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train_bsl.py
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train_bsl.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jul 7 16:00:00 2020
@author: CITI
"""
import os
import numpy as np
from models.BaselineBLSTM import RNNDownBeatProc as RNNmodel
import drumaware_dataset as mmdataset
from torch.utils.data import DataLoader
import tqdm
import torch
import torch.nn as nn
from pathlib import Path
import json
import utils
import time
import sys
from lookahead_pytorch import Lookahead
def train(model, device, train_loader, optimizer):
model.train()
train_loss = 0
pbar = tqdm.tqdm(train_loader, disable = False)
for x, y in pbar:
# break
pbar.set_description("Training batch")
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
y_hat = model(x) # beat activations (batch, timestep, 3) ==> nonbeat(0), donwbeat(1), beat(2)
y_hat = y_hat.reshape((-1, 3))
y = y.reshape((-1)).to(dtype = torch.long) # required type of loss function
weights = [1, 200, 67] # nonbeat, beat, downbeat
class_weights = torch.FloatTensor(weights).to(device)
CE = nn.CrossEntropyLoss(weight = class_weights)
loss = CE(y_hat, y)
loss.backward()
train_loss += loss
optimizer.step()
return train_loss/len(train_loader.dataset)
def valid(model, device, valid_loader ):
model.eval()
valid_loss = 0
with torch.no_grad():
for x, y in valid_loader:
x, y = x.to(device), y.to(device)
y_hat = model(x) # beat activations (batch, timestep, 3) ==> nonbeat(0), donwbeat(1), beat(2)
y_hat = y_hat.reshape((-1, 3))
y = y.reshape((-1)).to(dtype = torch.long) # required type of loss function
weights = [1, 200, 67] # nonbeat, beat, downbeat
class_weights = torch.FloatTensor(weights).to(device)
CE = nn.CrossEntropyLoss(weight = class_weights)
loss = CE(y_hat, y)
valid_loss += loss
return valid_loss/len(valid_loader.dataset)
def main():
cuda_num = 0#int(sys.argv[1])
cuda_str = 'cuda:'+str(cuda_num)
device = torch.device(cuda_str if torch.cuda.is_available() else 'cpu')
train_epochs = 2000
# must assign
date = '0615'
exp_num = 1#sys.argv[2] ## added for repeat experiments
exp_name = 'RNNBeat_bsl_V'+str(exp_num) + '_'+date
exp_dir = os.path.join('./experiments', exp_name)
target_jsonpath = exp_dir
lr = 1e-2
patience = 20
### extra information to save in json
model_type = 'bsl_blstm'
model_simpname = 'baseline_v'+str(exp_num)
model_dir = exp_dir
model_info = dict(model_type = model_type,
model_simpname = model_simpname,
model_dir = model_dir,
)
if not os.path.exists(exp_dir):
Path(exp_dir).mkdir(parents = True, exist_ok = True)
mix_main_dir = './datasets/original'
mix_dataset_dirs = os.listdir(mix_main_dir)
mixtrainset = utils.getMixset(mix_dataset_dirs, folderName = 'features', abname = 'train_dataset.ab' )
mixvalidset = utils.getMixset(mix_dataset_dirs, folderName= 'features', abname = 'valid_dataset.ab' )
trainset = mixtrainset
validset = mixvalidset
train_loader = DataLoader( trainset, batch_size=4, shuffle=True)
valid_loader = DataLoader( validset, batch_size = 2, shuffle = True)
model = RNNmodel()
model.cuda(cuda_num)
optimizer = torch.optim.Adam(
model.parameters(),
lr=lr,
weight_decay= 0.00001
)
optimizer = Lookahead(optimizer)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=0.3,
patience=80,
cooldown=10
)
es = utils.EarlyStopping(patience= patience)
t = tqdm.trange(1, train_epochs +1, disable = False)
train_losses = []
valid_losses = []
train_times = []
lr_change_epoch = []
best_epoch = 0
stop_t = 0
for epoch in t:
# break
t.set_description("Training Epoch")
end = time.time()
train_loss = train(model, device, train_loader, optimizer)
valid_loss = valid(model, device, valid_loader)
scheduler.step(valid_loss)
train_losses.append(train_loss.item())
valid_losses.append(valid_loss.item())
t.set_postfix(
train_loss=train_loss.item(), val_loss=valid_loss.item()
)
stop = es.step(valid_loss.item())
if valid_loss.item() == es.best:
best_epoch = epoch
utils.save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_loss': es.best,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict()
},
is_best=valid_loss == es.best,
path=exp_dir,
target='RNNBeatProc'
)
# save params
params = {
'epochs_trained': epoch,
'best_loss': es.best,
'best_epoch': best_epoch,
'train_loss_history': train_losses,
'valid_loss_history': valid_losses,
'train_time_history': train_times,
'num_bad_epochs': es.num_bad_epochs,
'lr_change_epoch': lr_change_epoch,
'stop_t': stop_t,
'model_info': model_info,
}
with open(os.path.join(target_jsonpath, 'RNNbeat' + '.json'), 'w') as outfile:
outfile.write(json.dumps(params, indent=4, sort_keys=True))
train_times.append(time.time() - end)
if stop:
print("Apply Early Stopping and retrain")
stop_t +=1
if stop_t >=5:
break
lr = lr*0.2
lr_change_epoch.append(epoch)
optimizer = torch.optim.Adam(
model.parameters(),
lr=lr,
weight_decay= 0.00001
)
optimizer = Lookahead(optimizer)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=0.3,
patience=80,
cooldown=10
)
es = utils.EarlyStopping(patience= patience, best_loss = es.best)
if __name__ == "__main__":
main()