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train_stage2.py
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train_stage2.py
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import os
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.optim as optim
from tqdm import tqdm
import logging
from model.MEFL import MEFARG
from dataset import *
from utils import *
from conf import get_config,set_logger,set_outdir,set_env
def get_dataloader(conf):
print('==> Preparing data...')
if conf.dataset == 'BP4D':
trainset = BP4D(conf.dataset_path, train=True, fold = conf.fold, transform=image_train(crop_size=conf.crop_size), crop_size=conf.crop_size, stage = 2)
train_loader = DataLoader(trainset, batch_size=conf.batch_size, shuffle=True, num_workers=conf.num_workers)
valset = BP4D(conf.dataset_path, train=False, fold=conf.fold, transform=image_test(crop_size=conf.crop_size), stage = 2)
val_loader = DataLoader(valset, batch_size=conf.batch_size, shuffle=False, num_workers=conf.num_workers)
elif conf.dataset == 'DISFA':
trainset = DISFA(conf.dataset_path, train=True, fold = conf.fold, transform=image_train(crop_size=conf.crop_size), crop_size=conf.crop_size, stage = 2)
train_loader = DataLoader(trainset, batch_size=conf.batch_size, shuffle=True, num_workers=conf.num_workers)
valset = DISFA(conf.dataset_path, train=False, fold=conf.fold, transform=image_test(crop_size=conf.crop_size), stage = 2)
val_loader = DataLoader(valset, batch_size=conf.batch_size, shuffle=False, num_workers=conf.num_workers)
return train_loader, val_loader, len(trainset), len(valset)
# Train
def train(conf, net, train_loader, optimizer, epoch, criterion):
losses = AverageMeter()
losses1 = AverageMeter()
losses2 = AverageMeter()
net.train()
train_loader_len = len(train_loader)
for batch_idx, (inputs, targets, relations) in enumerate(tqdm(train_loader)):
adjust_learning_rate(optimizer, epoch, conf.epochs, conf.learning_rate, batch_idx, train_loader_len)
targets, relations = targets.float(), relations.long()
if torch.cuda.is_available():
inputs, targets, relations = inputs.cuda(), targets.cuda(), relations.cuda()
optimizer.zero_grad()
outputs, outputs_relation = net(inputs)
wa_loss = criterion[0](outputs, targets)
edge_loss = criterion[1](outputs_relation.view(-1,4), relations.view(-1))
loss = wa_loss + conf.lam * edge_loss
loss.backward()
optimizer.step()
losses.update(loss.data.item(), inputs.size(0))
losses1.update(wa_loss.data.item(), inputs.size(0))
losses2.update(edge_loss.data.item(), inputs.size(0))
return losses.avg, losses1.avg, losses2.avg
# Val
def val(net, val_loader, criterion):
losses = AverageMeter()
net.eval()
statistics_list = None
for batch_idx, (inputs, targets) in enumerate(tqdm(val_loader)):
targets = targets.float()
with torch.no_grad():
if torch.cuda.is_available():
inputs, targets = inputs.cuda(), targets.cuda()
outputs, _ = net(inputs)
loss = criterion[0](outputs, targets)
losses.update(loss.data.item(), inputs.size(0))
update_list = statistics(outputs, targets.detach(), 0.5)
statistics_list = update_statistics_list(statistics_list, update_list)
mean_f1_score, f1_score_list = calc_f1_score(statistics_list)
mean_acc, acc_list = calc_acc(statistics_list)
return losses.avg, mean_f1_score, f1_score_list, mean_acc, acc_list
def main(conf):
if conf.dataset == 'BP4D':
dataset_info = BP4D_infolist
elif conf.dataset == 'DISFA':
dataset_info = DISFA_infolist
start_epoch = 0
# data
train_loader,val_loader,train_data_num,val_data_num = get_dataloader(conf)
train_weight = torch.from_numpy(np.loadtxt(os.path.join(conf.dataset_path, 'list', conf.dataset+'_weight_fold'+str(conf.fold)+'.txt')))
logging.info("Fold: [{} | {} val_data_num: {} ]".format(conf.fold + 1, conf.N_fold, val_data_num))
net = MEFARG(num_classes=conf.num_classes, backbone=conf.arc)
# resume
if conf.resume != '':
logging.info("Resume form | {} ]".format(conf.resume))
net = load_state_dict(net, conf.resume)
if torch.cuda.is_available():
net = nn.DataParallel(net).cuda()
train_weight = train_weight.cuda()
criterion = [WeightedAsymmetricLoss(weight=train_weight), nn.CrossEntropyLoss()]
optimizer = optim.AdamW(net.parameters(), betas=(0.9, 0.999), lr=conf.learning_rate, weight_decay=conf.weight_decay)
print('the init learning rate is ', conf.learning_rate)
#train and val
for epoch in range(start_epoch, conf.epochs):
lr = optimizer.param_groups[0]['lr']
logging.info("Epoch: [{} | {} LR: {} ]".format(epoch + 1, conf.epochs, lr))
train_loss, wa_loss, edge_loss = train(conf,net,train_loader,optimizer,epoch,criterion)
val_loss, val_mean_f1_score, val_f1_score, val_mean_acc, val_acc = val(net, val_loader, criterion)
# log
infostr = {'Epoch: {} train_loss: {:.5f} wa_loss: {:.5f} edge_loss: {:.5f} val_loss: {:.5f} val_mean_f1_score {:.2f},val_mean_acc {:.2f}'
.format(epoch + 1, train_loss, wa_loss, edge_loss, val_loss, 100.* val_mean_f1_score, 100.* val_mean_acc)}
logging.info(infostr)
infostr = {'F1-score-list:'}
logging.info(infostr)
infostr = dataset_info(val_f1_score)
logging.info(infostr)
infostr = {'Acc-list:'}
logging.info(infostr)
infostr = dataset_info(val_acc)
logging.info(infostr)
# save checkpoints
if (epoch+1) % 4 == 0:
checkpoint = {
'epoch': epoch,
'state_dict': net.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(checkpoint, os.path.join(conf['outdir'], 'epoch' + str(epoch + 1) + '_model_fold' + str(conf.fold + 1) + '.pth'))
checkpoint = {
'epoch': epoch,
'state_dict': net.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(checkpoint, os.path.join(conf['outdir'], 'cur_model_fold' + str(conf.fold + 1) + '.pth'))
# ---------------------------------------------------------------------------------
if __name__=="__main__":
conf = get_config()
set_env(conf)
# generate outdir name
set_outdir(conf)
# Set the logger
set_logger(conf)
main(conf)