def data_loaders(model, loss_func, train_dataset, valid_dataset, test_dataset):
data_transform = transforms.Compose([
transforms.Resize(model.input_size[1:]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
])
grayscale = model.input_size[0] != 3
if loss_func.__name__ != 'TripletLoss':
train_dataset = SiameseNetworkDataset(imageFolderDataset=train_dataset,
transform=data_transform,
grayscale=grayscale)
valid_dataset = SiameseNetworkDataset(imageFolderDataset=valid_dataset,
transform=data_transform,
grayscale=grayscale)
else:
train_dataset = TripletDataset(imageFolderDataset=train_dataset,
transform=data_transform,
grayscale=grayscale)
valid_dataset = TripletDataset(imageFolderDataset=valid_dataset,
transform=data_transform,
grayscale=grayscale)
test_dataset = SiameseNetworkDataset(imageFolderDataset=test_dataset,
transform=data_transform,
grayscale=grayscale)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=Config.train_batch_size, shuffle=True, num_workers=Config.num_workers)
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=Config.valid_batch_size, shuffle=True, num_workers=Config.num_workers)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, num_workers=Config.num_workers)
return train_loader, valid_loader, test_loader
def main():
"""
Training.
"""
global start_epoch, epoch, checkpoint
# Initialize model or load checkpoint
if checkpoint is None:
model = UNet(in_channels, out_channels)
# Initialize the optimizer
optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad,
model.parameters()),
lr=lr)
else:
checkpoint = torch.load(checkpoint)
start_epoch = checkpoint['epoch'] + 1
model = checkpoint['model']
optimizer = checkpoint['optimizer']
# Move to default device
model = model.to(device)
criterion = nn.L1Loss().to(device)
# Custom dataloaders
train_loader = torch.utils.data.DataLoader(TripletDataset(
train_folder, crop_size, scale),
batch_size=batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(TripletDataset(
test_folder, crop_size, scale),
batch_size=batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True)
# Total number of epochs to train for
epochs = int(iterations // len(train_loader) + 1)
# Epochs
for epoch in range(start_epoch, epochs):
# One epoch's training
train(train_loader=train_loader,
model=model,
criterion=criterion,
optimizer=optimizer,
epoch=epoch,
epochs=epochs)
test(test_loader=test_loader, model=model, criterion=criterion)
# Save checkpoint
torch.save({
'epoch': epoch,
'model': model,
'optimizer': optimizer
}, f'checkpoints/checkpoint_unet_{epoch}.pth.tar')
def __init__(self, path, transform, num_triplets, batchsize, resolution):
self.path = path
self.batchsize = batchsize
self.num_workers = 4
self.transform = transform
self.resolution= resolution
self.num_triplets= num_triplets
self.dataset = TripletDataset(self.path, self.transform, num_triplets = self.num_triplets, resolution = self.resolution)
self.dataloader = DataLoader(
dataset=self.dataset,
batch_size=self.batchsize,
shuffle=False,
num_workers=self.num_workers)
def train(self, config):
train_dataset = TripletDataset(config['dataset_path'], 'train',
config['data_augmentation_suffixes'])
train_dataset.prepare(config['num_train_pairs'])
val_dataset = TripletDataset(config['dataset_path'], 'validation')
val_dataset.prepare(config['num_val_pairs'])
train_generator = TripletDataGenerator(
train_dataset,
batch_size=config['batch_size'],
dim=self.config['input_shape'],
shuffle=config['shuffle_training_inputs'])
val_generator = TripletDataGenerator(
val_dataset,
batch_size=config['batch_size'],
dim=self.config['input_shape'],
shuffle=config['shuffle_training_inputs'])
model_path, _ = os.path.split(self.config['model_filename'])
callbacks = [
