def main():
global args, best_prec1
args = parser.parse_args()
print('\n\t\t\t\t Aum Sri Sai Ram\nFER on FERPLUS using OADN \n\n')
print(args)
print('\nimg_dir: ', args.root_path)
print('\nTraining mode: ', args.mode)
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
imagesize = args.imagesize
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.Resize((args.imagesize, args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
valid_transform = transforms.Compose([
transforms.Resize((args.imagesize, args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
#train_data = ImageList(root=args.root_path +'Images/FER2013TrainValid/' , fileList=args.train_list, transform=train_transform, mode = args.mode)
train_data = ImageList(root=args.root_path + 'Images/FER2013TrainValid/',
landmarksfile=args.train_landmarksfile,
fileList=args.train_list,
transform=train_transform,
mode='majority')
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
#test_data = ImageList(root=args.root_path+'Images/FER2013Test/', fileList = args.test_list, transform = valid_transform, mode = args.mode)
test_data = ImageList(root=args.root_path + 'Images/FER2013Test/',
landmarksfile=args.test_landmarksfile,
fileList=args.test_list,
transform=valid_transform,
mode='majority')
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=args.batch_size_t,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
print('length of train+valid Database for training: ' +
str(len(train_loader.dataset)))
print('length of test Database: ' + str(len(test_loader.dataset)))
# prepare model
basemodel = resnet50(pretrained=False)
attention_model = AttentionBranch(inputdim=2048,
num_maps=24,
num_classes=args.num_classes)
region_model = RegionBranch(inputdim=2048,
num_regions=4,
num_classes=args.num_classes)
basemodel = torch.nn.DataParallel(basemodel).to(device)
attention_model = torch.nn.DataParallel(attention_model).to(device)
region_model = torch.nn.DataParallel(region_model).to(device)
print('\nNumber of parameters:')
print(
'Base Model: {}, Attention Branch:{}, Region Branch:{} and Total: {}'.
format(
count_parameters(basemodel), count_parameters(attention_model),
count_parameters(region_model),
count_parameters(basemodel) + count_parameters(attention_model) +
count_parameters(region_model)))
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD([{
"params": region_model.parameters(),
"lr": args.lr,
"momentum": args.momentum,
"weight_decay": args.weight_decay
}])
optimizer.add_param_group({
"params": attention_model.parameters(),
"lr": args.lr,
"momentum": args.momentum,
"weight_decay": args.weight_decay
})
optimizer.add_param_group({
"params": basemodel.parameters(),
"lr": 0.0001,
"momentum": args.momentum,
"weight_decay": args.weight_decay
})
if args.pretrained:
util.load_state_dict(
basemodel, 'pretrainedmodels/vgg_msceleb_resnet50_ft_weight.pkl')
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
basemodel.load_state_dict(checkpoint['base_state_dict'])
attention_model.load_state_dict(checkpoint['attention_state_dict'])
region_model.load_state_dict(checkpoint['region_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
print('\nTraining starting:\n')
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, basemodel, attention_model, region_model,
criterion, optimizer, epoch)
prec1 = validate(test_loader, basemodel, attention_model, region_model,
criterion, epoch)
print("Epoch: {} Test Acc: {}".format(epoch, prec1))
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1.to(device).item(), best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'base_state_dict': basemodel.state_dict(),
'attention_state_dict': attention_model.state_dict(),
'region_state_dict': region_model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
}, is_best.item())
def main():
#Print args
global args, best_prec1
args = parser.parse_args()
print('\n\t\t\t\t Aum Sri Sai Ram\nFER on FEDRO using Local and global Attention along with region branch (non-overlapping patches)\n\n')
print(args)
