def train_model(train_dl,model):
criterion = nn.CrossEntropyLoss()
optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
n_total_steps = len(train_dl)
#print(n_total_steps)
for epoch in range(num_epoch):
if epoch % 2== 0:
checkpoint = {'state_dict': model.state_dict(),'optimizer':optimizer.state_dict()}
save_checkpoint(checkpoint)
for i, (inputs,targets) in enumerate(train_dl):
inputs = inputs.to(device)
targets = targets.to(device)
#print(targets)
# Forward Pass
yhat = model(inputs)
loss = criterion(yhat,targets)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 2 == 0:
print("Epoch:",epoch+1/num_epoch,"Step:", i+1/n_total_steps, "Loss:",loss.item())
def main():
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# os.mkdir(args.outdir)
train_dataset = get_dataset(args.dataset, 'train')
test_dataset = get_dataset(args.dataset, 'test')
pin_memory = (args.dataset == "imagenet")
train_loader = DataLoader(train_dataset,
shuffle=True,
batch_size=args.batch,
num_workers=args.workers,
pin_memory=pin_memory)
test_loader = DataLoader(test_dataset,
shuffle=False,
batch_size=args.batch,
num_workers=args.workers,
pin_memory=pin_memory)
model = get_architecture(args.arch, args.dataset)
logfilename = os.path.join(args.outdir, 'log.txt')
init_logfile(logfilename,
"epoch\ttime\tlr\ttrain loss\ttrain acc\ttestloss\ttest acc")
criterion = CrossEntropyLoss().cuda()
optimizer = SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = StepLR(optimizer,
step_size=args.lr_step_size,
gamma=args.gamma)
for epoch in range(args.epochs):
scheduler.step(epoch)
before = time.time()
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, epoch, args.noise_sd)
test_loss, test_acc = test(test_loader, model, criterion,
args.noise_sd)
after = time.time()
log(
logfilename, "{}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}".format(
epoch, str(datetime.timedelta(seconds=(after - before))),
scheduler.get_lr()[0], train_loss, train_acc, test_loss,
test_acc))
torch.save(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, os.path.join(args.outdir, 'checkpoint.pth.tar'))
def save_checkpoints(epoch: int, model: Module, optimizer: SGD, loss: _Loss,
path: str):
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss,
}, path)
def main():
cudnn.benchmark = True
batch_size = Config.gpu_count * Config.image_per_gpu
EPOCHS = Config.epoch
workers = Config.workers
global best_val_acc, best_test_acc
Config.distributed = Config.gpu_count > 4 # TODO!
model = set_model()
#if Config.gpu is not None:
model = model.cuda()
if Config.gpu_count > 1:
model = torch.nn.DataParallel(model).cuda()
criterion = nn.CrossEntropyLoss().cuda()
#weights = torch.FloatTensor(np.array([0.7, 0.3])).cuda()
#criterion = WeightCrossEntropy(num_classes=Config.out_class, weight=weights).cuda()
#criterion = LGMLoss(num_classes=Config.out_class, feat_dim=128).cuda()
optimizer = SGD(model.parameters(), lr=Config.lr, momentum=0.9,nesterov=True, weight_decay=0.0001)
#optimizer = Adam(model.parameters())
train_dir = os.path.join(DATA_DIR, 'train', '40X')
val_dir = os.path.join(DATA_DIR, 'val', '40X')
test_dir = os.path.join(DATA_DIR, 'test', '40X')
TRANSFORM_IMG = transforms.Compose([
transforms.Resize((256, 256)),
#ImageTransform(),
#lambda x: PIL.Image.fromarray(x),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.2, 0.2, 0.2])
])
train_loader = DataLoader(ImageFolder(root=train_dir, transform=TRANSFORM_IMG),
batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=workers)
val_loader = DataLoader(ImageFolder(root=test_dir, transform=TRANSFORM_IMG),
batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=workers)
#test_loader = DataLoader(ImageFolder(root=test_dir, transform=TRANSFORM_IMG),
# batch_size=batch_size, shuffle=True, pin_memory=True,
# num_workers=workers)
for epoch in range(EPOCHS):
adjust_learing_rate(optimizer, epoch)
train_losses, train_acc = train_epoch(train_loader, model, criterion, optimizer, epoch)
val_losses, val_acc = validate(val_loader, model, criterion)
is_best = val_acc.avg > best_val_acc
print('>>>>>>>>>>>>>>>>>>>>>>')
print('Epoch: {} train loss: {}, train acc: {}, valid loss: {}, valid acc: {}'.format(epoch, train_losses.avg, train_acc.avg,
val_losses.avg, val_acc.avg))
print('>>>>>>>>>>>>>>>>>>>>>>')
save_checkpoint({'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_val_acc': best_val_acc,
'optimizer': optimizer.state_dict(),}, is_best)
def main(args):
run = RunManager(args,
ignore=('device', 'evaluate', 'no_cuda'),
main='model')
print(run)
train_dataset = DatasetFolder('data/train',
load_sample, ('.npy', ),
transform=normalize_sample)
val_dataset = DatasetFolder('data/val',
load_sample, ('.npy', ),
transform=normalize_sample)
print(train_dataset)
print(val_dataset)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=8,
shuffle=True)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
num_workers=8)
if args.model == '1d-conv':
model = RectCNN(282)
else:
model = PaperCNN()
model = model.double().to(args.device)
optimizer = SGD(model.parameters(), lr=1e-2)
# evaluate(val_loader, model, args)
best = 0
progress = trange(1, args.epochs)
for epoch in progress:
progress.set_description('TRAIN [CurBestAcc={:.2%}]'.format(best))
train(train_loader, model, optimizer, args)
progress.set_description('EVAL [CurBestAcc={:.2%}]'.format(best))
metrics = evaluate(val_loader, model, args)
is_best = metrics['acc'] > best
best = max(metrics['acc'], best)
if is_best:
run.save_checkpoint(
{
'epoch': epoch,
'params': vars(args),
'model': model.state_dict(),
'optim': optimizer.state_dict(),
'metrics': metrics
}, is_best)
metrics.update({'epoch': epoch})
run.pushLog(metrics)
def main():
if args['gpu']:
os.environ['CUDA_VISIBLE_DEVICES'] = args['gpu']
if not os.path.exists(args['outdir']):
os.mkdir(args['outdir'])
train_loader, test_loader = loaddata(args)
if torch.cuda.is_available():
model = loadmodel(args)
model = model.cuda()
logfilename = os.path.join(args['outdir'], 'log.txt')
init_logfile(logfilename,
"epoch\ttime\tlr\ttrain loss\ttrain acc\ttestloss\ttest acc")
criterion = CrossEntropyLoss().cuda()
optimizer = SGD(model.parameters(),
lr=args['lr'],
momentum=args['momentum'],
weight_decay=args['weight_decay'])
scheduler = StepLR(optimizer,
step_size=args['lr_step_size'],
gamma=args['gamma'])
for epoch in range(args['epochs']):
scheduler.step(epoch)
before = time.time()
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, epoch, args['noise_sd'])
test_loss, test_acc = test(test_loader, model, criterion,
args['noise_sd'])
after = time.time()
log(
logfilename, "{}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}".format(
epoch, str(datetime.timedelta(seconds=(after - before))),
scheduler.get_lr()[0], train_loss, train_acc, test_loss,
test_acc))
torch.save(
{
'epoch': epoch + 1,
'dataset': args['dataset'],
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, os.path.join(args['outdir'], 'checkpoint.pth.tar'))
def main_train_worker(args):
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
print("=> creating model '{}'".format(args.arch))
network = MetaLearnerModelBuilder.construct_cifar_model(
args.arch, args.dataset)
model_path = '{}/train_pytorch_model/real_image_model/{}@{}@epoch_{}@lr_{}@batch_{}.pth.tar'.format(
PY_ROOT, args.dataset, args.arch, args.epochs, args.lr,
args.batch_size)
os.makedirs(os.path.dirname(model_path), exist_ok=True)
print("after train, model will be saved to {}".format(model_path))
network.cuda()
image_classifier_loss = nn.CrossEntropyLoss().cuda()
optimizer = SGD(network.parameters(),
args.lr,
weight_decay=args.weight_decay)
cudnn.benchmark = True
train_loader = DataLoaderMaker.get_img_label_data_loader(
args.dataset, args.batch_size, True)
val_loader = DataLoaderMaker.get_img_label_data_loader(
args.dataset, args.batch_size, False)
for epoch in range(0, args.epochs):
# adjust_learning_rate(optimizer, epoch, args)
# train_simulate_grad_mode for one epoch
train(train_loader, network, image_classifier_loss, optimizer, epoch,
args)
# evaluate_accuracy on validation set
validate(val_loader, network, image_classifier_loss, args)
# remember best acc@1 and save checkpoint
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': network.state_dict(),
'optimizer': optimizer.state_dict(),
},
filename=model_path)
def train(cont=False):
# for tensorboard tracking
logger = get_logger()
logger.info("(1) Initiating Training ... ")
logger.info("Training on device: {}".format(device))
writer = SummaryWriter()
# init model
aux_layers = None
if net == "SETR-PUP":
aux_layers, model = get_SETR_PUP()
elif net == "SETR-MLA":
aux_layers, model = get_SETR_MLA()
elif net == "TransUNet-Base":
model = get_TransUNet_base()
elif net == "TransUNet-Large":
model = get_TransUNet_large()
elif net == "UNet":
model = UNet(CLASS_NUM)
# prepare dataset
cluster_model = get_clustering_model(logger)
train_dataset = CityscapeDataset(img_dir=data_dir,
img_dim=IMG_DIM,
mode="train",
cluster_model=cluster_model)
valid_dataset = CityscapeDataset(img_dir=data_dir,
img_dim=IMG_DIM,
mode="val",
cluster_model=cluster_model)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
valid_loader = DataLoader(valid_dataset,
batch_size=batch_size,
shuffle=False)
logger.info("(2) Dataset Initiated. ")
# optimizer
epochs = epoch_num if epoch_num > 0 else iteration_num // len(
train_loader) + 1
optim = SGD(model.parameters(),
lr=lrate,
momentum=momentum,
weight_decay=wdecay)
# optim = Adam(model.parameters(), lr=lrate)
scheduler = lr_scheduler.MultiStepLR(
optim, milestones=[int(epochs * fine_tune_ratio)], gamma=0.1)
cur_epoch = 0
best_loss = float('inf')
epochs_since_improvement = 0
# for continue training
if cont:
model, optim, cur_epoch, best_loss = load_ckpt_continue_training(
best_ckpt_src, model, optim, logger)
logger.info("Current best loss: {0}".format(best_loss))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for i in range(cur_epoch):
scheduler.step()
else:
model = nn.DataParallel(model)
model = model.to(device)
logger.info("(3) Model Initiated ... ")
logger.info("Training model: {}".format(net) + ". Training Started.")
# loss
ce_loss = CrossEntropyLoss()
if use_dice_loss:
dice_loss = DiceLoss(CLASS_NUM)
# loop over epochs
iter_count = 0
epoch_bar = tqdm.tqdm(total=epochs,
desc="Epoch",
position=cur_epoch,
leave=True)
logger.info("Total epochs: {0}. Starting from epoch {1}.".format(
epochs, cur_epoch + 1))
for e in range(epochs - cur_epoch):
epoch = e + cur_epoch
# Training.
model.train()
trainLossMeter = LossMeter()
train_batch_bar = tqdm.tqdm(total=len(train_loader),
desc="TrainBatch",
position=0,
leave=True)
for batch_num, (orig_img, mask_img) in enumerate(train_loader):
orig_img, mask_img = orig_img.float().to(
device), mask_img.float().to(device)
if net == "TransUNet-Base" or net == "TransUNet-Large":
pred = model(orig_img)
elif net == "SETR-PUP" or net == "SETR-MLA":
if aux_layers is not None:
pred, _ = model(orig_img)
else:
pred = model(orig_img)
elif net == "UNet":
pred = model(orig_img)
loss_ce = ce_loss(pred, mask_img[:].long())
if use_dice_loss:
loss_dice = dice_loss(pred, mask_img, softmax=True)
loss = 0.5 * (loss_ce + loss_dice)
else:
loss = loss_ce
# Backward Propagation, Update weight and metrics
optim.zero_grad()
loss.backward()
optim.step()
# update learning rate
for param_group in optim.param_groups:
orig_lr = param_group['lr']
param_group['lr'] = orig_lr * (1.0 -
iter_count / iteration_num)**0.9
iter_count += 1
# Update loss
trainLossMeter.update(loss.item())
# print status
if (batch_num + 1) % print_freq == 0:
status = 'Epoch: [{0}][{1}/{2}]\t' \
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch+1, batch_num+1, len(train_loader), loss=trainLossMeter)
logger.info(status)
# log loss to tensorboard
if (batch_num + 1) % tensorboard_freq == 0:
writer.add_scalar(
'Train_Loss_{0}'.format(tensorboard_freq),
trainLossMeter.avg,
epoch * (len(train_loader) / tensorboard_freq) +
(batch_num + 1) / tensorboard_freq)
train_batch_bar.update(1)
writer.add_scalar('Train_Loss_epoch', trainLossMeter.avg, epoch)
# Validation.
model.eval()
validLossMeter = LossMeter()
valid_batch_bar = tqdm.tqdm(total=len(valid_loader),
desc="ValidBatch",
position=0,
leave=True)
with torch.no_grad():
for batch_num, (orig_img, mask_img) in enumerate(valid_loader):
orig_img, mask_img = orig_img.float().to(
device), mask_img.float().to(device)
if net == "TransUNet-Base" or net == "TransUNet-Large":
pred = model(orig_img)
elif net == "SETR-PUP" or net == "SETR-MLA":
if aux_layers is not None:
pred, _ = model(orig_img)
else:
pred = model(orig_img)
elif net == "UNet":
pred = model(orig_img)
loss_ce = ce_loss(pred, mask_img[:].long())
if use_dice_loss:
loss_dice = dice_loss(pred, mask_img, softmax=True)
loss = 0.5 * (loss_ce + loss_dice)
else:
loss = loss_ce
# Update loss
validLossMeter.update(loss.item())
# print status
if (batch_num + 1) % print_freq == 0:
status = 'Validation: [{0}][{1}/{2}]\t' \
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch+1, batch_num+1, len(valid_loader), loss=validLossMeter)
logger.info(status)
# log loss to tensorboard
if (batch_num + 1) % tensorboard_freq == 0:
writer.add_scalar(
'Valid_Loss_{0}'.format(tensorboard_freq),
validLossMeter.avg,
epoch * (len(valid_loader) / tensorboard_freq) +
(batch_num + 1) / tensorboard_freq)
valid_batch_bar.update(1)
valid_loss = validLossMeter.avg
writer.add_scalar('Valid_Loss_epoch', valid_loss, epoch)
logger.info("Validation Loss of epoch [{0}/{1}]: {2}\n".format(
epoch + 1, epochs, valid_loss))
# update optim scheduler
scheduler.step()
# save checkpoint
is_best = valid_loss < best_loss
best_loss_tmp = min(valid_loss, best_loss)
if not is_best:
epochs_since_improvement += 1
logger.info("Epochs since last improvement: %d\n" %
(epochs_since_improvement, ))
if epochs_since_improvement == early_stop_tolerance:
break # early stopping.
else:
epochs_since_improvement = 0
state = {
'epoch': epoch,
'loss': best_loss_tmp,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optim.state_dict(),
}
torch.save(state, ckpt_src)
logger.info("Checkpoint updated.")
best_loss = best_loss_tmp
epoch_bar.update(1)
writer.close()
def train(start_path, beta):
# prepare hyper-parameters
seed = 42
cuda_enabled = True
cuda_deterministic = False
batch_size = 2048
num_workers = 2
shared = False
stochastic = False
kkt_momentum = 0.0
create_graph = False
grad_correction = False
shift = 0.0
tol = 1e-5
damping = 0.1
maxiter = 50
lr = 0.1
momentum = 0.0
weight_decay = 0.0
num_steps = 10
verbose = False
# prepare path
ckpt_name = start_path.name.split('.')[0]
root_path = Path(__file__).resolve().parent
dataset_path = root_path / 'MultiMNIST'
ckpt_path = root_path / 'cpmtl' / ckpt_name
if not start_path.is_file():
raise RuntimeError('Pareto solutions not found.')
root_path.mkdir(parents=True, exist_ok=True)
dataset_path.mkdir(parents=True, exist_ok=True)
ckpt_path.mkdir(parents=True, exist_ok=True)
# fix random seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if cuda_enabled and torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
# prepare device
if cuda_enabled and torch.cuda.is_available():
import torch.backends.cudnn as cudnn
device = torch.device('cuda')
if cuda_deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
else:
cudnn.benchmark = True
else:
device = torch.device('cpu')
# prepare dataset
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
trainset = MultiMNIST(dataset_path,
train=True,
download=True,
transform=transform)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
testset = MultiMNIST(dataset_path,
train=False,
download=True,
transform=transform)
testloader = torch.utils.data.DataLoader(testset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
# prepare network
network = MultiLeNet()
network.to(device)
# initialize network
start_ckpt = torch.load(start_path, map_location='cpu')
network.load_state_dict(start_ckpt['state_dict'])
# prepare losses
criterion = F.cross_entropy
closures = [
lambda n, l, t: criterion(l[0], t[:, 0]),
lambda n, l, t: criterion(l[1], t[:, 1])
]
# prepare HVP solver
hvp_solver = VisionHVPSolver(network,
device,
trainloader,
closures,
shared=shared)
hvp_solver.set_grad(batch=False)
hvp_solver.set_hess(batch=True)
# prepare KKT solver
kkt_solver = MINRESKKTSolver(network,
hvp_solver,
device,
stochastic=stochastic,
kkt_momentum=kkt_momentum,
create_graph=create_graph,
grad_correction=grad_correction,
shift=shift,
tol=tol,
damping=damping,
maxiter=maxiter)
# prepare optimizer
optimizer = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
# first evaluation
losses, tops = evaluate(network, testloader, device, closures,
f'{ckpt_name}')
# prepare utilities
top_trace = TopTrace(len(closures))
top_trace.print(tops, show=False)
beta = beta.to(device)
# training
for step in range(1, num_steps + 1):
network.train(True)
optimizer.zero_grad()
kkt_solver.backward(beta, verbose=verbose)
optimizer.step()
losses, tops = evaluate(network, testloader, device, closures,
f'{ckpt_name}: {step}/{num_steps}')
top_trace.print(tops)
ckpt = {
'state_dict': network.state_dict(),
'optimizer': optimizer.state_dict(),
'beta': beta,
}
record = {'losses': losses, 'tops': tops}
ckpt['record'] = record
torch.save(ckpt, ckpt_path / f'{step:d}.pth')
hvp_solver.close()
class Trainer(object):
"""
Trainer encapsulates all the logic necessary for
training the Recurrent Attention Model.
All hyperparameters are provided by the user in the
config file.
"""
def __init__(self, config, data_loader):
"""
Construct a new Trainer instance.
