def dataloaders():
# Data loading code
# 1. all mages are already aligned to 218*178;
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = CelebA(
args.root,
'train600.txt',
transforms.Compose([
transforms.RandomRotation(30),
transforms.RandomHorizontalFlip(),
# transforms.Resize((178,178)),
# transforms.CenterCrop((178,178)),
transforms.RandomResizedCrop(178, scale=(0.8, 1.0)),
# should not cut many info for multi label classification
transforms.ToTensor(),
normalize,
]))
val_dataset = CelebA(
args.root,
'val200.txt',
transforms.Compose([
transforms.Resize((178, 178)),
# transforms.CenterCrop((178,178)),
transforms.ToTensor(),
normalize,
]))
test_dataset = CelebA(
args.root,
'test200.txt',
transforms.Compose([
transforms.Resize((178, 178)),
# transforms.CenterCrop((178,178)),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True)
return train_loader, val_loader, test_loader
def get_ds_train(self):
if self.ds_train is None:
self.celeba = CelebA(self.path_img, self.path_ann, self.path_bbox)
self.persons = self.celeba.persons
self.ds_train = BufferDS(self.buffer_size, self.celeba,
self.batch_size)
return self.ds_train
def dataloaders():
# Data loading code
# 1. all mages are already aligned to 218*178;
normalize0 = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = CelebA(
'/home/MSAI/cgong002/acv_project_celeba/',
'train_attr_list.txt',
transforms.Compose([
transforms.RandomRotation(30),
transforms.RandomHorizontalFlip(),
# transforms.Resize((178,178)),
# transforms.CenterCrop((178,178)),
transforms.RandomResizedCrop(178, scale=(0.8, 1.0)),
# should not cut many info for multi label classification
transforms.ToTensor(),
# normalize,
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=512,
shuffle=True,
num_workers=7,
pin_memory=True)
return train_loader
def __init__(self, split, align=False, partition='all'):
if not partition == 'all':
name = 'person_' + partition + '_' + split
elif partition == 'all':
name = 'person_' + split
if align and (partition == 'all' or partition == 'face'):
name += '_align'
Imdb.__init__(self, name)
# Load two children dataset wrappers
self._face = CelebA(split, align=align)
self._clothes = DeepFashion(split)
# The class list is a combination of face and clothing attributes
self._classes = self._face.classes + self._clothes.classes
self._face_class_idx = range(self._face.num_classes)
self._clothes_class_idx = range(
self._face.num_classes,
self._face.num_classes + self._clothes.num_classes)
# load data path
self._data_path = os.path.join(self.data_path, 'imdb_PersonAttributes')
# load the image lists and attributes.
self._load_dataset(split, align, partition)
def main():
global args, best_prec1
args = parser.parse_args()
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size)
# Use CUDA
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
use_cuda = torch.cuda.is_available()
# Random seed
if args.manual_seed is None:
args.manual_seed = random.randint(1, 10000)
random.seed(args.manual_seed)
torch.manual_seed(args.manual_seed)
if use_cuda:
torch.cuda.manual_seed_all(args.manual_seed)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
elif args.arch.startswith('resnext'):
model = models.__dict__[args.arch](
baseWidth=args.base_width,
cardinality=args.cardinality,
)
elif args.arch.startswith('shufflenet'):
model = models.__dict__[args.arch](
groups=args.groups
)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if not args.distributed:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
else:
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
title = 'CelebA-' + args.arch
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
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']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
args.checkpoint = os.path.dirname(args.resume)
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.'])
