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trainerClasses.py
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trainerClasses.py
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# from cv2 import UMAT_SUBMATRIX_FLAG
from scipy.io import savemat, loadmat
import numpy as np
from torch import nn
import torch.nn.functional as F
from scipy.io import loadmat, savemat
import time
from torch.utils.data import Dataset, DataLoader
import wandb
import torch
def transformDataset(data, imgSizes, rescaleVals, randVals = None):
dims = len(imgSizes)
reScaleMin, reScaleMax = rescaleVals
imgSize = data.shape
if dims == 2:
n1, n2 = imgSizes
data -= data.reshape(imgSize[0], imgSize[1], -1).min(dim = 2).values[:,:,None,None]
data /= data.reshape(imgSize[0], imgSize[1], -1).max(dim = 2).values[:,:,None,None]
if (n1 < data.shape[2]) or (n2 < data.shape[3]):
if randVals is None:
rand1 = torch.randint(low = 0, high = data.shape[2] - n1, size = (1,))
rand2 = torch.randint(low = 0, high = data.shape[3] - n2, size = (1,))
else:
rand1 = randVals[0]
rand2 = randVals[1]
else:
rand1 = 0
rand2 = 0
data = data[:, :, rand1:rand1 + n1, rand2:rand2 + n2]
else:
n1, n2, n3 = imgSizes # n1 is 16
data -= data.reshape(imgSize[0], imgSize[1], -1).min(dim = 2).values[:,:,None,None,None]
data /= data.reshape(imgSize[0], imgSize[1], -1).max(dim = 2).values[:,:,None,None,None]
diffS = np.array(imgSizes) - np.array(data.shape[2:])
diffS *= (diffS > 0)
data = F.pad(data, (diffS[2], diffS[2], diffS[1], diffS[1], diffS[0], diffS[0])) # new data size is
if (n1 < data.shape[2]) or (n2 < data.shape[3]) or (n3 < data.shape[3]):
if randVals is None:
rand1 = torch.randint(low = 0, high = data.shape[2] - n1, size = (1,))
rand2 = torch.randint(low = 0, high = data.shape[3] - n2, size = (1,))
rand3 = torch.randint(low = 0, high = data.shape[4] - n3, size = (1,))
else:
rand1 = randVals[0]
rand2 = randVals[1]
rand3 = randVals[2]
else:
rand1 = 0
rand2 = 0
rand3 = 0
data = data[:, :, rand1:rand1 + n1, rand2:rand2 + n2, rand3:rand3 + n3]
if reScaleMax > 0:
randScale = torch.rand((imgSize[0], 1, 1, 1), device = data.device) * reScaleMax + reScaleMin
if dims == 3:
randScale = randScale.reshape(imgSize[0], 1, 1, 1, 1)
else:
randScale = 1
data *= randScale
return data
def getNoisyData(data, stdVal, sysMtx, colored = 0):
genData = F.linear(data.reshape(data.shape[0], 1, -1), sysMtx)
if colored > 0:
if colored > 2: # laplacian noise
r1 = -torch.log(torch.rand_like(genData)) + torch.log(torch.rand_like(genData))
genNoise = (stdVal / (2)**(1/2)) * r1
# genNoise = stdVal * theScale * ( (1 - colored) + colored * torch.rand_like(genData)) * torch.randn_like(genData) # generate random
else:
# colored with max 1
# theScale = ( 3 * colored / ( 1 - (1 - colored)**3 ))**(1/2)
# genNoise = stdVal * theScale * ( (1 - colored) + colored * torch.rand_like(genData)) * torch.randn_like(genData) # generate random
# colored with mean 1
theScale = ( 3 * colored / ( (1+colored/2)**3 - (1 - colored/2)**3 ))**(1/2)
