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train.py
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train.py
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#%%
import torch
from torch import optim
from torch import nn
from torch.utils.data import DataLoader
from tqdm import tqdm
import argparse
from torch.utils.tensorboard import SummaryWriter
from pathlib import Path
from utilsTraining import MinExponentialLR, batch_inference, batch_random_inference, moduleGradMean, get_lr
import platform
from datetime import datetime
from tensorboard import program
import numpy as np
from model import ModelFullEnd2End as Model
from irishDataloading import irishSplitter, RandomDataset
# CUDA_LAUNCH_BLOCKING=1
#%%
# From generic random curves to specific irish notes
if __name__ == "__main__":
hostName = platform.uname().node
method = 'Curve2Melody'
######################################################################################
# Load Args and Params
######################################################################################
parser = argparse.ArgumentParser(description='Train Arguments')
parser.add_argument("--saveFolder", "-f", type=str, default='')
parser.add_argument("--sessionName", type=str, default='')
parser.add_argument("--info", type=str, default = '')
parser.add_argument('--cuda', type=int, default=0)
parser.add_argument('--wdecay', type=float, default=1e-6 )
parser.add_argument('--tfDecay', type=float, default=10000 )
parser.add_argument('--minTf', type=float, default=0.25 )
parser.add_argument('--seed', type=int, default=555)
parser.add_argument('--batchSize', type=int, default=128)
parser.add_argument('--epochsNum', type=int, default=100)
parser.add_argument('--savePeriod', type=int, default=1)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--lrFinal', type=float, default=1e-5)
parser.add_argument('--lrDecay', type=float, default=0.999975)
parser.add_argument('--densityType', type=str, default='quant') # quantNorm
parser.add_argument('--size', type=int, default = 0)
parser.add_argument('--workers', type=int, default=4)
parser.add_argument('--vaeBeta', type=float, default=0.1)
# parser.add_argument('--hiddenDims', type=int, default=2048)
parser.add_argument('--trainDecoder', type=int, default=0)
parser.add_argument('--detaching', type=int, default=0)
parser.add_argument('--randomDataProb', type=float, default=0.1)
# parser.add_argument('--seq', type=int, default=0)
parser.add_argument('--splitCurveEnc', type=int, default=0)
parser.add_argument('--musicDecoderStatus', type=str, default='finetune') # frozen, fineTune, train
parser.add_argument('--musicEncoderStatus', type=str, default='frozen') # frozen, fineTune, train
parser.add_argument('--trainFull', type=int, default=0) # frozen, fineTune, train
parser.add_argument('--includeCE', type=int, default = 1)
parser.add_argument('--dirWeight', type=float, default = 1.0)
parser.add_argument('--rhyWeight', type=float, default = 1.0)
parser.add_argument('--rsample', type=int, default = 1)
parser.add_argument('--zDirGt', type=int, default = 0)
parser.add_argument('--zRhyGt', type=int, default = 0)
parser.add_argument('--special', type=int, default = 0)
parser.add_argument('--diff', type=int, default = 0)
parser.add_argument('--onsetDistProb', type=float, default = 0.0)
parser.add_argument('--pitchDistProb', type=float, default = 0.0)
parser.add_argument('--curveDistProb', type=float, default = 0.0)
# Curve networks parameters
parser.add_argument('--channels', type=int, default=16)
parser.add_argument('--kernel', type=int, default=3)
parser.add_argument('--encoderType', type=str, default='CNN')