Args
----
- config: object containing command line arguments.
- data_loader: data iterator
"""
self.config = config
# glimpse network params
self.patch_size = config.patch_size
self.glimpse_scale = config.glimpse_scale
self.num_patches = config.num_patches
self.loc_hidden = config.loc_hidden
self.glimpse_hidden = config.glimpse_hidden
# core network params
self.num_glimpses = config.num_glimpses
self.hidden_size = config.hidden_size
# reinforce params
self.std = config.std
self.M = config.M
# data params
if config.is_train:
self.train_loader = data_loader[0]
self.valid_loader = data_loader[1]
self.num_train = len(self.train_loader.sampler.indices)
self.num_valid = len(self.valid_loader.sampler.indices)
else:
self.test_loader = data_loader
self.num_test = len(self.test_loader.dataset)
self.num_classes = 10
self.num_channels = 1
# training params
self.epochs = config.epochs
self.start_epoch = 0
self.momentum = config.momentum
self.lr = config.init_lr
# misc params
self.use_gpu = config.use_gpu
self.best = config.best
self.ckpt_dir = config.ckpt_dir
self.logs_dir = config.logs_dir
self.best_valid_acc = 0.
self.counter = 0
self.patience = config.patience
self.use_tensorboard = config.use_tensorboard
self.resume = config.resume
self.print_freq = config.print_freq
self.plot_freq = config.plot_freq
self.model_name = 'ram_{}_{}x{}_{}'.format(config.num_glimpses,
config.patch_size,
config.patch_size,
config.glimpse_scale)
self.plot_dir = './plots/' + self.model_name + '/'
if not os.path.exists(self.plot_dir):
os.makedirs(self.plot_dir)
# configure tensorboard logging
if self.use_tensorboard:
tensorboard_dir = self.logs_dir + self.model_name
print('[*] Saving tensorboard logs to {}'.format(tensorboard_dir))
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
configure(tensorboard_dir)
# build RAM model
self.model = RecurrentAttention(
self.patch_size,
self.num_patches,
self.glimpse_scale,
self.num_channels,
self.loc_hidden,
self.glimpse_hidden,
self.std,
self.hidden_size,
self.num_classes,
)
if self.use_gpu:
self.model.cuda()
print('[*] Number of model parameters: {:,}'.format(
sum([p.data.nelement() for p in self.model.parameters()])))
# initialize optimizer and scheduler
self.optimizer = SGD(
self.model.parameters(),
lr=self.lr,
momentum=self.momentum,
)
self.scheduler = ReduceLROnPlateau(self.optimizer,
'min',
patience=self.patience)
def reset(self):
"""
Initialize the hidden state of the core network
and the location vector.
This is called once every time a new minibatch
`x` is introduced.
"""
dtype = torch.cuda.FloatTensor if self.use_gpu else torch.FloatTensor
h_t = torch.zeros(self.batch_size, self.hidden_size)
h_t = Variable(h_t).type(dtype)
l_t = torch.Tensor(self.batch_size, 2).uniform_(-1, 1)
l_t = Variable(l_t).type(dtype)
return h_t, l_t
def train(self):
"""
Train the model on the training set.
A checkpoint of the model is saved after each epoch
and if the validation accuracy is improved upon,
a separate ckpt is created for use on the test set.
"""
# load the most recent checkpoint
if self.resume:
self.load_checkpoint(best=False)
print("\n[*] Train on {} samples, validate on {} samples".format(
self.num_train, self.num_valid))
for epoch in range(self.start_epoch, self.epochs):
print('\nEpoch: {}/{} - LR: {:.6f}'.format(epoch + 1, self.epochs,
self.lr))
# train for 1 epoch
train_loss, train_acc = self.train_one_epoch(epoch)
# evaluate on validation set
valid_loss, valid_acc = self.validate(epoch)
# reduce lr if validation loss plateaus
self.scheduler.step(valid_loss)
is_best = valid_acc > self.best_valid_acc
msg1 = "train loss: {:.3f} - train acc: {:.3f} "
msg2 = "- val loss: {:.3f} - val acc: {:.3f}"
if is_best:
msg2 += " [*]"
msg = msg1 + msg2
print(msg.format(train_loss, train_acc, valid_loss, valid_acc))
# check for improvement
if not is_best:
self.counter += 1
if self.counter > self.patience:
print("[!] No improvement in a while, stopping training.")
return
self.best_valid_acc = max(valid_acc, self.best_valid_acc)
self.save_checkpoint(
{
'epoch': epoch + 1,
'model_state': self.model.state_dict(),
'optim_state': self.optimizer.state_dict(),
'best_valid_acc': self.best_valid_acc,
}, is_best)
def train_one_epoch(self, epoch):
"""
Train the model for 1 epoch of the training set.
An epoch corresponds to one full pass through the entire
training set in successive mini-batches.
This is used by train() and should not be called manually.
"""
batch_time = AverageMeter()
losses = AverageMeter()
accs = AverageMeter()
tic = time.time()
with tqdm(total=self.num_train) as pbar:
for i, (x, y) in enumerate(self.train_loader):
if self.use_gpu:
x, y = x.cuda(), y.cuda()
x, y = Variable(x), Variable(y)
plot = False
if (epoch % self.plot_freq == 0) and (i == 0):
plot = True
# initialize location vector and hidden state
self.batch_size = x.shape[0]
h_t, l_t = self.reset()
# save images
imgs = []
imgs.append(x[0:9])
# extract the glimpses
locs = []
log_pi = []
baselines = []
for t in range(self.num_glimpses - 1):
# forward pass through model
h_t, l_t, b_t, p = self.model(x, l_t, h_t)
# store
locs.append(l_t[0:9])
baselines.append(b_t)
log_pi.append(p)
# last iteration
h_t, l_t, b_t, log_probas, p = self.model(x,
l_t,
h_t,
last=True)
log_pi.append(p)
baselines.append(b_t)
locs.append(l_t[0:9])
# convert list to tensors and reshape
baselines = torch.stack(baselines).transpose(1, 0)
log_pi = torch.stack(log_pi).transpose(1, 0)
# calculate reward
predicted = torch.max(log_probas, 1)[1]
R = (predicted.detach() == y).float()
R = R.unsqueeze(1).repeat(1, self.num_glimpses)
# compute losses for differentiable modules
loss_action = F.nll_loss(log_probas, y)
loss_baseline = F.mse_loss(baselines, R)
# compute reinforce loss
adjusted_reward = R - baselines.detach()
loss_reinforce = torch.mean(-log_pi * adjusted_reward)
# sum up into a hybrid loss
loss = loss_action + loss_baseline + loss_reinforce
# compute accuracy
correct = (predicted == y).float()
acc = 100 * (correct.sum() / len(y))
# store
losses.update(loss.data[0], x.size()[0])
accs.update(acc.data[0], x.size()[0])
# compute gradients and update SGD
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# measure elapsed time
toc = time.time()
batch_time.update(toc - tic)
pbar.set_description(
("{:.1f}s - loss: {:.3f} - acc: {:.3f}".format(
(toc - tic), loss.data[0], acc.data[0])))
pbar.update(self.batch_size)
# dump the glimpses and locs
if plot:
if self.use_gpu:
imgs = [g.cpu().data.numpy().squeeze() for g in imgs]
locs = [l.cpu().data.numpy() for l in locs]
else:
imgs = [g.data.numpy().squeeze() for g in imgs]
locs = [l.data.numpy() for l in locs]
pickle.dump(
imgs,
open(self.plot_dir + "g_{}.p".format(epoch + 1), "wb"))
pickle.dump(
locs,
open(self.plot_dir + "l_{}.p".format(epoch + 1), "wb"))
# log to tensorboard
if self.use_tensorboard:
iteration = epoch * len(self.train_loader) + i
log_value('train_loss', losses.avg, iteration)
log_value('train_acc', accs.avg, iteration)
return losses.avg, accs.avg
def validate(self, epoch):
"""
Evaluate the model on the validation set.
"""
losses = AverageMeter()
accs = AverageMeter()
for i, (x, y) in enumerate(self.valid_loader):
if self.use_gpu:
x, y = x.cuda(), y.cuda()
x, y = Variable(x), Variable(y)
# duplicate 10 times
x = x.repeat(self.M, 1, 1, 1)
# initialize location vector and hidden state
self.batch_size = x.shape[0]
h_t, l_t = self.reset()
# extract the glimpses
log_pi = []
baselines = []
for t in range(self.num_glimpses - 1):
# forward pass through model
h_t, l_t, b_t, p = self.model(x, l_t, h_t)
# store
baselines.append(b_t)
log_pi.append(p)
# last iteration
h_t, l_t, b_t, log_probas, p = self.model(x, l_t, h_t, last=True)
log_pi.append(p)
baselines.append(b_t)
# convert list to tensors and reshape
baselines = torch.stack(baselines).transpose(1, 0)
log_pi = torch.stack(log_pi).transpose(1, 0)
# average
log_probas = log_probas.view(self.M, -1, log_probas.shape[-1])
log_probas = torch.mean(log_probas, dim=0)
baselines = baselines.contiguous().view(self.M, -1,
baselines.shape[-1])
baselines = torch.mean(baselines, dim=0)
log_pi = log_pi.contiguous().view(self.M, -1, log_pi.shape[-1])
log_pi = torch.mean(log_pi, dim=0)
# calculate reward
predicted = torch.max(log_probas, 1)[1]
R = (predicted.detach() == y).float()
R = R.unsqueeze(1).repeat(1, self.num_glimpses)
# compute losses for differentiable modules
loss_action = F.nll_loss(log_probas, y)
loss_baseline = F.mse_loss(baselines, R)
# compute reinforce loss
adjusted_reward = R - baselines.detach()
loss_reinforce = torch.mean(-log_pi * adjusted_reward)
# sum up into a hybrid loss
loss = loss_action + loss_baseline + loss_reinforce
# compute accuracy
correct = (predicted == y).float()
acc = 100 * (correct.sum() / len(y))
# store
losses.update(loss.data[0], x.size()[0])
accs.update(acc.data[0], x.size()[0])
# log to tensorboard
if self.use_tensorboard:
iteration = epoch * len(self.valid_loader) + i
log_value('valid_loss', losses.avg, iteration)
log_value('valid_acc', accs.avg, iteration)
return losses.avg, accs.avg
def test(self):
"""
Test the model on the held-out test data.
This function should only be called at the very
end once the model has finished training.
"""
correct = 0
# load the best checkpoint
self.load_checkpoint(best=self.best)
for i, (x, y) in enumerate(self.test_loader):
if self.use_gpu:
x, y = x.cuda(), y.cuda()
x, y = Variable(x, volatile=True), Variable(y)
# duplicate 10 times
x = x.repeat(self.M, 1, 1, 1)
# initialize location vector and hidden state
self.batch_size = x.shape[0]
h_t, l_t = self.reset()
# extract the glimpses
for t in range(self.num_glimpses - 1):
# forward pass through model
h_t, l_t, b_t, p = self.model(x, l_t, h_t)
# last iteration
h_t, l_t, b_t, log_probas, p = self.model(x, l_t, h_t, last=True)
log_probas = log_probas.view(self.M, -1, log_probas.shape[-1])
log_probas = torch.mean(log_probas, dim=0)
pred = log_probas.data.max(1, keepdim=True)[1]
correct += pred.eq(y.data.view_as(pred)).cpu().sum()
perc = (100. * correct) / (self.num_test)
print('[*] Test Acc: {}/{} ({:.2f}%)'.format(correct, self.num_test,
perc))
def save_checkpoint(self, state, is_best):
"""
Save a copy of the model so that it can be loaded at a future
date. This function is used when the model is being evaluated
on the test data.
If this model has reached the best validation accuracy thus
far, a seperate file with the suffix `best` is created.
"""
# print("[*] Saving model to {}".format(self.ckpt_dir))
filename = self.model_name + '_ckpt.pth.tar'
ckpt_path = os.path.join(self.ckpt_dir, filename)
torch.save(state, ckpt_path)
if is_best:
filename = self.model_name + '_model_best.pth.tar'
shutil.copyfile(ckpt_path, os.path.join(self.ckpt_dir, filename))
def load_checkpoint(self, best=False):
"""
Load the best copy of a model. This is useful for 2 cases:
- Resuming training with the most recent model checkpoint.
- Loading the best validation model to evaluate on the test data.
Params
------
- best: if set to True, loads the best model. Use this if you want
to evaluate your model on the test data. Else, set to False in
which case the most recent version of the checkpoint is used.
"""
print("[*] Loading model from {}".format(self.ckpt_dir))
filename = self.model_name + '_ckpt.pth.tar'
if best:
filename = self.model_name + '_model_best.pth.tar'
ckpt_path = os.path.join(self.ckpt_dir, filename)
ckpt = torch.load(ckpt_path)
# load variables from checkpoint
self.start_epoch = ckpt['epoch']
self.best_valid_acc = ckpt['best_valid_acc']
self.model.load_state_dict(ckpt['model_state'])
self.optimizer.load_state_dict(ckpt['optim_state'])
if best:
print("[*] Loaded {} checkpoint @ epoch {} "
"with best valid acc of {:.3f}".format(
filename, ckpt['epoch'] + 1, ckpt['best_valid_acc']))
else:
print("[*] Loaded {} checkpoint @ epoch {}".format(
filename, ckpt['epoch'] + 1))
loss.backward()
optimizer.step()
trian_loss += loss.item() / len(train_loader)
_, y_pred = torch.max(y_pred, 1)
trian_acc += (y_pred == batch_y).sum().item() / len(y)
train_loss_temp.append(trian_loss)
train_acc_temp.append(trian_acc)
if epoch % checkpoint == 0:
torch.save(model.state_dict(),
f"./data/prob{prob_num}_model_ckpt_{epoch}.bin")
torch.save(optimizer.state_dict(),
f"./data/prob{prob_num}_optimizer_ckpt_{epoch}.bin")
model = model.eval()
with torch.no_grad():
y_pred = model(X_valid)
loss = loss_fn(y_pred, y_valid)
_, y_pred = torch.max(y_pred, 1)
acc = (y_pred == y_valid).sum().item() / len(y_valid)
valid_loss_temp.append(loss)
valid_acc_temp.append(acc)
train_loss_all.append(train_loss_temp)
train_acc_all.append(train_acc_temp)
valid_loss_all.append(valid_loss_temp)
class Trainer:
def __init__(self,
model: nn.Module,
dataset_root: str,
summary_writer: SummaryWriter,
device: Device,
batch_size: int = 128,
cc_loss: bool = False):
# load train/test splits of SALICON dataset
train_dataset = Salicon(dataset_root + "train.pkl")
test_dataset = Salicon(dataset_root + "val.pkl")
self.train_loader = DataLoader(
train_dataset,
shuffle=True,
batch_size=batch_size,
pin_memory=True,
num_workers=1,
)
self.val_loader = DataLoader(
test_dataset,
shuffle=False,
batch_size=batch_size,
num_workers=1,
pin_memory=True,
)
self.model = model.to(device)
self.device = device
if cc_loss:
self.criterion = CCLoss
else:
self.criterion = nn.MSELoss()
self.optimizer = SGD(self.model.parameters(),
lr=0.03,
momentum=0.9,
weight_decay=0.0005,
nesterov=True)
self.summary_writer = summary_writer
self.step = 0
def train(self,
epochs: int,
val_frequency: int,
log_frequency: int = 5,
start_epoch: int = 0):
lrs = np.linspace(0.03, 0.0001, epochs)
for epoch in range(start_epoch, epochs):
self.model.train()
for batch, gts in self.train_loader:
# LR decay
# need to update learning rate between 0.03 and 0.0001 (according to paper)
optimstate = self.optimizer.state_dict()
self.optimizer = SGD(self.model.parameters(),
lr=lrs[epoch],
momentum=0.9,
weight_decay=0.0005,
nesterov=True)
self.optimizer.load_state_dict(optimstate)
self.optimizer.zero_grad()
# load batch to device
batch = batch.to(self.device)
gts = gts.to(self.device)
# train step
step_start_time = time.time()
output = self.model.forward(batch)
loss = self.criterion(output, gts)
loss.backward()
self.optimizer.step()
# log step
if ((self.step + 1) % log_frequency) == 0:
step_time = time.time() - step_start_time
self.log_metrics(epoch, loss, step_time)
self.print_metrics(epoch, loss, step_time)
# count steps
self.step += 1
# log epoch
self.summary_writer.add_scalar("epoch", epoch, self.step)
# validate
if ((epoch + 1) % val_frequency) == 0:
self.validate()
self.model.train()
if (epoch + 1) % 10 == 0:
save(self.model, "checkp_model.pkl")
def print_metrics(self, epoch, loss, step_time):
epoch_step = self.step % len(self.train_loader)
print(f"epoch: [{epoch}], "
f"step: [{epoch_step}/{len(self.train_loader)}], "
f"batch loss: {loss:.5f}, "
f"step time: {step_time:.5f}")
def log_metrics(self, epoch, loss, step_time):
self.summary_writer.add_scalar("epoch", epoch, self.step)
self.summary_writer.add_scalars("loss", {"train": float(loss.item())},
self.step)
self.summary_writer.add_scalar("time/data", step_time, self.step)
def validate(self):
results = {"preds": [], "gts": []}
total_loss = 0
self.model.eval()
# No need to track gradients for validation, we're not optimizing.
with no_grad():
for batch, gts in self.val_loader:
batch = batch.to(self.device)
gts = gts.to(self.device)
output = self.model(batch)
loss = self.criterion(output, gts)
total_loss += loss.item()
preds = output.cpu().numpy()
results["preds"].extend(list(preds))
results["gts"].extend(list(gts.cpu().numpy()))
average_loss = total_loss / len(self.val_loader)
self.summary_writer.add_scalars("loss", {"test": average_loss},
self.step)
print(f"validation loss: {average_loss:.5f}")
def main(args: argparse.Namespace):
logger = CompleteLogger(args.log, args.phase)
print(args)
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
cudnn.benchmark = True
# Data loading code
normalize = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
train_transform = T.Compose([
T.RandomRotation(args.rotation),
T.RandomResizedCrop(size=args.image_size, scale=args.resize_scale),
T.ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25),
T.GaussianBlur(),
T.ToTensor(), normalize
])
val_transform = T.Compose(
[T.Resize(args.image_size),
T.ToTensor(), normalize])
image_size = (args.image_size, args.image_size)
heatmap_size = (args.heatmap_size, args.heatmap_size)
source_dataset = datasets.__dict__[args.source]
train_source_dataset = source_dataset(root=args.source_root,
transforms=train_transform,
image_size=image_size,
heatmap_size=heatmap_size)
train_source_loader = DataLoader(train_source_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
val_source_dataset = source_dataset(root=args.source_root,
split='test',
transforms=val_transform,
image_size=image_size,
heatmap_size=heatmap_size)
val_source_loader = DataLoader(val_source_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True)
target_dataset = datasets.__dict__[args.target]
train_target_dataset = target_dataset(root=args.target_root,
transforms=train_transform,
image_size=image_size,
heatmap_size=heatmap_size)
train_target_loader = DataLoader(train_target_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
val_target_dataset = target_dataset(root=args.target_root,
split='test',
transforms=val_transform,
image_size=image_size,
heatmap_size=heatmap_size)
val_target_loader = DataLoader(val_target_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True)
print("Source train:", len(train_source_loader))
print("Target train:", len(train_target_loader))
print("Source test:", len(val_source_loader))
print("Target test:", len(val_target_loader))
train_source_iter = ForeverDataIterator(train_source_loader)
train_target_iter = ForeverDataIterator(train_target_loader)
# create model
backbone = models.__dict__[args.arch](pretrained=True)
upsampling = Upsampling(backbone.out_features)
num_keypoints = train_source_dataset.num_keypoints
model = RegDAPoseResNet(backbone,
upsampling,
256,
num_keypoints,
num_head_layers=args.num_head_layers,
finetune=True).to(device)
# define loss function
criterion = JointsKLLoss()
pseudo_label_generator = PseudoLabelGenerator(num_keypoints,
args.heatmap_size,
args.heatmap_size)
regression_disparity = RegressionDisparity(pseudo_label_generator,
JointsKLLoss(epsilon=1e-7))
# define optimizer and lr scheduler
optimizer_f = SGD([
{
'params': backbone.parameters(),
'lr': 0.1
},
{
'params': upsampling.parameters(),
'lr': 0.1
},
],
lr=0.1,
momentum=args.momentum,
weight_decay=args.wd,
nesterov=True)
optimizer_h = SGD(model.head.parameters(),
lr=1.,
momentum=args.momentum,
weight_decay=args.wd,
nesterov=True)
optimizer_h_adv = SGD(model.head_adv.parameters(),
lr=1.,
momentum=args.momentum,
weight_decay=args.wd,
nesterov=True)
lr_decay_function = lambda x: args.lr * (1. + args.lr_gamma * float(x))**(
-args.lr_decay)
lr_scheduler_f = LambdaLR(optimizer_f, lr_decay_function)
lr_scheduler_h = LambdaLR(optimizer_h, lr_decay_function)
lr_scheduler_h_adv = LambdaLR(optimizer_h_adv, lr_decay_function)
start_epoch = 0
if args.resume is None:
if args.pretrain is None:
# first pretrain the backbone and upsampling
print("Pretraining the model on source domain.")