genNoise = stdVal * theScale * ( (1 - colored) + colored * torch.rand_like(genData)) * torch.randn_like(genData) # generate random
else:
genNoise = stdVal * torch.randn_like(genData) # generate random
genData += genNoise # add noise in original data domain
return genData
def admmInputGenerator(genData, U_, S_, V_, imgSize):
compData = F.linear(genData, U_.T) # compressed data
lsqrInp = F.linear(compData / (S_ + 1e-4), V_).reshape(imgSize)
return compData, lsqrInp
def trainADMMandE2EandImplicit(model, epoch_nb, loss, optimizer, scheduler, trainDataset, valDataset, batch_size_train, batch_size_val, theSysl, sysMtxl, Ul, Sl, Vl, Ml, sysMtxlHR = None, invCrimeFnc = None, imgSizes = [32, 32] \
, rescaleVals = [1, 1], saveModelEpoch=0, valEpoch=0, saveDirectory='', pSNRval=30, wandbFlag=False, fixedNoiseStdFlag=False, nbOfSingulars=0, lambdaVal = 1, mode = 0, coloredNoise = 0):
variablesEpsFlag = pSNRval == 0
stdVal = 0.41 * 10**(-(pSNRval) / 20)
N = np.prod(imgSizes)
# epsilonVal = stdVal * (N)**(1/2) * lambdaVal
if sysMtxlHR is None:
epsOffset = 0
else:
epsOffset = 0.087 * 0
epsilonVal = ((stdVal * (N)**(1/2) * lambdaVal)**2 + epsOffset)**(1/2)
trainLosses = torch.zeros(epoch_nb)
trainNrmses = torch.zeros(epoch_nb)
trainPsnrs = torch.zeros(epoch_nb)
valLosses = list()
valNrmses = list()
valPsnrs = list()
trainLoader = DataLoader(trainDataset, batch_size_train, shuffle=True)
valLoader = DataLoader(valDataset, valDataset.__len__(), shuffle=False)
maxPsnrVal = -1000
for epoch in range(int(epoch_nb)):
wandbLoggerDict = {'epoch': epoch}
tempLosses = list()
model.train()
tempNrmseNumeratorSquare = 0
tempNrmseDenumeratorSquare = 0
tempNumel = 0
tempTime = time.time()
if saveModelEpoch > 0:
if (epoch + 1 % saveModelEpoch == 0):
torch.save(model.state_dict(), saveDirectory+r"/" +
"epoch" + str(epoch + 1) + ".pth")
for idx, dataHR in enumerate(trainLoader, 0):
myRndInd = int(torch.randint(low = 0, high = len(Ul), size = (1,)))
dataHR = dataHR.cuda().float()
# compressedRep = 1, noisySys
if invCrimeFnc is None:
data = dataHR
data = transformDataset(data, imgSizes, rescaleVals)
else:
dataHR = transformDataset(dataHR, [b*2 for b in imgSizes], rescaleVals)
data = invCrimeFnc(dataHR)
if variablesEpsFlag:
stdVal = 0.41 * 10**(-(float(torch.randint(low = 20, high = 40, size = (1,)))) / 20)
epsilonVal = ((stdVal * (N)**(1/2) * lambdaVal)**2 + epsOffset)**(1/2)
if sysMtxlHR is None:
theNoisyData = getNoisyData(data, stdVal, sysMtxl[myRndInd], coloredNoise)
else:
theNoisyData = getNoisyData(dataHR, stdVal, sysMtxlHR[myRndInd], coloredNoise)
compData, lsqrInp = admmInputGenerator(theNoisyData, Ul[myRndInd], Sl[myRndInd], Vl[myRndInd], data.shape)
epsilonVal = ((stdVal * (sysMtxl[myRndInd].shape[0])**(1/2) * lambdaVal)**2 + epsOffset)**(1/2)
if (mode == 1) or (mode == 3): # ADMM
modelOut = model(datatC = theNoisyData, AtC = sysMtxl[myRndInd], x_in = lsqrInp, MtC = Ml[myRndInd], epsilon = epsilonVal)