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
color_jitter = transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)
train_dataset = CelebA(
args.data,
'train_40_att_list.txt',
transforms.Compose([
transforms.RandomResizedCrop(size=(218, 178)),
transforms.RandomHorizontalFlip(),
transforms.RandomApply([color_jitter], p=0.8),
transforms.RandomGrayscale(p=0.2),
GaussianBlur(kernel_size=int(0.1 * 178)),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.train_batch, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(
CelebA(args.data, 'val_40_att_list.txt', transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=args.test_batch, shuffle=False,
num_workers=args.workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(
CelebA(args.data, 'test_40_att_list.txt', transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=args.test_batch, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.evaluate:
validate(test_loader, model, criterion)
return
if args.private_test:
private_loader = torch.utils.data.DataLoader(
CelebA(args.data, 'testset', transforms.Compose([
transforms.ToTensor(),
normalize,
]),test=True),
batch_size=args.test_batch, shuffle=False,
num_workers=args.workers, pin_memory=True)
test(private_loader, model, criterion)
return
# visualization
writer = SummaryWriter(os.path.join(args.checkpoint, 'logs'))
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
lr = adjust_learning_rate(optimizer, epoch)
print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, lr))
# train for one epoch
train_loss, train_acc = train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
val_loss, prec1 = validate(val_loader, model, criterion)
# append logger file
logger.append([lr, train_loss, val_loss, train_acc, prec1])
# tensorboardX
writer.add_scalar('learning rate', lr, epoch + 1)
writer.add_scalars('loss', {'train loss': train_loss, 'validation loss': val_loss}, epoch + 1)
writer.add_scalars('accuracy', {'train accuracy': train_acc, 'validation accuracy': prec1}, epoch + 1)
#for name, param in model.named_parameters():
# writer.add_histogram(name, param.clone().cpu().data.numpy(), epoch + 1)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer' : optimizer.state_dict(),
}, is_best, checkpoint=args.checkpoint)
logger.close()
logger.plot()
savefig(os.path.join(args.checkpoint, 'log.eps'))
writer.close()
print('Best accuracy:')
print(best_prec1)
for f in f_list[-7:]:
f = os.path.join(path, f)
transfer_paths.append(f)
gif_images = []
for p in transfer_paths:
gif_images.append(imageio.imread(p))
gif_path = os.path.join(path, "transfer.gif")
imageio.mimsave(gif_path, gif_images, duration=0.1)
def get_middle_vectors(self, src, dst):
num = 5
delta = 1 / (num + 1)
alpha = delta
result = []
for i in range(num):
vec = src * (1 - alpha) + dst * alpha
result.append(vec)
alpha += delta
return result
if __name__ == '__main__':
path_img = '../samples/celeba/Img/img_align_celeba.zip'
path_ann = '../samples/celeba/Anno/identity_CelebA.txt'
path_bbox = '../samples/celeba/Anno/list_bbox_celeba.txt'
path_sample = '../samples/photos/'
ca = CelebA(path_img, path_ann, path_bbox)
# ca.pick_2_samples()
cfg = MyConfig()
cfg.from_cmd()
count = count + 1
iterr = count * show_every
# Show example output for the generator
images_grid = show_generator_output(
sess, 25, inp_z, data_shape[2], data_img_mode)
dst = os.path.join("output", str(epoch_i),
str(iterr) + ".png")
pyplot.imsave(dst, images_grid)
# saving the model
if epoch_i % 10 == 0:
if not os.path.exists('./model/'):
os.makedirs('./model')
saver.save(sess, './model/' + str(epoch_i))
# Get the data in a readable format
dataset = CelebA()
# Tensorflow
with tf.Graph().as_default():
train(cfg.NB_EPOCHS, cfg.BATCH_SIZE, cfg.SIZE_G_INPUT, cfg.LEARNING_RATE,
cfg.BETA1, dataset.get_batches, dataset.shape, dataset.image_mode)
for f in glob("output/**/*.png"):
image = cv2.imread(f)
# cv2.imshow('my_image', image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
large = cv2.resize(image, (0, 0), fx=3, fy=3)
cv2.imwrite(f, large)
split, align=True, partition='face'))
# PersonAttributes dataset (clothes partition)
for split in ['train', 'val', 'trainval', 'test']:
name = 'person_clothes_{}'.format(split)
