"""
##init weights
def weights_init(m):
classname=m.__class__.__name__
if classname.find('Conv') != -1:
xavier(m.weight.data)
xavier(m.bias.data)
"""
#build cnn
SVS = NET.SVS()
SVS.cuda()
#print(SVS)
#SVS.apply(weights_init)
#torch.save(SVS,'/home/lisa/SVSP/SVS.pkl')
optimizer = torch.optim.Adam(SVS.parameters(),
lr=args.LR) # optimize all paramenters
loss_func = nn.L1Loss().cuda()
lossM = nn.MSELoss().cuda()
if __name__ == '__main__':
for epoch in range(args.epochs):
##training------------------------------------------
for step, (batch_x, batch_y) in enumerate(trainloader):
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
args.num_classes = 10 if args.dataset == 'cifar10' else 1000
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
args.loss_exits = [0] * args.num_exits
args.losses_exits = []
args.exiterrors = []
# Create the model
#model = create_model(args.pretrained, args.dataset, args.arch,
# parallel=not args.load_serialized, device_ids=args.gpus)
model = NET.SVS()
compression_scheduler = None
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# capture thresholds for early-exit training
if args.earlyexit_thresholds:
msglogger.info('=> using early-exit threshold values of %s', args.earlyexit_thresholds)
# TODO(barrh): args.deprecated_resume is deprecated since v0.3.1
if args.deprecated_resume:
msglogger.warning('The "--resume" flag is deprecated. Please use "--resume-from=YOUR_PATH" instead.')
if not args.reset_optimizer:
msglogger.warning('If you wish to also reset the optimizer, call with: --reset-optimizer')