elif mode == 2: # ADMM Implicit
imgSize = data.shape
Nimg = N
AlsqrInp = F.linear(lsqrInp.reshape(imgSize[0], -1), sysMtxl[myRndInd])
d0, d2 = torch.zeros_like(lsqrInp).reshape(imgSize[0], -1), torch.zeros_like(AlsqrInp)
theIn = torch.cat((lsqrInp.reshape(imgSize[0], -1), d0.reshape(imgSize[0], -1), d2.reshape(imgSize[0], -1)), dim = 1 )
theFixedPts = (theNoisyData, sysMtxl[myRndInd], Ml[myRndInd], epsilonVal, imgSize)
modelOut = model(theIn, theFixedPts)[:, :Nimg].reshape(imgSize)
model.zero_grad()
model_loss = loss(modelOut, data)
model_loss.backward()
optimizer.step()
with torch.no_grad():
if len(data.shape) == 5:
percent = 10
if idx%int(trainDataset.__len__()/batch_size_train/percent)==0:
print('Epoch {0:d} | {1:d}% | batch nrmse: {2:.5f}'.format(epoch,percent*idx//int(trainDataset.__len__()/batch_size_train/percent),(float(torch.norm(modelOut-data)))/(float(torch.norm(data))))) # myflag
tempLosses.append(float(model_loss))
tempNrmseNumeratorSquare += (
float(torch.norm(modelOut-data)))**2
tempNrmseDenumeratorSquare += (float(torch.norm(data)))**2
tempNumel += modelOut.numel()
# back to epoch
model.eval()
scheduler.step()
trainLosses[epoch] = sum(tempLosses)/len(tempLosses)
trainNrmses[epoch] = (tempNrmseNumeratorSquare /
tempNrmseDenumeratorSquare)**(1/2)
trainPsnrs[epoch] = 20 * \
torch.log10(1 / (tempNrmseDenumeratorSquare**(1/2) * # Should we correct 1 -> valGround.max()
trainNrmses[epoch] / (tempNumel) ** (1/2)))
epochTime = time.time() - tempTime
if wandbFlag:
wandbLoggerDict["train_loss"] = trainLosses[epoch]
wandbLoggerDict["train_nrmse"] = trainNrmses[epoch]
wandbLoggerDict["train_psnr"] = trainPsnrs[epoch]
print("Epoch: {0}, Train Loss = {1:.6f}, Train nRMSE = {2:.6f}, Train pSNR = {3:.6f}, time elapsed = {4:.6f}".format(epoch,
trainLosses[epoch], trainNrmses[epoch], trainPsnrs[epoch], epochTime))
if valEpoch > 0:
if epoch % valEpoch == 0:
with torch.no_grad():
model.eval()
myRndInd = int(torch.randint(low = 0, high = len(Ul), size = (1,)))
valInpHR = next(iter(valLoader)).cuda().float()
if invCrimeFnc is None:
valInp = valInpHR
valInp = transformDataset(valInp, imgSizes, rescaleVals)
else:
valInpHR = transformDataset(valInpHR, [b*2 for b in imgSizes], rescaleVals)
valInp = invCrimeFnc(valInpHR)
if variablesEpsFlag:
stdVal = 0.41 * 10**(-(float(torch.randint(low = 20, high = 40, size = (1,)))) / 20)
epsilonVal = ((stdVal * (N)**(1/2) * lambdaVal)**2 + epsOffset)**(1/2)
if sysMtxlHR is None:
theNoisyData = getNoisyData(valInp, stdVal, sysMtxl[myRndInd], coloredNoise)
else:
theNoisyData = getNoisyData(valInpHR, stdVal, sysMtxlHR[myRndInd], coloredNoise)
# valInp = transformDataset(valInp, imgSizes, rescaleVals)
valGround = valInp.clone()
valOut = torch.zeros_like(valInp)
# theNoisyData = getNoisyData(valGround, stdVal, sysMtxl[myRndInd])
compData, valInp = admmInputGenerator(theNoisyData, Ul[myRndInd], Sl[myRndInd], Vl[myRndInd], valGround.shape)