parser.add_argument('--decoderType', type=str, default='CNN')
parser.add_argument('--nSamples', type=int, default=512)
parser.add_argument('--decoderDil', type=int, default=0)
parser.add_argument('--encoderDil', type=int, default=0)
parser.add_argument('--layers', type=int, default=7)
parser.add_argument('--upMode', type=str, default='linear')
parser.add_argument('--batchNorm', type=int, default = 1)
parser.add_argument('--pooling', type=str, default='None')
parser.add_argument('--activation', type=str, default='lrelu')
parser.add_argument('--finalActivation', type=str, default='None')
# parser.add_argument('--ablationInd', type=int, default=1)
# parser.add_argument('--unicorn', type=int, default=1)
parser.add_argument('--contextRhythmLink', type=int, default = 1)
parser.add_argument('--embeddings', type=int, default = 1)
parser.add_argument('--concatCond', type=int, default = 1)
parser.add_argument('--transpose', type=int, default = 1)
parser.add_argument('--seq', type=int, default=0)
parser.add_argument('--condDims', type=int, default = 3)
parser.add_argument('--condType', type=str, default='mean') # it can be mean also
parser.add_argument('--dirDims', type=int, default=7)
parser.add_argument('--dirType', type=str, default='bugFix') # it can be simple (linear interp)
parser.add_argument('--hiddenDims', type=int, default=2048)
parser.add_argument('--decHiddenDims', type=int, default=2048)
parser.add_argument('--encHiddenDims', type=int, default=2048)
parser.add_argument('--dirHiddenDims', type=int, default=2048)
parser.add_argument('--rhyHiddenDims', type=int, default=2048)
parser.add_argument('--decoderLayers', type=int, default=2)
parser.add_argument('--z1Dims', type=int, default=85)
parser.add_argument('--z2Dims', type=int, default=85)
parser.add_argument('--z3Dims', type=int, default=85)
parser.add_argument('--barsNum', type=int, default=3)
parser.add_argument('--pretrain', type=int, default=0)
parser.add_argument('--checkpointPath', type=str, default='04-05-2021_20:30:14_end2end_quantNorm_onsetOffset3_tied0_concat1_chroma1_Airgpustation2_Curve2Melody/modelEnd2End_end2end_quantNorm_onsetOffset3_tied0_concat1_chroma1_Airgpustation2_Curve2Melody_last.pt')
parser.add_argument('--chromaLossWeight', type=float, default=1)
parser.add_argument('--tiedEncoders', type=int, default=0)
parser.add_argument('--concatContext', type=int, default=1)
parser.add_argument('--onsetOffsetConds', type=int, default=0)# 3
parser.add_argument('--chromaLink', type=int, default=0)
cwd = Path.cwd()
checkpoints = Path("Checkpoints")
# ablationPath = Path("Ablation/MelodyVAE")
# datasetPath = Path("Dataset/Irish")
datasetPath = cwd
if hostName == 'AIR-GPU-STATION':
checkpoints = Path("/dataNVME/christos/Checkpoints")
datasetPath = Path("/dataNVME/christos/Dataset/Irish")
# ablationPath = Path("/dataNVME/christos/Ablation/MelodyVAE")
elif hostName in ['airgpustation3','Airgpustation2']:
checkpoints = Path("/storageNVME/christos/Checkpoints")
datasetPath = Path("/storageNVME/christos/Dataset/Irish")
# ablationPath = Path("/storageNVME/christos/Ablation/MelodyVAE")
args = parser.parse_args()
device = torch.device(f'cuda:{args.cuda}' if torch.cuda.is_available() else 'cpu')
print(f'Using cuda {args.cuda}', torch.cuda.get_device_name(device))
# device = torch.device("cpu")
now = datetime.now()
sessionInfo = f"{args.sessionName}_{hostName}_{method}"
saveFolder = checkpoints / Path(now.strftime("%d-%m-%Y_%H:%M:%S_") + sessionInfo)
print(saveFolder)
writer = SummaryWriter(saveFolder / "logs")
#%%
if args.pretrain == 1:
checkpointPreviousPath = checkpoints / args.checkpointPath
