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# -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li ([email protected])
# -----------------------------------------------------
import torch
import torch.utils.data
from .utils.dataset import coco
from opt import opt
from tqdm import tqdm
from models.FastPose import createModel
from .utils.eval import DataLogger, accuracy
from .utils.img import flip, shuffleLR
from .evaluation import prediction
from tensorboardX import SummaryWriter
import os
def train(train_loader, m, criterion, optimizer, writer):
lossLogger = DataLogger()
accLogger = DataLogger()
m.train()
train_loader_desc = tqdm(train_loader)
for i, (inps, labels, setMask, imgset) in enumerate(train_loader_desc):
inps = inps.requires_grad_()
labels = labels
setMask = setMask
out = m(inps)
loss = criterion(out.mul(setMask), labels)
acc = accuracy(out.data.mul(setMask), labels.data, train_loader.dataset)
accLogger.update(acc[0], inps.size(0))
lossLogger.update(loss.item(), inps.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
opt.trainIters += 1
# Tensorboard
writer.add_scalar(
'Train/Loss', lossLogger.avg, opt.trainIters)
writer.add_scalar(
'Train/Acc', accLogger.avg, opt.trainIters)
# TQDM
train_loader_desc.set_description(
'loss: {loss:.8f} | acc: {acc:.2f}'.format(
loss=lossLogger.avg,
acc=accLogger.avg * 100)
)
train_loader_desc.close()
return lossLogger.avg, accLogger.avg
def valid(val_loader, m, criterion, optimizer, writer):
lossLogger = DataLogger()
accLogger = DataLogger()
m.eval()
val_loader_desc = tqdm(val_loader)
for i, (inps, labels, setMask, imgset) in enumerate(val_loader_desc):
inps = inps
labels = labels
setMask = setMask
with torch.no_grad():
out = m(inps)
loss = criterion(out.mul(setMask), labels)
flip_out = m(flip(inps))
flip_out = flip(shuffleLR(flip_out, val_loader.dataset))
out = (flip_out + out) / 2
acc = accuracy(out.mul(setMask), labels, val_loader.dataset)
lossLogger.update(loss.item(), inps.size(0))
accLogger.update(acc[0], inps.size(0))
opt.valIters += 1
# Tensorboard
writer.add_scalar(
'Valid/Loss', lossLogger.avg, opt.valIters)
writer.add_scalar(
'Valid/Acc', accLogger.avg, opt.valIters)
val_loader_desc.set_description(
'loss: {loss:.8f} | acc: {acc:.2f}'.format(
loss=lossLogger.avg,
acc=accLogger.avg * 100)
)
val_loader_desc.close()
return lossLogger.avg, accLogger.avg
def main():
# Model Initialize
m = createModel()
if opt.loadModel:
print('Loading Model from {}'.format(opt.loadModel))
m.load_state_dict(torch.load(opt.loadModel, map_location=torch.device('cpu')))
if not os.path.exists("../exp/{}/{}".format(opt.dataset, opt.expID)):
try:
os.mkdir("../exp/{}/{}".format(opt.dataset, opt.expID))
except FileNotFoundError:
os.mkdir("../exp/{}".format(opt.dataset))
os.mkdir("../exp/{}/{}".format(opt.dataset, opt.expID))
else:
print('Create new model')
if not os.path.exists("../exp/{}/{}".format(opt.dataset, opt.expID)):
try:
os.mkdir("../exp/{}/{}".format(opt.dataset, opt.expID))
except FileNotFoundError:
os.mkdir("../exp/{}".format(opt.dataset))
os.mkdir("../exp/{}/{}".format(opt.dataset, opt.expID))
criterion = torch.nn.MSELoss()
if opt.optMethod == 'rmsprop':
optimizer = torch.optim.RMSprop(m.parameters(),
lr=opt.LR,
momentum=opt.momentum,
weight_decay=opt.weightDecay)
elif opt.optMethod == 'adam':
optimizer = torch.optim.Adam(
m.parameters(),
lr=opt.LR
)
else:
raise Exception
writer = SummaryWriter(
'.tensorboard/{}/{}'.format(opt.dataset, opt.expID))
# Prepare Dataset
if opt.dataset == 'coco':
train_dataset = coco.Mscoco(train=True)
val_dataset = coco.Mscoco(train=False)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=opt.trainBatch, shuffle=True, num_workers=opt.nThreads, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=opt.validBatch, shuffle=False, num_workers=opt.nThreads, pin_memory=True)
# Model Transfer
m = torch.nn.DataParallel(m)
# Start Training
for i in range(opt.nEpochs):
opt.epoch = i
print('############# Starting Epoch {} #############'.format(opt.epoch))
loss, acc = train(train_loader, m, criterion, optimizer, writer)
print('Train-{idx:d} epoch | loss:{loss:.8f} | acc:{acc:.4f}'.format(
idx=opt.epoch,
loss=loss,
acc=acc
))
opt.acc = acc
opt.loss = loss
m_dev = m.module
if i % opt.snapshot == 0:
torch.save(
m_dev.state_dict(), '../exp/{}/{}/model_{}.pkl'.format(opt.dataset, opt.expID, opt.epoch))
torch.save(
opt, '../exp/{}/{}/option.pkl'.format(opt.dataset, opt.expID, opt.epoch))
torch.save(
optimizer, '../exp/{}/{}/optimizer.pkl'.format(opt.dataset, opt.expID))
loss, acc = valid(val_loader, m, criterion, optimizer, writer)
print('Valid-{idx:d} epoch | loss:{loss:.8f} | acc:{acc:.4f}'.format(
idx=i,
loss=loss,
acc=acc
))
'''
if opt.dataset != 'mpii':
with torch.no_grad():
mAP, mAP5 = prediction(m)
print('Prediction-{idx:d} epoch | mAP:{mAP:.3f} | mAP0.5:{mAP5:.3f}'.format(
idx=i,
mAP=mAP,
mAP5=mAP5
))
'''
writer.close()
if __name__ == '__main__':
main()
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