iii = 0
epsilonVal = ((stdVal * (sysMtxl[myRndInd].shape[0])**(1/2) * lambdaVal)**2 + epsOffset)**(1/2)
# stdVal * (sysMtxl[myRndInd].shape[0])**(1/2) * lambdaVal
if (mode == 1) or (mode == 3): # ADMM
while(iii < valInp.shape[0]-(valInp.shape[0] % batch_size_val)):
valOut[iii:iii+batch_size_val] = model(datatC = theNoisyData[iii:iii+batch_size_val], AtC = sysMtxl[myRndInd], x_in = valInp[iii:iii+batch_size_val], MtC = Ml[myRndInd], epsilon = epsilonVal)
iii += batch_size_val
valOut[iii:] = model(datatC = theNoisyData[iii:], AtC = sysMtxl[myRndInd], x_in = valInp[iii:], MtC = Ml[myRndInd], epsilon = epsilonVal)
elif mode == 2: # ADMM Implicit
imgSize = valInp.shape
Nimg = N
AvalInp = F.linear(valInp.reshape(imgSize[0], -1), sysMtxl[myRndInd])
d0, d2 = torch.zeros_like(valInp).reshape(imgSize[0], -1), torch.zeros_like(AvalInp)
theIn = torch.cat((valInp.reshape(imgSize[0], -1), d0.reshape(imgSize[0], -1), d2.reshape(imgSize[0], -1)), dim = 1 )
while(iii < valInp.shape[0]-(valInp.shape[0] % batch_size_val)):
imgSize2 = (batch_size_val, *imgSize[1:])
theFixedPts = (theNoisyData[iii:iii+batch_size_val], sysMtxl[myRndInd], Ml[myRndInd], epsilonVal, imgSize2)
valOut[iii:iii+batch_size_val] = model(theIn[iii:iii+batch_size_val], theFixedPts)[:, :Nimg].reshape(imgSize2)
iii += batch_size_val
imgSize2 = (theNoisyData[iii:].shape[0], *imgSize[1:])
theFixedPts = (theNoisyData[iii:], sysMtxl[myRndInd], Ml[myRndInd], epsilonVal, imgSize2)
valOut[iii:] = model(theIn[iii:], theFixedPts)[:, :Nimg].reshape(imgSize2)
valLoss = float(nn.L1Loss()(valGround, valOut))
valNrmse = float(torch.norm(
valGround-valOut)/torch.norm(valGround))
valPSNR = float(20 *
torch.log10(1 / (torch.norm(valGround) * # Should we correct 1 -> valGround.max()
valNrmse / (valOut.numel()) ** (1/2))))
valPSNR_avg = (20 *
torch.log10(1 / (torch.norm((valGround-valOut).reshape(valGround.shape[0], -1), dim = (1)).squeeze() / (valOut[0,0].numel()) ** (1/2))))
valLosses.append(valLoss)
valNrmses.append(valNrmse)
valPsnrs.append(valPSNR)
if wandbFlag:
currentAvg = valPSNR_avg.mean(0)
if maxPsnrVal < currentAvg:
maxPsnrVal = currentAvg
torch.save(model.state_dict(), saveDirectory+r"/" +
"epoch" + str(epoch) + "max.pth")
wandbLoggerDict["valid_nRMSE"] = valNrmse
wandbLoggerDict["ref_nRMSE"] = torch.norm(valInp-valGround)/torch.norm(valGround)
wandbLoggerDict["valid_pSNR"] = valPSNR
wandbLoggerDict["valid_pSNRavg"] = valPSNR_avg.mean(0)
wandbLoggerDict["valid_pSNRstd"] = valPSNR_avg.std(0)
wandbLoggerDict["valid_maxPSNR"] = maxPsnrVal
wandbLoggerDict["valid_loss"] = valLoss
print("---Epoch: {0}, Val Loss = {1:.6f}, Val nRMSE = {2:.6f}, Val pSNR = {3:.6f}".format(
epoch, valLoss, valNrmse, valPSNR))
if wandbFlag:
wandb.log(wandbLoggerDict)
torch.save(model.state_dict(), saveDirectory+r"/" +
"epoch" + str(epoch + 1) + "END.pth")
return model, [trainLosses.numpy(), trainNrmses.numpy(), trainPsnrs.numpy()], [np.array(valLosses), np.array(valNrmses), np.array(valPsnrs)]
