def main():
model = VGG(depth=16, init_weights=True, cfg=None)
# model = VGG_shaokai("vgg16")
# model = ConvNet()
# model = ResNet18()
# model = torch.nn.DataParallel(model)
model.load_state_dict(torch.load("./model_pruned/2019-04-09 11:14:52.016169/column-filter-fivelabels-masked_retrain/cifar10_vgg16_retrained_acc_93.960_4rhos_config_vgg16_v2.yaml.pt"))
model.cuda()
criterion = F.cross_entropy
# criterion = CrossEntropyLossMaybeSmooth(smooth_eps=0).cuda()
validate(test_loader, criterion, model)
# test(model, criterion, test_loader)
print("\n------------------------------\n")
print('here')
for name, weight in model.named_parameters():
if (len(weight.size()) == 4 and "shortcut" not in name):
print(name, weight.size())
print('here now')
test_column_sparsity(model)
# test_chanel_sparsity(model)
test_filter_sparsity(model)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
# Model
print('==> Building model..')
net = VGG('VGG13', n)
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
# calculate total number of threshold parameters
no_thresh_p = 0
for name, p in net.named_parameters():
check = name.split('.')[-1]
if check == 'thresh':
no_thresh_p += p.numel()
lam = 1 / no_thresh_p
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
scheduler = CyclicLR(optimizer)
if args.resume:
# Load checkpoint.