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variation.py
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import numpy as np
import argparse
import torch
from torch import nn
import flow
import source
import train
import utils
import math
import h5py
#torch.manual_seed(42)
parser = argparse.ArgumentParser(description='')
group = parser.add_argument_group('Learning parameters')
parser.add_argument("-folder", default=None,help = "Folder to save and load")
group.add_argument("-epochs", type=int, default=5000, help="Number of epoches to train")
group.add_argument("-batch", type=int, default=200, help="Batch size of the training")
group.add_argument("-cuda", type=int, default=-1, help="Which device to use with -1 standing for CPU, number bigger than -1 is N.O. of GPU")
group.add_argument("-lr", type=float, default=0.001, help="Learning rate")
group.add_argument("-save", action='store_true',help="If save or not")
group.add_argument("-load", action='store_true' ,help="If load or not")
group.add_argument("-save_period", type=int, default=10, help="Save after how many steps")
group.add_argument("-K",type=float, default=300, help="Temperature")
group.add_argument("-double", action='store_true',help="Use double or single")
group = parser.add_argument_group('Network parameters')
group.add_argument("-hdim", type=int, default=128, help="Hidden dimension of mlps")
group.add_argument("-numFlow", type=int, default=1, help="Number of flows layers")
group.add_argument("-nlayers", type=int, default=8, help="Number of mlps layers in the rnvp")
group.add_argument("-nmlp", type=int, default=2, help="Number of layers in each mlp")
group.add_argument("-shift",action="store_true",help="Shift latent variable or not")
group.add_argument("-relax",action="store_true",help="Trainable latent p or not")
group = parser.add_argument_group('Target parameters')
group.add_argument("-source",default="Ring2d",help="Which source to train")
args = parser.parse_args()
device = torch.device("cpu" if args.cuda<0 else "cuda:"+str(args.cuda))
if args.folder is None:
rootFolder = './opt/variationModel_source_'+str(args.source)+'_Shift_'+str(args.shift) +"_T_"+str(args.K)+"_depthLevel_"+str(args.numFlow)+'_l'+str(args.nlayers)+'_M'+str(args.nmlp)+'_H'+str(args.hdim)+"/"
print("No specified saving path, using",rootFolder)
else:
rootFolder = args.folder
if rootFolder[-1] != '/':
rootFolder += '/'
utils.createWorkSpace(rootFolder)
if not args.load:
numFlow = args.numFlow
lossPlotStep = args.save_period
hidden = args.hdim
nlayers = args.nlayers
nmlp = args.nmlp
lr = args.lr
batchSize = args.batch
epochs = args.epochs
K = args.K
with h5py.File(rootFolder+"/parameter.hdf5","w") as f:
f.create_dataset("numFlow",data=numFlow)
f.create_dataset("lossPlotStep",data=lossPlotStep)
f.create_dataset("hidden",data=hidden)
f.create_dataset("nlayers",data=nlayers)
f.create_dataset("nmlp",data=nmlp)
f.create_dataset("lr",data=lr)
f.create_dataset("batchSize",data=batchSize)
f.create_dataset("epochs",data=epochs)
f.create_dataset("K",data=K)
else:
with h5py.File(rootFolder+"/parameter.hdf5","r") as f:
numFlow = int(np.array(f["numFlow"]))
lossPlotStep = int(np.array(f["lossPlotStep"]))
hidden = int(np.array(f["hidden"]))
nlayers = int(np.array(f["nlayers"]))
nmlp = int(np.array(f["nmlp"]))
lr = int(np.array(f["lr"]))
batchSize = int(np.array(f["batchSize"]))
epochs = int(np.array(f["epochs"]))
K = int(np.array(f["K"]))
if args.source == "Ring2d":
target = source.Ring2d().to(device)
elif args.source == "HarmonicChain":
target = source.HarmonicChain(32,1).to(device)
else:
raise Exception("No such source for target")
n = target.nvars[0]//2
if args.double:
target = target.to(torch.float64)
def innerBuilder(num):
maskList = []
for i in range(nlayers):
if i %2==0:
b = torch.zeros(num)
i = torch.randperm(b.numel()).narrow(0, 0, b.numel() // 2)
b.zero_()[i] = 1
b=b.reshape(1,num)
else:
b = 1-b
maskList.append(b)
maskList = torch.cat(maskList,0).to(torch.float32)
fl = flow.RNVP(maskList, [utils.SimpleMLPreshape([num]+[hidden]*nmlp+[num],[nn.Softplus()]*nmlp+[None]) for _ in range(nlayers)], [utils.SimpleMLPreshape([num]+[hidden]*nmlp+[num],[nn.Softplus()]*nmlp+[utils.ScalableTanh(num)]) for _ in range(nlayers)])
return fl
from utils import flowBuilder
f = flowBuilder(n,numFlow,innerBuilder,1,relax=args.relax,shift=args.shift).to(device)
if not args.double:
f = f.to(torch.float32)
LOSS = train.learn(target,f,batchSize,epochs,lr,saveSteps = lossPlotStep,savePath=rootFolder)