def returnNsTerm(fixedNoiseStdFlag = False,shape=0, maxNoiseStd=0.05, minNoiseStd=0, useDev = torch.device("cpu")):
if fixedNoiseStdFlag:
noiseStd = maxNoiseStd
return torch.randn(shape, device = useDev) * noiseStd
else:
noiseStd = (torch.rand(shape, device = useDev)*(maxNoiseStd-minNoiseStd)+minNoiseStd)
return torch.randn(shape, device = useDev) * noiseStd
def trainDenoiser(model, epoch_nb, loss, optimizer, scheduler, trainDataset, valDataset, batch_size_train, batch_size_val, rescaleVals = [1, 1], saveModelEpoch=0, valEpoch=0, saveDirectory='', maxNoiseStd = 0.1, optionalMessage="", wandbFlag=False, fixedNoiseStdFlag = False, minNoiseStd =0, dims = 2):
trainLosses = torch.zeros(epoch_nb)
trainNrmses = torch.zeros(epoch_nb)
trainPsnrs = torch.zeros(epoch_nb)
valLosses = list()
valNrmses = list()
valPsnrs = list()
trainLoader = DataLoader(trainDataset, batch_size_train, shuffle=True)
valLoader = DataLoader(valDataset, valDataset.__len__(), shuffle=False)
reScaleMin, reScaleMax = rescaleVals
for epoch in range(1,1+int(epoch_nb)):
tempLosses = list()
model.train()
tempNrmseNumeratorSquare = 0
tempNrmseDenumeratorSquare = 0
tempNumel = 0
tempTime = time.time()
if saveModelEpoch > 0:
if (epoch % saveModelEpoch == 0):
torch.save(model.state_dict(), saveDirectory+r"/"+ optionalMessage +"epoch"+ str(epoch)+ ".pth")
for idx, data in enumerate(trainLoader, 0):
data = data.float().cuda()
data = transformDataset(data, [*data.shape[2:]], rescaleVals)
noiseTerm = returnNsTerm(fixedNoiseStdFlag = fixedNoiseStdFlag, shape=data.shape,\
maxNoiseStd=maxNoiseStd, minNoiseStd=minNoiseStd, useDev = data.device)
noisyInp = data + noiseTerm
modelOut = model(noisyInp)
model.zero_grad()
model_loss = loss(modelOut, data)
model_loss.backward()
optimizer.step()
if dims == 3:
percent = 10
if idx%int(trainDataset.__len__()/batch_size_train/percent)==0:
print('Epoch {0:d} | {1:d}% | batch nrmse: {2:.5f}'.format(epoch,percent*idx//int(trainDataset.__len__()/batch_size_train/percent),(float(torch.norm(modelOut-data)))/(float(torch.norm(data))))) # myflag
with torch.no_grad():
tempLosses.append(float(model_loss))
tempNrmseNumeratorSquare += (float(torch.norm(modelOut-data)))**2
tempNrmseDenumeratorSquare += (float(torch.norm(data)))**2
tempNumel += modelOut.numel()
# back to epoch
model.eval()
scheduler.step()
trainLosses[epoch-1] = sum(tempLosses)/len(tempLosses)
trainNrmses[epoch-1] = (tempNrmseNumeratorSquare/tempNrmseDenumeratorSquare)**(1/2)
trainPsnrs[epoch-1] = 20 * \
torch.log10(1 / (tempNrmseDenumeratorSquare**(1/2) * #Should we correct 1 -> valGround.max()
trainNrmses[epoch-1] / (tempNumel) ** (1/2)))
epochTime = time.time() - tempTime
if wandbFlag:
wandb.log({"train_loss": trainLosses[epoch-1], "train_nrmse": trainNrmses[epoch-1], "train_psnr": trainPsnrs[epoch-1]})
print("Epoch: {0}, Train Loss = {1:.6f}, Train nRMSE = {2:.6f}, Train pSNR = {3:.6f}, time elapsed = {4:.6f}".format(epoch,