print('\nimg_dir: ', args.root_path)
print('\ntrain rule: ',args.train_rule, ' and loss type: ', args.loss_type, '\n')
print('\nlr is : ', args.lr)
print('img_dir:', args.root_path)
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
imagesize = args.imagesize
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.ColorJitter(brightness=0.4, contrast = 0.3, saturation = 0.25, hue = 0.05),
transforms.Resize((args.imagesize, args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean,std)
])
valid_transform = transforms.Compose([
transforms.Resize((args.imagesize,args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean,std)
])
val_data = TestList(root='../data/FED_RO/FED_RO_crop/', fileList=args.valid_list,
transform=valid_transform)
val_loader = torch.utils.data.DataLoader(val_data, args.batch_size, shuffle=False, num_workers=8)
train_dataset = TrainList(root=args.root_path, fileList = args.train_list,
transform=train_transform)
if args.train_rule == 'None':
train_sampler = None
per_cls_weights = None
elif args.train_rule == 'Resample':
train_sampler = ImbalancedDatasetSampler(train_dataset)
per_cls_weights = None
elif args.train_rule == 'Reweight':
train_sampler = None
beta = 0.9999 #0:normal weighting
effective_num = 1.0 - np.power(beta, cls_num_list)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
per_cls_weights = per_cls_weights / np.sum(per_cls_weights) * len(cls_num_list)
per_cls_weights = torch.FloatTensor(per_cls_weights).to(device)
if args.loss_type == 'CE':
criterion = nn.CrossEntropyLoss(weight=per_cls_weights).to(device)
elif args.loss_type == 'Focal':
criterion = FocalLoss(weight=per_cls_weights, gamma=2).to(device)
else:
warnings.warn('Loss type is not listed')
return
train_loader = torch.utils.data.DataLoader(train_dataset, args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
print('length of AffectNet + RAFDB train Database: ' + str(len(train_dataset)))
print('length of FEDRO occlusion Database: ' + str(len(val_loader.dataset)))
# prepare model
basemodel = resnet50(pretrained = False)
attention_model = AttentionBranch(inputdim = 512, num_regions = args.num_attentive_regions, num_classes = args.num_classes)
region_model = RegionBranch(inputdim = 1024, num_regions = args.num_regions, num_classes = args.num_classes)
basemodel = torch.nn.DataParallel(basemodel).to(device)
attention_model = torch.nn.DataParallel(attention_model).to(device)
region_model = torch.nn.DataParallel(region_model).to(device)
print('\nNumber of parameters:')
print('Base Model: {}, Attention Branch:{}, Region Branch:{} and Total: {}'.format(count_parameters(basemodel),count_parameters(attention_model), count_parameters(region_model), count_parameters(basemodel)+count_parameters(attention_model)+count_parameters(region_model)))
optimizer = torch.optim.SGD([{"params": basemodel.parameters(), "lr": 0.0001, "momentum":args.momentum,
"weight_decay":args.weight_decay}])
optimizer.add_param_group({"params": attention_model.parameters(), "lr": args.lr, "momentum":args.momentum,
"weight_decay":args.weight_decay})
optimizer.add_param_group({"params": region_model.parameters(), "lr": args.lr, "momentum":args.momentum,
"weight_decay":args.weight_decay})
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
basemodel.load_state_dict(checkpoint['base_state_dict'])
attention_model.load_state_dict(checkpoint['attention_state_dict'])
region_model.load_state_dict(checkpoint['region_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
print('\nTraining starting:\n')
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train(train_loader, basemodel, attention_model, region_model, criterion, optimizer, epoch)
adjust_learning_rate(optimizer, epoch)
prec1 = validate(val_loader, basemodel, attention_model, region_model, criterion, epoch)
print("Epoch: {} Test Acc: {}".format(epoch, prec1))
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1.to(device).item(), best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'base_state_dict': basemodel.state_dict(),