args.pretrain = logger.get_checkpoint_path('pretrain')
pretrained_model = PoseResNet(backbone, upsampling, 256,
num_keypoints, True).to(device)
optimizer = SGD(pretrained_model.get_parameters(lr=args.lr),
momentum=args.momentum,
weight_decay=args.wd,
nesterov=True)
lr_scheduler = MultiStepLR(optimizer, args.lr_step, args.lr_factor)
best_acc = 0
for epoch in range(args.pretrain_epochs):
lr_scheduler.step()
print(lr_scheduler.get_lr())
pretrain(train_source_iter, pretrained_model, criterion,
optimizer, epoch, args)
source_val_acc = validate(val_source_loader, pretrained_model,
criterion, None, args)
# remember best acc and save checkpoint
if source_val_acc['all'] > best_acc:
best_acc = source_val_acc['all']
torch.save({'model': pretrained_model.state_dict()},
args.pretrain)
print("Source: {} best: {}".format(source_val_acc['all'],
best_acc))
# load from the pretrained checkpoint
pretrained_dict = torch.load(args.pretrain,
map_location='cpu')['model']
model_dict = model.state_dict()
# remove keys from pretrained dict that doesn't appear in model dict
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
model.load_state_dict(pretrained_dict, strict=False)
else:
# optionally resume from a checkpoint
checkpoint = torch.load(args.resume, map_location='cpu')
model.load_state_dict(checkpoint['model'])
optimizer_f.load_state_dict(checkpoint['optimizer_f'])
optimizer_h.load_state_dict(checkpoint['optimizer_h'])
optimizer_h_adv.load_state_dict(checkpoint['optimizer_h_adv'])
lr_scheduler_f.load_state_dict(checkpoint['lr_scheduler_f'])
lr_scheduler_h.load_state_dict(checkpoint['lr_scheduler_h'])
lr_scheduler_h_adv.load_state_dict(checkpoint['lr_scheduler_h_adv'])
start_epoch = checkpoint['epoch'] + 1
# define visualization function
tensor_to_image = Compose([
Denormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
ToPILImage()
])
def visualize(image, keypoint2d, name, heatmaps=None):
"""
Args:
image (tensor): image in shape 3 x H x W
keypoint2d (tensor): keypoints in shape K x 2
name: name of the saving image
"""
train_source_dataset.visualize(
tensor_to_image(image), keypoint2d,
logger.get_image_path("{}.jpg".format(name)))
if args.phase == 'test':
# evaluate on validation set
source_val_acc = validate(val_source_loader, model, criterion, None,
args)
target_val_acc = validate(val_target_loader, model, criterion,
visualize, args)
print("Source: {:4.3f} Target: {:4.3f}".format(source_val_acc['all'],
target_val_acc['all']))
for name, acc in target_val_acc.items():
print("{}: {:4.3f}".format(name, acc))
return
# start training
best_acc = 0
print("Start regression domain adaptation.")
for epoch in range(start_epoch, args.epochs):
logger.set_epoch(epoch)
print(lr_scheduler_f.get_lr(), lr_scheduler_h.get_lr(),
lr_scheduler_h_adv.get_lr())
# train for one epoch
train(train_source_iter, train_target_iter, model, criterion,
regression_disparity, optimizer_f, optimizer_h, optimizer_h_adv,
lr_scheduler_f, lr_scheduler_h, lr_scheduler_h_adv, epoch,
visualize if args.debug else None, args)
# evaluate on validation set
source_val_acc = validate(val_source_loader, model, criterion, None,
args)
target_val_acc = validate(val_target_loader, model, criterion,
visualize if args.debug else None, args)
# remember best acc and save checkpoint
torch.save(
{
'model': model.state_dict(),
'optimizer_f': optimizer_f.state_dict(),
'optimizer_h': optimizer_h.state_dict(),
'optimizer_h_adv': optimizer_h_adv.state_dict(),
'lr_scheduler_f': lr_scheduler_f.state_dict(),
'lr_scheduler_h': lr_scheduler_h.state_dict(),
'lr_scheduler_h_adv': lr_scheduler_h_adv.state_dict(),
'epoch': epoch,
'args': args
}, logger.get_checkpoint_path(epoch))
if target_val_acc['all'] > best_acc:
shutil.copy(logger.get_checkpoint_path(epoch),
logger.get_checkpoint_path('best'))
best_acc = target_val_acc['all']
print("Source: {:4.3f} Target: {:4.3f} Target(best): {:4.3f}".format(
source_val_acc['all'], target_val_acc['all'], best_acc))
for name, acc in target_val_acc.items():
print("{}: {:4.3f}".format(name, acc))
logger.close()
class Trainer(object):
def __init__(self, args):
super(Trainer, self).__init__()
train_transform = transforms.Compose([
transforms.Resize((args.scale_size, args.scale_size)),
transforms.RandomChoice([
transforms.RandomCrop(640),
transforms.RandomCrop(576),
transforms.RandomCrop(512),
transforms.RandomCrop(384),
transforms.RandomCrop(320)
]),
transforms.Resize((args.crop_size, args.crop_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
train_dataset = MLDataset(args.train_path, args.label_path,
train_transform)
self.train_loader = DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
val_transform = transforms.Compose([
transforms.Resize((args.scale_size, args.scale_size)),
transforms.CenterCrop(args.crop_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
val_dataset = MLDataset(args.val_path, args.label_path, val_transform)
self.val_loader = DataLoader(dataset=val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
self.model = model_factory[args.model](args, args.num_classes)
self.model.cuda()
trainable_parameters = filter(lambda param: param.requires_grad,
self.model.parameters())
if args.optimizer == 'Adam':
self.optimizer = Adam(trainable_parameters, lr=args.lr)
elif args.optimizer == 'SGD':
self.optimizer = SGD(trainable_parameters, lr=args.lr)
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer,
mode='max',
patience=2,
verbose=True)
if args.loss == 'BCElogitloss':
self.criterion = nn.BCEWithLogitsLoss()
elif args.loss == 'tencentloss':
self.criterion = TencentLoss(args.num_classes)
elif args.loss == 'focalloss':
self.criterion = FocalLoss()
self.early_stopping = EarlyStopping(patience=5)
self.voc12_mAP = VOC12mAP(args.num_classes)
self.average_loss = AverageLoss()
self.average_topk_meter = TopkAverageMeter(args.num_classes,
topk=args.topk)
self.average_threshold_meter = ThresholdAverageMeter(
args.num_classes, threshold=args.threshold)
self.args = args
self.global_step = 0
self.writer = SummaryWriter(log_dir=args.log_dir)
def run(self):
s_epoch = 0
if self.args.resume:
checkpoint = torch.load(self.args.ckpt_latest_path)
s_epoch = checkpoint['epoch']
self.global_step = checkpoint['global_step']
self.model.load_state_dict(checkpoint['model_state_dict'])
self.optimizer.load_state_dict(checkpoint['optim_state_dict'])
self.early_stopping.best_score = checkpoint['best_score']
print('loading checkpoint success (epoch {})'.format(s_epoch))
for epoch in range(s_epoch, self.args.max_epoch):
self.train(epoch)
save_dict = {
'epoch': epoch + 1,
'global_step': self.global_step,
'model_state_dict': self.model.state_dict(),
'optim_state_dict': self.optimizer.state_dict(),
'best_score': self.early_stopping.best_score
}
torch.save(save_dict, self.args.ckpt_latest_path)
mAP = self.validation(epoch)
self.lr_scheduler.step(mAP)
is_save, is_terminate = self.early_stopping(mAP)
if is_terminate:
break
if is_save:
torch.save(self.model.state_dict(), self.args.ckpt_best_path)
def train(self, epoch):
self.model.train()
if self.args.model == 'ssgrl':
self.model.resnet_101.eval()
self.model.resnet_101.layer4.train()
for _, batch in enumerate(self.train_loader):
x, y = batch[0].cuda(), batch[1].cuda()
pred_y = self.model(x)
loss = self.criterion(pred_y, y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if self.global_step % 400 == 0:
self.writer.add_scalar('Loss/train', loss, self.global_step)
print('TRAIN [epoch {}] loss: {:4f}'.format(epoch, loss))
self.global_step += 1
def validation(self, epoch):
self.model.eval()
self.voc12_mAP.reset()
self.average_loss.reset()
self.average_topk_meter.reset()
self.average_threshold_meter.reset()
with torch.no_grad():
for _, batch in enumerate(self.val_loader):
x, y = batch[0].cuda(), batch[1].cuda()
pred_y = self.model(x)
loss = self.criterion(pred_y, y)
y = y.cpu().numpy()
pred_y = pred_y.cpu().numpy()
loss = loss.cpu().numpy()
self.voc12_mAP.update(pred_y, y)
self.average_loss.update(loss, x.size(0))
self.average_topk_meter.update(pred_y, y)
self.average_threshold_meter.update(pred_y, y)
_, mAP = self.voc12_mAP.compute()
mLoss = self.average_loss.compute()
self.average_topk_meter.compute()
self.average_threshold_meter.compute()
self.writer.add_scalar('Loss/val', mLoss, self.global_step)
self.writer.add_scalar('mAP/val', mAP, self.global_step)
print("Validation [epoch {}] mAP: {:.4f} loss: {:.4f}".format(
epoch, mAP, mLoss))
return mAP
optimizer.step()
print(torch.cuda.max_memory_allocated(device=0))
print(output)
with open('./Logs/log_triplet_new.txt', 'a') as f:
val_list = [
epoch + 1, batch_idx,
float(output),
float(triplet_loss_sum)
]
log = '\t'.join(str(value) for value in val_list)
f.writelines(log + '\n')
avg_triplet_loss = triplet_loss_sum / batches_per_epoch
with open('./Logs/log_triplet_new.txt', 'a') as f:
val_list = ['FINAL', epoch + 1, float(avg_triplet_loss)]
log = '\t'.join(str(value) for value in val_list)
f.writelines(log + '\n')
print('Epoch {}:\tAverage Triplet Loss: {:.4f}\t'.format(
epoch + 1, avg_triplet_loss))
torch.save(
{
'epoch': epoch,
'model_state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'avg_triplet_loss': avg_triplet_loss
}, './Train_Checkpoints/' + 'checkpoint_' + str(epoch) + '_' +
str(round(float(avg_triplet_loss), 4)) + '.tar')
class MetaFrameWork(object):
def __init__(self, name='normal_all', train_num=1, source='GSIM',
target='C', network=Net, resume=True, dataset=DGMetaDataSets,
inner_lr=1e-3, outer_lr=5e-3, train_size=8, test_size=16, no_source_test=True, bn='torch'):
super(MetaFrameWork, self).__init__()
self.no_source_test = no_source_test
self.train_num = train_num
self.exp_name = name
self.resume = resume
self.inner_update_lr = inner_lr
self.outer_update_lr = outer_lr
self.network = network
self.dataset = dataset
self.train_size = train_size
self.test_size = test_size
self.source = source
self.target = target
self.bn = bn
self.epoch = 1
self.best_target_acc = 0
self.best_target_acc_source = 0
self.best_target_epoch = 1
self.best_source_acc = 0
self.best_source_acc_target = 0
self.best_source_epoch = 0
self.total_epoch = 120
self.save_interval = 1
self.save_path = Path(self.exp_name)
self.init()
def init(self):
kwargs = {'bn': self.bn, 'output_stride': 8}
self.backbone = nn.DataParallel(self.network(**kwargs)).cuda()
kwargs.update({'pretrained': False})
self.updated_net = nn.DataParallel(self.network(**kwargs)).cuda()
self.ce = nn.CrossEntropyLoss(ignore_index=-1)
self.nim = NaturalImageMeasure(nclass=19)
batch_size = self.train_size
workers = len(self.source) * 4
dataloader = functools.partial(DataLoader, num_workers=workers, pin_memory=True, batch_size=batch_size, shuffle=True)
self.train_loader = dataloader(self.dataset(mode='train', domains=self.source, force_cache=True))
dataloader = functools.partial(DataLoader, num_workers=workers, pin_memory=True, batch_size=self.test_size, shuffle=False)
self.source_val_loader = dataloader(self.dataset(mode='val', domains=self.source, force_cache=True))
target_dataset, folder = get_dataset(self.target)
self.target_loader = dataloader(target_dataset(root=ROOT + folder, mode='val'))
self.target_test_loader = dataloader(target_dataset(root=ROOT + 'cityscapes', mode='test'))
self.opt_old = SGD(self.backbone.parameters(), lr=self.outer_update_lr, momentum=0.9, weight_decay=5e-4)
self.scheduler_old = PolyLR(self.opt_old, self.total_epoch, len(self.train_loader), 0, True, power=0.9)
self.logger = get_logger('train', self.exp_name)
self.log('exp_name : {}, train_num = {}, source domains = {}, target_domain = {}, lr : inner = {}, outer = {},'
'dataset : {}, net : {}, bn : {}\n'.