trainLosses[epoch-1], trainNrmses[epoch-1], trainPsnrs[epoch-1], epochTime))
if valEpoch>0:
if epoch % valEpoch == 0:
with torch.no_grad():
model.eval()
valInp = next(iter(valLoader))
valGround = valInp.clone()
valGround = transformDataset(valGround, [*valInp.shape[2:]], rescaleVals)
noiseTerm = returnNsTerm(fixedNoiseStdFlag = fixedNoiseStdFlag,shape=valInp.shape, \
maxNoiseStd=maxNoiseStd, minNoiseStd=minNoiseStd, useDev = valGround.device)
valInpVal = valGround + noiseTerm
valOut = torch.zeros_like(valGround)
deviceVal = valGround.device #'cuda' if valOut.is_cuda else 'cpu'
iii = 0
while(iii < valInpVal.shape[0]-(valInpVal.shape[0] % batch_size_val)):
valInpC = valInpVal[iii:iii+batch_size_val].float().cuda()
valOut[iii:iii+batch_size_val] = model(valInpC).to(deviceVal)
iii += batch_size_val
valInpC = valInpVal[iii:].float().cuda()
valOut[iii:] = model(valInpC).to(deviceVal)
valLoss = float(nn.L1Loss()(valGround, valOut))
valNrmse = float(torch.norm(valGround-valOut)/torch.norm(valGround))
valPSNR= float(20 * \
torch.log10(1 / (torch.norm(valGround) * #Should we correct 1 -> valGround.max()
valNrmse / (valOut.numel()) ** (1/2))))
# valPSNR_avg = (20 *
# torch.log10(1 / (torch.norm(valGround-valOut, dim = (2, 3)).squeeze() / (valOut[0,0].numel()) ** (1/2))))
valPSNR_avg = (20 *
torch.log10(1 / (torch.norm(valGround.reshape(valGround.shape[0],-1)-valOut.reshape(valGround.shape[0],-1), dim = (1)).squeeze() / (valOut[0,0].numel()) ** (1/2))))
valLosses.append(valLoss)
valNrmses.append(valNrmse)
valPsnrs.append(valPSNR)
if wandbFlag:
wandb.log({"valid_nRMSE": valNrmse,
"ref_nRMSE": torch.norm(valInp-valGround)/torch.norm(valGround),
'valid_pSNR': valPSNR,
'valid_pSNRavg': valPSNR_avg.mean(0),
'valid_pSNRstd': valPSNR_avg.std(0),
'valid_loss': valLoss})
print("---Epoch: {0}, Val Loss = {1:.6f}, Val nRMSE = {2:.6f}, Val pSNR = {3:.6f}".format(epoch, valLoss, valNrmse, valPSNR))
if wandbFlag:
wandb.log({"valid_nRMSE": valNrmse,
'valid_pSNR': valPSNR,
'valid_pSNRavg': valPSNR_avg.mean(0),
'valid_pSNRstd': valPSNR_avg.std(0),
'valid_loss': valLoss})
print("---Epoch: {0}, Val Loss = {1:.6f}, Val nRMSE = {2:.6f}, Val pSNR = {3:.6f}".format(epoch, valLoss, valNrmse, valPSNR))
torch.save(model.state_dict(), saveDirectory+r"/"+ optionalMessage +"epoch"+ str(epoch)+ "END.pth")
return model, [trainLosses.numpy(), trainNrmses.numpy(), trainPsnrs.numpy()], [np.array(valLosses), np.array(valNrmses), np.array(valPsnrs)]
def getDCNoisyData(data, stdVal, sysMtx):
noiselessData = F.linear(data.reshape(data.shape[0], 1, -1), sysMtx)
genNoise = stdVal * torch.randn_like(noiselessData) # generate random
genData = noiselessData + genNoise # add noise in original data domain
return noiselessData, genData
from reconUtils import proj2Tmtx
def trainDCdenoiserPsi(model, epoch_nb, loss, optimizer, scheduler, trainDataset, valDataset, batch_size_train, batch_size_val, theSysl, sysMtxl, Ul, Sl, Vl, noisySys = 0, imgSizes = [32, 32] \
