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baseline_ck_plus.py
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baseline_ck_plus.py
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import os
import pdb
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision.models as models
from basic_code import load, util, networks
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def main():
parser = argparse.ArgumentParser(description='PyTorch Frame Attention Network Training')
parser.add_argument('--epochs', default=60, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--lr', '--learning-rate', default=1e-1, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('-f', '--fold', default=10, type=int, help='which fold used for ck+ test')
parser.add_argument('-e', '--evaluate', default=False, dest='evaluate', action='store_true',
help='evaluate model on validation set')
args = parser.parse_args()
best_acc = 0
logger = util.Logger('./log/','baseline_ckplus')
''' Load data '''
video_root = './data/face/ck_face'
video_list = './data/txt/CK+_10-fold_sample_IDascendorder_step10.txt'
batchsize_train= 48
batchsize_eval= 64
train_loader, val_loader = load.ckplus_faces_baseline(video_root, video_list, args.fold, batchsize_train, batchsize_eval)
''' Load model '''
_structure = models.resnet18(num_classes=7)
_parameterDir = './pretrain_model/Resnet18_FER+_pytorch.pth.tar'
model = load.model_parameters(_structure, _parameterDir)
''' Loss & Optimizer '''
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), args.lr, momentum=0.9, weight_decay=1e-4)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.2)
cudnn.benchmark = True
''' Train & Eval '''
if args.evaluate == True:
logger.print('args.evaluate: {:}', args.evaluate)
val(val_loader, model, logger)
return
logger.print('baseline ck+ dataset, learning rate: {:}'.format(args.lr))
for epoch in range(args.epochs):
train(train_loader, model, optimizer, epoch)
acc_epoch = val(val_loader, model)
is_best = acc_epoch > best_acc
if is_best:
logger.print('better model!')
best_acc = max(acc_epoch, best_acc)
util.save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'accuracy': acc_epoch,
}, at_type='baseline')
lr_scheduler.step()
logger.print("epoch: {:} learning rate:{:}".format(epoch+1, optimizer.param_groups[0]['lr']))
def train(train_loader, model, optimizer, epoch):
losses = util.AverageMeter()
topframe = util.AverageMeter()
topVideoSoft = util.AverageMeter()
# switch to train mode
output_store_soft = []
target_store = []
index_vector = []
model.train()
for i, (input_var, target_var, index) in enumerate(train_loader):
target_var = target_var.to(DEVICE)
input_var = input_var.to(DEVICE)
# model
pred_score = model(input_var)
loss = F.cross_entropy(pred_score, target_var).sum()
output_store_soft.append(F.softmax(pred_score, dim=1))
target_store.append(target_var)
index_vector.append(index)
# measure accuracy and record loss
acc_iter = util.accuracy(pred_score.data, target_var, topk=(1,))
losses.update(loss.item(), input_var.size(0))
topframe.update(acc_iter[0], input_var.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % 200 == 0:
logger.print('Epoch: [{:3d}][{:3d}/{:3d}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc_Iter@1 {topframe.val:.3f} ({topframe.avg:.3f})\t'
.format(
epoch, i, len(train_loader), loss=losses, topframe=topframe))
index_vector = torch.cat(index_vector, dim=0) # [256] ... [256] ---> [21570]
index_matrix = []
for i in range(int(max(index_vector)) + 1):
index_matrix.append(index_vector == i)
index_matrix = torch.stack(index_matrix, dim=0).to(DEVICE).float() # [21570] ---> [380, 21570]
output_store_soft = torch.cat(output_store_soft, dim=0)
target_store = torch.cat(target_store, dim=0).float() # [256] ... [256] ---> [21570]
output_store_soft = index_matrix.mm(output_store_soft)
target_vector = index_matrix.mm(target_store.unsqueeze(1)).squeeze(1).div(
index_matrix.sum(1)).long() # [380,21570] * [21570,1] -> [380,1] / sum([21570,1]) -> [380]
prec_video_soft = util.accuracy(output_store_soft, target_vector, topk=(1,))
topVideoSoft.update(prec_video_soft[0].item(), i + 1)
logger.print(' *Acc@Video_soft {topsoft.avg:.3f} *Acc@Frame {topframe.avg:.3f} '.format(topsoft=topVideoSoft, topframe=topframe))
def val(train_loader, model):
topframe = util.AverageMeter()
topVideoSoft = util.AverageMeter()
# switch to train mode
output_store_soft = []
target_store = []
index_vector = []
model.eval()
with torch.no_grad():
for i, (input_var, target_var, index) in enumerate(train_loader):
target_var = target_var.to(DEVICE)
input_var = input_var.to(DEVICE)
# model
pred_score = model(input_var)
output_store_soft.append(F.softmax(pred_score, dim=1))
target_store.append(target_var)
index_vector.append(index)
# measure accuracy and record loss
acc_iter = util.accuracy(pred_score.data, target_var, topk=(1,))
topframe.update(acc_iter[0], input_var.size(0))
index_vector = torch.cat(index_vector, dim=0) # [256] ... [256] ---> [21570]
index_matrix = []
for i in range(int(max(index_vector)) + 1):
index_matrix.append(index_vector == i)
index_matrix = torch.stack(index_matrix, dim=0).to(DEVICE).float() # [21570] ---> [380, 21570]
output_store_soft = torch.cat(output_store_soft, dim=0)
target_store = torch.cat(target_store, dim=0).float() # [256] ... [256] ---> [21570]
output_store_soft = index_matrix.mm(output_store_soft)
target_vector = index_matrix.mm(target_store.unsqueeze(1)).squeeze(1).div(
index_matrix.sum(1)).long() # [380,21570] * [21570,1] -> [380,1] / sum([21570,1]) -> [380]
prec_video_soft = util.accuracy(output_store_soft, target_vector, topk=(1,))
topVideoSoft.update(prec_video_soft[0].item(), i + 1)
logger.print(' *Acc@Video {topVideo.avg:.3f} '.format(topVideo=topVideoSoft))
return topVideoSoft.avg
if __name__ == '__main__':
main()