format(self.exp_name, self.train_num, self.source, self.target, self.inner_update_lr, self.outer_update_lr, self.dataset,
self.network, self.bn))
self.log(self.exp_name + '\n')
self.train_timer, self.test_timer = Timer(), Timer()
def train(self, epoch, it, inputs):
# imgs : batch x domains x C x H x W
# targets : batch x domains x 1 x H x W
imgs, targets = inputs
B, D, C, H, W = imgs.size()
meta_train_imgs = imgs.view(-1, C, H, W)
meta_train_targets = targets.view(-1, 1, H, W)
tr_logits = self.backbone(meta_train_imgs)[0]
tr_logits = make_same_size(tr_logits, meta_train_targets)
ds_loss = self.ce(tr_logits, meta_train_targets[:, 0])
with torch.no_grad():
self.nim(tr_logits, meta_train_targets)
self.opt_old.zero_grad()
ds_loss.backward()
self.opt_old.step()
self.scheduler_old.step(epoch, it)
losses = {
'dg': 0,
'ds': ds_loss.item()
}
acc = {
'iou': self.nim.get_res()[0]
}
return losses, acc, self.scheduler_old.get_lr(epoch, it)[0]
def meta_train(self, epoch, it, inputs):
# imgs : batch x domains x C x H x W
# targets : batch x domains x 1 x H x W
imgs, targets = inputs
B, D, C, H, W = imgs.size()
split_idx = np.random.permutation(D)
i = np.random.randint(1, D)
train_idx = split_idx[:i]
test_idx = split_idx[i:]
# train_idx = split_idx[:D // 2]
# test_idx = split_idx[D // 2:]
# self.print(split_idx, B, D, C, H, W)'
meta_train_imgs = imgs[:, train_idx].reshape(-1, C, H, W)
meta_train_targets = targets[:, train_idx].reshape(-1, 1, H, W)
meta_test_imgs = imgs[:, test_idx].reshape(-1, C, H, W)
meta_test_targets = targets[:, test_idx].reshape(-1, 1, H, W)
# Meta-Train
tr_logits = self.backbone(meta_train_imgs)[0]
tr_logits = make_same_size(tr_logits, meta_train_targets)
ds_loss = self.ce(tr_logits, meta_train_targets[:, 0])
# Update new network
self.opt_old.zero_grad()
ds_loss.backward(retain_graph=True)
updated_net = get_updated_network(self.backbone, self.updated_net, self.inner_update_lr).train().cuda()
# Meta-Test
te_logits = updated_net(meta_test_imgs)[0]
# te_logits = test_res[0]
te_logits = make_same_size(te_logits, meta_test_targets)
dg_loss = self.ce(te_logits, meta_test_targets[:, 0])
with torch.no_grad():
self.nim(te_logits, meta_test_targets)
# Update old network
dg_loss.backward()
self.opt_old.step()
self.scheduler_old.step(epoch, it)
losses = {
'dg': dg_loss.item(),
'ds': ds_loss.item()
}
acc = {
'iou': self.nim.get_res()[0],
}
return losses, acc, self.scheduler_old.get_lr(epoch, it)[0]
def do_train(self):
if self.resume:
self.load()
self.writer = SummaryWriter(str(self.save_path / 'tensorboard'), filename_suffix=time.strftime('_%Y-%m-%d_%H-%M-%S'))
self.log('Start epoch : {}\n'.format(self.epoch))
for epoch in range(self.epoch, self.total_epoch + 1):
loss_meters, acc_meters = MeterDicts(), MeterDicts(averaged=['iou'])
self.nim.clear_cache()
self.backbone.train()
self.epoch = epoch
with self.train_timer:
for it, (paths, imgs, target) in enumerate(self.train_loader):
meta = (it + 1) % self.train_num == 0
if meta:
losses, acc, lr = self.meta_train(epoch - 1, it, to_cuda([imgs, target]))
else:
losses, acc, lr = self.train(epoch - 1, it, to_cuda([imgs, target]))
loss_meters.update_meters(losses, skips=['dg'] if not meta else [])
acc_meters.update_meters(acc)
self.print(self.get_string(epoch, it, loss_meters, acc_meters, lr, meta), end='')
self.tfb_log(epoch, it, loss_meters, acc_meters)
self.print(self.train_timer.get_formatted_duration())
self.log(self.get_string(epoch, it, loss_meters, acc_meters, lr, meta) + '\n')
self.save('ckpt')
if epoch % self.save_interval == 0:
with self.test_timer:
city_acc = self.val(self.target_loader)
self.save_best(city_acc, epoch)
total_duration = self.train_timer.duration + self.test_timer.duration
self.print('Time Left : ' + self.train_timer.get_formatted_duration(total_duration * (self.total_epoch - epoch)) + '\n')
self.log('Best city acc : \n city : {}, origin : {}, epoch : {}\n'.format(
self.best_target_acc, self.best_target_acc_source, self.best_target_epoch))
self.log('Best origin acc : \n city : {}, origin : {}, epoch : {}\n'.format(
self.best_source_acc_target, self.best_source_acc, self.best_source_epoch))
def save_best(self, city_acc, epoch):
self.writer.add_scalar('acc/citys', city_acc, epoch)
if not self.no_source_test:
origin_acc = self.val(self.source_val_loader)
self.writer.add_scalar('acc/origin', origin_acc, epoch)
else:
origin_acc = 0
self.writer.flush()
if city_acc > self.best_target_acc:
self.best_target_acc = city_acc
self.best_target_acc_source = origin_acc
self.best_target_epoch = epoch
self.save('best_city')
if origin_acc > self.best_source_acc:
self.best_source_acc = origin_acc
self.best_source_acc_target = city_acc
self.best_source_epoch = epoch
self.save('best_origin')
def val(self, dataset):
self.backbone.eval()
with torch.no_grad():
self.nim.clear_cache()
self.nim.set_max_len(len(dataset))
for p, img, target in dataset:
img, target = to_cuda(get_img_target(img, target))
logits = self.backbone(img)[0]
self.nim(logits, target)
self.log('\nNormal validation : {}\n'.format(self.nim.get_acc()))
if hasattr(dataset.dataset, 'format_class_iou'):
self.log(dataset.dataset.format_class_iou(self.nim.get_class_acc()[0]) + '\n')
return self.nim.get_acc()[0]
def target_specific_val(self, loader):
self.nim.clear_cache()
self.nim.set_max_len(len(loader))
# eval for dropout
self.backbone.module.remove_dropout()
self.backbone.module.not_track()
for idx, (p, img, target) in enumerate(loader):
if len(img.size()) == 5:
B, D, C, H, W = img.size()
else:
B, C, H, W = img.size()
D = 1
img, target = to_cuda([img.reshape(B, D, C, H, W), target.reshape(B, D, 1, H, W)])
for d in range(img.size(1)):
img_d, target_d, = img[:, d], target[:, d]
self.backbone.train()
with torch.no_grad():
new_logits = self.backbone(img_d)[0]
self.nim(new_logits, target_d)
self.backbone.module.recover_dropout()
self.log('\nTarget specific validation : {}\n'.format(self.nim.get_acc()))
if hasattr(loader.dataset, 'format_class_iou'):
self.log(loader.dataset.format_class_iou(self.nim.get_class_acc()[0]) + '\n')
return self.nim.get_acc()[0]
def predict_target(self, load_path='best_city', color=False, train=False, output_path='predictions'):
self.load(load_path)
import skimage.io as skio
dataset = self.target_test_loader
output_path = Path(self.save_path / output_path)
output_path.mkdir(exist_ok=True)
if train:
self.backbone.module.remove_dropout()
self.backbone.train()
else:
self.backbone.eval()
with torch.no_grad():
self.nim.clear_cache()
self.nim.set_max_len(len(dataset))
for names, img, target in tqdm(dataset):
img = to_cuda(img)
logits = self.backbone(img)[0]
logits = F.interpolate(logits, img.size()[2:], mode='bilinear', align_corners=True)
preds = get_prediction(logits).cpu().numpy()
if color:
trainId_preds = preds
else:
trainId_preds = dataset.dataset.predict(preds)
for pred, name in zip(trainId_preds, names):
file_name = name.split('/')[-1]
if color:
pred = class_map_2_color_map(pred).transpose(1, 2, 0).astype(np.uint8)
skio.imsave(str(output_path / file_name), pred)
def get_string(self, epoch, it, loss_meters, acc_meters, lr, meta):
string = '\repoch {:4}, iter : {:4}, '.format(epoch, it)
for k, v in loss_meters.items():
string += k + ' : {:.4f}, '.format(v.avg)
for k, v in acc_meters.items():
string += k + ' : {:.4f}, '.format(v.avg)
string += 'lr : {:.6f}, meta : {}'.format(lr, meta)
return string
def log(self, strs):
self.logger.info(strs)
def print(self, strs, **kwargs):
print(strs, **kwargs)
def tfb_log(self, epoch, it, losses, acc):
iteration = epoch * len(self.train_loader) + it
for k, v in losses.items():
self.writer.add_scalar('loss/' + k, v.val, iteration)
for k, v in acc.items():
self.writer.add_scalar('acc/' + k, v.val, iteration)
def save(self, name='ckpt'):
info = [self.best_source_acc, self.best_source_acc_target, self.best_source_epoch,
self.best_target_acc, self.best_target_acc_source, self.best_target_epoch]
dicts = {
'backbone': self.backbone.state_dict(),
'opt': self.opt_old.state_dict(),
'epoch': self.epoch + 1,
'best': self.best_target_acc,
'info': info
}
self.print('Saving epoch : {}'.format(self.epoch))
torch.save(dicts, self.save_path / '{}.pth'.format(name))
def load(self, path=None, strict=False):
if path is None:
path = self.save_path / 'ckpt.pth'
else:
if 'pth' in path:
path = path
else:
path = self.save_path / '{}.pth'.format(path)
try:
dicts = torch.load(path, map_location='cpu')
msg = self.backbone.load_state_dict(dicts['backbone'], strict=strict)
self.print(msg)
if 'opt' in dicts:
self.opt_old.load_state_dict(dicts['opt'])
if 'epoch' in dicts:
self.epoch = dicts['epoch']
else:
self.epoch = 1
if 'best' in dicts:
self.best_target_acc = dicts['best']
if 'info' in dicts:
self.best_source_acc, self.best_source_acc_target, self.best_source_epoch, \
self.best_target_acc, self.best_target_acc_source, self.best_target_epoch = dicts['info']
self.log('Loaded from {}, next epoch : {}, best_target : {}, best_epoch : {}\n'
.format(str(path), self.epoch, self.best_target_acc, self.best_target_epoch))
return True
except Exception as e:
self.print(e)
self.log('No ckpt found in {}\n'.format(str(path)))
self.epoch = 1
return False
def main(args):
""" Main function
Here, you should instantiate
1) Dataset objects for training and test datasets
2) DataLoaders for training and testing
3) model
4) optimizer: SGD with initial learning rate 0.01 and momentum 0.9
5) cost function: use torch.nn.CrossEntropyLoss
"""
# write your codes here
# Configuration
mode = args.mode
model_name = args.model
options = args.o
if mode == 'train':
train_data_dir = args.d + '/train/'
elif mode == 'test':
test_data_dir = args.d + '/test/'
elif mode == 'graph_compare':
if model_name == 'LeNet5':
models_name = [
'LeNet5', 'LeNet5_insert_noise_s0.1_m0.0',
'LeNet5_insert_noise_s0.2_m0.0',
'LeNet5_insert_noise_s0.3_m0.0', 'LeNet5_weight_decay_0.0001',
'LeNet5_weight_decay_0.001', 'LeNet5_weight_decay_0.01'
]
elif model_name == 'CustomMLP_6':
models_name = [
'CustomMLP_6', 'CustomMLP_6_weight_decay_1e-05',
'CustomMLP_6_weight_decay_0.0001',
'CustomMLP_6_weight_decay_0.001'
]
else:
models_name = [
'LeNet5', 'CustomMLP_1', 'CustomMLP_2', 'CustomMLP_3',
'CustomMLP_4', 'CustomMLP_5', 'CustomMLP_6'
]
model_path = args.m
device = torch.device("cuda:" + str(args.cuda))
lr = 0.01
momentum = 0.6
batch_size = args.b
epoch = args.e
use_ckpt = args.c
if model_name == "CustomMLP_1":
layer_option = [54, 47, 35, 10, 39]
elif model_name == "CustomMLP_2":
layer_option = [55, 35, 30, 34]
elif model_name == "CustomMLP_3":
layer_option = [55, 34, 33, 31]
elif model_name == "CustomMLP_4":
layer_option = [55, 41, 41]
elif model_name == "CustomMLP_5":
layer_option = [56, 51]
elif model_name == "CustomMLP_6":
layer_option = [58]
##change models
if mode != "graph_compare":
if model_name.split('_')[0] == "LeNet5":
model = LeNet5(device).to(device)
elif model_name.split('_')[0] == "CustomMLP":
model = CustomMLP(layer_option).to(device)
##change model name
if options:
model_name = model_name + '_' + options
if options == "weight_decay":
weight_decay = args.w
gausian_noise_mean = 0.
gausian_noise_std = 0.
model_name += '_' + str(weight_decay)
elif options == "insert_noise":
weight_decay = 0.
gausian_noise_mean = args.mean
gausian_noise_std = args.std
model_name += '_s' + str(gausian_noise_std) + "_m" + str(
gausian_noise_mean)
else:
weight_decay = 0.
##change criterion
criterion = CrossEntropyLoss()
#Custom TimeModule
mytime = CheckTime()
if mode == "train":
# Load Dataset
print(
"{} Start Loading Train Dataset ==================================="
.format(mytime.get_running_time_str()))
train_dataset = dataset.MNIST(train_data_dir, gausian_noise_mean,
gausian_noise_std)
train_dataloader = DataLoader(train_dataset, batch_size, shuffle=True)
# initiate optimizer
optimizer = SGD(model.parameters(),
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
# If use checkpoint ...
if use_ckpt:
ckpt_files = glob(model_path + '{}_model_*.pt'.format(model_name))
ckpt_files.sort()
ckpt_model_path = ckpt_files[-1]
epoch_info = torch.load(ckpt_model_path, map_location=device)
start_epoch = epoch_info['epoch'] - 1
model.load_state_dict(epoch_info['model'])
optimizer.load_state_dic(epoch_info['optimizer'])
total_trn_loss = epoch_info['total_trn_loss']
total_trn_acc = epoch_info['total_trn_acc']
else:
start_epoch = 0
total_trn_loss = []
total_trn_acc = []
# Check Random Parameter Model Loss & Accuracy
print(
"{} Check Random Parameter Model {}========================================= "
.format(mytime.get_running_time_str(), model_name))
with torch.no_grad():
trn_loss, trn_acc = test(model, train_dataloader, device,
criterion)
total_trn_loss.append(trn_loss.item())
total_trn_acc.append(trn_acc.item())
i = 0
torch.save(
{
'epoch': i,
'model': model.state_dict(),
'opimizer': optimizer.state_dict(),
'total_trn_loss': total_trn_loss,
'total_trn_acc': total_trn_acc
}, model_path + '{}_model_{:04d}.pt'.format(model_name, i))
print("{} train {} // epoch: {} // loss: {:.6f} // accuracy: {:.2f} ".
format(mytime.get_running_time_str(), model_name, i, trn_loss,
trn_acc))
# Start traing model
print("{} Start Training {}========================================= ".
format(mytime.get_running_time_str(), model_name))
for i in range(start_epoch, epoch):
trn_loss, trn_acc = train(model, train_dataloader, device,
criterion, optimizer)
total_trn_loss.append(trn_loss.item())
total_trn_acc.append(trn_acc.item())
torch.save(
{
'epoch': i,
'model': model.state_dict(),
'opimizer': optimizer.state_dict(),
'total_trn_loss': total_trn_loss,
'total_trn_acc': total_trn_acc
}, model_path + '{}_model_{:04d}.pt'.format(model_name, i + 1))
print(
"{} train {} // epoch: {} // loss: {:.6f} // accuracy: {:.2f} "
.format(mytime.get_running_time_str(), model_name, i + 1,
trn_loss, trn_acc))
if mode == "test":
#Start Loading Test Dataset
print(
"{} Start Loading Test Dataset ==================================="
.format(mytime.get_running_time_str()))
test_dataset = dataset.MNIST(test_data_dir)
test_dataloader = DataLoader(test_dataset, batch_size, shuffle=True)
# Start Testing model
with torch.no_grad():
ckpt_files = glob(model_path + '{}_model_*.pt'.format(model_name))
ckpt_files.sort()
total_tst_loss = []
total_tst_acc = []
for i, ckpt_model_path in enumerate(ckpt_files):
epoch_info = torch.load(ckpt_model_path, map_location=device)
model.load_state_dict(epoch_info['model'])
tst_loss, tst_acc = test(model, test_dataloader, device,
criterion)
total_tst_loss.append(tst_loss.item())
total_tst_acc.append(tst_acc.item())
epoch_info['total_tst_loss'] = total_tst_loss
epoch_info['total_tst_acc'] = total_tst_acc
torch.save(epoch_info, ckpt_model_path)
print(
"{} test {} // model_num: {} // loss: {:.6f} // accuracy: {:.2f} "
.format(mytime.get_running_time_str(), model_name, i,
tst_loss, tst_acc))
if mode == "graph":
#Load models to draw graph
ckpt_files = glob(model_path + '{}_model_*.pt'.format(model_name))
ckpt_files.sort()
epoch_info = torch.load(ckpt_files[-1])
#initiate loss and accuracy dictionary
loss_dic = {}
acc_dic = {}
#add loss and accuracy list
loss_dic['train'] = epoch_info['total_trn_loss']
loss_dic['test'] = epoch_info['total_tst_loss']
acc_dic['train'] = epoch_info['total_trn_acc']
acc_dic['test'] = epoch_info['total_tst_acc']
num_epoch = len(loss_dic['train'])
#Draw Graph per model: trn_loss + tst_loss
graph_name = "Loss (model - {}) ".format(model_name)
draw_model_graph(graph_name,
num_epoch,
loss_dic,
graph_mode="loss",
save=args.s,
zoom_plot=args.z)
#Draw Graph per model: trn_acc + tst_acc
graph_name = "Accuracy (model - {}) ".format(model_name)
draw_model_graph(graph_name,
num_epoch,
acc_dic,
graph_mode="acc",
save=args.s,
zoom_plot=args.z)
if mode == "graph_compare":
tst_loss_dic = {}
tst_acc_dic = {}
print(models_name)
#Load pre-defined models
for model_name in models_name:
model_file_name = model_path + '{}_model_{:04d}.pt'.format(
model_name, epoch)
epoch_info = torch.load(model_file_name)
tst_loss_dic[model_name] = epoch_info['total_tst_loss']
tst_acc_dic[model_name] = epoch_info['total_tst_acc']
num_epoch = len(tst_loss_dic[model_name])
#Comparison models: tst_loss
graph_name = "Compare Loss"
draw_model_graph(graph_name,
num_epoch,
tst_loss_dic,
graph_mode="loss",
save=args.s,
zoom_plot=args.z)
#Comparison models: tst_acc
graph_name = "Compare Accuracy"
draw_model_graph(graph_name,
num_epoch,
tst_acc_dic,
graph_mode="acc",
save=args.s,
zoom_plot=args.z)
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best-{}.pth.tar'.format(arch))
model = Model().cuda()
optimizer = SGD(filter(lambda p: p.requires_grad, model.parameters()), lr,
momentum, weight_decay)
criterion = nn.CrossEntropyLoss().cuda()
best_loss = 10
for epoch in range(epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
loss = validate(val_loader, model, criterion)
# remember best prec@1 and save checkpoint
is_best = loss < best_loss
best_loss = min(loss, best_loss)
print(' * Best Loss: {}'.format(best_loss))
save_checkpoint(
{
'epoch': epoch + 1,
'arch': arch,
'state_dict': model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
}, is_best)
class Trainer(object):
def __init__(self, config):
self.config = config
self.device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
start_time = datetime.datetime.now().strftime('%m%d_%H%M%S')
self.log_path = os.path.join(config['train']['save_dir'], start_time)
tb_path = os.path.join(self.log_path, 'logs')
mkdir_p(tb_path)
self.writer = WriterTensorboardX(tb_path)
data_manager = CSVDataManager(config['data'])
self.data_loader = data_manager.get_loader('train')
self.valid_data_loader = data_manager.get_loader('val')
self.model = AttentionalFactorizationMachine(data_manager.dims, config)
self.model = self.model.to(self.device)
trainable_params = filter(lambda p: p.requires_grad,
self.model.parameters())
self.optimizer = SGD(trainable_params, **config['optimizer'])
self.lr_scheduler = StepLR(self.optimizer, **config['lr_scheduler'])
self.best_val_loss = float('inf')
self.satur_count = 0
def _train_epoch(self, epoch):
self.model.train()
total_loss = 0
self.writer.set_step(epoch)
_trange = tqdm(self.data_loader, leave=True, desc='')
for batch_idx, batch in enumerate(_trange):
batch = [b.to(self.device) for b in batch]
data, target = batch[:-1], batch[-1]
# data -> users, items, gens
self.optimizer.zero_grad()
output = self.model(data)
loss = F.mse_loss(output, target)
loss.backward()
self.optimizer.step()
total_loss += loss.item()
if batch_idx % 10 == 0:
_str = 'Train Epoch: {} Loss: {:.6f}'.format(
epoch, loss.item())
_trange.set_description(_str)
loss = total_loss / len(self.data_loader)
self.writer.add_scalar('loss', loss)
log = {'loss': loss}
val_log = self._valid_epoch(epoch)
log = {**log, **val_log}
self.lr_scheduler.step()
return log
def _valid_epoch(self, epoch):
self.model.eval()
total_val_loss = 0
self.writer.set_step(epoch, 'valid')
with torch.no_grad():
for batch_idx, batch in enumerate(self.valid_data_loader):
batch = [b.to(self.device) for b in batch]
data, target = batch[:-1], batch[-1]
output = self.model(data)
loss = F.mse_loss(output, target)
total_val_loss += loss.item()
val_loss = total_val_loss / len(self.valid_data_loader)
self.writer.add_scalar('loss', val_loss)
# for name, param in self.model.named_parameters():
# if param.requires_grad:
# self.writer.add_histogram(name, param.clone().cpu().numpy(), bins='doane')
return {'val_loss': val_loss}
def train(self):
print(self.model)
for epoch in range(1, self.config['train']['epochs'] + 1):
result = self._train_epoch(epoch)
c_lr = self.optimizer.param_groups[0]['lr']
self.writer.add_scalar('lr', c_lr)
log = pd.DataFrame([result]).T
log.columns = ['']
print(log)
if self.best_val_loss > result['val_loss']:
print('[IMPROVED]')
chk_path = os.path.join(self.log_path, 'checkpoints')
mkdir_p(chk_path)
state = {
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict()
}
torch.save(state, os.path.join(chk_path, 'model_best.pth'))
with open(os.path.join(chk_path, 'config.json'), 'w') as wj:
json.dump(self.config, wj)
else:
self.satur_count += 1
if self.satur_count > self.config['train']['early_stop']:
break
class Trainer(object):
def __init__(self, config_path=None, **kwargs):
# general
self.run_name = None
# code parameters
self.use_ecc = None
self.n_symbols = None
# channel
self.memory_length = None
self.channel_type = None
self.channel_coefficients = None
self.noisy_est_var = None
self.fading_in_channel = None
self.fading_in_decoder = None
self.fading_taps_type = None
self.subframes_in_frame = None
self.gamma = None
# validation hyperparameters
self.val_block_length = None
self.val_frames = None
self.val_SNR_start = None
self.val_SNR_end = None
self.val_SNR_step = None
self.eval_mode = None
# training hyperparameters
self.train_block_length = None
self.train_frames = None
self.train_minibatch_num = None
self.train_minibatch_size = None
self.train_SNR_start = None
self.train_SNR_end = None
self.train_SNR_step = None
self.lr = None # learning rate
self.loss_type = None
self.optimizer_type = None
# self-supervised online training
self.self_supervised = None
self.self_supervised_iterations = None
self.ser_thresh = None
self.meta_lr = None
self.MAML = None
self.online_meta = None
self.weights_init = None
self.window_size = None
self.buffer_empty = None
self.meta_train_iterations = None
self.meta_j_num = None
self.meta_subframes = None
# seed
self.noise_seed = None
self.word_seed = None
# weights dir
self.weights_dir = None
# if any kwargs are passed, initialize the dict with them
self.initialize_by_kwargs(**kwargs)
# initializes all none parameters above from config
self.param_parser(config_path)
# initializes word and noise generator from seed
self.rand_gen = np.random.RandomState(self.noise_seed)
self.word_rand_gen = np.random.RandomState(self.word_seed)
self.n_states = 2**self.memory_length
# initialize matrices, datasets and detector
self.initialize_dataloaders()
self.initialize_detector()
self.initialize_meta_detector()