, rescaleVals = [1, 1], saveModelEpoch=0, valEpoch=0, saveDirectory='', pSNRval=40, wandbFlag=False, fixedNoiseStdFlag=False, nbOfSingulars=0, lambdaVal = 100):
trainingNs = 0.41 * 10**(-pSNRval / 20)
trainLosses = torch.zeros(epoch_nb)
trainNrmses = torch.zeros(epoch_nb)
trainPsnrs = torch.zeros(epoch_nb)
valLosses = list()
valNrmses = list()
valPsnrs = list()
trainLoader = DataLoader(trainDataset, batch_size_train, shuffle=True)
valLoader = DataLoader(valDataset, valDataset.__len__(), shuffle=False)
for epoch in range(int(epoch_nb)):
wandbLoggerDict = {'epoch': epoch}
tempLosses = list()
model.train()
tempNrmseNumeratorSquare = 0
tempRefNrmseNumeratorSquare = 0
tempNrmseDenumeratorSquare = 0
tempNumel = 0
tempTime = time.time()
if saveModelEpoch > 0:
if (epoch + 1 % saveModelEpoch == 0):
torch.save(model.state_dict(), saveDirectory+r"/" +
"epoch" + str(epoch + 1) + ".pth")
for idx, data in enumerate(trainLoader, 0):
myRndInd = int(torch.randint(low = 0, high = len(sysMtxl), size = (1,)))
data = data.float().cuda()
data = transformDataset(data, imgSizes, rescaleVals)
# compressedRep = 1, noisySys
noiselessData, theNoisyData = getDCNoisyData(data, noisySys, sysMtxl[myRndInd])
# trainingAddNoise = theNoisyData + trainingNs * torch.randn_like(theNoisyData)
trainingAddNoise = noiselessData + trainingNs * torch.randn_like(theNoisyData)
Nbatch = noiselessData.shape[0]
in1 = trainingAddNoise.reshape(Nbatch, 1, -1)
in2 = theNoisyData.reshape(Nbatch, 1, -1)
noiselessData = noiselessData.reshape(Nbatch, 1, -1)
epsilonVal = noisySys * (sysMtxl[myRndInd].shape[0])**(1/2) * lambdaVal
modelExpected = proj2Tmtx(in1, in2, epsilonVal).reshape(Nbatch, 1, -1)
modelOut = model(in1,
in2, epsilonVal, Ua = 1).reshape(Nbatch, 1, -1)
model.zero_grad()
model_loss = loss(modelOut, modelExpected)
model_loss.backward()
optimizer.step()
with torch.no_grad():
if len(data.shape) == 5:
percent = 10
if idx%int(trainDataset.__len__()/batch_size_train/percent)==0:
print('Epoch {0:d} | {1:d}% | batch nrmse: {2:.5f}'.format(epoch,percent*idx//int(trainDataset.__len__()/batch_size_train/percent),(float(torch.norm(modelOut-data)))/(float(torch.norm(data))))) # myflag
tempLosses.append(float(model_loss))
tempNrmseNumeratorSquare += (
float(torch.norm(modelOut-modelExpected)))**2
tempRefNrmseNumeratorSquare += (
float(torch.norm(theNoisyData-modelExpected)))**2
tempNrmseDenumeratorSquare += (float(torch.norm(modelExpected)))**2
tempNumel += modelOut.numel()
# back to epoch
model.eval()
scheduler.step()
trainLosses[epoch] = sum(tempLosses)/len(tempLosses)
trainNrmses[epoch] = (tempNrmseNumeratorSquare /
tempNrmseDenumeratorSquare)**(1/2)
trainPsnrs[epoch] = 20 * \
torch.log10(1 / (tempNrmseDenumeratorSquare**(1/2) * # Should we correct 1 -> valGround.max()
trainNrmses[epoch] / (tempNumel) ** (1/2)))
epochTime = time.time() - tempTime
if wandbFlag:
