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train_graph_au.py
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import os
import numpy as np
import torch
from torch.utils.data import DataLoader
import torch.optim as optim
from tqdm import tqdm
import logging
from model.graph_au_pre import *
from data_loader import *
from pre_process import *
from utils import *
from losses import *
from lr_schedule import *
def get_dataloader(conf_graph_au):
print('==> Preparing data...')
if conf_graph_au.dataset == 'BP4D':
if conf_graph_au.info_nce == None:
trainset = BP4D(root_path=conf_graph_au.dataset_path, train = True, if_200 = conf_graph_au.if_200, info_nce = conf_graph_au.info_nce,
fold = conf_graph_au.fold, transform = image_train_enhance(crop_size=conf_graph_au.crop_size), target_transform = None,
crop_size = conf_graph_au.crop_size)
train_loader = DataLoader(trainset, batch_size = conf_graph_au.batch_size, shuffle = True, num_workers = conf_graph_au.num_workers, pin_memory=True)
valset = BP4D(root_path=conf_graph_au.dataset_path, train = False, if_200 = conf_graph_au.if_200, info_nce = conf_graph_au.info_nce,
fold = conf_graph_au.fold, transform = image_test(crop_size=conf_graph_au.crop_size), target_transform = None,
crop_size = conf_graph_au.crop_size)
val_loader = DataLoader(valset, batch_size = conf_graph_au.batch_size, shuffle = False, num_workers = conf_graph_au.num_workers, pin_memory=True)
return train_loader, val_loader, len(trainset), len(valset)
elif conf_graph_au.info_nce == 'enhance':
trainset = BP4D(root_path=conf_graph_au.dataset_path, train = True, if_200 = conf_graph_au.if_200, info_nce = conf_graph_au.info_nce,
fold = conf_graph_au.fold, transform = image_train_enhance(crop_size=conf_graph_au.crop_size),
target_transform = image_train_enhance(crop_size=conf_graph_au.crop_size), crop_size = conf_graph_au.crop_size)
train_loader = DataLoader(trainset, batch_size = conf_graph_au.batch_size, shuffle = True, num_workers = conf_graph_au.num_workers, pin_memory=True)
return train_loader, len(trainset)
elif conf_graph_au.dataset == 'DISFA':
if conf_graph_au.info_nce == None:
trainset = DISFA(root_path=conf_graph_au.dataset_path, train = True, if_200 = conf_graph_au.if_200, info_nce = conf_graph_au.info_nce,
fold = conf_graph_au.fold, transform = image_train_enhance(crop_size=conf_graph_au.crop_size), target_transform = None,
crop_size = conf_graph_au.crop_size)
train_loader = DataLoader(trainset, batch_size = conf_graph_au.batch_size, shuffle = True, num_workers = conf_graph_au.num_workers, pin_memory=True)
valset = DISFA(root_path=conf_graph_au.dataset_path, train = False, if_200 = conf_graph_au.if_200, info_nce = conf_graph_au.info_nce,
fold = conf_graph_au.fold, transform = image_test(crop_size=conf_graph_au.crop_size), target_transform = None,
crop_size = conf_graph_au.crop_size)
val_loader = DataLoader(valset, batch_size = conf_graph_au.batch_size, shuffle = False, num_workers = conf_graph_au.num_workers, pin_memory=True)
return train_loader, val_loader, len(trainset), len(valset)
elif conf_graph_au.info_nce == 'enhance':
trainset = DISFA(root_path=conf_graph_au.dataset_path, train = True, if_200 = conf_graph_au.if_200, info_nce = conf_graph_au.info_nce,
fold = conf_graph_au.fold, transform = image_train_enhance(crop_size=conf_graph_au.crop_size),
target_transform = image_train_enhance(crop_size=conf_graph_au.crop_size), crop_size = conf_graph_au.crop_size)
train_loader = DataLoader(trainset, batch_size = conf_graph_au.batch_size, shuffle = True, num_workers = conf_graph_au.num_workers, pin_memory=True)
return train_loader, len(trainset)
def train_graphau(conf_graph_au, net, train_loader, optimizer, epoch, train_weight, lr, lr_scheduler):
net.train()
losses = AverageMeter()
for batch_idx, (inputs, labels) in enumerate(tqdm(train_loader)):
optimizer = lr_scheduler(param_lr = lr, optimizer = optimizer, epoch = epoch, gamma = conf_graph_au.gamma, stepsize = conf_graph_au.stepsize)
inputs, labels = inputs.float(), labels.float()
if torch.cuda.is_available():
inputs, labels = inputs.cuda(), labels.cuda()
optimizer.zero_grad()
outputs = net(inputs)
loss = au_softmax_loss(outputs, labels, train_weight)
loss.backward()
optimizer.step()
losses.update(loss.data.item(), inputs.size(0))
if batch_idx % 10 == 0:
print("epoch: {} batch_idx: {} loss_avg: {:.5f}".format(epoch, batch_idx, losses.avg))
return losses.avg
def train_graphau_nce(conf_graph_au, net, train_loader, optimizer, epoch, train_weight, lr, lr_scheduler):
net.train()
losses = AverageMeter()
for batch_idx, (inputs, inputs_e, labels) in enumerate(tqdm(train_loader)):
optimizer = lr_scheduler(param_lr = lr, optimizer = optimizer, epoch = epoch, gamma = conf_graph_au.gamma, stepsize = conf_graph_au.stepsize)
inputs, inputs_e, labels = inputs.float(), inputs_e.float(), labels.float()