# calculate data subframes indices. We will calculate ser only over these values.
self.data_indices = torch.Tensor(
list(
filter(lambda x: x % self.subframes_in_frame != 0, [
i for i in range(self.val_frames * self.subframes_in_frame)
]))).long()
def initialize_by_kwargs(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
def param_parser(self, config_path: str):
"""
Parse the config, load all attributes into the trainer
:param config_path: path to config
"""
if config_path is None:
config_path = CONFIG_PATH
with open(config_path) as f:
self.config = yaml.load(f, Loader=yaml.FullLoader)
# set attribute of Trainer with every config item
for k, v in self.config.items():
try:
if getattr(self, k) is None:
setattr(self, k, v)
except AttributeError:
pass
if self.weights_dir is None:
self.weights_dir = os.path.join(WEIGHTS_DIR, self.run_name)
if not os.path.exists(self.weights_dir) and len(self.weights_dir):
os.makedirs(self.weights_dir)
# save config in output dir
copyfile(config_path,
os.path.join(self.weights_dir, "config.yaml"))
def get_name(self):
return self.__name__()
def initialize_detector(self):
"""
Every trainer must have some base detector model
"""
self.detector = None
pass
def initialize_meta_detector(self):
"""
Every trainer must have some base detector model
"""
self.meta_detector = None
pass
def check_eval_mode(self):
if self.eval_mode != 'aggregated' and self.eval_mode != 'by_word':
raise ValueError("No such eval mode!!!")
# calculate train loss
def calc_loss(self, soft_estimation: torch.Tensor,
transmitted_words: torch.Tensor) -> torch.Tensor:
"""
Every trainer must have some loss calculation
"""
pass
# setup the optimization algorithm
def deep_learning_setup(self):
"""
Sets up the optimizer and loss criterion
"""
if self.optimizer_type == 'Adam':
self.optimizer = Adam(filter(lambda p: p.requires_grad,
self.detector.parameters()),
lr=self.lr)
elif self.optimizer_type == 'RMSprop':
self.optimizer = RMSprop(filter(lambda p: p.requires_grad,
self.detector.parameters()),
lr=self.lr)
elif self.optimizer_type == 'SGD':
self.optimizer = SGD(filter(lambda p: p.requires_grad,
self.detector.parameters()),
lr=self.lr)
else:
raise NotImplementedError("No such optimizer implemented!!!")
if self.loss_type == 'BCE':
self.criterion = BCELoss().to(device)
elif self.loss_type == 'CrossEntropy':
self.criterion = CrossEntropyLoss().to(device)
elif self.loss_type == 'MSE':
self.criterion = MSELoss().to(device)
else:
raise NotImplementedError("No such loss function implemented!!!")
def initialize_dataloaders(self):
"""
Sets up the data loader - a generator from which we draw batches, in iterations
"""
self.snr_range = {
'train':
np.arange(self.train_SNR_start,
self.train_SNR_end + 1,
step=self.train_SNR_step),
'val':
np.arange(self.val_SNR_start,
self.val_SNR_end + 1,
step=self.val_SNR_step)
}
self.frames_per_phase = {
'train': self.train_frames,
'val': self.val_frames
}
self.block_lengths = {
'train': self.train_block_length,
'val': self.val_block_length
}
self.channel_coefficients = {
'train': 'time_decay',
'val': self.channel_coefficients
}
self.transmission_lengths = {
'train':
self.train_block_length if not self.use_ecc else
self.train_block_length + 8 * self.n_symbols,
'val':
self.val_block_length
if not self.use_ecc else self.val_block_length + 8 * self.n_symbols
}
self.channel_dataset = {
phase: ChannelModelDataset(
channel_type=self.channel_type,
block_length=self.block_lengths[phase],
transmission_length=self.transmission_lengths[phase],
words=self.frames_per_phase[phase] * self.subframes_in_frame,
memory_length=self.memory_length,
channel_coefficients=self.channel_coefficients[phase],
random=self.rand_gen,
word_rand_gen=self.word_rand_gen,
noisy_est_var=self.noisy_est_var,
use_ecc=self.use_ecc,
n_symbols=self.n_symbols,
fading_taps_type=self.fading_taps_type,
fading_in_channel=self.fading_in_channel,
fading_in_decoder=self.fading_in_decoder,
phase=phase)
for phase in ['train', 'val']
}
self.dataloaders = {
phase: torch.utils.data.DataLoader(self.channel_dataset[phase])
for phase in ['train', 'val']
}
def online_training(self, tx: torch.Tensor, rx: torch.Tensor):
pass
def single_eval_at_point(self, snr: float, gamma: float) -> float:
"""
Evaluation at a single snr.
:param snr: indice of snr in the snrs vector
:return: ser for batch
"""
# draw words of given gamma for all snrs
transmitted_words, received_words = self.channel_dataset[
'val'].__getitem__(snr_list=[snr], gamma=gamma)
# decode and calculate accuracy
detected_words = self.detector(received_words, 'val', snr, gamma)
if self.use_ecc:
decoded_words = [
decode(detected_word, self.n_symbols)
for detected_word in detected_words.cpu().numpy()
]
detected_words = torch.Tensor(decoded_words).to(device)
ser, fer, err_indices = calculate_error_rates(
detected_words[self.data_indices],
transmitted_words[self.data_indices])
return ser
def gamma_eval(self, gamma: float) -> np.ndarray:
"""
Evaluation at a single gamma value.
:return: ser for batch.
"""
ser_total = np.zeros(len(self.snr_range['val']))
for snr_ind, snr in enumerate(self.snr_range['val']):
self.load_weights(snr, gamma)
ser_total[snr_ind] = self.single_eval_at_point(snr, gamma)
return ser_total
def evaluate_at_point(self) -> np.ndarray:
"""
Monte-Carlo simulation over validation SNRs range
:return: ber, fer, iterations vectors
"""
ser_total = np.zeros(len(self.snr_range['val']))
with torch.no_grad():
print(f'Starts evaluation at gamma {self.gamma}')
start = time()
ser_total += self.gamma_eval(self.gamma)
print(f'Done. time: {time() - start}, ser: {ser_total}')
return ser_total
def eval_by_word(self, snr: float,
gamma: float) -> Union[float, np.ndarray]:
if self.self_supervised:
self.deep_learning_setup()
total_ser = 0
# draw words of given gamma for all snrs
transmitted_words, received_words = self.channel_dataset[
'val'].__getitem__(snr_list=[snr], gamma=gamma)
ser_by_word = np.zeros(transmitted_words.shape[0])
# saved detector is used to initialize the decoder in meta learning loops
self.saved_detector = copy.deepcopy(self.detector)
# query for all detected words
if self.buffer_empty:
buffer_rx = torch.empty([0, received_words.shape[1]]).to(device)
buffer_tx = torch.empty([0, received_words.shape[1]]).to(device)
buffer_ser = torch.empty([0]).to(device)
else:
# draw words from different channels
buffer_tx, buffer_rx = self.channel_dataset['train'].__getitem__(
snr_list=[snr], gamma=gamma)
buffer_ser = torch.zeros(buffer_rx.shape[0]).to(device)
buffer_tx = torch.cat([
torch.Tensor(
encode(transmitted_word.int().cpu().numpy(),
self.n_symbols).reshape(1, -1)).to(device)
for transmitted_word in buffer_tx
],
dim=0)
support_idx = torch.arange(-self.window_size - 1, -1).long().to(device)
query_idx = -1 * torch.ones(1).long().to(device)
for count, (transmitted_word, received_word) in enumerate(
zip(transmitted_words, received_words)):
transmitted_word, received_word = transmitted_word.reshape(
1, -1), received_word.reshape(1, -1)
# detect
detected_word = self.detector(received_word, 'val', snr, gamma,
count)
if count in self.data_indices:
# decode
decoded_word = [
decode(detected_word, self.n_symbols)
for detected_word in detected_word.cpu().numpy()
]
decoded_word = torch.Tensor(decoded_word).to(device)
# calculate accuracy
ser, fer, err_indices = calculate_error_rates(
decoded_word, transmitted_word)
# encode word again
decoded_word_array = decoded_word.int().cpu().numpy()
encoded_word = torch.Tensor(
encode(decoded_word_array,
self.n_symbols).reshape(1, -1)).to(device)
errors_num = torch.sum(torch.abs(encoded_word -
detected_word)).item()
print('*' * 20)
print(f'current: {count, ser, errors_num}')
total_ser += ser
ser_by_word[count] = ser
else:
print('*' * 20)
print(f'current: {count}, Pilot')
# encode word again
decoded_word_array = transmitted_word.int().cpu().numpy()
encoded_word = torch.Tensor(
encode(decoded_word_array,
self.n_symbols).reshape(1, -1)).to(device)
ser = 0
# save the encoded word in the buffer
if ser <= self.ser_thresh:
buffer_rx = torch.cat([buffer_rx, received_word])
buffer_tx = torch.cat([
buffer_tx,
detected_word.reshape(1, -1)
if ser > 0 else encoded_word.reshape(1, -1)
],
dim=0)
buffer_ser = torch.cat(
[buffer_ser,
torch.FloatTensor([ser]).to(device)])
if not self.buffer_empty:
buffer_rx = buffer_rx[1:]
buffer_tx = buffer_tx[1:]
buffer_ser = buffer_ser[1:]
if self.online_meta and count % self.meta_subframes == 0 and count >= self.meta_subframes and \
buffer_rx.shape[0] > 2: # self.subframes_in_frame
print('meta-training')
self.meta_weights_init()
for i in range(self.meta_train_iterations):
j_hat_values = torch.unique(
torch.randint(low=0,
high=buffer_rx.shape[0] - 2,
size=[self.meta_j_num])).to(device)
for j_hat in j_hat_values:
cur_support_idx = j_hat + support_idx + 1
cur_query_idx = j_hat + query_idx + 1
self.meta_train_loop(buffer_rx, buffer_tx,
cur_support_idx, cur_query_idx)
copy_model(source_model=self.detector,
dest_model=self.saved_detector)
if self.self_supervised and ser <= self.ser_thresh:
# use last word inserted in the buffer for training
self.online_training(buffer_tx[-1].reshape(1, -1),
buffer_rx[-1].reshape(1, -1))
if (count + 1) % 10 == 0:
print(
f'Self-supervised: {count + 1}/{transmitted_words.shape[0]}, SER {total_ser / (count + 1)}'
)
total_ser /= transmitted_words.shape[0]
print(f'Final ser: {total_ser}')
return ser_by_word
def meta_weights_init(self):
if self.weights_init == 'random':
self.initialize_detector()
self.deep_learning_setup()
elif self.weights_init == 'last_frame':
copy_model(source_model=self.saved_detector,
dest_model=self.detector)
elif self.weights_init == 'meta_training':
snr = self.snr_range['val'][0]
self.load_weights(snr, self.gamma)
else:
raise ValueError('No such weights init!!!')
def evaluate(self) -> np.ndarray:
"""
Evaluation either happens in a point aggregation way, or in a word-by-word fashion
"""
# eval with training
self.check_eval_mode()
if self.eval_mode == 'by_word':
if not self.use_ecc:
raise ValueError('Only supports ecc')
snr = self.snr_range['val'][0]
self.load_weights(snr, self.gamma)
return self.eval_by_word(snr, self.gamma)
else:
return self.evaluate_at_point()
def meta_train(self):
"""
Main meta-training loop. Runs in minibatches, each minibatch is split to pairs of following words.
The pairs are comprised of (support,query) words.
Evaluates performance over validation SNRs.
Saves weights every so and so iterations.
"""
# initialize weights and loss
for snr in self.snr_range['train']:
print(f'SNR - {snr}, Gamma - {self.gamma}')
# initialize weights and loss
self.initialize_detector()
self.deep_learning_setup()
for minibatch in range(1, self.train_minibatch_num + 1):
# draw words from different channels
transmitted_words, received_words = self.channel_dataset[
'train'].__getitem__(snr_list=[snr], gamma=self.gamma)
support_idx = torch.arange(-self.window_size - 1,
-1).long().to(device)
query_idx = -1 * torch.ones(1).long().to(device)
j_hat_values = torch.unique(
torch.randint(low=self.window_size,
high=transmitted_words.shape[0],
size=[self.meta_j_num])).to(device)
if self.use_ecc:
transmitted_words = torch.cat([
torch.Tensor(
encode(transmitted_word.int().cpu().numpy(),
self.n_symbols).reshape(1, -1)).to(device)
for transmitted_word in transmitted_words
],
dim=0)
loss_query = 0
for j_hat in j_hat_values:
cur_support_idx = j_hat + support_idx + 1
cur_query_idx = j_hat + query_idx + 1
loss_query += self.meta_train_loop(received_words,
transmitted_words,
cur_support_idx,
cur_query_idx)
# evaluate performance
ser = self.single_eval_at_point(snr, self.gamma)
print(
f'Minibatch {minibatch}, ser - {ser}, loss - {loss_query}')
# save best weights
self.save_weights(float(loss_query), snr, self.gamma)
def meta_train_loop(self, received_words: torch.Tensor,
transmitted_words: torch.Tensor,
support_idx: torch.Tensor, query_idx: torch.Tensor):
# divide the words to following pairs - (support,query)
support_tx, support_rx = transmitted_words[
support_idx], received_words[support_idx]
query_tx, query_rx = transmitted_words[query_idx], received_words[
query_idx]
# local update (with support set)
para_list_detector = list(
map(lambda p: p[0], zip(self.detector.parameters())))
soft_estimation_supp = self.meta_detector(support_rx, 'train',
para_list_detector)
loss_supp = self.calc_loss(soft_estimation=soft_estimation_supp,
transmitted_words=support_tx)
# set create_graph to True for MAML, False for FO-MAML
local_grad = torch.autograd.grad(loss_supp,
para_list_detector,
create_graph=self.MAML)
updated_para_list_detector = list(
map(lambda p: p[1] - self.meta_lr * p[0],
zip(local_grad, para_list_detector)))
# meta-update (with query set) should be same channel with support set
soft_estimation_query = self.meta_detector(query_rx, 'train',
updated_para_list_detector)
loss_query = self.calc_loss(soft_estimation=soft_estimation_query,
transmitted_words=query_tx)
meta_grad = torch.autograd.grad(loss_query,
para_list_detector,
create_graph=False)
ind_param = 0
for param in self.detector.parameters():
param.grad = None # zero_grad
param.grad = meta_grad[ind_param]
ind_param += 1
self.optimizer.step()
return loss_query
def train(self):
"""
Main training loop. Runs in minibatches.
Evaluates performance over validation SNRs.
Saves weights every so and so iterations.
