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main.py
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import torch
from torch import nn
import numpy as np
import utils
import flow
import train
import source
#from profilehooks import profile
import math
import h5py
import argparse
torch.manual_seed(42)
parser = argparse.ArgumentParser(description='')
parser.add_argument("-folder", default=None)
parser.add_argument("-name", default=None, help='name of flow')
group = parser.add_argument_group('learning parameters')
group.add_argument("-epochs", type=int, default=1000, help="")
group.add_argument("-batch", type=int, default=32, help="")
group.add_argument("-cuda", type=int, default=-1, help="use GPU")
group.add_argument("-double", action='store_true', help="use float64")
group.add_argument("-lr", type=float, default=0.001, help="learning rate")
group.add_argument("-savePeriod", type=int, default=10, help="")
group.add_argument("-alpha", type=float, default=1, help="")
group.add_argument("-skipHMC", action='store_true', help="")
group = parser.add_argument_group('network parameters')
group.add_argument("-load", action='store_true', help="if load from folder")
group.add_argument("-nlayers", type=int, default=4, help="# of layers in RNVP block")
group.add_argument("-nmlp",type = int, default=2,help="# of layers in MLP")
group.add_argument("-nhidden", type=int, default=32, help="")
group.add_argument("-nrepeat", type=int, default=2, help="repeat of mera block")
group.add_argument("-depthMERA", type=int, default=-1, help="maximum depth of MERA flow")
group.add_argument("-symmetry", action='store_true', help="")
group = parser.add_argument_group('Ising target parameters')
#
group.add_argument("-L",type=int, default=4,help="linear size")
group.add_argument("-d",type=int, default=2,help="dimension")
group.add_argument("-T",type=float, default=2.269185314213022, help="Temperature")
args = parser.parse_args()
if args.folder is None:
rootFolder = './opt/replyMERA_ising_' + str(args.L)+"_T_"+str(args.T)+"_depthLevel_"+str(args.depthMERA)+"_MERA"+'_l'+str(args.nlayers)+'_M'+str(args.nmlp)+'_H'+str(args.nhidden)+'_R'+str(args.nrepeat)+"/"
print("No specified saving path, using",rootFolder)
else:
rootFolder = args.folder
if rootFolder[-1] != '/':
rootFolder += '/'
utils.createWorkSpace(rootFolder)
if args.load:
with h5py.File(rootFolder+"/parameters.hdf5","r") as f:
epochs = int(np.array(f["epochs"]))
batch = int(np.array(f["batch"]))
cuda = int(np.array(f["cuda"]))
double = bool(np.array(f["double"]))
lr = float(np.array(f["lr"]))
savePeriod = int(np.array(f["savePeriod"]))
nlayers = int(np.array(f["nlayers"]))
nmlp = int(np.array(f["nmlp"]))
nhidden = int(np.array(f["nhidden"]))
nrepeat = int(np.array(f["nrepeat"]))
depthMERA = int(np.array(f["depthMERA"]))
L = int(np.array(f["L"]))
d = int(np.array(f["d"]))
T = float(np.array(f["T"]))
else:
epochs = args.epochs
batch = args.batch
cuda = args.cuda
double = args.double
lr = args.lr
savePeriod = args.savePeriod
nlayers = args.nlayers
nmlp = args.nmlp
nhidden = args.nhidden
nrepeat = args.nrepeat
depthMERA = args.depthMERA
L = args.L
d = args.d
T = args.T
with h5py.File(rootFolder+"parameters.hdf5","w") as f:
f.create_dataset("epochs",data=args.epochs)
f.create_dataset("batch",data=args.batch)
f.create_dataset("cuda",data=args.cuda)
f.create_dataset("double",data=args.double)
f.create_dataset("lr",data=args.lr)
f.create_dataset("savePeriod",data=args.savePeriod)
f.create_dataset("nlayers",data=args.nlayers)
f.create_dataset("nmlp",data=args.nmlp)
f.create_dataset("nhidden",data=args.nhidden)
f.create_dataset("nrepeat",data=args.nrepeat)
f.create_dataset("depthMERA",data=args.depthMERA)
f.create_dataset("L",data=args.L)
f.create_dataset("d",data=args.d)
f.create_dataset("T",data=args.T)
device = torch.device("cpu" if cuda<0 else "cuda:"+str(cuda))
if double:
dtype = torch.float64
else:
dtype = torch.float32
target = source.Ising(L, d, T)
target = target.to(device=device,dtype=dtype)
if args.name is None:
name = "SymmMERA"+'_l'+str(nlayers)+'_M'+str(nmlp)+'H'+str(nhidden)+'_R'+str(nrepeat)+'_Ising'
else:
name = args.name
if args.symmetry:
def op(x):
return -x
sym = [op]
print("Using symmetry")
else:
sym = None
if depthMERA == -1:
depthMERA = None
fw = train.symmetryMERAInit(L,d,nlayers,nmlp,nhidden,nrepeat,sym,device,dtype,name,depthMERA=depthMERA)
#fw = train.symmetryMERAInit(L,d,nlayers,nmlp,nhidden,nrepeat,sym,device,dtype,name)
if args.load:
import os
import glob
name = max(glob.iglob(rootFolder+'savings/*.saving'), key=os.path.getctime)
print("load saving at "+name)
saved = torch.load(name)
fw.load(saved)
def measure(x):
p = torch.sigmoid(2.*x).reshape(-1, target.nvars[0])
s = 2.*p.data.cpu().numpy() - 1.
sf = (s.mean(axis=1))**2 - (s**2).sum(axis=1)/target.nvars[0]**2 +1./target.nvars[0] #structure factor
return sf
LOSS,ZACC,ZOBS,XACC,XOBS = train.learnInterface(target,fw,batch,epochs,save=True,saveSteps = savePeriod,savePath=rootFolder,measureFn = measure,alpha=args.alpha,skipHMC=args.skipHMC)
#LOSS,ZACC,ZOBS,XACC,XOBS = train.learnInterface(target,fw,batch,epochs,save=True,saveSteps = savePeriod,savePath=rootFolder,measureFn = measure)