keras.callbacks.TensorBoard(log_dir=self.log_dir,
histogram_freq=0,
write_graph=True,
write_images=False),
keras.callbacks.ModelCheckpoint(self.checkpoint_path,
verbose=0,
save_weights_only=True)
]
self.keras_model.compile(loss=utils.l2_loss,
optimizer=Adam(lr=config['learning_rate']))
self.keras_model.fit_generator(generator=train_generator,
validation_data=val_generator,
epochs=config['epochs'],
use_multiprocessing=True,
callbacks=callbacks,
workers=multiprocessing.cpu_count())
self.keras_model.save(self.config['model_filename'])
def get_triplet_dataloader(root=None, batch_size=1, transforms=None):
dataset = TripletDataset(root=root, transforms=transforms)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
return dataloader
def main(args):
assert args.save_interval % 10 == 0, "save_interval must be a multiple of 10"
# prepare dirs
os.makedirs(args.log_dir, exist_ok=True)
os.makedirs(args.save_model, exist_ok=True)
device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")
print("Device is", device)
# img path loading
with open("data/3d_data.pkl", mode='rb') as f:
data_3d = pickle.load(f)
train_path_list = data_3d.train_pl
val_path_list = data_3d.val_pl
train_dataset = TripletDataset(transform=ImageTransform(), flist=train_path_list)
train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
val_dataset = TripletDataset(transform=ImageTransform(), flist=val_path_list)
val_dataloader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False)
model = TripletNet()
model.to(device)
criterion = nn.MarginRankingLoss(margin=args.margin)
# choose params to train
update_params_name = []
for name, _ in model.named_parameters():
if 'layer4' in name:
update_params_name.append(name)
elif 'fc' in name:
update_params_name.append(name)
print("**-----** update params **-----**")
print(update_params_name)
print("**-----------------------------**")
print()
params_to_update = choose_update_params(update_params_name, model)
# set optimizer
optimizer = optim.SGD(params_to_update, lr=1e-4, momentum=0.9)
# run epoch
log_writer = SummaryWriter(log_dir=args.log_dir)
for epoch in range(args.num_epochs):
print("-"*80)
print('Epoch {}/{}'.format(epoch+1, args.num_epochs))
epoch_loss, epoch_acc = [], []
for inputs, labels in tqdm(train_dataloader):
batch_loss, batch_acc = train_one_batch(inputs, labels, model, criterion, optimizer, device)
epoch_loss.append(batch_loss.item())
epoch_acc.append(batch_acc.item())
epoch_loss = np.array(epoch_loss)
epoch_acc = np.array(epoch_acc)
print('[Loss: {:.4f}], [Acc: {:.4f}] \n'.format(np.mean(epoch_loss), np.mean(epoch_acc)))
log_writer.add_scalar("train/loss", np.mean(epoch_loss), epoch+1)
log_writer.add_scalar("train/acc", np.mean(epoch_acc), epoch+1)
# validation
if (epoch+1) % 10 == 0:
print("Run Validation")
epoch_loss, epoch_acc = [], []
for inputs, labels in tqdm(val_dataloader):
batch_loss, batch_acc = validation(inputs, labels, model, criterion, device)
epoch_loss.append(batch_loss.item())
epoch_acc.append(batch_acc.item())
epoch_loss = np.array(epoch_loss)
epoch_acc = np.array(epoch_acc)
print('[Validation Loss: {:.4f}], [Validation Acc: {:.4f}]'.format(np.mean(epoch_loss), np.mean(epoch_acc)))
log_writer.add_scalar("val/loss", np.mean(epoch_loss), epoch+1)
log_writer.add_scalar("val/acc", np.mean(epoch_acc), epoch+1)
# save model
if (args.save_interval > 0) and ((epoch+1) % args.save_interval == 0):
save_path = os.path.join(args.save_model, '{}_epoch_{:.1f}.pth'.format(epoch+1, np.mean(epoch_loss)))
torch.save(model.state_dict(), save_path)
log_writer.close()
name = 'arcface1.pt'
load_local_model = False
# os.environ['CUDA_LAUNCH_BLOCKING']='1'
# device: cpu or cuda
os.environ[
'CUDA_VISIBLE_DEVICES'] = '2' # specify which gpu you want to use