"""
# batches loop
for snr in self.snr_range['train']:
print(f'SNR - {snr}, Gamma - {self.gamma}')
# initialize weights and loss
self.initialize_detector()
self.deep_learning_setup()
best_ser = math.inf
for minibatch in range(1, self.train_minibatch_num + 1):
# draw words
transmitted_words, received_words = self.channel_dataset[
'train'].__getitem__(snr_list=[snr], gamma=self.gamma)
# run training loops
current_loss = 0
for i in range(self.train_frames * self.subframes_in_frame):
# pass through detector
soft_estimation = self.detector(
received_words[i].reshape(1, -1), 'train')
current_loss += self.run_train_loop(
soft_estimation, transmitted_words[i].reshape(1, -1))
# evaluate performance
ser = self.single_eval_at_point(snr, self.gamma)
print(
f'Minibatch {minibatch}, ser - {ser}, loss {current_loss}')
# save best weights
if ser < best_ser:
self.save_weights(current_loss, snr, self.gamma)
best_ser = ser
print(f'best ser - {best_ser}')
print('*' * 50)
def run_train_loop(self, soft_estimation: torch.Tensor,
transmitted_words: torch.Tensor):
# calculate loss
loss = self.calc_loss(soft_estimation=soft_estimation,
transmitted_words=transmitted_words)
# if loss is Nan inform the user
if torch.sum(torch.isnan(loss)):
print('Nan value')
return np.nan
current_loss = loss.item()
# back propagation
for param in self.detector.parameters():
param.grad = None
loss.backward()
self.optimizer.step()
return current_loss
def save_weights(self, current_loss: float, snr: float, gamma: float):
torch.save(
{
'model_state_dict': self.detector.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'loss': current_loss
}, os.path.join(self.weights_dir, f'snr_{snr}_gamma_{gamma}.pt'))
def load_weights(self, snr: float, gamma: float):
"""
Loads detector's weights defined by the [snr,gamma] from checkpoint, if exists
"""
if os.path.join(self.weights_dir, f'snr_{snr}_gamma_{gamma}.pt'):
print(f'loading model from snr {snr} and gamma {gamma}')
weights_path = os.path.join(self.weights_dir,
f'snr_{snr}_gamma_{gamma}.pt')
if not os.path.isfile(weights_path):
# if weights do not exist, train on the synthetic channel. Then validate on the test channel.
self.fading_taps_type = 1
os.makedirs(self.weights_dir, exist_ok=True)
self.train()
self.fading_taps_type = 2
checkpoint = torch.load(weights_path)
try:
self.detector.load_state_dict(checkpoint['model_state_dict'])
except Exception:
raise ValueError("Wrong run directory!!!")
else:
print(
f'No checkpoint for snr {snr} and gamma {gamma} in run "{self.run_name}", starting from scratch'
)
def select_batch(
self, gt_examples: torch.LongTensor, soft_estimation: torch.Tensor
) -> Tuple[torch.LongTensor, torch.Tensor]:
"""
Select a batch from the input and gt labels
:param gt_examples: training labels
:param soft_estimation: the soft approximation, distribution over states (per word)
:return: selected batch from the entire "epoch", contains both labels and the NN soft approximation
"""
rand_ind = torch.multinomial(
torch.arange(gt_examples.shape[0]).float(),
self.train_minibatch_size).long().to(device)
return gt_examples[rand_ind], soft_estimation[rand_ind]
def train(model,
state,
path,
annotations,
val_path,
val_annotations,
resize,
max_size,
jitter,
batch_size,
iterations,
val_iterations,
mixed_precision,
lr,
warmup,
milestones,
gamma,
rank=0,
world=1,
no_apex=False,
use_dali=True,
verbose=True,
metrics_url=None,
logdir=None,
rotate_augment=False,
augment_brightness=0.0,
augment_contrast=0.0,
augment_hue=0.0,
augment_saturation=0.0,
regularization_l2=0.0001,
rotated_bbox=False,
absolute_angle=False):
'Train the model on the given dataset'
# Prepare model
nn_model = model
stride = model.stride
model = convert_fixedbn_model(model)
if torch.cuda.is_available():
model = model.to(memory_format=torch.channels_last).cuda()
# Setup optimizer and schedule
optimizer = SGD(model.parameters(),
lr=lr,
weight_decay=regularization_l2,
momentum=0.9)
is_master = rank == 0
if not no_apex:
loss_scale = "dynamic" if use_dali else "128.0"
model, optimizer = amp.initialize(
model,
optimizer,
opt_level='O2' if mixed_precision else 'O0',
keep_batchnorm_fp32=True,
loss_scale=loss_scale,
verbosity=is_master)
if world > 1:
model = DDP(model, device_ids=[rank]) if no_apex else ADDP(model)
model.train()
if 'optimizer' in state:
optimizer.load_state_dict(state['optimizer'])
def schedule(train_iter):
if warmup and train_iter <= warmup:
return 0.9 * train_iter / warmup + 0.1
return gamma**len([m for m in milestones if m <= train_iter])
scheduler = LambdaLR(optimizer, schedule)
if 'scheduler' in state:
scheduler.load_state_dict(state['scheduler'])
# Prepare dataset
if verbose: print('Preparing dataset...')
if rotated_bbox:
if use_dali:
raise NotImplementedError(
"This repo does not currently support DALI for rotated bbox detections."
)
data_iterator = RotatedDataIterator(
path,
jitter,
max_size,
batch_size,
stride,
world,
annotations,
training=True,
rotate_augment=rotate_augment,
augment_brightness=augment_brightness,
augment_contrast=augment_contrast,
augment_hue=augment_hue,
augment_saturation=augment_saturation,
absolute_angle=absolute_angle)
else:
data_iterator = (DaliDataIterator if use_dali else DataIterator)(
path,
jitter,
max_size,
batch_size,
stride,
world,
annotations,
training=True,
rotate_augment=rotate_augment,
augment_brightness=augment_brightness,
augment_contrast=augment_contrast,
augment_hue=augment_hue,
augment_saturation=augment_saturation)
if verbose: print(data_iterator)
if verbose:
print(' device: {} {}'.format(
world, 'cpu' if not torch.cuda.is_available() else
'GPU' if world == 1 else 'GPUs'))
print(' batch: {}, precision: {}'.format(
batch_size, 'mixed' if mixed_precision else 'full'))
print(' BBOX type:', 'rotated' if rotated_bbox else 'axis aligned')
print('Training model for {} iterations...'.format(iterations))
# Create TensorBoard writer
if is_master and logdir is not None:
from torch.utils.tensorboard import SummaryWriter
if verbose:
print('Writing TensorBoard logs to: {}'.format(logdir))
writer = SummaryWriter(log_dir=logdir)
scaler = GradScaler()
profiler = Profiler(['train', 'fw', 'bw'])
iteration = state.get('iteration', 0)
while iteration < iterations:
cls_losses, box_losses = [], []
for i, (data, target) in enumerate(data_iterator):
if iteration >= iterations:
break
# Forward pass
profiler.start('fw')
optimizer.zero_grad()
if not no_apex:
cls_loss, box_loss = model([
data.contiguous(memory_format=torch.channels_last), target
])
else:
with autocast():
cls_loss, box_loss = model([
data.contiguous(memory_format=torch.channels_last),
target
])
del data
profiler.stop('fw')
# Backward pass
profiler.start('bw')
if not no_apex:
with amp.scale_loss(cls_loss + box_loss,
optimizer) as scaled_loss:
scaled_loss.backward()
optimizer.step()
else:
scaler.scale(cls_loss + box_loss).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
# Reduce all losses
cls_loss, box_loss = cls_loss.mean().clone(), box_loss.mean(
).clone()
if world > 1:
torch.distributed.all_reduce(cls_loss)
torch.distributed.all_reduce(box_loss)
cls_loss /= world
box_loss /= world
if is_master:
cls_losses.append(cls_loss)
box_losses.append(box_loss)
if is_master and not isfinite(cls_loss + box_loss):
raise RuntimeError('Loss is diverging!\n{}'.format(
'Try lowering the learning rate.'))
del cls_loss, box_loss
profiler.stop('bw')
iteration += 1
profiler.bump('train')
if is_master and (profiler.totals['train'] > 60
or iteration == iterations):
focal_loss = torch.stack(list(cls_losses)).mean().item()
box_loss = torch.stack(list(box_losses)).mean().item()
learning_rate = optimizer.param_groups[0]['lr']
if verbose:
msg = '[{:{len}}/{}]'.format(iteration,
iterations,
len=len(str(iterations)))
msg += ' focal loss: {:.3f}'.format(focal_loss)
msg += ', box loss: {:.3f}'.format(box_loss)
msg += ', {:.3f}s/{}-batch'.format(profiler.means['train'],
batch_size)
msg += ' (fw: {:.3f}s, bw: {:.3f}s)'.format(
profiler.means['fw'], profiler.means['bw'])
msg += ', {:.1f} im/s'.format(batch_size /
profiler.means['train'])
msg += ', lr: {:.2g}'.format(learning_rate)
print(msg, flush=True)
if is_master and logdir is not None:
writer.add_scalar('focal_loss', focal_loss, iteration)
writer.add_scalar('box_loss', box_loss, iteration)
writer.add_scalar('learning_rate', learning_rate,
iteration)
del box_loss, focal_loss
if metrics_url:
post_metrics(
metrics_url, {
'focal loss': mean(cls_losses),
'box loss': mean(box_losses),
'im_s': batch_size / profiler.means['train'],
'lr': learning_rate
})
# Save model weights
state.update({
'iteration': iteration,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
})
with ignore_sigint():
nn_model.save(state)
profiler.reset()
del cls_losses[:], box_losses[:]
if val_annotations and (iteration == iterations
or iteration % val_iterations == 0):
stats = infer(model,
val_path,
None,
resize,
max_size,
batch_size,
annotations=val_annotations,
mixed_precision=mixed_precision,
is_master=is_master,
world=world,
use_dali=use_dali,
no_apex=no_apex,
is_validation=True,
verbose=False,
rotated_bbox=rotated_bbox)
model.train()
if is_master and logdir is not None and stats is not None:
writer.add_scalar('Validation_Precision/mAP', stats[0],
iteration)
writer.add_scalar('Validation_Precision/[email protected]',
stats[1], iteration)
writer.add_scalar('Validation_Precision/[email protected]',
stats[2], iteration)
writer.add_scalar('Validation_Precision/mAP (small)',
stats[3], iteration)
writer.add_scalar('Validation_Precision/mAP (medium)',
stats[4], iteration)
writer.add_scalar('Validation_Precision/mAP (large)',
stats[5], iteration)
writer.add_scalar('Validation_Recall/mAR (max 1 Dets)',
stats[6], iteration)
writer.add_scalar('Validation_Recall/mAR (max 10 Dets)',
stats[7], iteration)
writer.add_scalar('Validation_Recall/mAR (max 100 Dets)',
stats[8], iteration)
writer.add_scalar('Validation_Recall/mAR (small)',
stats[9], iteration)
writer.add_scalar('Validation_Recall/mAR (medium)',
stats[10], iteration)
writer.add_scalar('Validation_Recall/mAR (large)',
stats[11], iteration)
if (iteration == iterations
and not rotated_bbox) or (iteration > iterations
and rotated_bbox):
break
if is_master and logdir is not None:
writer.close()
def main(args: argparse.Namespace):
logger = CompleteLogger(args.log, args.phase)
print(args)
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
cudnn.benchmark = True
# Data loading code
source_dataset = datasets.__dict__[args.source]
train_source_dataset = source_dataset(
root=args.source_root,
transforms=T.Compose([
T.RandomResizedCrop(size=args.train_size,
ratio=args.resize_ratio,
scale=(0.5, 1.)),
T.ColorJitter(brightness=0.3, contrast=0.3),
T.RandomHorizontalFlip(),
T.NormalizeAndTranspose(),
]),
)
train_source_loader = DataLoader(train_source_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
target_dataset = datasets.__dict__[args.target]
train_target_dataset = target_dataset(
root=args.target_root,
transforms=T.Compose([
T.RandomResizedCrop(size=args.train_size,
ratio=(2., 2.),
scale=(0.5, 1.)),
T.RandomHorizontalFlip(),
T.NormalizeAndTranspose(),
]),
)
train_target_loader = DataLoader(train_target_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
val_target_dataset = target_dataset(
root=args.target_root,
split='val',
transforms=T.Compose([
T.Resize(image_size=args.test_input_size,
label_size=args.test_output_size),
T.NormalizeAndTranspose(),
]),
)
val_target_loader = DataLoader(val_target_dataset,
batch_size=1,
shuffle=False,
pin_memory=True)
train_source_iter = ForeverDataIterator(train_source_loader)
train_target_iter = ForeverDataIterator(train_target_loader)
# create model
num_classes = train_source_dataset.num_classes
model = models.__dict__[args.arch](num_classes=num_classes).to(device)
discriminator = Discriminator(num_classes=num_classes).to(device)
# define optimizer and lr scheduler
optimizer = SGD(model.get_parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer_d = Adam(discriminator.parameters(),
lr=args.lr_d,
betas=(0.9, 0.99))
lr_scheduler = LambdaLR(
optimizer, lambda x: args.lr *
(1. - float(x) / args.epochs / args.iters_per_epoch)**(args.lr_power))
lr_scheduler_d = LambdaLR(
optimizer_d, lambda x:
(1. - float(x) / args.epochs / args.iters_per_epoch)**(args.lr_power))
# optionally resume from a checkpoint
if args.resume:
checkpoint = torch.load(args.resume, map_location='cpu')
model.load_state_dict(checkpoint['model'])
discriminator.load_state_dict(checkpoint['discriminator'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
optimizer_d.load_state_dict(checkpoint['optimizer_d'])
lr_scheduler_d.load_state_dict(checkpoint['lr_scheduler_d'])
args.start_epoch = checkpoint['epoch'] + 1
# define loss function (criterion)
criterion = torch.nn.CrossEntropyLoss(
ignore_index=args.ignore_label).to(device)
dann = DomainAdversarialEntropyLoss(discriminator)
interp_train = nn.Upsample(size=args.train_size[::-1],
mode='bilinear',
align_corners=True)
interp_val = nn.Upsample(size=args.test_output_size[::-1],
mode='bilinear',
align_corners=True)
# define visualization function
decode = train_source_dataset.decode_target
def visualize(image, pred, label, prefix):
"""
Args:
image (tensor): 3 x H x W
pred (tensor): C x H x W
label (tensor): H x W
prefix: prefix of the saving image
"""
image = image.detach().cpu().numpy()
pred = pred.detach().max(dim=0)[1].cpu().numpy()
label = label.cpu().numpy()
for tensor, name in [
(Image.fromarray(np.uint8(DeNormalizeAndTranspose()(image))),
"image"), (decode(label), "label"), (decode(pred), "pred")
]:
tensor.save(logger.get_image_path("{}_{}.png".format(prefix,
name)))
if args.phase == 'test':
confmat = validate(val_target_loader, model, interp_val, criterion,
visualize, args)
print(confmat)
return
# start training
best_iou = 0.
for epoch in range(args.start_epoch, args.epochs):
logger.set_epoch(epoch)
print(lr_scheduler.get_lr(), lr_scheduler_d.get_lr())
# train for one epoch
train(train_source_iter, train_target_iter, model, interp_train,
criterion, dann, optimizer, lr_scheduler, optimizer_d,
lr_scheduler_d, epoch, visualize if args.debug else None, args)
# evaluate on validation set
confmat = validate(val_target_loader, model, interp_val, criterion,
None, args)
print(confmat.format(train_source_dataset.classes))
acc_global, acc, iu = confmat.compute()
# calculate the mean iou over partial classes
indexes = [
train_source_dataset.classes.index(name)
for name in train_source_dataset.evaluate_classes
]
iu = iu[indexes]
mean_iou = iu.mean()
# remember best acc@1 and save checkpoint
torch.save(
{
'model': model.state_dict(),
'discriminator': discriminator.state_dict(),
'optimizer': optimizer.state_dict(),
'optimizer_d': optimizer_d.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'lr_scheduler_d': lr_scheduler_d.state_dict(),
'epoch': epoch,
'args': args
}, logger.get_checkpoint_path(epoch))
if mean_iou > best_iou:
shutil.copy(logger.get_checkpoint_path(epoch),
logger.get_checkpoint_path('best'))
best_iou = max(best_iou, mean_iou)
print("Target: {} Best: {}".format(mean_iou, best_iou))
logger.close()
def main_worker(gpu, ngpus_per_node, args):
global best_acc
args.gpu = gpu
assert args.gpu is not None
print("Use GPU: {} for training".format(args.gpu))
log = open(
os.path.join(
args.save_path,
'log_seed{}{}.txt'.format(args.manualSeed,
'_eval' if args.evaluate else '')), 'w')
log = (log, args.gpu)
net = models.__dict__[args.arch](pretrained=True)
disable_dropout(net)
net = to_bayesian(net, args.psi_init_range)
net.apply(unfreeze)
print_log("Python version : {}".format(sys.version.replace('\n', ' ')),
log)
print_log("PyTorch version : {}".format(torch.__version__), log)
print_log("CuDNN version : {}".format(torch.backends.cudnn.version()),
log)
print_log(
"Number of parameters: {}".format(
sum([p.numel() for p in net.parameters()])), log)
print_log(str(args), log)
if args.distributed:
if args.multiprocessing_distributed:
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url + ":" +
args.dist_port,
world_size=args.world_size,
rank=args.rank)
torch.cuda.set_device(args.gpu)
net.cuda(args.gpu)
args.batch_size = int(args.batch_size / ngpus_per_node)
net = torch.nn.parallel.DistributedDataParallel(net,
device_ids=[args.gpu])
else:
torch.cuda.set_device(args.gpu)
net = net.cuda(args.gpu)
criterion = torch.nn.CrossEntropyLoss().cuda(args.gpu)
mus, psis = [], []
for name, param in net.named_parameters():
if 'psi' in name: psis.append(param)
else: mus.append(param)
mu_optimizer = SGD(mus,
args.learning_rate,
args.momentum,
weight_decay=args.decay,
nesterov=(args.momentum > 0.0))
psi_optimizer = PsiSGD(psis,
args.learning_rate,
args.momentum,
weight_decay=args.decay,
nesterov=(args.momentum > 0.0))
recorder = RecorderMeter(args.epochs)
if args.resume:
if args.resume == 'auto':
args.resume = os.path.join(args.save_path, 'checkpoint.pth.tar')
if os.path.isfile(args.resume):
print_log("=> loading checkpoint '{}'".format(args.resume), log)
checkpoint = torch.load(args.resume,
map_location='cuda:{}'.format(args.gpu))
recorder = checkpoint['recorder']
recorder.refresh(args.epochs)
args.start_epoch = checkpoint['epoch']
net.load_state_dict(
checkpoint['state_dict'] if args.distributed else {
k.replace('module.', ''): v
for k, v in checkpoint['state_dict'].items()
})
mu_optimizer.load_state_dict(checkpoint['mu_optimizer'])
psi_optimizer.load_state_dict(checkpoint['psi_optimizer'])
best_acc = recorder.max_accuracy(False)
print_log(
"=> loaded checkpoint '{}' accuracy={} (epoch {})".format(
args.resume, best_acc, checkpoint['epoch']), log)
else:
print_log("=> no checkpoint found at '{}'".format(args.resume),
log)
else:
print_log("=> do not use any checkpoint for the model", log)
cudnn.benchmark = True
train_loader, ood_train_loader, test_loader, adv_loader, \
fake_loader, adv_loader2 = load_dataset_ft(args)
psi_optimizer.num_data = len(train_loader.dataset)
if args.evaluate:
evaluate(test_loader, adv_loader, fake_loader, adv_loader2, net,
criterion, args, log, 20, 100)
return
start_time = time.time()
epoch_time = AverageMeter()
train_los = -1
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_loader.sampler.set_epoch(epoch)
ood_train_loader.sampler.set_epoch(epoch)
cur_lr, cur_slr = adjust_learning_rate(mu_optimizer, psi_optimizer,
epoch, args)
need_hour, need_mins, need_secs = convert_secs2time(
epoch_time.avg * (args.epochs - epoch))
need_time = '[Need: {:02d}:{:02d}:{:02d}]'.format(
need_hour, need_mins, need_secs)
print_log('\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} [learning_rate={:6.4f} {:6.4f}]'.format(
time_string(), epoch, args.epochs, need_time, cur_lr, cur_slr) \
+ ' [Best : Accuracy={:.2f}, Error={:.2f}]'.format(recorder.max_accuracy(False), 100-recorder.max_accuracy(False)), log)
train_acc, train_los = train(train_loader, ood_train_loader, net,
criterion, mu_optimizer, psi_optimizer,
epoch, args, log)
val_acc, val_los = 0, 0
recorder.update(epoch, train_los, train_acc, val_acc, val_los)
is_best = False
if val_acc > best_acc:
is_best = True
best_acc = val_acc
if args.gpu == 0:
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': net.state_dict(),
'recorder': recorder,
'mu_optimizer': mu_optimizer.state_dict(),
'psi_optimizer': psi_optimizer.state_dict(),
}, False, args.save_path, 'checkpoint.pth.tar')
epoch_time.update(time.time() - start_time)
start_time = time.time()
recorder.plot_curve(os.path.join(args.save_path, 'log.png'))
evaluate(test_loader, adv_loader, fake_loader, adv_loader2, net, criterion,
args, log, 20, 100)
log[0].close()
triplet_loss = TripletLoss(margin=margin).forward(
anchor=anc_hard_embedding,
positive=pos_hard_embedding,
negative=neg_hard_embedding).cuda()
triplet_loss_sum += triplet_loss.item()
num_valid_training_triplets += len(anc_hard_embedding)
optimizer_model.zero_grad()
triplet_loss.backward()
optimizer_model.step()
avg_triplet_loss = 0 if (
num_valid_training_triplets
== 0) else triplet_loss_sum / num_valid_training_triplets
print(
'Epoch {}:\tAverage Triplet Loss: {:.4f}\tNumber of valid training triplets in epoch: {}'
.format(epoch + 1, avg_triplet_loss, num_valid_training_triplets))
torch.save(
{
'epoch': epoch,
'model_state_dict': net.state_dict(),
'optimizer_state_dict': optimizer_model.state_dict(),
'avg_triplet_loss': avg_triplet_loss,
'valid_training_triplets': num_valid_training_triplets
}, './train_checkpoints/' + 'checkpoint_' + str(total_triplets) + '_' +
str(epoch) + '_' + str(num_valid_training_triplets) + '.tar')
def main(input_len, epochs_num, hidden_size, batch_size, output_size, lr):
start = datetime.datetime(1999, 1, 8)
end = datetime.datetime(2016, 12, 31)
#test_start = datetime.datetime(2015, 1, 8)
#test_end = datetime.datetime(2016, 12, 31)
training_size = 0
test_size = 0
train_x, train_t, test_x, test_t = mkDataSet(start, end, input_len)
model = Predictor(6, hidden_size, output_size)
test_x = torch.Tensor(test_x)
test_t = torch.Tensor(test_t)
train_x = torch.Tensor(train_x)
train_t = torch.Tensor(train_t)
#print(test_x.size())
#print(test_t.size())
#print(test_x)
#print(test_t)
#exit
#test_x = torch.Tensor(test_x)
#test_t = torch.Tensor(test_t)
dataset = TensorDataset(train_x, train_t)
loader_train = DataLoader(dataset, batch_size=batch_size, shuffle=True)
dataset = TensorDataset(test_x, test_t)
loader_test = DataLoader(dataset, batch_size=batch_size, shuffle=False)
#dataset_loader = torch.utils.data.DataLoader(dataset,batch_size=4, shuffle=True,num_workers=2)
#torch.backends.cudnn.benchmark=True
optimizer = SGD(model.parameters(), lr)
criterion = torch.nn.BCELoss(size_average=False)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[epochs_num * 0.3, epochs_num * 0.7],
gamma=0.1,
last_epoch=-1)
loss_record = []
count = 0
for epoch in range(epochs_num):
# training
running_loss = 0.0
training_accuracy = 0.0
training_num = 0
#scheduler.step()
model.train()
for i, data in enumerate(loader_train, 0):
#入力データ・ラベルに分割
# get the inputs
inputs, labels = data
# optimizerの初期化
# zero the parameter gradients
optimizer.zero_grad()
#一連の流れ
# forward + backward + optimize
outputs = model(inputs)
labels = labels.float()
#ここでラベルデータに対するCross-Entropyがとられる
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# ロスの表示
# print statistics
#running_loss += loss.data[0]
running_loss += loss.data.item() * 100
training_accuracy += np.sum(
np.abs((outputs.data - labels.data).numpy()) <= 0.5)
training_num += np.sum(
np.abs((outputs.data - labels.data).numpy()) != 10000)
#test
test_accuracy = 0.0
test_num = 0
model.eval()
for i, data in enumerate(loader_test, 0):
inputs, labels = data
outputs = model(inputs)
labels = labels.float()
#print("#######################")
#print(outputs)
#print(labels)
#print(output.t_(),label.t_())
#print(np.abs((output.data - label.data).numpy()))
test_accuracy += np.sum(
np.abs((outputs.data - labels.data).numpy()) <= 0.5)
test_num += np.sum(
np.abs((outputs.data - labels.data).numpy()) != 100000)
training_accuracy /= training_num
test_accuracy /= test_num
if ((epoch + 1) % 1 == 0):
print(
'%d loss: %.3f, training_accuracy: %.5f, test_accuracy: %.5f' %
(epoch + 1, running_loss, training_accuracy, test_accuracy))
#print(output)
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss,
}, 'jikkenpath')
#loss_record.append(running_loss)
else:
print(training_num)
print(test_num)
#print(loss_record)
if (1):
test_x, test_t = mkTestModelset(input_len)
test_x = torch.Tensor(test_x)
test_t = torch.Tensor(test_t)
dataset = TensorDataset(test_x, test_t)
loader_test = DataLoader(dataset, batch_size=5, shuffle=False)
test_accuracy = 0.0
test_num = 0
av_testac = 0
model.eval()
for i, data in enumerate(loader_test, 0):
inputs, labels = data
outputs = model(inputs)
labels = labels.float()
test_accuracy = 0.0
test_num = 0
test_accuracy += np.sum(
np.abs((outputs.data - labels.data).numpy()) <= 0.5)
test_num += np.sum(
np.abs((outputs.data - labels.data).numpy()) != 100000)
test_accuracy /= test_num
av_testac += test_accuracy
print(i, test_accuracy)
else:
print(av_testac / (i + 1))
torch.save(model.state_dict(), 'weight.pth')
def train(train_dir, model_dir, config_path, checkpoint_path,
n_steps, save_every, test_every, decay_every,
n_speakers, n_utterances, seg_len):
"""Train a d-vector network."""
