classes (real/fake) =====
NUM_CLASSES = 2
base_model = model.module # unwrap DataParallel
# DRN uses a Conv2d "fc" with out_dim -> num_classes, then avgpool
in_channels = base_model.out_dim # this is channels[-1]
base_model.fc = nn.Conv2d(in_channels, NUM_CLASSES, kernel_size=1, stride=1, padding=0, bias=True)
nn.init.kaiming_normal_(base_model.fc.weight, mode='fan_out', nonlinearity='relu')
if base_model.fc.bias is not None:
nn.init.constant_(base_model.fc.bias, 0.)
# ===========================================================


if args.resume:
if os.path.isfile(args.resume):
Expand All @@ -178,12 +201,12 @@ def test_model(args):
cudnn.benchmark = True

# Data loading code
valdir = os.path.join(args.data, 'val')
valdir = os.path.join(args.data, 'validation')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])

t = transforms.Compose([
transforms.Scale(args.scale_size),
transforms.Resize(256),,
transforms.CenterCrop(args.crop_size),
transforms.ToTensor(),
normalize])
Expand Down Expand Up @@ -212,17 +235,14 @@ def train(args, train_loader, model, criterion, optimizer, epoch):
# measure data loading time
data_time.update(time.time() - end)

target = target.cuda(async=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)

# compute output
output = model(input_var)
loss = criterion(output, target_var)
output = model(input)
loss = criterion(output, target)

losses.update(loss.item(), input.size(0))

# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))

Expand Down Expand Up @@ -257,17 +277,15 @@ def validate(args, val_loader, model, criterion):

end = time.time()
for i, (input, target) in enumerate(val_loader):
target = target.cuda(async=True)
input_var = torch.autograd.Variable(input, volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
input = input.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)

with torch.no_grad():
output = model(input)
loss = criterion(output, target)

# compute output
output = model(input_var)
loss = criterion(output, target_var)
losses.update(loss.item(), input.size(0))

# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))

Expand Down