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print("Device:", device)
df_eval1 = pd.read_csv('../Data/eval_same.csv')
df_eval2 = pd.read_csv('../Data/eval_diff.csv')
df_test = pd.read_csv('../Data/test.csv')
eval_dataset1 = TripletDataset(df_eval1, mode='eval')
eval_dataset2 = TripletDataset(df_eval2, mode='eval')
test_dataset = TripletDataset(df_test, mode='test')
eval_loader1 = DataLoader(eval_dataset1,
batch_size=BATCH_SIZE,
num_workers=NUM_WORKERS,
drop_last=False)
eval_loader2 = DataLoader(eval_dataset2,
batch_size=BATCH_SIZE,
num_workers=NUM_WORKERS,
drop_last=False)
test_loader = DataLoader(test_dataset,
batch_size=BATCH_SIZE,
num_workers=NUM_WORKERS,
drop_last=False)
def train_triplet_module():
"""
:return:
"""
# 设置超参数
LR = 1e-5 # 学习率
EPOCH = 15 # 训练轮数
BATCH_SIZE = 10 # Class Batch size
N_CLASS = 10 # 类别个数
num_sub_dataset = 10 # 子集数量
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
resume = False # 是否断点训练
workers = 0 # Number of workers for dataloader
margin = 1e-1 # triplet loss 超参数 margin
k = 1
# k in topk
interval = 5 # diff loss和triplet loss间隔epoch
balance = 4e-2 # diff loss和triplet loss间权重
# 加载数据
train_dataset = TripletDataset(num_sub_dataset)
train_loader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=workers)
diff_dataset = DifferenceDataset()
# 定义模型
model_set = []
for i in range(num_sub_dataset):
model = TripletModule().float().to(device)
model_set.append(model)
# 定义优化器
params_set = []
for model in model_set:
params = [{'params': model.parameters(), 'lr': LR}]
params_set.extend(params)
optimizer = optim.Adam(params_set, lr=LR)
# 断点训练，加载模型权重
if resume:
print('==> Resuming from checkpoint..')
assert os.path.isdir(
'Checkpoint'), 'Error: no Checkpoint directory found!'
state = torch.load('Checkpoint/models/ckpt.pth')
for i in range(num_sub_dataset):
model = model_set[i]
model.load_state_dict(state['net'][i])
optimizer.load_state_dict(state['optim'])
start_epoch = state['epoch']
# 损失函数
cosloss = nn.CosineSimilarity(dim=1, eps=1e-6).to(device)
# 训练模型
for epoch in range(start_epoch, EPOCH):
print(
"####################################################################################"
)
# 学习率调度机制
adjust_learning_rate(optimizer, epoch)
print('Learning rate is {}'.format(optimizer.param_groups[0]['lr']))
############################
# 训练
############################
# 训练模式
for i in range(num_sub_dataset):
model = model_set[i]
model.train()
# 迭代次数
cnt = 0
# triplet损失
sum_triplet_loss = 0.
# diff损失
sum_diff_loss = 0.
# 损失
sum_loss = 0.
for data in train_loader:
cnt += 1
# 加载Triplet数据集数据
x, y = data
batch_size = x.size(0)
inputs, labels = torch.cat(tuple([x[:, i] for i in range(num_sub_dataset)]), dim=0),\
torch.cat(tuple([y[:, i] for i in range(num_sub_dataset)]), dim=0)
inputs, labels = inputs.view(
(-1, inputs.size(-1))), labels.view(-1)
inputs, labels = inputs.float().to(device), labels.int().to(device)
# 梯度置零
optimizer.zero_grad()
# 前向传播、后向传播
num_subset_sample = batch_size * N_CLASS # 每个子集对应batch的样本数量
embeddings = torch.cat(tuple([
model_set[i](inputs[num_subset_sample * i:num_subset_sample *
(i + 1)]) for i in range(num_sub_dataset)
]),
dim=0)
triplet_loss = batch_hard_triplet_loss(k,
num_subset_sample,
labels,
embeddings,
margin=margin,
device=device)
# triplet loss
sum_triplet_loss += triplet_loss.item()
# 加载Difference数据集数据
x, y = diff_dataset.getsamples(batch_size)
inputs, labels = torch.from_numpy(x), torch.from_numpy(y)
inputs, labels = inputs.float().to(device), labels.int().to(device)
outputs = []
outputs_sum = None
for model in model_set:
output = model(inputs)
outputs.append(output)
if outputs_sum is None:
outputs_sum = output
else:
outputs_sum += output
diff_loss = 0.