# setup
total_steps = 0
# load data
dataset = SEDataset(train_dir, n_utterances, seg_len)
train_set, valid_set = random_split(dataset, [len(dataset)-2*n_speakers,
2*n_speakers])
train_loader = DataLoader(train_set, batch_size=n_speakers,
shuffle=True, num_workers=4,
collate_fn=pad_batch, drop_last=True)
valid_loader = DataLoader(valid_set, batch_size=n_speakers,
shuffle=True, num_workers=4,
collate_fn=pad_batch, drop_last=True)
train_iter = iter(train_loader)
assert len(train_set) >= n_speakers
assert len(valid_set) >= n_speakers
print(f"Training starts with {len(train_set)} speakers. "
f"(and {len(valid_set)} speakers for validation)")
# build network and training tools
dvector = DVector().load_config_file(config_path)
criterion = GE2ELoss()
optimizer = SGD(list(dvector.parameters()) +
list(criterion.parameters()), lr=0.01)
scheduler = StepLR(optimizer, step_size=decay_every, gamma=0.5)
# load checkpoint
if checkpoint_path is not None:
ckpt = torch.load(checkpoint_path)
total_steps = ckpt["total_steps"]
dvector.load_state_dict(ckpt["state_dict"])
criterion.load_state_dict(ckpt["criterion"])
optimizer.load_state_dict(ckpt["optimizer"])
scheduler.load_state_dict(ckpt["scheduler"])
# prepare for training
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dvector = dvector.to(device)
criterion = criterion.to(device)
writer = SummaryWriter(model_dir)
pbar = tqdm.trange(n_steps)
# start training
for step in pbar:
total_steps += 1
try:
batch = next(train_iter)
except StopIteration:
train_iter = iter(train_loader)
batch = next(train_iter)
embd = dvector(batch.to(device)).view(n_speakers, n_utterances, -1)
loss = criterion(embd)
optimizer.zero_grad()
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(
list(dvector.parameters()) + list(criterion.parameters()), max_norm=3)
dvector.embedding.weight.grad.data *= 0.5
criterion.w.grad.data *= 0.01
criterion.b.grad.data *= 0.01
optimizer.step()
scheduler.step()
pbar.set_description(f"global = {total_steps}, loss = {loss:.4f}")
writer.add_scalar("Training loss", loss, total_steps)
writer.add_scalar("Gradient norm", grad_norm, total_steps)
if (step + 1) % test_every == 0:
batch = next(iter(valid_loader))
embd = dvector(batch.to(device)).view(n_speakers, n_utterances, -1)
loss = criterion(embd)
writer.add_scalar("validation loss", loss, total_steps)
if (step + 1) % save_every == 0:
ckpt_path = os.path.join(model_dir, f"ckpt-{total_steps}.tar")
ckpt_dict = {
"total_steps": total_steps,
"state_dict": dvector.state_dict(),
"criterion": criterion.state_dict(),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
}
torch.save(ckpt_dict, ckpt_path)
print("Training completed.")
class LightHeadRCNN_Learner(Module):
def __init__(self, training=True):
super(LightHeadRCNN_Learner, self).__init__()
self.conf = Config()
self.class_2_color = get_class_colors(self.conf)
self.extractor = ResNet101Extractor(self.conf.pretrained_model_path).to(self.conf.device)
self.rpn = RegionProposalNetwork().to(self.conf.device)
# self.head = LightHeadRCNNResNet101_Head(self.conf.class_num + 1, self.conf.roi_size).to(self.conf.device)
self.loc_normalize_mean=(0., 0., 0., 0.),
self.loc_normalize_std=(0.1, 0.1, 0.2, 0.2)
self.head = LightHeadRCNNResNet101_Head(self.conf.class_num + 1,
self.conf.roi_size,
roi_align = self.conf.use_roi_align).to(self.conf.device)
self.class_2_color = get_class_colors(self.conf)
self.detections = namedtuple('detections', ['roi_cls_locs', 'roi_scores', 'rois'])
if training:
self.train_dataset = coco_dataset(self.conf, mode = 'train')
self.train_length = len(self.train_dataset)
self.val_dataset = coco_dataset(self.conf, mode = 'val')
self.val_length = len(self.val_dataset)
self.anchor_target_creator = AnchorTargetCreator()
self.proposal_target_creator = ProposalTargetCreator(loc_normalize_mean = self.loc_normalize_mean,
loc_normalize_std = self.loc_normalize_std)
self.step = 0
self.optimizer = SGD([
{'params' : get_trainables(self.extractor.parameters())},
{'params' : self.rpn.parameters()},
{'params' : [*self.head.parameters()][:8], 'lr' : self.conf.lr*3},
{'params' : [*self.head.parameters()][8:]},
], lr = self.conf.lr, momentum=self.conf.momentum, weight_decay=self.conf.weight_decay)
self.base_lrs = [params['lr'] for params in self.optimizer.param_groups]
self.warm_up_duration = 5000
self.warm_up_rate = 1 / 5
self.train_outputs = namedtuple('train_outputs',
['loss_total',
'rpn_loc_loss',
'rpn_cls_loss',
'ohem_roi_loc_loss',
'ohem_roi_cls_loss',
'total_roi_loc_loss',
'total_roi_cls_loss'])
self.writer = SummaryWriter(self.conf.log_path)
self.board_loss_every = self.train_length // self.conf.board_loss_interval
self.evaluate_every = self.train_length // self.conf.eval_interval
self.eva_on_coco_every = self.train_length // self.conf.eval_coco_interval
self.board_pred_image_every = self.train_length // self.conf.board_pred_image_interval
self.save_every = self.train_length // self.conf.save_interval
# only for debugging
# self.board_loss_every = 5
# self.evaluate_every = 6
# self.eva_on_coco_every = 7
# self.board_pred_image_every = 8
# self.save_every = 10
def set_training(self):
self.train()
self.extractor.set_bn_eval()
def lr_warmup(self):
assert self.step <= self.warm_up_duration, 'stop warm up after {} steps'.format(self.warm_up_duration)
rate = self.warm_up_rate + (1 - self.warm_up_rate) * self.step / self.warm_up_duration
for i, params in enumerate(self.optimizer.param_groups):
params['lr'] = self.base_lrs[i] * rate
def lr_schedule(self, epoch):
if epoch < 13:
return
elif epoch < 16:
rate = 0.1
else:
rate = 0.01
for i, params in enumerate(self.optimizer.param_groups):
params['lr'] = self.base_lrs[i] * rate
print(self.optimizer)
def forward(self, img_tensor, scale, bboxes=None, labels=None, force_eval=False):
img_tensor = img_tensor.to(self.conf.device)
img_size = (img_tensor.shape[2], img_tensor.shape[3]) # H,W
rpn_feature, roi_feature = self.extractor(img_tensor)
rpn_locs, rpn_scores, rois, roi_indices, anchor = self.rpn(rpn_feature, img_size, scale)
if self.training or force_eval:
gt_rpn_loc, gt_rpn_labels = self.anchor_target_creator(bboxes, anchor, img_size)
gt_rpn_labels = torch.tensor(gt_rpn_labels, dtype=torch.long).to(self.conf.device)
if len(bboxes) == 0:
rpn_cls_loss = F.cross_entropy(rpn_scores[0], gt_rpn_labels, ignore_index = -1)
return self.train_outputs(rpn_cls_loss, 0, 0, 0, 0, 0, 0)
sample_roi, gt_roi_locs, gt_roi_labels = self.proposal_target_creator(rois, bboxes, labels)
roi_cls_locs, roi_scores = self.head(roi_feature, sample_roi)
# roi_cls_locs, roi_scores, pool, h, rois = self.head(roi_feature, sample_roi)
gt_rpn_loc = torch.tensor(gt_rpn_loc, dtype=torch.float).to(self.conf.device)
gt_roi_locs = torch.tensor(gt_roi_locs, dtype=torch.float).to(self.conf.device)
gt_roi_labels = torch.tensor(gt_roi_labels, dtype=torch.long).to(self.conf.device)
rpn_loc_loss = fast_rcnn_loc_loss(rpn_locs[0], gt_rpn_loc, gt_rpn_labels, sigma=self.conf.rpn_sigma)
rpn_cls_loss = F.cross_entropy(rpn_scores[0], gt_rpn_labels, ignore_index = -1)
ohem_roi_loc_loss, \
ohem_roi_cls_loss, \
total_roi_loc_loss, \
total_roi_cls_loss = OHEM_loss(roi_cls_locs,
roi_scores,
gt_roi_locs,
gt_roi_labels,
self.conf.n_ohem_sample,
self.conf.roi_sigma)
loss_total = rpn_loc_loss + rpn_cls_loss + ohem_roi_loc_loss + ohem_roi_cls_loss
# if loss_total.item() > 1000.:
# print('ohem_roi_loc_loss : {}, ohem_roi_cls_loss : {}'.format(ohem_roi_loc_loss, ohem_roi_cls_loss))
# torch.save(pool, 'pool_debug.pth')
# torch.save(h, 'h_debug.pth')
# np.save('rois_debug', rois)
# torch.save(roi_cls_locs, 'roi_cls_locs_debug.pth')
# torch.save(roi_scores, 'roi_scores_debug.pth')
# torch.save(gt_roi_locs, 'gt_roi_locs_debug.pth')
# torch.save(gt_roi_labels, 'gt_roi_labels_debug.pth')
# pdb.set_trace()
return self.train_outputs(loss_total,
rpn_loc_loss.item(),
rpn_cls_loss.item(),
ohem_roi_loc_loss.item(),
ohem_roi_cls_loss.item(),
total_roi_loc_loss,
total_roi_cls_loss)
else:
roi_cls_locs, roi_scores = self.head(roi_feature, rois)
return self.detections(roi_cls_locs, roi_scores, rois)
def eval_predict(self, img, preset = 'evaluate', use_softnms = False):
if type(img) == list:
img = img[0]
img = Image.fromarray(img.transpose(1,2,0).astype('uint8'))
bboxes, labels, scores = self.predict_on_img(img, preset, use_softnms, original_size = True)
bboxes = y1x1y2x2_2_x1y1x2y2(bboxes)
return [bboxes], [labels], [scores]
def predict_on_img(self, img, preset = 'evaluate', use_softnms=False, return_img = False, with_scores = False, original_size = False):
'''
inputs :
imgs : PIL Image
return : PIL Image (if return_img) or bboxes_group and labels_group
'''
self.eval()
self.use_preset(preset)
with torch.no_grad():
orig_size = img.size # W,H
img = np.asarray(img).transpose(2,0,1)
img, scale = prepare_img(self.conf, img, -1)
img = torch.tensor(img).unsqueeze(0)
img_size = (img.shape[2], img.shape[3]) # H,W
detections = self.forward(img, scale)
n_sample = len(detections.roi_cls_locs)
n_class = self.conf.class_num + 1
roi_cls_locs = detections.roi_cls_locs.reshape((n_sample, -1, 4)).reshape([-1,4])
roi_cls_locs = roi_cls_locs * torch.tensor(self.loc_normalize_std, device=self.conf.device) + torch.tensor(self.loc_normalize_mean, device=self.conf.device)
rois = torch.tensor(detections.rois.repeat(n_class,0), dtype=torch.float).to(self.conf.device)
raw_cls_bboxes = loc2bbox(rois, roi_cls_locs)
torch.clamp(raw_cls_bboxes[:,0::2], 0, img_size[1], out = raw_cls_bboxes[:,0::2] )
torch.clamp(raw_cls_bboxes[:,1::2], 0, img_size[0], out = raw_cls_bboxes[:,1::2] )
raw_cls_bboxes = raw_cls_bboxes.reshape([n_sample, n_class, 4])
raw_prob = F.softmax(detections.roi_scores, dim=1)
bboxes, labels, scores = self._suppress(raw_cls_bboxes, raw_prob, use_softnms)
if len(bboxes) == len(labels) == len(scores) == 0:
if not return_img:
return [], [], []
else:
return to_img(self.conf, img[0])
_, indices = scores.sort(descending=True)
bboxes = bboxes[indices]
labels = labels[indices]
scores = scores[indices]
if len(bboxes) > self.max_n_predict:
bboxes = bboxes[:self.max_n_predict]
labels = labels[:self.max_n_predict]
scores = scores[:self.max_n_predict]
# now, implement drawing
bboxes = bboxes.cpu().numpy()
labels = labels.cpu().numpy()
scores = scores.cpu().numpy()
if original_size:
bboxes = adjust_bbox(scale, bboxes, detect=True)
if not return_img:
return bboxes, labels, scores
else:
if with_scores:
scores_ = scores
else:
scores_ = []
predicted_img = to_img(self.conf, img[0])
if original_size:
predicted_img = predicted_img.resize(orig_size)
if len(bboxes) != 0 and len(labels) != 0:
predicted_img = draw_bbox_class(self.conf,
predicted_img,
labels,
bboxes,
self.conf.correct_id_2_class,
self.class_2_color,
scores = scores_)
return predicted_img
def _suppress(self, raw_cls_bboxes, raw_prob, use_softnms):
bbox = []
label = []
prob = []
for l in range(1, self.conf.class_num + 1):
cls_bbox_l = raw_cls_bboxes[:, l, :]
prob_l = raw_prob[:, l]
mask = prob_l > self.score_thresh
if not mask.any():
continue
cls_bbox_l = cls_bbox_l[mask]
prob_l = prob_l[mask]
if use_softnms:
keep, _ = soft_nms(torch.cat((cls_bbox_l, prob_l.unsqueeze(-1)), dim=1).cpu().numpy(),
Nt = self.conf.softnms_Nt,
method = self.conf.softnms_method,
sigma = self.conf.softnms_sigma,
min_score = self.conf.softnms_min_score)
keep = keep.tolist()
else:
# prob_l, order = torch.sort(prob_l, descending=True)
# cls_bbox_l = cls_bbox_l[order]
keep = nms(torch.cat((cls_bbox_l, prob_l.unsqueeze(-1)), dim=1), self.nms_thresh).tolist()
bbox.append(cls_bbox_l[keep])
# The labels are in [0, 79].
label.append((l - 1) * torch.ones((len(keep),), dtype = torch.long))
prob.append(prob_l[keep])
if len(bbox) == 0:
print("looks like there is no prediction have a prob larger than thresh")
return [], [], []
bbox = torch.cat(bbox)
label = torch.cat(label)
prob = torch.cat(prob)
return bbox, label, prob
def board_scalars(self,
key,
loss_total,
rpn_loc_loss,
rpn_cls_loss,
ohem_roi_loc_loss,
ohem_roi_cls_loss,
total_roi_loc_loss,
total_roi_cls_loss):
self.writer.add_scalar('{}_loss_total'.format(key), loss_total, self.step)
self.writer.add_scalar('{}_rpn_loc_loss'.format(key), rpn_loc_loss, self.step)
self.writer.add_scalar('{}_rpn_cls_loss'.format(key), rpn_cls_loss, self.step)
self.writer.add_scalar('{}_ohem_roi_loc_loss'.format(key), ohem_roi_loc_loss, self.step)
self.writer.add_scalar('{}_ohem_roi_cls_loss'.format(key), ohem_roi_cls_loss, self.step)
self.writer.add_scalar('{}_total_roi_loc_loss'.format(key), total_roi_loc_loss, self.step)
self.writer.add_scalar('{}_total_roi_cls_loss'.format(key), total_roi_cls_loss, self.step)
def use_preset(self, preset):
"""Use the given preset during prediction.
