torch.set_default_dtype(argPrec)
cvt = lambda x: x.type(argPrec).to(device, non_blocking=True)
# load model
logger.info(' ')
logger.info("loading model: {:}".format(args.resume))
logger.info(' ')
checkpt = torch.load(args.resume,
map_location=lambda storage, loc: storage)
m = checkpt['args'].m
alph = checkpt['args'].alph
nTh = checkpt['args'].nTh
data = checkpt['args'].data
prob, x0, _, xInit = initProb(data, 10, 11, var0=0.5, alph=alph, cvt=cvt)
d = x0.size(1)
net = Phi(nTh=nTh, m=m, d=d, alph=alph) # the phi aka the value function
net.load_state_dict(checkpt["state_dict"])
net = net.to(argPrec).to(device)
nt = args.nt
xtarget = prob.xtarget
strTitle = 'eval_' + os.path.basename(args.resume)[:-12]
with torch.no_grad():
net.eval()
sPath = args.save + '/figs/' + strTitle + '.png'
print(i, err.item())
if nIters / 4 == 0: # lower lr
lr = lr * 0.1
print('lr: ', lr)
return ubest
if __name__ == '__main__':
alphG = args.alph[0]
prob, _, _, xInit = initProb(
args.data,
10,
10,
var0=1.0,
cvt=cvt,
alph=[alphG, args.alph[1], args.alph[2], 0.0, 0.0, 0.0])
prob.train()
d = xInit.numel()
strTitle = 'baseline_' + args.data + '_{:}_{:}_{:}'.format(
int(alphG), int(prob.alph_Q), int(prob.alph_W))
x0 = xInit # x0 can be more than one point
traj = cvt(torch.zeros(x0.size(0), d, nt + 1))
h = 1. / nt
for i in range(x0.size(0)):
z0 = x0[i, :]
if args.resume is not None: # load a previous model
str(args.gpu) if torch.cuda.is_available() else 'cpu')
print('device: ', device)
if __name__ == '__main__':
torch.set_default_dtype(argPrec)
cvt = lambda x: x.type(argPrec).to(device, non_blocking=True)
n_train = args.n_train
nVal = n_train
alph = args.alph
# set-up problem
prob, x0, x0v, xInit = initProb(args.data,
n_train,
nVal,
var0=args.var0,
alph=alph,
cvt=cvt)
# set-up model
d = x0.size(1) # dimension of the problem
m = args.m
nt = args.nt
nt_val = args.nt_val
nTh = args.nTh
tspan = [0.0, 1.0] # throughout we solve on [ 0 , T=1 ]
net = Phi(nTh=nTh, d=d, m=m, alph=alph)
net = net.to(argPrec).to(device)
# resume training on a model that's already had some training