checkpointPretrained = torch.load(checkpointPreviousPath, map_location=device)
argsMusicVae = checkpointPretrained['args']
else:
argsMusicVae = args
concatCond = argsMusicVae.concatCond
transpose = argsMusicVae.transpose
condDims = argsMusicVae.condDims
dirDims = argsMusicVae.dirDims
hiddenDims = argsMusicVae.hiddenDims
z1Dims = argsMusicVae.z1Dims
z2Dims = argsMusicVae.z2Dims
z3Dims = argsMusicVae.z3Dims
inputDims = 130
rhythmDims = 3
seqLen = 6 * 4
cpcDims = 13
finalActivation = args.finalActivation
if finalActivation == 'None':
finalActivation = None
pooling = args.pooling
if pooling == 'None':
pooling = None
musicVae = Model(device, hidden_dims = argsMusicVae.hiddenDims, dir_dims = argsMusicVae.dirDims,
cond_dims = argsMusicVae.condDims,
zCpc_dims = argsMusicVae.z1Dims, zRhy_dims = argsMusicVae.z2Dims, zDir_dims = argsMusicVae.z3Dims,
rhythm_dims = 3, input_dims = 130, useCPC = 0, orderlessCpc = 0,
cpc_dims = cpcDims, seq_len = 24, decay = args.tfDecay, detaching = argsMusicVae.detaching, seq = argsMusicVae.seq,
decoderLayers = argsMusicVae.decoderLayers, dec_hidden_dims = argsMusicVae.decHiddenDims,
enc_hidden_dims = argsMusicVae.encHiddenDims,
rhy_hidden_dims = argsMusicVae.rhyHiddenDims, dir_hidden_dims=argsMusicVae.dirHiddenDims,
concat_cond = concatCond,
contextRhythmLink = argsMusicVae.contextRhythmLink,
embeddings = argsMusicVae.embeddings, embeddingsDim = 128,
tiedEncoders = args.tiedEncoders,
concatContext= args.concatContext,
onsetOffsetConds = args.onsetOffsetConds,
chromaLink = argsMusicVae.chromaLink,
minTf = args.minTf,
channels = argsMusicVae.channels, kernel = argsMusicVae.kernel, encoderType = 'CNN', nSamples = argsMusicVae.nSamples,
encoderDil = False, layers = argsMusicVae.layers, batchNorm = bool(argsMusicVae.batchNorm), pooling = None,
activation = 'lrelu'
)
if args.pretrain == 1:
musicVae.load_state_dict(checkpointPretrained['state_dict'])
print("AAAAAAAAAAAA")
musicVae.to(device)
print(f"GRAND MEAN IS {moduleGradMean(musicVae)}")
parametersList = list(musicVae.parameters())
optimizer = optim.Adam(parametersList, lr = args.lr)
if args.lrDecay > 0:
scheduler = MinExponentialLR(optimizer, gamma = args.lrDecay, minimum = args.lrFinal)
#%%
print(argsMusicVae.dirDims)
if args.size == 0:
args.size = None
half = args.barsNum //2
print(argsMusicVae)
trainDataset, validDataset, testDataset = irishSplitter(datasetPath, size = args.size, includeCurves = True,
dirs = argsMusicVae.dirDims,
conds = argsMusicVae.condDims,
transpose = argsMusicVae.transpose,
densityType = argsMusicVae.densityType,
dirType = argsMusicVae.dirType,
condType = argsMusicVae.condType,
onsetDistProb = args.onsetDistProb,
pitchDistProb = args.pitchDistProb,
curveDistProb = args.curveDistProb,
barsNum = args.barsNum
)
print(f"dirType {argsMusicVae.dirType} condType {argsMusicVae.condType}")
print(len(trainDataset),len(validDataset),len(testDataset))
print(argsMusicVae.transpose)
trainLoader = DataLoader(trainDataset, batch_size=args.batchSize,
shuffle=True, num_workers=args.workers)
validLoader = DataLoader(validDataset, batch_size=args.batchSize,
shuffle=True, num_workers=args.workers)
validIter = iter(validLoader)
trainIter = iter(trainLoader)
testLoader = DataLoader(testDataset, batch_size=args.batchSize,
shuffle=False, num_workers=0)
randomDataset = RandomDataset(p1=0.0, p2=0.0,
includeCurves=True, densityType=args.densityType,
condType = argsMusicVae.condType, dirType = argsMusicVae.dirType,
curveOrigin = 'linear', dirs = argsMusicVae.dirDims,
conds = argsMusicVae.condDims, transpose = 1, nSamples=512,
onsetDistProb = args.onsetDistProb, pitchDistProb = args.pitchDistProb,