for output in outputs:
# diff_loss += torch.sum(torch.abs(cosloss(output, (outputs_sum-output)/(num_sub_dataset-1)))) / inputs.size(0)
diff_loss += torch.sum(
cosloss(output, (outputs_sum - output) /
(num_sub_dataset - 1))) / inputs.size(0)
diff_loss /= num_sub_dataset
sum_diff_loss += diff_loss.item()
loss = triplet_loss + balance * diff_loss
sum_loss += loss.item()
if (epoch + 1) % interval == 0:
loss.backward()
else:
triplet_loss.backward()
# 梯度更新
optimizer.step()
# 打印日志
if cnt % 5 == 0 or cnt == len(train_loader):
print(
'[%d/%d]--[%d/%d]\tTriplet Loss: %.6f\tDiff Loss: %.6f\tLoss: %.6f'
% (epoch + 1, EPOCH, cnt, len(train_loader),
sum_triplet_loss / cnt, sum_diff_loss / cnt,
sum_loss / cnt))
# 模型状态
net_state_set = [model.state_dict() for model in model_set]
# 保存断点模型
state = {
'net': net_state_set,
'optim': optimizer.state_dict(),
'epoch': epoch
}
torch.save(state, './Checkpoint/models/ckpt.pth')
def train_worker(dataset, device, rank=0, world_size=None):
torch.cuda.set_device(device)
criterion = TripletMarginRankingLoss(args.loss_margin)
model = TransformerPool(args.vocab_size, args.embedding_dim,
args.hidden_dim, pre_trained=GLOVE)
if args.re_train:
model.load_state_dict(torch.load(
args.train_model, map_location='cuda:{}'.format(device)))
else:
model.apply(init_weights)
model, criterion = model.to(device), criterion.to(device)
triplet_dataset = TripletDataset(dataset)
in_distributed_mode = True if world_size else False
if in_distributed_mode:
rank, device = torch.distributed.get_rank(), torch.cuda.current_device()
print("rank:{}, device:{}".format(rank, device))
if in_distributed_mode:
model = DistributedDataParallel(
model, device_ids=[device])
datasampler = DistributedSampler(triplet_dataset)
dataloader = DataLoader(triplet_dataset, shuffle=False,
pin_memory=True, num_workers=0,
batch_size=args.batch_size, sampler=datasampler)
else:
dataloader = DataLoader(triplet_dataset, shuffle=True,
pin_memory=True, num_workers=4,
batch_size=args.batch_size)
optimizer = RAdam(
model.parameters(), lr=args.learning_rate)
scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=args.t_max, eta_min=args.eta_min)
model.train()
best_avg_loss = None
t1 = time.time()
for epoch in range(args.epoch):
datasampler.set_epoch(epoch) if in_distributed_mode else None
total_loss = []
bar = tqdm(desc='EPOCH {:02d}'.format(epoch), total=len(
dataloader), leave=False) if rank == 0 else None
for triplet in dataloader:
optimizer.zero_grad()
anchor, positive, negative = model(triplet)
loss = criterion(anchor, positive, negative)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5)
optimizer.step()
total_loss.append(loss.item())
bar.update() if rank == 0 else None
if rank == 0:
bar.close()
epoch_avg_loss = np.mean(total_loss)
scheduler.step(epoch_avg_loss)
print("Epoch {:02d}, Time {:.02f}s, AvgLoss {:.08f}, lr {:.8f}".format(
epoch, time.time()-t1, epoch_avg_loss, optimizer.param_groups[0]['lr']))
if best_avg_loss is None or epoch_avg_loss < best_avg_loss:
best_avg_loss = epoch_avg_loss
state_dict = model.module.state_dict() if in_distributed_mode else model.state_dict()
torch.save(state_dict, args.model_path)
t1 = time.time()
scheduler.step(epoch)
torch.cuda.empty_cache()
return