'attention_state_dict': attention_model.state_dict(),
'region_state_dict': region_model.state_dict(),
'best_prec1': best_prec1,
'optimizer' : optimizer.state_dict(),
}, is_best.item())
def main():
#Print args
global args, best_prec1
args = parser.parse_args()
print('\n\t\t\t\t Aum Sri Sai Ram\nFER on SFEW using OADN\n\n')
print(args)
print('\nimg_dir: ', args.root_path)
print('\ntrain rule: ', args.train_rule, ' and loss type: ',
args.loss_type, '\n')
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
imagesize = args.imagesize
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.Resize((args.imagesize, args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
valid_transform = transforms.Compose([
transforms.Resize((args.imagesize, args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
val_data = ImageList(root=args.root_path + 'Val_Aligned_Faces_SAN/',
fileList=args.valid_list,
landmarksfile=args.test_landmarksfile,
transform=valid_transform)
val_loader = torch.utils.data.DataLoader(val_data,
args.batch_size,
shuffle=False,
num_workers=8)
train_dataset = ImageList(root=args.root_path + 'Train_Aligned_Faces_SAN/',
fileList=args.train_list,
landmarksfile=args.train_landmarksfile,
transform=train_transform)
if args.train_rule == 'None':
train_sampler = None
per_cls_weights = None
if args.loss_type == 'CE':
criterion = nn.CrossEntropyLoss(weight=per_cls_weights).to(device)
else:
warnings.warn('Loss type is not listed')
return
train_loader = torch.utils.data.DataLoader(train_dataset,
args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
print('length of SFEW train Database: ' + str(len(train_dataset)))
print('length of SFEW valid Database: ' + str(len(val_loader.dataset)))
# prepare model
basemodel = resnet50(pretrained=False)
attention_model = AttentionBranch(inputdim=2048,
num_maps=24,
num_classes=args.num_classes)
region_model = RegionBranch(inputdim=2048,
num_regions=4,
num_classes=args.num_classes)
basemodel = torch.nn.DataParallel(basemodel).to(device)
attention_model = torch.nn.DataParallel(attention_model).to(device)
region_model = torch.nn.DataParallel(region_model).to(device)
print('\nNumber of parameters:')
print(
'Base Model: {}, Attention Branch:{}, Region Branch:{} and Total: {}'.
format(
count_parameters(basemodel), count_parameters(attention_model),
count_parameters(region_model),
count_parameters(basemodel) + count_parameters(attention_model) +
count_parameters(region_model)))
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD([{
"params": region_model.parameters(),
"lr": args.lr,
"momentum": args.momentum,
"weight_decay": args.weight_decay
}])
optimizer.add_param_group({
"params": attention_model.parameters(),
"lr": args.lr,
"momentum": args.momentum,
"weight_decay": args.weight_decay
})
optimizer.add_param_group({
"params": basemodel.parameters(),
"lr": 0.0001,
"momentum": args.momentum,
"weight_decay": args.weight_decay
})
if args.pretrained:
util.load_state_dict(
basemodel, 'pretrainedmodels/vgg_msceleb_resnet50_ft_weight.pkl')
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
basemodel.load_state_dict(checkpoint['base_state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
print('\nTraining starting:\n')
for epoch in range(args.start_epoch, args.epochs):
train(train_loader, basemodel, attention_model, region_model,
criterion, optimizer, epoch)
prec1 = validate(val_loader, basemodel, attention_model, region_model,
criterion, epoch)
print("Epoch: {} Test Acc: {}".format(epoch, prec1))
is_best = prec1 > best_prec1
best_prec1 = max(prec1.to(device).item(), best_prec1)
adjust_learning_rate(optimizer, epoch)
save_checkpoint(
{
'epoch': epoch + 1,
'base_state_dict': basemodel.state_dict(),
'attention_state_dict': attention_model.state_dict(),
'region_state_dict': region_model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
}, is_best.item())
def main():
global args, best_prec1
args = parser.parse_args()
print(