if torch.cuda.is_available():
inputs, inputs_e, labels = inputs.cuda(), inputs_e.cuda(), labels.cuda()
optimizer.zero_grad()
outputs = net(inputs)
outputs_e = net(inputs_e)
loss = au_infonce_loss(outputs, outputs_e, labels, train_weight, probability=conf_graph_au.probability)
loss.backward()
optimizer.step()
losses.update(loss.data.item(), inputs.size(0))
if batch_idx % 10 == 0:
print("epoch: {} batch_idx: {} loss_avg: {:.5f}".format(epoch, batch_idx, losses.avg))
return losses.avg
def val_graphau(conf_graph_au, net, val_loader, test_weight):
net.eval()
losses = AverageMeter()
statistics_list = None
for batch_idx, (inputs, labels) in enumerate(tqdm(val_loader)):
with torch.no_grad():
inputs, labels = inputs.float(), labels.float()
if torch.cuda.is_available():
inputs, labels = inputs.cuda(), labels.cuda()
outputs = net(inputs)
loss = au_softmax_loss(outputs, labels, test_weight)
losses.update(loss.data.item(), inputs.size(0))
update_list = statistics(outputs[:, 0, :], labels.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_graph_au):
if conf_graph_au.dataset == 'BP4D':
dataset_info = BP4D_infolist
elif conf_graph_au.dataset == 'DISFA':
dataset_info = DISFA_infolist
if conf_graph_au.info_nce == None:
train_loader, val_loader, train_data_num, val_data_num = get_dataloader(conf_graph_au)
elif conf_graph_au.info_nce == 'enhance':
train_loader, train_data_num = get_dataloader(conf_graph_au)
train_weight = torch.from_numpy(np.loadtxt(os.path.join(conf_graph_au.dataset_path, 'list_256x256', conf_graph_au.dataset+'_train_weight_fold' + str(conf_graph_au.fold)+'.txt'))).cuda()
test_weight = torch.from_numpy(np.loadtxt(os.path.join(conf_graph_au.dataset_path, 'list_256x256', conf_graph_au.dataset+'_test_weight_fold' + str(conf_graph_au.fold)+'.txt'))).cuda()
logging.info("Fold: [{} | {}]".format(conf_graph_au.fold, conf_graph_au.N_fold))
if conf_graph_au.info_nce == None:
net = Graph_au_net(au_num_classes=conf_graph_au.au_num_classes, backbone=conf_graph_au.backbone,
neighbor_num=conf_graph_au.neighbor_num, metric=conf_graph_au.metric).cuda()
for p in net.parameters():
p.requires_grad = True
elif conf_graph_au.info_nce == 'enhance':
net = Graph_au_net_NCE(au_num_classes=conf_graph_au.au_num_classes, backbone=conf_graph_au.backbone,
neighbor_num=conf_graph_au.neighbor_num, metric=conf_graph_au.metric).cuda()
for p in net.parameters():
p.requires_grad = True
if conf_graph_au.start_epoch > 0:
logging.info("Resume form epoch {} ".format(conf_graph_au.start_epoch))
if conf_graph_au.info_nce == None:
net.load_state_dict(torch.load(os.path.join(conf_graph_au['outdir'], 'epoch' + str(conf_graph_au.start_epoch) + '_graphau_net_model_fold' + str(conf_graph_au.fold) + '.pth')))
elif conf_graph_au.info_nce == 'enhance':
net.load_state_dict(torch.load(os.path.join(conf_graph_au['outdir'], 'epoch' + str(conf_graph_au.start_epoch) + '_graphau_net_nce_model_fold' + str(conf_graph_au.fold) + '.pth')))
if conf_graph_au.optimizer_type == 'SGD':
optimizer = optim.SGD(net.parameters(), lr=conf_graph_au.init_lr, momentum = conf_graph_au.momentum, weight_decay = conf_graph_au.weight_decay, nesterov = conf_graph_au.use_nesterov)
elif conf_graph_au.optimizer_type == 'AdamW':
optimizer = optim.AdamW(net.parameters(), lr=conf_graph_au.init_lr, betas=(0.9, 0.999), weight_decay = conf_graph_au.weight_decay)
lr_scheduler = schedule_dict[conf_graph_au.lr_type]
print('the init learning rate is ', conf_graph_au.init_lr)
for epoch in range(conf_graph_au.start_epoch + 1, conf_graph_au.n_epochs + 1):
lr = optimizer.param_groups[0]['lr']
logging.info("Epoch: [{} | {} LR: {} ]".format(epoch, conf_graph_au.n_epochs, lr))
if conf_graph_au.info_nce == None:
train_loss = train_graphau(conf_graph_au, net, train_loader, optimizer, epoch, train_weight, lr, lr_scheduler)
val_loss, val_mean_f1_score, val_f1_score, val_mean_acc, val_acc = val_graphau(conf_graph_au, net, val_loader, test_weight)
infostr = {'Epoch: {} train_loss: {:.5f} val_loss: {:.5f} val_mean_f1_score {:.2f} val_mean_acc {:.2f}'
.format(epoch, train_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)
torch.save(net.state_dict(), os.path.join(conf_graph_au['outdir'], 'epoch' + str(epoch) + '_graphau_net_model_fold' + str(conf_graph_au.fold) + '.pth'))
elif conf_graph_au.info_nce == 'enhance':
train_loss = train_graphau_nce(conf_graph_au, net, train_loader, optimizer, epoch, train_weight, lr, lr_scheduler)
infostr = {'Epoch: {} train_loss: {:.5f}'.format(epoch, train_loss)}
logging.info(infostr)
torch.save(net.state_dict(), os.path.join(conf_graph_au['outdir'], 'epoch' + str(epoch) + '_graphau_net_nce_model_fold' + str(conf_graph_au.fold) + '.pth'))
if __name__=="__main__":
conf_graph_au = get_config_graphau()
set_env(conf_graph_au)
set_outdir(conf_graph_au)
set_logger(conf_graph_au)
main(conf_graph_au)