wandbLoggerDict["train_loss"] = trainLosses[epoch]
wandbLoggerDict["train_nrmse"] = trainNrmses[epoch]
wandbLoggerDict["train_refnrmse"] = (tempRefNrmseNumeratorSquare / tempNrmseDenumeratorSquare)**(1/2)
wandbLoggerDict["train_psnr"] = trainPsnrs[epoch]
print("Epoch: {0}, Train Loss = {1:.6f}, Train nRMSE = {2:.6f}, Train pSNR = {3:.6f}, time elapsed = {4:.6f}".format(epoch,
trainLosses[epoch], trainNrmses[epoch], trainPsnrs[epoch], epochTime))
if valEpoch > 0:
if epoch % valEpoch == 0:
with torch.no_grad():
model.eval()
myRndInd = int(torch.randint(low = 0, high = len(sysMtxl), size = (1,)))
valInp = next(iter(valLoader)).cuda().float()
valInp = transformDataset(valInp, imgSizes, rescaleVals)
# noiselessData, theNoisyData = getDCNoisyData(data, noisySys, sysMtxl[myRndInd])
noiselessData, theNoisyData = getDCNoisyData(valInp, noisySys, sysMtxl[myRndInd])
Nbatch = noiselessData.shape[0]
noiselessData = noiselessData.reshape(Nbatch, 1, -1)
theNoisyData = theNoisyData.reshape(Nbatch, 1, -1)
trainingAddNoise = noiselessData + trainingNs * torch.randn_like(theNoisyData)
# trainingAddNoise = theNoisyData + trainingNs * torch.randn_like(theNoisyData)
valOut = torch.zeros_like(noiselessData)
iii = 0
epsilonVal = noisySys * (sysMtxl[myRndInd].shape[0])**(1/2) * lambdaVal
in1 = trainingAddNoise
in2 = theNoisyData
modelExpected = proj2Tmtx(in1, in2, epsilonVal).reshape(Nbatch, 1, -1)
while(iii < valInp.shape[0]-(valInp.shape[0] % batch_size_val)):
valOut[iii:iii+batch_size_val] = model(in1[iii:iii+batch_size_val], in2[iii:iii+batch_size_val], \
epsilonVal, Ua = 1)
iii += batch_size_val
valOut[iii:] = model(in1[iii:], in2[iii:], epsilonVal, Ua = 1)
valLoss = float(nn.L1Loss()(modelExpected, valOut))
valNrmse = float(torch.norm(
modelExpected-valOut)/torch.norm(modelExpected))
valPSNR = float(20 *
torch.log10(1 / (torch.norm(modelExpected) * # Should we correct 1 -> valGround.max()
valNrmse / (valOut.numel()) ** (1/2))))
valPSNR_avg = (20 *
torch.log10(1 / (torch.norm((modelExpected-valOut).reshape(modelExpected.shape[0], -1), dim = (1)).squeeze() / (valOut[0,0].numel()) ** (1/2))))
valLosses.append(valLoss)
valNrmses.append(valNrmse)
valPsnrs.append(valPSNR)
if wandbFlag:
wandbLoggerDict["valid_nRMSE"] = valNrmse
wandbLoggerDict["ref_nRMSE"] = torch.norm(theNoisyData-modelExpected)/torch.norm(modelExpected)
wandbLoggerDict["valid_pSNR"] = valPSNR
wandbLoggerDict["valid_pSNRavg"] = valPSNR_avg.mean(0)
wandbLoggerDict["valid_pSNRstd"] = valPSNR_avg.std(0)
wandbLoggerDict["valid_loss"] = valLoss
print("---Epoch: {0}, Val Loss = {1:.6f}, Val nRMSE = {2:.6f}, Val pSNR = {3:.6f}".format(
epoch, valLoss, valNrmse, valPSNR))
if wandbFlag:
wandb.log(wandbLoggerDict)
torch.save(model.state_dict(), saveDirectory+r"/" +
"epoch" + str(epoch + 1) + "END.pth")
return model, [trainLosses.numpy(), trainNrmses.numpy(), trainPsnrs.numpy()], [np.array(valLosses), np.array(valNrmses), np.array(valPsnrs)]