'\n\t\t Aum Sri Sai Ram\n\t\tRAFDB FER using Attention branch based on gaussian maps with region branch\n\n'
)
print(args)
print('img_dir:', args.root_path)
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
imagesize = args.imagesize
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.Resize((args.imagesize, args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
valid_transform = transforms.Compose([
transforms.Resize((args.imagesize, args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
train_dataset = ImageList(root=args.root_path,
landmarksfile=args.landmarksfile,
fileList=args.train_list,
transform=train_transform)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
test_data = ImageList(root=args.root_path,
fileList=args.test_list,
landmarksfile=args.landmarksfile,
transform=valid_transform)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=args.batch_size_t,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
print('length of RAFDB train Database: ' + str(len(train_dataset)))
print('length of RAFDB test Database: ' + str(len(test_loader.dataset)))
# prepare model
# prepare model
basemodel = resnet50(pretrained=False)
attention_model = LandmarksAttentionBranch(inputdim=1024,
num_maps=24,
num_classes=args.num_classes)
region_model = RegionBranch(inputdim=1024,
num_regions=args.num_regions,
num_classes=args.num_classes)
basemodel = torch.nn.DataParallel(basemodel).to(device)
attention_model = torch.nn.DataParallel(attention_model).to(device)
region_model = torch.nn.DataParallel(region_model).to(device)
print('\nNumber of parameters:')
print(
'Base Model: {}, Attention Branch:{}, Region Branch:{} and Total: {}'.
format(
count_parameters(basemodel), count_parameters(attention_model),
count_parameters(region_model),
count_parameters(basemodel) + count_parameters(attention_model) +
count_parameters(region_model)))
criterion = nn.CrossEntropyLoss().to(device)
optimizer1 = torch.optim.SGD([{
"params": basemodel.parameters(),
"lr": args.lr,
"momentum": args.momentum,
"weight_decay": args.weight_decay
}])
optimizer1.add_param_group({
"params": attention_model.parameters(),
"lr": args.lr,
"momentum": args.momentum,
"weight_decay": args.weight_decay
})
optimizer1.add_param_group({
"params": region_model.parameters(),
"lr": args.lr,
"momentum": args.momentum,
"weight_decay": args.weight_decay
})
if args.pretrained:
pretrained_state_dict = torch.load(
'pretrainedmodels/resnet50-19c8e357.pth')
model_state_dict = basemodel.state_dict()
#print(model_state_dict.keys())
for key in pretrained_state_dict:
if ((key == 'fc.weight') | (key == 'fc.bias') |
(key == 'feature.weight') | (key == 'feature.bias') |
(key.find('layer4.') > -1)):
pass
else:
#print(key)
model_state_dict['module.' + key] = pretrained_state_dict[key]
basemodel.load_state_dict(model_state_dict, strict=True)
print('\nLoaded resent50 pretrained on imagenet.\n')
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
basemodel.load_state_dict(checkpoint['base_state_dict'])
attention_model.load_state_dict(checkpoint['attention_state_dict'])
region_model.load_state_dict(checkpoint['region_state_dict'])
optimizer1.load_state_dict(checkpoint['optimizer1'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
prec1 = validate(test_loader, basemodel, attention_model, region_model,
criterion, 0)
print("Epoch: {} Test Acc: {}".format(0, prec1))
assert (False)
print('\nTraining starting:\n')
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
adjust_learning_rate(optimizer1, epoch)
# train for one epoch
train(train_loader, basemodel, attention_model, region_model,
criterion, optimizer1, epoch)
prec1 = validate(test_loader, basemodel, attention_model, region_model,
criterion, epoch)
print("Epoch: {} Test Acc: {}".format(epoch, prec1))
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1.to(device).item(), best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'base_state_dict': basemodel.state_dict(),
'attention_state_dict': attention_model.state_dict(),
'region_state_dict': region_model.state_dict(),