__sets[name] = (
lambda split=split: PersonAttributes(split, partition='clothes'))
# setup DeepFashion dataset
for split in ['train', 'val', 'test', 'trainval']:
name = 'deepfashion_{}'.format(split)
__sets[name] = (lambda split=split: DeepFashion(split))
# setup CelebA dataset
for split in ['train', 'val', 'test', 'trainval']:
name = 'celeba_{}'.format(split)
__sets[name] = (lambda split=split: CelebA(split))
# setup CelebA (aligned) dataset
for split in ['train', 'val', 'test', 'trainval']:
name = 'celeba_{}_align'.format(split)
__sets[name] = (lambda split=split: CelebA(split, align=True))
# setup CelebA+Webcam dataset
for split in ['train', 'val']:
name = 'celeba_plus_webcam_cls_{}'.format(split)
__sets[name] = (lambda split=split: CelebA_Plus_Webcam_Cls(split))
# setup IBMattributes dataset
for split in ['train', 'val']:
name = 'IBMattributes_{}'.format(split)
__sets[name] = (lambda split=split: IBMAttributes(split))
def dataloaders():
# Data loading code
# 1. all mages are already aligned to 218*178;
normalize_old = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
#newly computed after data augmentation
normalize_new = transforms.Normalize([0.4807, 0.3973, 0.3534],
[0.2838, 0.2535, 0.2443])
normalize = normalize_new
train_dataset = CelebA(
args.root,
'train_attr_list.txt',
transforms.Compose([
transforms.RandomRotation(30),
transforms.RandomHorizontalFlip(),
# transforms.Resize((178,178)),
# transforms.CenterCrop((178,178)),
transforms.RandomResizedCrop(178, scale=(0.8, 1.0)),
# should not cut many info for multi label classification
transforms.ToTensor(),
normalize,
]))
val_dataset = CelebA(
args.root,
'val_attr_list.txt',
transforms.Compose([
transforms.Resize((178, 178)),
# transforms.CenterCrop((178,178)),
transforms.ToTensor(),
normalize,
]))
test_dataset = CelebA(
args.root,
'test_attr_list.txt',
transforms.Compose([
transforms.Resize((178, 178)),
# transforms.CenterCrop((178,178)),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=7,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=7,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=7,
pin_memory=True)
return train_loader, val_loader, test_loader
def main(c: dict):
global args, best_prec1
# if using tune, config will overwrite the args
# TODO: write in a generic way
# configable_list = ["lr", "arch", "lr_decay", "step", "loss"]
# for c_name in configable_list:
# if c_name in c:
# args.
if "lr" in c:
args.lr = c["lr"]
if "arch" in c:
args.arch = c["arch"]
if "lr_decay" in c:
args.lr_decay = c["lr_decay"]
if "step" in c:
args.step = c["step"]
if "loss" in c:
args.loss = c["loss"]
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size)
# Use CUDA
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
use_cuda = torch.cuda.is_available()
# Random seed
if args.manual_seed is None:
args.manual_seed = random.randint(1, 10000)
random.seed(args.manual_seed)
torch.manual_seed(args.manual_seed)
if use_cuda:
torch.cuda.manual_seed_all(args.manual_seed)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
# elif args.arch.startswith('resnext'):
# model = models.__dict__[args.arch](
# baseWidth=args.base_width,
# cardinality=args.cardinality,
# )
elif args.arch.startswith('shufflenet'):
model = models.__dict__[args.arch](groups=args.groups)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if not args.distributed:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.to(device) #.cuda()
else:
model = torch.nn.DataParallel(model).to(device) #.cuda()
else:
model.to(device) #.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
# define loss function (criterion) and optimizer
if args.loss == "ce":
criterion = nn.CrossEntropyLoss().to(device) #.cuda()
elif args.loss == "focalloss":
criterion = FocalLoss(device)
else:
print("ERROR: ------ Unkown loss !!! ------")
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
title = 'CelebA-' + args.arch
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location=device)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
args.checkpoint = os.path.dirname(args.resume)
logger = Logger(os.path.join(args.checkpoint, 'log.txt'),
title=title,
resume=True)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names([
'Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.',
'Valid Acc.'
])
# Comment to avoid the cudnn cloud pickle error
# cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = CelebA(
args.data, 'train_40_att_list.txt',
transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.train_batch,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(CelebA(
args.data, 'val_40_att_list.txt',
transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(CelebA(
args.data, args.e_file,
transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.evaluate:
val_loss, prec1, top1_avg_att = validate(test_loader, model, criterion)
print(top1_avg_att)
return
# visualization
if not args.tune:
writer = SummaryWriter(os.path.join(args.checkpoint, 'logs'))
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
lr = adjust_learning_rate(optimizer, epoch)
print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, lr))
# train for one epoch
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, epoch)
# evaluate on validation set
val_loss, prec1, top1_avg_att = validate(val_loader, model, criterion)
# append logger file
logger.append([lr, train_loss, val_loss, train_acc, prec1])
acc_att_dict = {}
for i, acc in enumerate(top1_avg_att):
acc_att_dict[f"att-{i}"] = acc
# tensorboardX
if not args.tune:
writer.add_scalar('learning rate', lr, epoch + 1)
writer.add_scalars('loss', {
'train loss': train_loss,
'validation loss': val_loss
}, epoch + 1)
writer.add_scalars('accuracy', {
'train accuracy': train_acc,
'validation accuracy': prec1
}, epoch + 1)
writer.add_scalars('att-accuracy', acc_att_dict, epoch + 1)
#for name, param in model.named_parameters():
# writer.add_histogram(name, param.clone().cpu().data.numpy(), epoch + 1)
if args.tune:
tune.report(train_loss=train_loss,
train_acc=train_acc,
val_loss=val_loss,
prec1=prec1,
att_acc=acc_att_dict,
lr=lr)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
},
is_best,
checkpoint=args.checkpoint)
logger.close()
logger.plot()
savefig(os.path.join(args.checkpoint, 'log.eps'))
if not args.tune:
writer.close()
print('Best accuracy:')
print(best_prec1)
def main():
global args, best_prec1
args = parser.parse_args()
# Use CUDA
# os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
use_cuda = torch.cuda.is_available()
# Random seed
if args.manual_seed is None:
args.manual_seed = random.randint(1, 10000)
random.seed(args.manual_seed)
torch.manual_seed(args.manual_seed)
if use_cuda:
torch.cuda.manual_seed_all(args.manual_seed)
# create model
if args.resume == "" and args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
elif args.arch.startswith("resnext"):
model = models.__dict__[args.arch](
baseWidth=args.base_width, cardinality=args.cardinality,
)
elif args.arch.startswith("shufflenet"):
model = models.__dict__[args.arch](groups=args.groups)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=False)
if args.ft:
for param in model.parameters():
param.requires_grad = False
classifier_numbers = model.num_attributes
# newly constructed classifiers have requires_grad=True
for i in range(classifier_numbers):
setattr(
model,
"classifier" + str(i).zfill(2),
nn.Sequential(fc_block(512, 256), nn.Linear(256, 1)),
)
args.sampler = "balance"
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(
backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
)
if not args.distributed:
if args.arch.startswith("alexnet") or args.arch.startswith("vgg"):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
else:
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
# optionally resume from a checkpoint
title = "CelebA-" + args.arch
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize( # statistics from CelebA TrainSet
mean=[0.5084, 0.4287, 0.3879], std=[0.2656, 0.2451, 0.2419]
)
print("=> using {} sampler to load data.".format(args.sampler))
train_dataset = CelebA(
args.data,
"train_attr_list.txt",
transforms.Compose(
[
transforms.RandomHorizontalFlip(),
transforms.RandomResizedCrop(size=(256, 256), scale=(0.5, 1.0)),
transforms.ToTensor(),
normalize,
transforms.RandomErasing(),
]
),
sampler=args.sampler,
)
train_sample_prob = train_dataset._class_sample_prob()
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
if args.rs:
train_sampler = WeightedRandomSampler(
1 / train_sample_prob, len(train_dataset)
)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler,
)
val_loader = torch.utils.data.DataLoader(
CelebA(
args.data,
"val_attr_list.txt",
transforms.Compose(
[transforms.Resize(size=(256, 256)), transforms.ToTensor(), normalize,]
),
),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
test_loader = torch.utils.data.DataLoader(
CelebA(
args.data,
"test_attr_list.txt",
transforms.Compose(
[transforms.Resize(size=(256, 256)), transforms.ToTensor(), normalize,]
),
),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
lfw_test_loader = torch.utils.data.DataLoader(
LFW(
args.data_lfw, transforms.Compose([transforms.ToTensor(), normalize,]),
), # celebA mean variance
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
# define loss function (criterion) and optimizer
if args.lw: # loss weight
print("=> loading CE loss_weight")
criterion = nn.BCEWithLogitsLoss(
reduction="mean", weight=1 / torch.sqrt(train_sample_prob)
).cuda()
else:
# criterion = nn.CrossEntropyLoss().cuda()
criterion = nn.BCEWithLogitsLoss(reduction="mean").cuda()
if args.focal:
print("=> using focal loss")
criterion = FocalLoss(criterion, balance_param=5)
print("=> using wd {}".format(args.weight_decay))
optimizer = torch.optim.SGD(
model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
if args.resume:
if os.path.isfile(args.resume):
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint["epoch"]
best_prec1 = checkpoint["best_prec1"]
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
print(
"=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint["epoch"]
)
)
args.checkpoint = os.path.dirname(args.resume)
logger = Logger(
os.path.join(args.checkpoint, "log.txt"), title=title, resume=True
)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(os.path.join(args.checkpoint, "log.txt"), title=title)
logger.set_names(
[
"Learning Rate",
"Train Loss",
"Valid Loss",
"Train Acc.",
"Valid Acc.",
"LFW Loss.",
"LFW Acc.",
]
)
if args.evaluate: # TODO
validate(test_loader, model, criterion)
# stat(train_loader)
return
if args.validate: # TODO
validate(val_loader, model, criterion)
# stat(train_loader)
return
if args.evaluate_lfw:
validate(val_loader, model, criterion)
validate(lfw_test_loader, model, criterion)
return
# visualization
writer = SummaryWriter(os.path.join(args.checkpoint, "logs"))
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
lr = adjust_learning_rate(optimizer, epoch)
print("\nEpoch: [%d | %d] LR: %f" % (epoch + 1, args.epochs, lr))
# train for one epoch
train_loss, train_acc = train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
val_loss, prec1 = validate(val_loader, model, criterion)
# evaluate on lfw
lfw_loss, lfw_prec1 = validate(lfw_test_loader, model, criterion)
# append logger file
logger.append([lr, train_loss, val_loss, train_acc, prec1, lfw_loss, lfw_prec1])
# tensorboardX
writer.add_scalar("learning rate", lr, epoch + 1)
writer.add_scalars(
"loss",
{
"train loss": train_loss,
"validation loss": val_loss,
"lfw loss": lfw_loss,
},
epoch + 1,
)
writer.add_scalars(
"accuracy",
{
"train accuracy": train_acc,
"validation accuracy": prec1,
"lfw accuracy": lfw_prec1,
},
epoch + 1,
)
# for name, param in model.named_parameters():
# writer.add_histogram(name, param.clone().cpu().data.numpy(), epoch + 1)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_prec1": best_prec1,
"optimizer": optimizer.state_dict(),
},
is_best,
checkpoint=args.checkpoint,
)
logger.close()
logger.plot()
savefig(os.path.join(args.checkpoint, "log.eps"))
writer.close()
print("Best accuracy:")
print(best_prec1)