curveDistProb = args.curveDistProb)
randomLoader = DataLoader(randomDataset, shuffle=False, batch_size=args.batchSize, num_workers=args.workers)
randomIter = iter(randomLoader)
#%%
tb = program.TensorBoard()
tb.configure(argv=[None, '--logdir', str(saveFolder/'logs')])
url = tb.launch()
print(url)
#%%
print(sum(p.numel() for p in musicVae.curveEncoder.parameters() if p.requires_grad))
epoch = 0
step=0
if args.pretrain == 1:
epoch += checkpointPretrained['epoch']
step += checkpointPretrained['step']
while epoch < 10000:
epoch += 1
with tqdm(trainLoader, unit="batch") as t:
for batchInd in range(len(trainLoader)):
if np.random.random() < args.randomDataProb:
irishBatch = False
try:
batchRandom = next(randomIter)
except:
randomIter = iter(randomLoader)
batchRandom = next(randomIter)
batch = batchRandom
else:
irishBatch = True
try:
batchIrish = next(trainIter)
except:
trainIter = iter(trainLoader)
batchIrish = next(trainIter)
batch = batchIrish
try:
batchValid = next(validIter)
except:
validIter = iter(validLoader)
batchValid = next(validIter)
musicVae.train()
optimizer.zero_grad()
if irishBatch is True:
out = batch_inference(batch, musicVae, args, device)
out['loss']['total'].backward()
else:
outRandom = batch_random_inference(batch, musicVae, args, device)
outRandom['loss']['total'].backward()
# torch.nn.utils.clip_grad_norm_(musicVae.parameters(), 1)
optimizer.step()
# VALIDATION
musicVae.eval()
with torch.no_grad():
outVal = batch_inference(batchValid, musicVae, args, device)
step += 1
if args.lrDecay > 0:
scheduler.step()
if irishBatch:
writer.add_scalars('totalLoss', {'train':out['loss']['total'].item(),
'valid':outVal['loss']['total'].item()
},
step)
writer.add_scalars('klLoss', {'train':out['loss']['kl'].item(),
'valid':outVal['loss']['kl'].item()
},
step)
writer.add_scalars('reconLoss', {'train':out['loss']['melody'].item(),
'valid':outVal['loss']['melody'].item()
},
step)
writer.add_scalars('chromaLoss', {'train':out['loss']['chroma'].item(),
'valid':outVal['loss']['chroma'].item()
},
step)
writer.add_scalars('rhythmLoss',{'train':out['loss']['rhythm'].item(),
'valid':outVal['loss']['rhythm'].item()
},
step)
writer.add_scalars('dirLoss', {'train':out['loss']['dir'].item(),
'valid':outVal['loss']['dir'].item()
},
step)
writer.add_scalars('accMelody', {'train':out['acc']['melody'].item(),
'valid':outVal['acc']['melody'].item()
},
step)
writer.add_scalars('accRhy', {
'train':out['acc']['rhythm'].item(),
'valid':outVal['acc']['rhythm'].item()
},
step)
writer.add_scalars('accDir', {
'train':out['acc']['dir'].item(),
'valid':outVal['acc']['dir'].item()
},
step)
writer.add_scalars('modelParams', {'eps': musicVae.eps,
'beta': args.vaeBeta,
'lr': optimizer.param_groups[0]['lr'],
'lr2' : get_lr(optimizer)
},
step)
else:
writer.add_scalars('klLossRR', {'train':outRandom['loss']['kl'].item()}, step)
writer.add_scalars('dirLossRR', {'train':outRandom['loss']['dir'].item()}, step)
writer.add_scalars('accDirRR', { 'train':outRandom['acc']['dir'].item()}, step)
t.update(1)
if (epoch + 1) % args.savePeriod == 0:
if args.pretrain == 1:
filename = f"{sessionInfo}_fromCheckpoint_last.pt"
else:
filename = f"{sessionInfo}_last.pt" #
state1 = {
'epoch': epoch,
'state_dict': musicVae.state_dict(),
# 'minValLoss': minValLoss,
# 'optimizer': optimizer.state_dict(),
'args': args,
'argsMusicVae':argsMusicVae,
'checkpointPath':saveFolder,
'step': step,
'lr' : get_lr(optimizer),
# 'argsHist' : prevArgs.append(args),
# 'epocs' : epocLocal,
'info' : args.info
}
torch.save(state1, Path(saveFolder) / ("modelEnd2End_"+filename))
writer.close()
print(checkpoints)
print(url)