Datasets:
jblitzar
/
github-python-corpus

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for y, m in enumerate(model.classifier.modules()):
if isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
m.bias.data.fill_(0.1)
model.top_layer.bias.data.fill_(0.1)
if args.fc6_8:
# freeze some layers
for param in model.features.parameters():
param.requires_grad = False
# unfreeze batchnorm scaling
if args.train_batchnorm:
for layer in model.modules():
if isinstance(layer, torch.nn.BatchNorm2d):
for param in layer.parameters():
param.requires_grad = True
# set optimizer
optimizer = torch.optim.SGD(
filter(lambda x: x.requires_grad, model.parameters()),
lr=args.lr,
momentum=0.9,
weight_decay=args.wd,
)
criterion = nn.BCEWithLogitsLoss(reduction='none')
print('Start training')
it = 0
losses = AverageMeter()
while it < args.nit:
it = train(
loader,
model,
optimizer,
criterion,
args.fc6_8,
losses,
it=it,
total_iterations=args.nit,
stepsize=args.stepsize,
)
print('Evaluation')
if args.eval_random_crops:
transform_eval = [
transforms.RandomHorizontalFlip(),
transforms.RandomResizedCrop(224, scale=(args.min_scale, args.max_scale), ratio=(1, 1)),
transforms.ToTensor(),
normalize,
]
else:
transform_eval = [
transforms.Resize(256),
transforms.TenCrop(224),
transforms.Lambda(lambda crops: torch.stack([normalize(transforms.ToTensor()(crop)) for crop in crops]))
]
print('Train set')
train_dataset = VOC2007_dataset(args.vocdir, split=args.split, transform=transforms.Compose(transform_eval))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=1,
shuffle=False,
num_workers=24,
pin_memory=True,
)
evaluate(train_loader, model, args.eval_random_crops)
print('Test set')
test_dataset = VOC2007_dataset(args.vocdir, split=args.test, transform=transforms.Compose(transform_eval))
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1,
shuffle=False,
num_workers=24,
pin_memory=True,
)
evaluate(test_loader, model, args.eval_random_crops)
def evaluate(loader, model, eval_random_crops):
model.eval()
gts = []
scr = []
for crop in range(9 * eval_random_crops + 1):
for i, (input, target) in enumerate(loader):
# move input to gpu and optionally reshape it
if len(input.size()) == 5:
bs, ncrops, c, h, w = input.size()
input = input.view(-1, c, h, w)
input = input.cuda(non_blocking=True)
# forward pass without grad computation
with torch.no_grad():
output = model(input)
if crop < 1 :
scr.append(torch.sum(output, 0, keepdim=True).cpu().numpy())
gts.append(target)
else: