def pt_1():
epochs = 25
training_width = 80
training_height = 60
face_dataset = FaceDataset(1, 32, root_dir, training_width,
training_height)
training = create_dataloader(face_dataset, 5)
validation_dataset = FaceDataset(33, 40, root_dir, training_width,
training_height)
validation = create_dataloader(validation_dataset, 1)
#test_dataloader(dataloader, training_width, training_height)
net = Net()
loss = nn.MSELoss()
opt = Adam(net.parameters(), lr=0.001)
training_losses = []
validation_losses = []
for epoch in range(epochs):
epoch_loss = torch.zeros((1, 1))
for i, (images, labels) in enumerate(training):
prediction = net(images)
output = loss(prediction, labels[:, -6])
epoch_loss += output
output.backward()
opt.step()
opt.zero_grad()
epoch_loss = epoch_loss / len(face_dataset)
training_losses.append([epoch, epoch_loss.item() * 100])
epoch_loss = torch.zeros((1, 1))
for i, (images, labels) in enumerate(validation):
prediction = net(images)
output = loss(prediction, labels[:, -6])
epoch_loss += output
opt.zero_grad()
epoch_loss = epoch_loss / len(face_dataset)
validation_losses.append([epoch, epoch_loss.item() * 100])
training_losses = np.array(training_losses)
validation_losses = np.array(validation_losses)
plt.plot(training_losses[:, 0], training_losses[:, 1])
plt.plot(validation_losses[:, 0], validation_losses[:, 1])
plt.plot()
plt.savefig('results/pt_1/epoch_loss_decrease.png')
plt.show()
def main():
args = get_args()
if args.opts:
cfg.merge_from_list(args.opts)
cfg.freeze()
# create model
print("=> creating model '{}'".format(cfg.MODEL.ARCH))
model = get_model(model_name=cfg.MODEL.ARCH, pretrained=None)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# load checkpoint
resume_path = args.resume
if Path(resume_path).is_file():
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path, map_location="cpu")
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(resume_path))
else:
raise ValueError("=> no checkpoint found at '{}'".format(resume_path))
if device == "cuda":
cudnn.benchmark = True
test_dataset = FaceDataset(args.data_dir, "test", img_size=cfg.MODEL.IMG_SIZE, augment=False)
test_loader = DataLoader(test_dataset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False,
num_workers=cfg.TRAIN.WORKERS, drop_last=False)
print("=> start testing")
_, _, test_mae = validate(test_loader, model, None, 0, device)
print(f"test mae: {test_mae:.3f}")
def evaluate(self):
"""
评估准确率
:return: accuracy rate
"""
self.model.eval()
dataset = FaceDataset(self.args, mode="test")
steps = 100
accuracy = 0.0
for step in range(steps):
log.info("step: %d", step)
names, params, images = dataset.get_batch(batch_size=self.args.batch_size, edge=False)
loss, _ = self.itr_train(images)
accuracy += 1.0 - loss
accuracy = accuracy / steps
log.info("accuracy rate is %f", accuracy)
return accuracy
def main():
args = get_args()
model = get_model()
# precompute validation Features
valid_dataset = FaceDataset(args.data_dir,
"valid",
img_size=cfg.MODEL.IMG_SIZE,
augment=False)
valid_loader = DataLoader(valid_dataset,
batch_size=1,
shuffle=True,
num_workers=cfg.TRAIN.WORKERS,
drop_last=True)
valid_features, valid_labels = preconvfeat(valid_loader, model)
with open('valid_features.pkl', 'wb') as f:
pickle.dump(valid_features, f)
with open('valid_labels.pkl', 'wb') as f:
pickle.dump(valid_labels, f)
# precompute training Features
train_dataset = FaceDataset(args.data_dir,
"train",
img_size=cfg.MODEL.IMG_SIZE,
augment=True,
age_stddev=cfg.TRAIN.AGE_STDDEV)
train_loader = DataLoader(train_dataset,
batch_size=1,
shuffle=True,
num_workers=cfg.TRAIN.WORKERS,
drop_last=True)
train_features, train_labels = preconvfeat(train_loader, model)
with open('train_features.pkl', 'wb') as f:
pickle.dump(train_features, f)
with open('train_labels.pkl', 'wb') as f:
pickle.dump(train_labels, f)
def batch_train(self, cuda=False):
"""
batch training
:param cuda: 是否开启gpu加速运算
"""
rnd_input = torch.randn(self.args.batch_size, self.args.params_cnt)
if cuda:
rnd_input = rnd_input.cuda()
self.writer.add_graph(self, input_to_model=rnd_input)
self.model.train()
dataset = FaceDataset(self.args, mode="train")
initial_step = self.initial_step
total_steps = self.args.total_steps
progress = tqdm(range(initial_step, total_steps + 1),
initial=initial_step,
total=total_steps)
for step in progress:
names, params, images = dataset.get_batch(
batch_size=self.args.batch_size, edge=False)
if cuda:
params = params.cuda()
images = images.cuda()
loss, y_ = self.itr_train(params, images)
loss_ = loss.cpu().detach().numpy()
progress.set_description("loss: {:.3f}".format(loss_))
self.writer.add_scalar('imitator/loss', loss_, step)
if (step + 1) % self.args.prev_freq == 0:
path = "{1}/imit_{0}.jpg".format(step + 1, self.prev_path)
self.capture(path, images, y_, self.args.parsing_checkpoint,
cuda)
x = step / float(total_steps)
lr = self.args.learning_rate * (x**2 - 2 * x + 1) + 2e-3
utils.update_optimizer_lr(self.optimizer, lr)
self.writer.add_scalar('imitator/learning rate', lr, step)
self.upload_weights(step)
if (step + 1) % self.args.save_freq == 0:
self.save(step)
self.writer.close()
def main():
args = get_args()
if args.opts:
cfg.merge_from_list(args.opts)
cfg.freeze()
# create model
print("=> creating model '{}'".format(cfg.MODEL.ARCH))
model = get_model(model_name=cfg.MODEL.ARCH, pretrained=None)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# TODO: delete
if torch.cuda.device_count() > 1:
print("Let's use [1,2,4,5] GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
model = nn.DataParallel(model, device_ids=[1, 2, 4, 5])
model.to(device)
# load checkpoint
resume_path = args.resume
if Path(resume_path).is_file():
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path, map_location="cpu")
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(resume_path))
else:
raise ValueError("=> no checkpoint found at '{}'".format(resume_path))
if device == "cuda":
cudnn.benchmark = True
test_dataset = FaceDataset(args.data_dir,
"test",
img_size=cfg.MODEL.IMG_SIZE,
augment=False)
test_loader = DataLoader(test_dataset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.TRAIN.WORKERS,
drop_last=False)
criterion = nn.CrossEntropyLoss().to(device)
print("=> start testing")
_, _, test_mae, gen_acc = validate(test_loader, model, criterion, 0,
device)
print(f"Test age mae: {test_mae:.3f}")
print(f"Test gender accuracy: {gen_acc:.2f}")
def batch_train(self, cuda):
log.info("feature extractor train")
initial_step = self.initial_step
total_steps = self.args.total_extractor_steps
self.training = True
self.dataset = FaceDataset(self.args, mode="train")
rnd_input = torch.randn(self.args.batch_size, 1, 64, 64)
if cuda:
rnd_input = rnd_input.cuda()
self.writer.add_graph(self, input_to_model=rnd_input)
progress = tqdm(range(initial_step, total_steps + 1), initial=initial_step, total=total_steps)
for step in progress:
if self.train_mode == Extractor.TRAIN_SYNC:
progress.set_description("sync mode ")
names, _, images = self.dataset.get_batch(batch_size=self.args.batch_size, edge=True)
if cuda:
images = images.cuda()
self.sync_train(images, names, step)
else:
image1, image2, name = self.dataset.get_cache(cuda)
if image1 is None or image2 is None:
self.change_mode(Extractor.TRAIN_SYNC)
continue
loss = self.asyn_train(image1, image2)
loss_ = loss.detach().numpy()
loss_display = loss_ * 1000
progress.set_description("loss: {:.3f}".format(loss_display))
self.writer.add_scalar('extractor/loss', loss_display, step)
if step % self.args.extractor_prev_freq == 0:
self.capture(image1, image2, name, step, cuda)
lr = self.args.extractor_learning_rate * loss_display
self.writer.add_scalar('extractor/learning rate', lr, step)
utils.update_optimizer_lr(self.optimizer, lr)
if step % self.args.extractor_save_freq == 0:
self.save(step)
self.writer.close()
'ngf': 512,
'ndf': 16,
'nzf': 100,
'optimizer': 'sgd',
'lr_D': 0.01,
'lr_G': 0.01,
'momentum': 0.9,
'nestrov': False,
'lr_schedule': None,
'epochs': 100
}
image_size = config['image_size']
batch_size = config['batch_size']
data_path = '/home/scott/Desktop/dataset/face3k/train.txt'
train_set = FaceDataset(data_path, 128, augment=True, cache_image=False)
train_loader = DataLoader(train_set,
batch_size=batch_size,
shuffle=True,
num_workers=8,
pin_memory=True)
netG = Generator(input_size=(batch_size, 100, 1, 1),
image_size=image_size,
ngf=config['ngf'],
leaky_relu=True)
netD = Discriminator(image_size=config['image_size'],
ndf=config['ndf'],
leaky_relu=True)
'''
load trained weight
from torch.utils.data import DataLoader
from util.config import DATASET_PARAMETERS, NETWORKS_PARAMETERS
from util.parse_dataset import csv_to_list
from network import restore_train, get_network
from utils import Meter, cycle, save_model, get_collate_fn, Logger
from dataset import VoiceDataset, FaceDataset
# dataset and dataloader
print('Parsing your dataset...')
voice_list, face_list, id_class_num, emotion_class_num = csv_to_list(
DATASET_PARAMETERS)
print('voice samples num = %d, face samples num = %d' %
(len(voice_list), len(face_list)))
print('Preparing the datasets...')
voice_dataset = VoiceDataset(voice_list, DATASET_PARAMETERS['nframe_range'])
face_dataset = FaceDataset(face_list)
print('Preparing the dataloaders...')
collate_fn = get_collate_fn(DATASET_PARAMETERS['nframe_range'])
voice_loader = DataLoader(
voice_dataset,
shuffle=True,
drop_last=True,
batch_size=DATASET_PARAMETERS['batch_size'],
num_workers=DATASET_PARAMETERS['workers_num'], # 使用多进程加载的进程数
collate_fn=collate_fn) # 如何将多个样本数据拼接成一个batch
face_loader = DataLoader(face_dataset,
shuffle=True,
drop_last=True,
batch_size=DATASET_PARAMETERS['batch_size'],
num_workers=DATASET_PARAMETERS['workers_num'])
def run():
opt = Config()
if opt.display:
visualizer = Visualizer()
# device = torch.device("cuda")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_dataset = FaceDataset(opt.train_root,
opt.train_list,
phase='train',
input_shape=opt.input_shape)
trainloader = torch.utils.data.DataLoader(train_dataset,
batch_size=opt.train_batch_size,
shuffle=True,
num_workers=opt.num_workers)
print('{} train iters per epoch:'.format(len(trainloader)))
# Focal Loss, 解决类别不均衡问题,减少易分类样本的权重，使得模型在训练时更专注于难分类的样本
# https://blog.csdn.net/u014380165/article/details/77019084
#
#定义损失函数
if opt.loss == 'focal_loss':
criterion = FocalLoss(gamma=2) #
else:
criterion = torch.nn.CrossEntropyLoss()
#定义模型
if opt.backbone == 'resnet18':
model = resnet_face18(use_se=opt.use_se)
elif opt.backbone == 'resnet34':
model = resnet34()
elif opt.backbone == 'resnet50':
model = resnet50()
#全连接层？
if opt.metric == 'add_margin':
metric_fc = AddMarginProduct(512, opt.num_classes, s=30, m=0.35)
elif opt.metric == 'arc_margin':
metric_fc = ArcMarginProduct(512,
opt.num_classes,
s=30,
m=0.5,
easy_margin=opt.easy_margin)
elif opt.metric == 'sphere':
metric_fc = SphereProduct(512, opt.num_classes, m=4)
else:
metric_fc = nn.Linear(512, opt.num_classes)
# view_model(model, opt.input_shape)
print(model)
model.to(device)
model = DataParallel(model)
metric_fc.to(device)
metric_fc = DataParallel(metric_fc)
#定义优化算法
if opt.optimizer == 'sgd':
optimizer = torch.optim.SGD([{
'params': model.parameters()
}, {
'params': metric_fc.parameters()
}],
lr=opt.lr,
weight_decay=opt.weight_decay)
else:
optimizer = torch.optim.Adam([{
'params': model.parameters()
}, {
'params': metric_fc.parameters()
}],
lr=opt.lr,
weight_decay=opt.weight_decay)
# https://www.programcreek.com/python/example/98143/torch.optim.lr_scheduler.StepLR
# ? 每过{lr_step}个epoch训练，学习率就乘gamma
scheduler = StepLR(optimizer, step_size=opt.lr_step, gamma=0.1)
start = time.time()
for i in range(opt.max_epoch):
scheduler.step()
model.train() # train模式，eval模式
for ii, data in enumerate(trainloader):
data_input, label = data
data_input = data_input.to(device)
label = label.to(device).long()
feature = model(data_input)
output = metric_fc(feature,
label) # 全连接层？ 将原本用于输出分类的层，改成输出512维向量？似乎不是？
loss = criterion(output, label) # criterion:做出判断的依据
optimizer.zero_grad()
loss.backward()
optimizer.step()
iters = i * len(trainloader) + ii
if iters % opt.print_freq == 0:
output = output.data.cpu().numpy()
output = np.argmax(output,
axis=1) #最大值所在的索引？ index <-> one-hot相互转换
label = label.data.cpu().numpy()
# print(output)
# print(label)
acc = np.mean((output == label).astype(int))
speed = opt.print_freq / (time.time() - start)
time_str = time.asctime(time.localtime(time.time()))
print('{} train epoch {} iter {} {} iters/s loss {} acc {}'.
format(time_str, i, ii, speed, loss.item(), acc))
if opt.display:
visualizer.display_current_results(iters,
loss.item(),
name='train_loss')
visualizer.display_current_results(iters,
acc,
name='train_acc')
start = time.time()
if i % opt.save_interval == 0 or i == opt.max_epoch:
save_model(model, opt.checkpoints_path, opt.backbone, i)
# train结束，模型设置为eval模式
model.eval()
#测试？
identity_list = get_lfw_list(opt.lfw_test_list)
img_paths = [
os.path.join(opt.lfw_root, each) for each in identity_list
]
acc = lfw_test(model, img_paths, identity_list, opt.lfw_test_list,
opt.test_batch_size)
if opt.display:
visualizer.display_current_results(iters, acc, name='test_acc')
def main():
global args, config, best_loss
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
for k, v in config['common'].items():
setattr(args, k, v)
config = EasyDict(config['common'])
rank, world_size, device_id = dist_init(
os.path.join(args.distributed_path, config.distributed_file))
args.save_path_dated = args.save_path + '/' + args.datetime
if args.run_tag != '':
args.save_path_dated += '-' + args.run_tag
# create model
model = model_entry(config.model)
model.cuda()
model = nn.parallel.DistributedDataParallel(model, device_ids=[device_id])
# create optimizer
opt_config = config.optimizer
opt_config.kwargs.lr = config.lr_scheduler.base_lr
opt_config.kwargs.params = model.parameters()
optimizer = optim_entry(opt_config)
# optionally resume from a checkpoint
last_iter = -1
best_loss = 1e9
if args.load_path:
if args.recover:
best_loss, last_iter = load_state(args.load_path,
model,
optimizer=optimizer)
else:
load_state(args.load_path, model)
cudnn.benchmark = True
# train augmentation
if config.augmentation.get('imgnet_mean', False):
model_mean = (0.485, 0.456, 0.406)
model_std = (0.229, 0.224, 0.225)
else:
model_mean = (0.5, 0.5, 0.5)
model_std = (0.5, 0.5, 0.5)
trans = albumentations.Compose([
RandomResizedCrop(config.augmentation.input_size,
config.augmentation.input_size,
scale=(config.augmentation.min_scale**2., 1.),
ratio=(1., 1.)),
HorizontalFlip(p=0.5),
RandomBrightnessContrast(brightness_limit=0.25,
contrast_limit=0.1,
p=0.5),
JpegCompression(p=.2, quality_lower=50),
MotionBlur(p=0.5),
Normalize(mean=model_mean, std=model_std),
ToTensorV2()
])
train_dataset = FaceDataset(config.train_root,
config.train_source,
transform=trans,
resize=config.augmentation.input_size,
image_format=config.get('image_format', None),
random_frame=config.get(
'train_random_frame', False),
bgr=config.augmentation.get('bgr', False))
train_sampler = DistributedGivenIterationSampler(
train_dataset,
config.lr_scheduler.max_iter,
config.batch_size,
last_iter=last_iter)
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.workers,
pin_memory=True,
sampler=train_sampler)
# validation augmentation
trans = albumentations.Compose([
Resize(config.augmentation.input_size, config.augmentation.input_size),
Normalize(mean=model_mean, std=model_std),
ToTensorV2()
])
val_multi_loader = []
if args.val_source != '':
for dataset_idx in range(len(args.val_source)):
val_dataset = FaceDataset(
args.val_root[dataset_idx],
args.val_source[dataset_idx],
transform=trans,
output_index=True,
resize=config.augmentation.input_size,
image_format=config.get('image_format', None),
bgr=config.augmentation.get('bgr', False))
val_sampler = DistributedSampler(val_dataset, round_up=False)
val_loader = DataLoader(val_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.workers,
pin_memory=True,
sampler=val_sampler)
val_multi_loader.append(val_loader)
config.lr_scheduler['optimizer'] = optimizer
config.lr_scheduler['last_iter'] = last_iter
lr_scheduler = get_scheduler(config.lr_scheduler)
if rank == 0:
mkdir(args.save_path)
mkdir(args.save_path_dated)
tb_logger = SummaryWriter(args.save_path_dated)
logger = create_logger('global_logger',
args.save_path_dated + '-log.txt')
logger.info('{}'.format(args))
logger.info(model)
logger.info(parameters_string(model))
logger.info('len(train dataset) = %d' % len(train_loader.dataset))
for dataset_idx in range(len(val_multi_loader)):
logger.info(
'len(val%d dataset) = %d' %
(dataset_idx, len(val_multi_loader[dataset_idx].dataset)))
mkdir(args.save_path_dated + '/saves')
else:
tb_logger = None
positive_weight = config.get('positive_weight', 0.5)
weight = torch.tensor([1. - positive_weight, positive_weight]) * 2.
if rank == 0:
logger.info('using class weights: {}'.format(weight.tolist()))
criterion = nn.CrossEntropyLoss(weight=weight).cuda()
if args.evaluate:
if args.evaluate_path:
all_ckpt = get_all_checkpoint(args.evaluate_path, args.range_list,
rank)
for ckpt in all_ckpt:
if rank == 0:
logger.info('Testing ckpt: ' + ckpt)
last_iter = -1
_, last_iter = load_state(ckpt, model, optimizer=optimizer)
for dataset_idx in range(len(val_multi_loader)):
validate(dataset_idx,
val_multi_loader[dataset_idx],
model,
criterion,
tb_logger,
curr_step=last_iter,
save_softmax=True)
else:
for dataset_idx in range(len(val_multi_loader)):
validate(dataset_idx,
val_multi_loader[dataset_idx],
model,
criterion,
tb_logger,
curr_step=last_iter,
save_softmax=True)
return
train(train_loader, val_multi_loader, model, criterion, optimizer,
lr_scheduler, last_iter + 1, tb_logger)
return
def main():
args = get_args()
if args.opts:
cfg.merge_from_list(args.opts)
cfg.freeze()
start_epoch = 0
checkpoint_dir = Path(args.checkpoint)
checkpoint_dir.mkdir(parents=True, exist_ok=True)
# create model
print("=> creating model '{}'".format(cfg.MODEL.ARCH))
model = get_model(model_name=cfg.MODEL.ARCH)
multitask(model)
if cfg.TRAIN.OPT == "sgd":
optimizer = torch.optim.SGD(model.parameters(), lr=cfg.TRAIN.LR,
momentum=cfg.TRAIN.MOMENTUM,
weight_decay=cfg.TRAIN.WEIGHT_DECAY)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=cfg.TRAIN.LR)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# optionally resume from a checkpoint
resume_path = args.resume
if resume_path:
if Path(resume_path).is_file():
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path, map_location="cpu")
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(resume_path, checkpoint['epoch']))
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
print("=> no checkpoint found at '{}'".format(resume_path))
if args.multi_gpu:
model = nn.DataParallel(model)
if device == "cuda":
cudnn.benchmark = True
get_ca = True if "megaage" in args.dataset.lower() else True # display cummulative acuracy
value_ca = True if "megaage" in args.dataset.lower() else False # use CA to update saved model
if get_ca:
print("Cummulative Accuracy will be calculated for", args.dataset)
if value_ca:
print("Cummulative Accuracy will be compared to update saved model")
criterion = nn.CrossEntropyLoss().to(device)
train_dataset = FaceDataset(args.data_dir, "train", args.dataset, img_size=cfg.MODEL.IMG_SIZE, augment=args.aug,
age_stddev=cfg.TRAIN.AGE_STDDEV, label=True, expand= args.expand, gender=True)
train_loader = DataLoader(train_dataset, batch_size=cfg.TRAIN.BATCH_SIZE, shuffle=True,
num_workers=cfg.TRAIN.WORKERS, drop_last=False)
val_dataset = FaceDataset(args.data_dir, "valid", args.dataset,
img_size=cfg.MODEL.IMG_SIZE, augment=False, label=True, expand= args.expand, gender=True)
val_loader = DataLoader(val_dataset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False,
num_workers=cfg.TRAIN.WORKERS, drop_last=False)
scheduler = StepLR(optimizer, step_size=cfg.TRAIN.LR_DECAY_STEP, gamma=cfg.TRAIN.LR_DECAY_RATE,
last_epoch=start_epoch - 1)
best_val_mae = 10000.0
train_writer = None
global_ca = {3: 0.0, 5: 0.0, 7: 0.0}
train_count = len(train_dataset)
val_count = len(val_dataset)
all_train_loss = []
all_train_accu = []
all_val_loss = []
all_val_accu = []
# range(start_epoch, cfg.TRAIN.EPOCHS):
for epoch in range(cfg.TRAIN.EPOCHS):
# train
train_loss, train_acc = train(
train_loader, model, criterion, optimizer, epoch, device, train_count)
# validate
val_loss, val_acc, val_mae, new_ca= validate(
val_loader, model, criterion, epoch, device, val_count, get_ca)
all_train_loss.append(float(train_loss))
all_train_accu.append(float(train_acc))
all_val_loss.append(float(val_loss))
all_val_accu.append(float(val_mae))
# checkpoint
if ((not value_ca) and (val_mae < best_val_mae)) or ((get_ca and value_ca) and (new_ca[3] > global_ca[3])):
print(
f"=> [epoch {epoch:03d}] best val mae was improved from {best_val_mae:.3f} to {val_mae:.3f}")
model_state_dict = model.module.state_dict(
) if args.multi_gpu else model.state_dict()
torch.save(
{
'epoch': epoch + 1,
'arch': cfg.MODEL.ARCH,
'state_dict': model_state_dict,
'optimizer_state_dict': optimizer.state_dict()
},
str(checkpoint_dir.joinpath("epoch{:03d}_{}_{:.5f}_{:.4f}_{}_{}_ldl.pth".format(
epoch, args.dataset, val_loss, val_mae, datetime.now().strftime("%Y%m%d"), cfg.MODEL.ARCH)))
)
best_val_mae = val_mae
best_checkpoint = str(checkpoint_dir.joinpath("epoch{:03d}_{}_{:.5f}_{:.4f}_{}_{}_ldl.pth".format(epoch, args.dataset, val_loss, val_mae, datetime.now().strftime("%Y%m%d"), cfg.MODEL.ARCH)))
if get_ca:
global_ca = new_ca
else:
print(
f"=> [epoch {epoch:03d}] best val mae was not improved from {best_val_mae:.3f} ({val_mae:.3f})")
# adjust learning rate
scheduler.step()
print("=> training finished")
print(f"additional opts: {args.opts}")
print(f"best val mae: {best_val_mae:.3f}")
if get_ca:
print("CA3: {:.2f} CA5: {:.2f} CA7: {:.2f}".format(global_ca[3] * 100, global_ca[5]*100, global_ca[7]*100))
print("best mae saved model:", best_checkpoint)
x = np.arange(cfg.TRAIN.EPOCHS)
plt.xlabel("Epoch")
plt.ylabel("Train Loss")
plt.plot(x, all_train_loss)
plt.savefig("savefig/{}_{}_{}_train_loss.png".format(args.dataset,
cfg.MODEL.ARCH, datetime.now().strftime("%Y%m%d")))
plt.clf()
plt.ylabel("Train Accuracy")
plt.plot(x, all_train_accu)
plt.savefig("savefig/{}_{}_{}_train_accu.png".format(args.dataset,
cfg.MODEL.ARCH, datetime.now().strftime("%Y%m%d")))
plt.clf()
plt.ylabel("Validation Loss")
plt.plot(x, all_val_loss)
plt.savefig("savefig/{}_{}_{}_val_loss.png".format(args.dataset,
cfg.MODEL.ARCH, datetime.now().strftime("%Y%m%d")))
plt.clf()
plt.ylabel("Validation Accuracy")
plt.plot(x, all_val_accu)
plt.savefig("savefig/{}_{}_{}_val_mae.png".format(args.dataset,
cfg.MODEL.ARCH, datetime.now().strftime("%Y%m%d")))
class Extractor(nn.Module):
TRAIN_ASYN = 1
TRAIN_SYNC = 2
def __init__(self, name, args, imitator=None, momentum=0.5):
"""
feature extractor
:param name: model name
:param args: argparse options
:param imitator: imitate engine's behaviour
:param momentum: momentum for optimizer
"""
super(Extractor, self).__init__()
log.info("construct feature_extractor %s", name)
self.name = name
self.imitator = imitator
self.initial_step = 0
self.args = args
self.model_path = "./output/extractor"
self.prev_path = "./output/preview"
self.training = False
self.params_cnt = self.args.params_cnt
self.dataset = None
self.train_mode = Extractor.TRAIN_SYNC
self.train_refer = 32
self.net = Net(args.udp_port, args)
self.clean()
self.writer = SummaryWriter(comment="feature extractor", log_dir=args.path_tensor_log)
self.model = nn.Sequential(
nn.Conv2d(1, 4, kernel_size=7, stride=2, padding=3), # 1. (batch, 4, 32, 32)
nn.MaxPool2d(kernel_size=3, stride=2, padding=1), # 2. (batch, 4, 16, 16)
group(4, 8, kernel_size=3, stride=1, padding=1), # 3. (batch, 8, 16, 16)
ResidualBlock.make_layer(8, channels=8), # 4. (batch, 8, 16, 16)
group(8, 16, kernel_size=3, stride=1, padding=1), # 5. (batch, 16, 16, 16)
ResidualBlock.make_layer(8, channels=16), # 6. (batch, 16, 16, 16)
group(16, 64, kernel_size=3, stride=1, padding=1), # 7. (batch, 64, 16, 16)
ResidualBlock.make_layer(8, channels=64), # 8. (batch, 64, 16, 16)
group(64, self.params_cnt, kernel_size=3, stride=1, padding=1), # 9. (batch, params_cnt, 16, 16)
ResidualBlock.make_layer(4, channels=self.params_cnt), # 10. (batch, params_cnt, 16, 16)
nn.Dropout(0.5),
)
self.fc = nn.Linear(self.params_cnt * 16 * 16, self.params_cnt)
self.optimizer = optim.Adam(self.parameters(), lr=args.extractor_learning_rate)
utils.debug_parameters(self, "_extractor_")
def forward(self, input):
output = self.model(input)
output = output.view(output.size(0), -1)
output = self.fc(output)
output = F.dropout(output, training=self.training)
output = torch.sigmoid(output)
return output
def itr_train(self, image):
"""
第一种方法 这里train的方式使用的是imitator （同步）
:param image: [batch, 3, 512, 512]
:return: loss scalar
"""
self.optimizer.zero_grad()
param_ = self.forward(image)
img_ = self.imitator.forward(param_)
loss = utils.content_loss(image, img_)
loss.backward()
self.optimizer.step()
return loss, param_
def sync_train(self, image, name, step):
"""
第二种方法是 通过net把params发生引擎生成image (异步)
(这种方法需要保证同步，但效果肯定比imitator效果好)
:param step: train step
:param name: 图片名 [batch]
:param image: [batch, 1, 64, 64]
"""
self.train_refer = self.train_refer - 1
if self.train_refer <= 0:
self.change_mode(Extractor.TRAIN_ASYN)
param_ = self.forward(image)
self.net.send_params(param_, name, step)
def asyn_train(self, image1, image2):
"""
cache 中累计一定量的时候就可以asyn train
:param image1: input image
:param image2: generate image
:return: loss, type scalar
"""
self.train_refer = self.train_refer - 1
if self.train_refer <= 0:
self.change_mode(Extractor.TRAIN_SYNC)
self.optimizer.zero_grad()
loss = F.mse_loss(image1, image2)
loss.backward()
self.optimizer.step()
return loss
def change_mode(self, mode):
"""
切换train mode 并恢复计数
:param mode: train mode
"""
self.train_refer = 32
if mode == Extractor.TRAIN_ASYN:
self.train_refer = 36
self.train_mode = mode
def batch_train(self, cuda):
log.info("feature extractor train")
initial_step = self.initial_step
total_steps = self.args.total_extractor_steps
self.training = True
self.dataset = FaceDataset(self.args, mode="train")
rnd_input = torch.randn(self.args.batch_size, 1, 64, 64)
if cuda:
rnd_input = rnd_input.cuda()
self.writer.add_graph(self, input_to_model=rnd_input)
progress = tqdm(range(initial_step, total_steps + 1), initial=initial_step, total=total_steps)
for step in progress:
if self.train_mode == Extractor.TRAIN_SYNC:
progress.set_description("sync mode ")
names, _, images = self.dataset.get_batch(batch_size=self.args.batch_size, edge=True)
if cuda:
images = images.cuda()
self.sync_train(images, names, step)
else:
image1, image2, name = self.dataset.get_cache(cuda)
if image1 is None or image2 is None:
self.change_mode(Extractor.TRAIN_SYNC)
continue
loss = self.asyn_train(image1, image2)
loss_ = loss.detach().numpy()
loss_display = loss_ * 1000
progress.set_description("loss: {:.3f}".format(loss_display))
self.writer.add_scalar('extractor/loss', loss_display, step)
if step % self.args.extractor_prev_freq == 0:
self.capture(image1, image2, name, step, cuda)
lr = self.args.extractor_learning_rate * loss_display
self.writer.add_scalar('extractor/learning rate', lr, step)
utils.update_optimizer_lr(self.optimizer, lr)
if step % self.args.extractor_save_freq == 0:
self.save(step)
self.writer.close()
def load_checkpoint(self, path, training=False, cuda=False):
"""
从checkpoint 中恢复net
:param path: checkpoint's path
:param training: 恢复之后 是否接着train
:param cuda: gpu speedup
"""
path_ = self.args.path_to_inference + "/" + path
if not os.path.exists(path_):
raise NeuralException("not exist checkpoint of extractor with path " + path)
if cuda:
checkpoint = torch.load(path_)
else:
checkpoint = torch.load(path_, map_location='cpu')
self.load_state_dict(checkpoint['net'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.initial_step = checkpoint['epoch']
log.info("recovery imitator from %s", path)
if training:
self.batch_train(cuda)
def clean(self):
"""
清空前记得备份
"""
ops.clear_folder(self.model_path)
ops.clear_files(self.args.path_to_cache)
ops.clear_files(self.args.path_tensor_log)
ops.clear_files(self.prev_path)
def save(self, step):
"""
save checkpoint
:param step: train step
"""
state = {'net': self.state_dict(), 'optimizer': self.optimizer.state_dict(), 'epoch': step}
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
ext = "cuda" if self.cuda() else "cpu"
torch.save(state, '{1}/extractor_{0}_{2}.pth'.format(step, self.model_path, ext))
def inference(self, cp_name, photo_path, cuda):
"""
feature extractor: 由图片生成捏脸参数
:param cuda: gpu speed up
:param cp_name: checkpoint's path
:param photo_path: input photo's path
:return: params [1, params_cnt]
"""
img = cv2.imread(photo_path)
scaled = align.align_face(img, size=(64, 64))
self.load_checkpoint(cp_name, training=False, cuda=cuda)
img = utils.faceparsing_ndarray(scaled, self.args.parsing_checkpoint, cuda)
img = utils.img_edge(img)
with torch.no_grad:
input = torch.from_numpy(img)
input = input.view([1, 1, 64, 64])
params_ = self(input)
log.info(params_)
return params_
def evaluate(self):
"""
评估准确率
:return: accuracy rate
"""
self.model.eval()
dataset = FaceDataset(self.args, mode="test")
steps = 100
accuracy = 0.0
for step in range(steps):
log.info("step: %d", step)
names, params, images = dataset.get_batch(batch_size=self.args.batch_size, edge=False)
loss, _ = self.itr_train(images)
accuracy += 1.0 - loss
accuracy = accuracy / steps
log.info("accuracy rate is %f", accuracy)
return accuracy
def capture(self, tensor1, tensor2, name, step, cuda):
"""
extractor 快照
:param tensor1: input photo
:param tensor2: generated image
:param cuda: use gpu to speed up
:param step: train step
:param name: picture name
"""
path = "{1}/{2}_{0}.jpg".format(step, self.prev_path, name[3:-6])
orig_path = os.path.join(self.args.path_to_dataset + "2", name)
img3 = cv2.imread(orig_path)
img4 = utils.faceparsing_ndarray(img3, self.args.parsing_checkpoint, cuda)
image1 = 255 - tensor1.cpu().detach().numpy() * 255
image2 = 255 - tensor2.cpu().detach().numpy() * 255
shape = image1.shape
if len(shape) == 2:
image1 = image1[:, :, np.newaxis]
image2 = image2[:, :, np.newaxis]
img1 = ops.fill_gray(image1)
img2 = ops.fill_gray(image2)
img = ops.merge_4image(img1, img2, img3, img4)
cv2.imwrite(path, img)
print("Number of train images :", len(tr_img_paths))
print("Number of test images :", len(te_img_paths))
print("-" * 30)
# exit()
""" setup data """
# TODO: リサイズのサイズ策定
# Normalizeは？
transforms = Compose([
Resize((224, 224)), # for vgg16
ToTensor(),
Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
])
tr_dataset = FaceDataset(tr_img_paths,
tr_raw_labels,
embedding_dict,
transform=transforms)
te_dataset = FaceDataset(te_img_paths,
te_raw_labels,
None,
transform=transforms)
tr_loader = DataLoader(tr_dataset, batch_size=batch_size, shuffle=True)
te_loader = DataLoader(te_dataset, batch_size=batch_size, shuffle=True)
""" setup model """
# NOTE: 特徴抽出層は完全に凍結してるが、学習する内容的に学習し直した方がいい
# 人物分類で事前学習したほうがよいかもしれない
model = my_vgg16_bn(out_features=n_classes)
if weight:
model.load_state_dict(torch.load(weight))
# HACK: featuresの上にmodelという階層ができてしまっているので、モデルクラス内での学習済みモデルの利用を改良したい
# CNNのrequires_gradをTrueにするとメモリーが枯渇する
if args.l is not None:
LOAD_MODEL = True
LOAD_MODEL_NAME = args.l
detecteur_image = False
if args.d is not None:
IMAGE_PATH = (args.d == 1)
detecteur_image = True
if args.c is not None:
CONFIDENCE = (args.c == 1)
transform = tf.Compose([tf.RandomHorizontalFlip(), tf.RandomVerticalFlip(), tf.RandomRotation(90), tf.ColorJitter(brightness=0.5, contrast=0.75, saturation=0, hue=0), tf.ToTensor(), tf.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
transform_test = tf.Compose([tf.ToTensor(), tf.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = FaceDataset(TRAIN_DATA, CLASSIFIED_TRAIN_DATA_55000, transform=transform)
validset = FaceDataset(TRAIN_DATA, CLASSIFIED_VALID_DATA_36000, transform=transform)
trainloader = {'train': torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,
shuffle=True, num_workers=WORKERS),
'val': torch.utils.data.DataLoader(validset, batch_size=BATCH_SIZE,
shuffle=True, num_workers=WORKERS)}
testset = TestDataset(TEST_DATA, CLASSIFIED_TEST_DATA, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE,
shuffle=False, num_workers=WORKERS)
ttset = FaceDataset(TRAIN_DATA, CLASSIFIED_TRAIN_DATA, transform=transform_test)
ttloader = torch.utils.data.DataLoader(ttset, batch_size=BATCH_SIZE,
shuffle=False, num_workers=WORKERS)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(resume_path, checkpoint['epoch']))
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
print("=> no checkpoint found at '{}'".format(resume_path))
if args.multi_gpu:
print(args.multi_gpu)
model = nn.DataParallel(model)
if device == "cuda":
cudnn.benchmark = True
criterion = homosedastic_case if cfg.MODEL.HOMOSCEDASTIC else aleatoric_loss
train_dataset = FaceDataset(args.data_dir, "train", img_size=cfg.MODEL.IMG_SIZE, augment=True,
age_stddev=cfg.TRAIN.AGE_STDDEV)
train_loader = DataLoader(train_dataset, batch_size=cfg.TRAIN.BATCH_SIZE, shuffle=True,
num_workers=cfg.TRAIN.WORKERS, drop_last=True)
val_dataset = FaceDataset(args.data_dir, "valid", img_size=cfg.MODEL.IMG_SIZE, augment=False)
val_loader = DataLoader(val_dataset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False,
num_workers=cfg.TRAIN.WORKERS, drop_last=False)
scheduler = StepLR(optimizer, step_size=cfg.TRAIN.LR_DECAY_STEP, gamma=cfg.TRAIN.LR_DECAY_RATE,
last_epoch=start_epoch - 1)
best_val_mae = 10000.0
train_writer = None
if args.tensorboard is not None:
opts_prefix = "_".join(args.opts)
train_writer = SummaryWriter(log_dir=args.tensorboard + "/" + opts_prefix + "_train")
val_writer = SummaryWriter(log_dir=args.tensorboard + "/" + opts_prefix + "_val")
def main():
args = get_args()
if args.opts:
cfg.merge_from_list(args.opts)
cfg.freeze()
start_epoch = 0
checkpoint_dir = Path(args.checkpoint)
checkpoint_dir.mkdir(parents=True, exist_ok=True)
# display nb of workers
print(f"number of train workers {cfg.TRAIN.WORKERS}")
# create model
print("=> creating model '{}'".format(cfg.MODEL.ARCH))
model = get_model(model_name=cfg.MODEL.ARCH)
if cfg.TRAIN.OPT == "sgd":
optimizer = torch.optim.SGD(model.parameters(),
lr=cfg.TRAIN.LR,
momentum=cfg.TRAIN.MOMENTUM,
weight_decay=cfg.TRAIN.WEIGHT_DECAY)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=cfg.TRAIN.LR)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# optionally resume from a checkpoint
resume_path = args.resume
if resume_path:
if Path(resume_path).is_file():
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path, map_location="cpu")
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
resume_path, checkpoint['epoch']))
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
print("=> no checkpoint found at '{}'".format(resume_path))
if args.multi_gpu:
model = nn.DataParallel(model)
if device == "cuda":
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss().to(device)
train_dataset = FaceDataset(args.data_dir,
"train",
img_size=cfg.MODEL.IMG_SIZE,
augment=True,
age_stddev=cfg.TRAIN.AGE_STDDEV)
train_loader = DataLoader(train_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE,
shuffle=True,
num_workers=cfg.TRAIN.WORKERS,
drop_last=True)
val_dataset = FaceDataset(args.data_dir,
"valid",
img_size=cfg.MODEL.IMG_SIZE,
augment=False)
val_loader = DataLoader(val_dataset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.TRAIN.WORKERS,
drop_last=False)
scheduler = StepLR(optimizer,
step_size=cfg.TRAIN.LR_DECAY_STEP,
gamma=cfg.TRAIN.LR_DECAY_RATE,
last_epoch=start_epoch - 1)
best_val_mae = 10000.0
train_writer = None
if args.tensorboard is not None:
opts_prefix = "_".join(args.opts)
train_writer = SummaryWriter(log_dir=args.tensorboard + "/" +
opts_prefix + "_train")
val_writer = SummaryWriter(log_dir=args.tensorboard + "/" +
opts_prefix + "_val")
for epoch in range(start_epoch, cfg.TRAIN.EPOCHS):
# train
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, epoch, device)
# validate
val_loss, val_acc, val_mae = validate(val_loader, model, criterion,
epoch, device)
if args.tensorboard is not None:
train_writer.add_scalar("loss", train_loss, epoch)
train_writer.add_scalar("acc", train_acc, epoch)
val_writer.add_scalar("loss", val_loss, epoch)
val_writer.add_scalar("acc", val_acc, epoch)
val_writer.add_scalar("mae", val_mae, epoch)
# checkpoint
if val_mae < best_val_mae:
print(
f"=> [epoch {epoch:03d}] best val mae was improved from {best_val_mae:.3f} to {val_mae:.3f}"
)
model_state_dict = model.module.state_dict(
) if args.multi_gpu else model.state_dict()
torch.save(
{
'epoch': epoch + 1,
'arch': cfg.MODEL.ARCH,
'state_dict': model_state_dict,
'optimizer_state_dict': optimizer.state_dict()
},
str(
checkpoint_dir.joinpath(
"epoch{:03d}_{:.5f}_{:.4f}.pth".format(
epoch, val_loss, val_mae))))
best_val_mae = val_mae
else:
print(
f"=> [epoch {epoch:03d}] best val mae was not improved from {best_val_mae:.3f} ({val_mae:.3f})"
)
# adjust learning rate
scheduler.step()
print("=> training finished")
print(f"additional opts: {args.opts}")
print(f"best val mae: {best_val_mae:.3f}")
def train_style_transfer(args):
if not (args.train_data and args.valid_data):
print("must chose train_data and valid_data")
sys.exit()
# make dataset
trans = transforms.ToTensor()
train_dataset = FaceDataset(args.train_data, transform=trans)
label_dict = train_dataset.get_label_dict()
valid_dataset = FaceDataset(args.valid_data, transform=trans)
valid_dataset.give_label_dict(label_dict)
train_loader = data_utils.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
valid_loader = data_utils.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
train_size = len(train_dataset)
valid_size = len(valid_dataset)
loaders = {"train": train_loader, "valid": valid_loader}
dataset_sizes = {"train": train_size, "valid": valid_size}
if args.gpu:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
# make network
if args.model_type == "VAE":
net = Autoencoder(train_dataset.label_num()).to(device)
optimizer = optim.Adam(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
best_model_wts = net.state_dict()
best_loss = 1e10
if args.generator_model and os.path.exists(args.generator_model):
net.load_state_dict(torch.load(args.generator_model))
elif args.model_type == "VAEGAN":
generator = Autoencoder(train_dataset.label_num()).to(device)
discriminator = Discriminator().to(device)
classifier = Classifier(train_dataset.label_num()).to(device)
generator_optimizer = optim.Adam(generator.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
discriminator_optimizer = optim.Adam(discriminator.parameters(),
lr=args.lr * 0.1,
weight_decay=args.weight_decay)
best_generator_wts = generator.state_dict()
best_discriminator_wts = discriminator.state_dict()
best_generator_loss = 1e10
best_discriminator_loss = 1e10
if args.generator_model and os.path.exists(args.generator_model):
generator.load_state_dict(torch.load(args.generator_model))
if args.discriminator_model and os.path.exists(
args.discriminator_model):
discriminator.load_state_dict(torch.load(args.discriminator_model))
if args.classifier_model:
classifier.load_state_dict(torch.load(args.classifier_model))
# make loss function and optimizer
criterion = nn.BCELoss(reduction="sum")
classifier_criterion = nn.CrossEntropyLoss(reduction="sum")
# initialize loss
loss_history = {"train": [], "valid": []}
# start training
start_time = time.time()
for epoch in range(args.epochs):
print("epoch {}".format(epoch + 1))
for phase in ["train", "valid"]:
if phase == "train":
if args.model_type == "VAE":
net.train(True)
elif args.model_type == "VAEGAN":
generator.train(True)
discriminator.train(True)
else:
if args.model_type == "VAE":
net.train(False)
elif args.model_type == "VAEGAN":
generator.train(False)
discriminator.train(False)
# initialize running loss
generator_running_loss = 0.0
discriminator_running_loss = 0.0
for i, data in enumerate(loaders[phase]):
inputs, label = data
# wrap the in valiables
if phase == "train":
inputs = Variable(inputs).to(device)
label = Variable(label).to(device)
torch.set_grad_enabled(True)
else:
inputs = Variable(inputs).to(device)
label = Variable(label).to(device)
torch.set_grad_enabled(False)
# zero gradients
if args.model_type == "VAE":
optimizer.zero_grad()
mu, var, outputs = net(inputs, label)
loss = loss_func(inputs, outputs, mu, var)
if phase == "train":
loss.backward()
optimizer.step()
generator_running_loss += loss.item()
elif args.model_type == "VAEGAN":
real_label = Variable(
torch.ones((inputs.size()[0], 1), dtype=torch.float) -
0.2 * (torch.rand(inputs.size()[0], 1))).to(device)
fake_label = Variable(
torch.zeros((inputs.size()[0], 1), dtype=torch.float) +
0.2 * (torch.rand(inputs.size()[0], 1))).to(device)
discriminator_optimizer.zero_grad()
real_pred = discriminator(inputs)
real_loss = criterion(real_pred, real_label)
random_index = np.random.randint(0,
train_dataset.label_num(),
inputs.size()[0])
generate_label = Variable(
torch.zeros_like(label)).to(device)
for i, index in enumerate(random_index):
generate_label[i][index] = 1
mu, var, outputs = generator(inputs, label)
fake_pred = discriminator(outputs.detach())
fake_loss = criterion(fake_pred, fake_label)
discriminator_loss = real_loss + fake_loss
if phase == "train":
discriminator_loss.backward()
discriminator_optimizer.step()
generator_optimizer.zero_grad()
#class_loss = classifier_criterion(classifier(outputs), torch.max(label, 1)[1])
dis_loss = criterion(discriminator(outputs), real_label)
gen_loss = loss_func(inputs, outputs, mu, var)
generator_loss = dis_loss + gen_loss
if phase == "train":
generator_loss.backward()
generator_optimizer.step()
discriminator_running_loss += discriminator_loss.item()
generator_running_loss += generator_loss.item()
if args.model_type == "VAE":
epoch_loss = generator_running_loss / dataset_sizes[
phase] * args.batch_size
loss_history[phase].append(epoch_loss)
print("{} loss {:.4f}".format(phase, epoch_loss))
if phase == "valid" and epoch_loss < best_loss:
best_model_wts = net.state_dict()
best_loss = epoch_loss
elif args.model_type == "VAEGAN":
epoch_generator_loss = generator_running_loss / dataset_sizes[
phase] * args.batch_size
epoch_discriminator_loss = discriminator_running_loss / dataset_sizes[
phase] * args.batch_size
print("{} generator loss {:.4f}".format(
phase, epoch_generator_loss))
print("{} discriminator loss {:.4f}".format(
phase, epoch_discriminator_loss))
if phase == "valid" and epoch_generator_loss < best_generator_loss:
best_generator_wts = generator.state_dict()
best_generator_loss = epoch_generator_loss
if phase == "valid" and epoch_discriminator_loss < best_discriminator_loss:
best_discriminator_wts = discriminator.state_dict()
best_generator_loss = epoch_discriminator_loss
elapsed_time = time.time() - start_time
print("training complete in {:.0f}s".format(elapsed_time))
if args.model_type == "VAE":
net.load_state_dict(best_model_wts)
return net, label_dict
elif args.model_type == "VAEGAN":
generator.load_state_dict(best_generator_wts)
discriminator.load_state_dict(best_discriminator_wts)
return (generator, discriminator), label_dict
def train_classifier(args):
if not (args.train_data and args.valid_data):
print("must chose train_data and valid_data")
sys.exit()
trans = transforms.ToTensor()
train_dataset = FaceDataset(args.train_data, transform=trans)
label_dict = train_dataset.get_label_dict()
valid_dataset = FaceDataset(args.valid_data, transform=trans)
valid_dataset.give_label_dict(label_dict)
train_loader = data_utils.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
valid_loader = data_utils.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
loaders = {"train": train_loader, "valid": valid_loader}
dataset_sizes = {"train": len(train_dataset), "valid": len(valid_dataset)}
if args.gpu:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
classifier = Classifier(len(label_dict)).to(device).float()
optimizer = optim.Adam(classifier.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
best_model_wts = classifier.state_dict()
best_loss = 1e10
if args.classifier_model and os.path.exists(args.classifier_model):
classifier.load_state_dict(torch.load(args.classifier_model))
criterion = nn.CrossEntropyLoss(reduction="sum")
start_time = time.time()
for epoch in range(args.epochs):
print("epoch {}".format(epoch + 1))
for phase in ["train", "valid"]:
if phase == "train":
classifier.train(True)
else:
classifier.train(False)
running_loss = 0.0
running_acc = 0
for i, data in enumerate(loaders[phase]):
inputs, label = data
inputs = Variable(inputs).to(device)
label = Variable(label).to(device)
if phase == "train":
torch.set_grad_enabled(True)
else:
torch.set_grad_enabled(False)
optimizer.zero_grad()
pred = classifier(inputs)
reg_loss = 0
for param in classifier.parameters():
reg_loss += (param * param).sum()
loss = criterion(pred,
torch.max(label,
1)[1]) + 1e-9 * reg_loss * reg_loss
if phase == "train":
loss.backward()
optimizer.step()
running_loss += loss.item()
running_acc += (torch.max(pred, 1)[1] == torch.max(
label, 1)[1]).sum().item()
epoch_loss = running_loss / dataset_sizes[phase] * args.batch_size
epoch_acc = running_acc / dataset_sizes[phase]
print("{} loss {:.4f}".format(phase, epoch_loss))
print("{} acc {:.6f}".format(phase, epoch_acc))
if phase == "valid" and epoch_loss < best_loss:
best_model_wts = classifier.state_dict()
best_loss = epoch_loss
elapsed_time = time.time() - start_time
print("training_complete in {:.0f}".format(elapsed_time))
classifier.load_state_dict(best_model_wts)
return classifier, label_dict
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--device_ids', type=str, default='0')
arg('--folds', type=str, help='fold', default='0,1,2,3,4,5,6,7,8,9')
arg('--model',
type=str,
default='se_resnext101',
choices=list(model_list.keys()))
arg('--batch_size', type=int, default=BS)
args = parser.parse_args()
GPUs = [int(i) for i in args.device_ids.split(',')]
folds_to_use = [int(i) for i in args.folds.split(',')]
path_images = list(
filter(lambda x: x.endswith('.jpg'), os.listdir('../data/test/')))
unet_base_model = model_list[args.model]
test_data_loader_normal = DataLoader(FaceDataset(
'../data/test', path_images, transforms=test_norm_transforms),
batch_size=args.batch_size,
num_workers=10,
shuffle=False)
test_data_loader_flip = DataLoader(FaceDataset(
'../data/test', path_images, transforms=test_flip_transforms),
batch_size=args.batch_size,
num_workers=10,
shuffle=False)
savedir_base = f'logits/{args.model}/'
os.makedirs(savedir_base, exist_ok=True)
for cur_fold_num in folds_to_use:
search_dir = f'checkpoints/{args.model}_fold_{cur_fold_num}/'
files_in_dir = os.listdir(search_dir)
scores = [
float('0.' + i.split('_')[1].split('.')[0]) for i in files_in_dir
]
chckp_to_use = files_in_dir[np.argmin(scores)]
chkp_pth = f'{search_dir}{chckp_to_use}'
print('use checkpoint ', chkp_pth)
savedir = f'{savedir_base}fold_{cur_fold_num}/'
os.makedirs(savedir, exist_ok=True)
unet = unet_base_model(pretrained=False)
if torch.cuda.is_available():
unet = unet.cuda()
unet = nn.DataParallel(unet, GPUs)
unet.load_state_dict(torch.load(chkp_pth)['model'])
unet.eval()
img_cntr = 0
with torch.no_grad():
for batch_n, batch_f in tqdm(zip(test_data_loader_normal,
test_data_loader_flip),
total=len(test_data_loader_normal)):
inp_n = cuda(batch_n[0])
inp_f = cuda(batch_f[0])
output_n = unet.forward(inp_n)
output_f = unet.forward(inp_f)
for img_batch_index in range(output_n.shape[0]):
img_n = output_n[img_batch_index].cpu().numpy()[0]
img_f = output_f[img_batch_index].cpu().numpy()[0]
img_f = np.fliplr(img_f)
img_id = path_images[img_cntr].split('.')[0]
np.save(f'{savedir}/id_{img_id}_normal', img_n)
np.save(f'{savedir}/id_{img_id}_tta', img_n)
img_cntr += 1
def pt_2():
epochs = 25
H = 120
W = 160
face_dataset = FaceDataset(1, 32, root_dir, W, H, CustomTransforms())
training = create_dataloader(face_dataset, 5)
validation_dataset = FaceDataset(33, 40, root_dir, W, H,
CustomTransforms())
validation = create_dataloader(validation_dataset, 1)
#test_dataloader(training, W, H)
net = Net()
loss = nn.MSELoss()
opt = Adam(net.parameters(), lr=0.001)
training_losses = []
validation_losses = []
for epoch in range(epochs):
epoch_loss = torch.zeros((1, 1))
for i, (images, labels) in enumerate(training):
prediction = net(images)
output = loss(prediction, labels.type(torch.float32).view(-1, 116))
epoch_loss += output
output.backward()
opt.step()
opt.zero_grad()
epoch_loss = epoch_loss / len(face_dataset)
print("EPOCH " + str(i) + " LOSS: " + str(epoch_loss))
training_losses.append([epoch, epoch_loss.item() * 100])
epoch_loss = torch.zeros((1, 1), requires_grad=False)
for i, (images, labels) in enumerate(validation):
prediction = net(images)
output = loss(prediction, labels.type(torch.float32).view(-1, 116))
epoch_loss += output
opt.zero_grad()
epoch_loss = epoch_loss / len(face_dataset)
validation_losses.append([epoch, epoch_loss.item() * 100])
training_losses = np.array(training_losses)
validation_losses = np.array(validation_losses)
plt.plot(training_losses[:, 0], training_losses[:, 1])
plt.plot(validation_losses[:, 0], validation_losses[:, 1])
plt.plot()
plt.savefig('results/pt_2/epoch_loss_decrease.png')
plt.show()
"""
Handy visualization code copied and pasted from:
https://colab.research.google.com/github/Niranjankumar-c/DeepLearning-PadhAI/blob/master/DeepLearning_Materials/6_VisualizationCNN_Pytorch/CNNVisualisation.ipynb#scrollTo=cWmfCalUvzbS
as linked on the piazza.
"""
def plot_filters_single_channel(i, t):
#kernels depth * number of kernels
nplots = t.shape[0] * t.shape[1]
ncols = 12
nrows = 1 + nplots // ncols
#convert tensor to numpy image
npimg = np.array(t.numpy(), np.float32)
count = 0
fig = plt.figure(figsize=(ncols, nrows))
#looping through all the kernels in each channel
for i in range(t.shape[0]):
for j in range(t.shape[1]):
count += 1
ax1 = fig.add_subplot(nrows, ncols, count)
npimg = np.array(t[i, j].numpy(), np.float32)
npimg = (npimg - np.mean(npimg)) / np.std(npimg)
npimg = np.minimum(1, np.maximum(0, (npimg + 0.5)))
ax1.imshow(npimg)
ax1.set_title(str(i) + ',' + str(j))
ax1.axis('off')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.tight_layout()
plt.savefig(str(i) + 'weight_visualization.png')
plt.show()
for i in range(len(net.conv)):
if i == 0:
plot_filters_single_channel(i, net.conv[i].weight.data)
validation_dataset = FaceDataset(33, 40, root_dir, W, H,
CustomTransforms())
dataloader = create_dataloader(validation_dataset, 1)
with torch.no_grad():
for i, (image, label) in enumerate(dataloader):
prediction = net(image)
output = loss(prediction, label.type(torch.float32).view(-1, 116))
print("LOSS FOR IMAGE IS: " + str(output))
prediction = prediction.view(-1, 58, 2)
plt.imshow(image[0][0], cmap='gray')
plt.scatter(prediction[0, :, 0] * W,
prediction[0, :, 1] * H,
s=10,
marker='o',
c='r')
plt.scatter(label[0, :, 0] * W,
label[0, :, 1] * H,
marker='o',
color='green')
plt.savefig('results/prediction_' + str(i) + '_' + str(epochs))
plt.show()
log.info('feature extractor train mode')
extractor = Extractor("neural extractor", args)
if cuda:
extractor.cuda()
extractor.batch_train(cuda)
elif args.phase == "inference_imitator":
log.info("inference imitator")
imitator = Imitator("neural imitator", args, clean=False)
if cuda:
imitator.cuda()
imitator.load_checkpoint(args.imitator_model, True, cuda=cuda)
elif args.phase == "prev_imitator":
log.info("preview imitator")
imitator = Imitator("neural imitator", args, clean=False)
imitator.load_checkpoint(args.imitator_model, False, cuda=False)
dataset = FaceDataset(args)
name, param, img = dataset.get_picture()
param = np.array(param, dtype=np.float32)
b_param = param[np.newaxis, :]
log.info(b_param.shape)
t_param = torch.from_numpy(b_param)
output = imitator(t_param)
output = output.cpu().detach().numpy()
output = np.squeeze(output, axis=0)
output = output.swapaxes(0, 2) * 255
cv2.imwrite('./output/{0}.jpg'.format(name), output)
elif args.phase == "inference_extractor":
log.info("inference extractor")
extractor = Extractor("neural extractor", args)
if cuda:
extractor.cuda()
netD.apply(weights_init)
netG.apply(weights_init)
criterion = nn.BCELoss()
fixed_noise = torch.randn(64, n_vector, 1, 1, device=device)
real_label = 1
fake_label = 0
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
facedataset = FaceDataset(dataset_dir=dataset_dir,
transform=transforms.Compose([
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
dataloader = DataLoader(facedataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
# Training loop
img_list = []
G_losses = []
D_losses = []
iters = 0
print("Starting Training Loop...")
for epoch in range(num_epochs):