'best_prec1': best_prec1,
'optimizer1': optimizer1.state_dict(),
#'optimizer2' : optimizer2.state_dict(),
},
is_best.item())
def main():
#Print args
global args, best_prec1
args = parser.parse_args()
print('\n\t\t\t\t Aum Sri Sai Ram\nFER on AffectWild2 using Local and global Attention along with region branch (non-overlapping patches)\n\n')
print(args)
print('\nimg_dir: ', args.root_path)
print('\ntrain rule: ',args.train_rule, ' and loss type: ', args.loss_type, '\n')
print('\n lr is : ', args.lr)
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
imagesize = args.imagesize
best_expr_f1 = 0
final_cm = 0
final_mcm = 0
best_prec1 = 0
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.ColorJitter(brightness=0.4, contrast = 0.3, saturation = 0.25, hue = 0.05),
transforms.Resize((args.imagesize,args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean,std)
])
valid_transform = transforms.Compose([
transforms.Resize((args.imagesize,args.imagesize)),
transforms.ToTensor(),
transforms.Normalize(mean,std)
])
val_data = ImageList(root=args.root_path, fileList = args.metafile, train_mode='Validation',
transform=valid_transform)
val_loader = torch.utils.data.DataLoader(val_data, args.batch_size, shuffle=False, num_workers=8)
train_dataset = ImageList(root=args.root_path, fileList = args.metafile,train_mode='Train',
transform=train_transform)
cls_num_list = train_dataset.get_cls_num_list()
print('\nTrain cls num list:', cls_num_list)
if args.train_rule == 'None':
train_sampler = None
per_cls_weights = None
elif args.train_rule == 'Reweight':
train_sampler = None
beta = 0.9999 #0:normal weighting
effective_num = 1.0 - np.power(beta, cls_num_list)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
per_cls_weights = per_cls_weights / np.sum(per_cls_weights) * len(cls_num_list)
per_cls_weights = torch.FloatTensor(per_cls_weights).to(device)
if args.loss_type == 'CE':
criterion = nn.CrossEntropyLoss(weight=per_cls_weights).to(device)
elif args.loss_type == 'Focal':
criterion = FocalLoss(weight=per_cls_weights, gamma=2).to(device)
else:
warnings.warn('Loss type is not listed')
return
train_loader = torch.utils.data.DataLoader(train_dataset, args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
print('\nlength of AffectWild2 train Database: ' + str(len(train_dataset)))
print('\nlength of AffectWild2 valid Database: ' + str(len(val_loader.dataset)))
# prepare model
basemodel = resnet50(pretrained = False)
attention_model = AttentionBranch(inputdim = 512, num_regions = args.num_attentive_regions, num_classes = args.num_classes)
region_model = RegionBranch(inputdim = 1024, num_regions = args.num_regions, num_classes = args.num_classes)
basemodel = torch.nn.DataParallel(basemodel).to(device)
attention_model = torch.nn.DataParallel(attention_model).to(device)
region_model = torch.nn.DataParallel(region_model).to(device)
print('\nNumber of parameters:')
print('Base Model: {}, Attention Branch:{}, Region Branch:{} and Total: {}'.format(count_parameters(basemodel),count_parameters(attention_model), count_parameters(region_model), count_parameters(basemodel)+count_parameters(attention_model)+count_parameters(region_model)))
optimizer = torch.optim.SGD([{"params": basemodel.parameters(), "lr": 0.0001, "momentum":args.momentum,
"weight_decay":args.weight_decay}])
optimizer.add_param_group({"params": attention_model.parameters(), "lr": args.lr, "momentum":args.momentum,
"weight_decay":args.weight_decay})
optimizer.add_param_group({"params": region_model.parameters(), "lr": args.lr, "momentum":args.momentum,
"weight_decay":args.weight_decay})
if args.pretrained:
util.load_state_dict(basemodel,'pretrainedmodels/vgg_msceleb_resnet50_ft_weight.pkl')
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
basemodel.load_state_dict(checkpoint['base_state_dict'])
attention_model.load_state_dict(checkpoint['attention_state_dict'])
region_model.load_state_dict(checkpoint['region_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