This method changes values of :obj:`self.nms_thresh` and
:obj:`self.score_thresh`. These values are a threshold value
used for non maximum suppression and a threshold value
to discard low confidence proposals in :meth:`predict`,
respectively.
If the attributes need to be changed to something
other than the values provided in the presets, please modify
them by directly accessing the public attributes.
Args:
preset ({'visualize', 'evaluate', 'debug'): A string to determine the
preset to use.
"""
if preset == 'visualize':
self.nms_thresh = 0.5
self.score_thresh = 0.25
self.max_n_predict = 40
elif preset == 'evaluate':
self.nms_thresh = 0.5
self.score_thresh = 0.0
self.max_n_predict = 100
# """
# We finally replace origi-nal 0.3 threshold with 0.5 for Non-maximum Suppression
# (NMS). It improves 0.6 points of mmAP by improving the
# recall rate especially for the crowd cases.
# """
elif preset == 'debug':
self.nms_thresh = 0.5
self.score_thresh = 0.0
self.max_n_predict = 10
else:
raise ValueError('preset must be visualize or evaluate')
def fit(self, epochs=20, resume=False, from_save_folder=False):
if resume:
self.resume_training_load(from_save_folder)
self.set_training()
running_loss = 0.
running_rpn_loc_loss = 0.
running_rpn_cls_loss = 0.
running_ohem_roi_loc_loss = 0.
running_ohem_roi_cls_loss = 0.
running_total_roi_loc_loss = 0.
running_total_roi_cls_loss = 0.
map05 = None
val_loss = None
epoch = self.step // self.train_length
while epoch <= epochs:
print('start the training of epoch : {}'.format(epoch))
self.lr_schedule(epoch)
# for index in tqdm(np.random.permutation(self.train_length), total = self.train_length):
for index in tqdm(range(self.train_length), total = self.train_length):
try:
inputs = self.train_dataset[index]
except:
print('loading index {} from train dataset failed}'.format(index))
# print(self.train_dataset.orig_dataset._datasets[0].id_to_prop[self.train_dataset.orig_dataset._datasets[0].ids[index]])
continue
self.optimizer.zero_grad()
train_outputs = self.forward(torch.tensor(inputs.img).unsqueeze(0),
inputs.scale,
inputs.bboxes,
inputs.labels)
train_outputs.loss_total.backward()
if epoch == 0:
if self.step <= self.warm_up_duration:
self.lr_warmup()
self.optimizer.step()
torch.cuda.empty_cache()
running_loss += train_outputs.loss_total.item()
running_rpn_loc_loss += train_outputs.rpn_loc_loss
running_rpn_cls_loss += train_outputs.rpn_cls_loss
running_ohem_roi_loc_loss += train_outputs.ohem_roi_loc_loss
running_ohem_roi_cls_loss += train_outputs.ohem_roi_cls_loss
running_total_roi_loc_loss += train_outputs.total_roi_loc_loss
running_total_roi_cls_loss += train_outputs.total_roi_cls_loss
if self.step != 0:
if self.step % self.board_loss_every == 0:
self.board_scalars('train',
running_loss / self.board_loss_every,
running_rpn_loc_loss / self.board_loss_every,
running_rpn_cls_loss / self.board_loss_every,
running_ohem_roi_loc_loss / self.board_loss_every,
running_ohem_roi_cls_loss / self.board_loss_every,
running_total_roi_loc_loss / self.board_loss_every,
running_total_roi_cls_loss / self.board_loss_every)
running_loss = 0.
running_rpn_loc_loss = 0.
running_rpn_cls_loss = 0.
running_ohem_roi_loc_loss = 0.
running_ohem_roi_cls_loss = 0.
running_total_roi_loc_loss = 0.
running_total_roi_cls_loss = 0.
if self.step % self.evaluate_every == 0:
val_loss, val_rpn_loc_loss, \
val_rpn_cls_loss, \
ohem_val_roi_loc_loss, \
ohem_val_roi_cls_loss, \
total_val_roi_loc_loss, \
total_val_roi_cls_loss = self.evaluate(num = self.conf.eva_num_during_training)
self.set_training()
self.board_scalars('val',
val_loss,
val_rpn_loc_loss,
val_rpn_cls_loss,
ohem_val_roi_loc_loss,
ohem_val_roi_cls_loss,
total_val_roi_loc_loss,
total_val_roi_cls_loss)
if self.step % self.eva_on_coco_every == 0:
try:
cocoEval = self.eva_on_coco(limit = self.conf.coco_eva_num_during_training)
self.set_training()
map05 = cocoEval[1]
mmap = cocoEval[0]
except:
print('eval on coco failed')
map05 = -1
mmap = -1
self.writer.add_scalar('0.5IoU MAP', map05, self.step)
self.writer.add_scalar('0.5::0.9 - MMAP', mmap, self.step)
if self.step % self.board_pred_image_every == 0:
for i in range(20):
img, _, _, _ , _= self.val_dataset.orig_dataset[i]
img = Image.fromarray(img.astype('uint8').transpose(1,2,0))
predicted_img = self.predict_on_img(img, preset='visualize', return_img=True, with_scores=True, original_size=True)
# if type(predicted_img) == tuple:
# self.writer.add_image('pred_image_{}'.format(i), trans.ToTensor()(img), global_step=self.step)
# else: ## should be deleted after test
self.writer.add_image('pred_image_{}'.format(i), trans.ToTensor()(predicted_img), global_step=self.step)
self.set_training()
if self.step % self.save_every == 0:
try:
self.save_state(val_loss, map05)
except:
print('save state failed')
self.step += 1
continue
self.step += 1
epoch = self.step // self.train_length
try:
self.save_state(val_loss, map05, to_save_folder=True)
except:
print('save state failed')
def eva_on_coco(self, limit = 1000, preset = 'evaluate', use_softnms = False):
self.eval()
return eva_coco(self.val_dataset.orig_dataset, lambda x : self.eval_predict(x, preset, use_softnms), limit, preset)
def evaluate(self, num=None):
self.eval()
running_loss = 0.
running_rpn_loc_loss = 0.
running_rpn_cls_loss = 0.
running_ohem_roi_loc_loss = 0.
running_ohem_roi_cls_loss = 0.
running_total_roi_loc_loss = 0.
running_total_roi_cls_loss = 0.
if num == None:
total_num = self.val_length
else:
total_num = num
with torch.no_grad():
for index in tqdm(range(total_num)):
inputs = self.val_dataset[index]
if inputs.bboxes == []:
continue
val_outputs = self.forward(torch.tensor(inputs.img).unsqueeze(0),
inputs.scale,
inputs.bboxes,
inputs.labels,
force_eval = True)
running_loss += val_outputs.loss_total.item()
running_rpn_loc_loss += val_outputs.rpn_loc_loss
running_rpn_cls_loss += val_outputs.rpn_cls_loss
running_ohem_roi_loc_loss += val_outputs.ohem_roi_loc_loss
running_ohem_roi_cls_loss += val_outputs.ohem_roi_cls_loss
running_total_roi_loc_loss += val_outputs.total_roi_loc_loss
running_total_roi_cls_loss += val_outputs.total_roi_cls_loss
return running_loss / total_num, \
running_rpn_loc_loss / total_num, \
running_rpn_cls_loss / total_num, \
running_ohem_roi_loc_loss / total_num, \
running_ohem_roi_cls_loss / total_num,\
running_total_roi_loc_loss / total_num, \
running_total_roi_cls_loss / total_num
def save_state(self, val_loss, map05, to_save_folder=False, model_only=False):
if to_save_folder:
save_path = self.conf.work_space/'save'
else:
save_path = self.conf.work_space/'model'
time = get_time()
torch.save(
self.state_dict(), save_path /
('model_{}_val_loss:{}_map05:{}_step:{}.pth'.format(time,
val_loss,
map05,
self.step)))
if not model_only:
torch.save(
self.optimizer.state_dict(), save_path /
('optimizer_{}_val_loss:{}_map05:{}_step:{}.pth'.format(time,
val_loss,
map05,
self.step)))
def load_state(self, fixed_str, from_save_folder=False, model_only=False):
if from_save_folder:
save_path = self.conf.work_space/'save'
else:
save_path = self.conf.work_space/'model'
self.load_state_dict(torch.load(save_path/'model_{}'.format(fixed_str)))
print('load model_{}'.format(fixed_str))
if not model_only:
self.optimizer.load_state_dict(torch.load(save_path/'optimizer_{}'.format(fixed_str)))
print('load optimizer_{}'.format(fixed_str))
def resume_training_load(self, from_save_folder=False):
if from_save_folder:
save_path = self.conf.work_space/'save'
else:
save_path = self.conf.work_space/'model'
sorted_files = sorted([*save_path.iterdir()], key=lambda x: os.path.getmtime(x), reverse=True)
seeking_flag = True
index = 0
while seeking_flag:
if index > len(sorted_files) - 2:
break
file_a = sorted_files[index]
file_b = sorted_files[index + 1]
if file_a.name.startswith('model'):
fix_str = file_a.name[6:]
self.step = int(fix_str.split(':')[-1].split('.')[0]) + 1
if file_b.name == ''.join(['optimizer', '_', fix_str]):
self.load_state(fix_str, from_save_folder)
return
else:
index += 1
continue
elif file_a.name.startswith('optimizer'):
fix_str = file_a.name[10:]
self.step = int(fix_str.split(':')[-1].split('.')[0]) + 1
if file_b.name == ''.join(['model', '_', fix_str]):
self.load_state(fix_str, from_save_folder)
return
else:
index += 1
continue
else:
index += 1
continue
print('no available files founded')
return
def train(model,
state,
path,
annotations,
val_path,
val_annotations,
resize,
max_size,
jitter,
batch_size,
iterations,
val_iterations,
mixed_precision,
lr,
warmup,
milestones,
gamma,
is_master=True,
world=1,
use_dali=True,
verbose=True,
metrics_url=None,
logdir=None):
'Train the model on the given dataset'
# Prepare model
nn_model = model
stride = model.stride
model = convert_fixedbn_model(model)
if torch.cuda.is_available():
model = model.cuda()
# Setup optimizer and schedule
optimizer = SGD(model.parameters(),
lr=lr,
weight_decay=0.0001,
momentum=0.9)
model, optimizer = amp.initialize(
model,
optimizer,
opt_level='O2' if mixed_precision else 'O0',
keep_batchnorm_fp32=True,
loss_scale=128.0,
verbosity=is_master)
if world > 1:
model = DistributedDataParallel(model)
model.train()
if 'optimizer' in state:
optimizer.load_state_dict(state['optimizer'])
def schedule(train_iter):
if warmup and train_iter <= warmup:
return 0.9 * train_iter / warmup + 0.1
return gamma**len([m for m in milestones if m <= train_iter])
scheduler = LambdaLR(optimizer.optimizer if mixed_precision else optimizer,
schedule)
# Prepare dataset
if verbose: print('Preparing dataset...')
data_iterator = (DaliDataIterator if use_dali else DataIterator)(
path,
jitter,
max_size,
batch_size,
stride,
world,
annotations,
training=True)
if verbose: print(data_iterator)
if verbose:
print(' device: {} {}'.format(
world, 'cpu' if not torch.cuda.is_available() else
'gpu' if world == 1 else 'gpus'))
print(' batch: {}, precision: {}'.format(
batch_size, 'mixed' if mixed_precision else 'full'))
print('Training model for {} iterations...'.format(iterations))
# Create TensorBoard writer
if logdir is not None:
from tensorboardX import SummaryWriter
if is_master and verbose:
print('Writing TensorBoard logs to: {}'.format(logdir))
writer = SummaryWriter(log_dir=logdir)
profiler = Profiler(['train', 'fw', 'bw'])
iteration = state.get('iteration', 0)
while iteration < iterations:
cls_losses, box_losses = [], []
for i, (data, target) in enumerate(data_iterator):
scheduler.step(iteration)
# Forward pass
profiler.start('fw')
optimizer.zero_grad()
cls_loss, box_loss = model([data, target])
del data
profiler.stop('fw')
# Backward pass
profiler.start('bw')
with amp.scale_loss(cls_loss + box_loss, optimizer) as scaled_loss:
scaled_loss.backward()
optimizer.step()
# Reduce all losses
cls_loss, box_loss = cls_loss.mean().clone(), box_loss.mean(
).clone()
if world > 1:
torch.distributed.all_reduce(cls_loss)
torch.distributed.all_reduce(box_loss)
cls_loss /= world
box_loss /= world
if is_master:
cls_losses.append(cls_loss)
box_losses.append(box_loss)
if is_master and not isfinite(cls_loss + box_loss):
raise RuntimeError('Loss is diverging!\n{}'.format(
'Try lowering the learning rate.'))
del cls_loss, box_loss
profiler.stop('bw')
iteration += 1
profiler.bump('train')
if is_master and (profiler.totals['train'] > 60
or iteration == iterations):
focal_loss = torch.stack(list(cls_losses)).mean().item()
box_loss = torch.stack(list(box_losses)).mean().item()
learning_rate = optimizer.param_groups[0]['lr']
if verbose:
msg = '[{:{len}}/{}]'.format(iteration,
iterations,
len=len(str(iterations)))
msg += ' focal loss: {:.3f}'.format(focal_loss)
msg += ', box loss: {:.3f}'.format(box_loss)
msg += ', {:.3f}s/{}-batch'.format(profiler.means['train'],
batch_size)
msg += ' (fw: {:.3f}s, bw: {:.3f}s)'.format(
profiler.means['fw'], profiler.means['bw'])
msg += ', {:.1f} im/s'.format(batch_size /
profiler.means['train'])
msg += ', lr: {:.2g}'.format(learning_rate)
print(msg, flush=True)
if logdir is not None:
writer.add_scalar('focal_loss', focal_loss, iteration)
writer.add_scalar('box_loss', box_loss, iteration)
writer.add_scalar('learning_rate', learning_rate,
iteration)
del box_loss, focal_loss
if metrics_url:
post_metrics(
metrics_url, {
'focal loss': mean(cls_losses),
'box loss': mean(box_losses),
'im_s': batch_size / profiler.means['train'],
'lr': learning_rate
})
# Save model weights
state.update({
'iteration': iteration,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
})
with ignore_sigint():
nn_model.save(state)
profiler.reset()
del cls_losses[:], box_losses[:]
if val_annotations and (iteration == iterations
or iteration % val_iterations == 0):
infer(model,
val_path,
None,
resize,
max_size,
batch_size,
annotations=val_annotations,
mixed_precision=mixed_precision,
is_master=is_master,
world=world,
use_dali=use_dali,
verbose=False)
model.train()
if iteration == iterations:
break
if logdir is not None:
writer.close()
def main():
global args, best_prec1
args = parser.parse_args()
model = create_model(args.arch,
args.pretrained,
args.finetune,
num_classes=args.num_classes)
# define loss function (criterion) and optimizer
criterion = CrossEntropyLoss().cuda()
optimizer = SGD(
filter(lambda p: p.requires_grad,
model.parameters()), # Only finetunable params
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
load_model_from_checkpoint(args, model, optimizer)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# data loading
train_path = os.path.join(args.data, 'train')
test_path = os.path.join(args.data, 'test')
if os.path.exists(train_path):
train_loader = read_fer2013_data(train_path,
dataset_type='train',
batch_size=args.batch_size,
num_workers=args.workers)
if os.path.exists(test_path):
test_loader = read_fer2013_data(test_path,
dataset_type='test',
batch_size=args.batch_size,
num_workers=args.workers)
if args.evaluate:
test(test_loader, model, criterion, args.print_freq)
return
summary_writer = SummaryWriter()
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(args.lr, optimizer, epoch, args.lr_decay,
args.lr_decay_freq)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch,
args.print_freq, summary_writer)
# evaluate on test set
prec1 = test(test_loader, model, criterion, args.print_freq)
# remember best prec@1 and save all checkpoints
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, is_best)
summary_writer.close()
def train_model(train_loader,
test_loader,
device,
lr,
epochs,
output_path,
valid_loader=False):
model = CNN().to(device)
optimizer = SGD(model.parameters(), lr=lr)
average_loss_train = []
average_loss_test = []
accuracy_train = []
accuracy_test = []
for epoch in range(epochs):
model.train()
correct_train, loss_train, _ = loop_dataset(model, train_loader,
device, optimizer)
print(
f'Epoch {epoch} : average train loss - {np.mean(loss_train)}, train accuracy - {correct_train}'
)
average_loss_train.append(np.mean(loss_train))
accuracy_train.append(correct_train)
model.eval()
correct_test, loss_test, _ = loop_dataset(model, test_loader, device)
print(
f'Epoch {epoch} : average test loss - {np.mean(loss_test)}, test accuracy - {correct_test}'
)
average_loss_test.append(np.mean(loss_test))
accuracy_test.append(correct_test)
model.eval()
for i in range(0, len(model.layers)):
model.layers[i].register_forward_hook(forward_hook)
if valid_loader:
correct_valid, _, output = loop_dataset(model, valid_loader, device)
print('\033[99m' + f'Accuracy on VALID test: {correct_valid}' +
'\033[0m')
checkpoint = {
'model': CNN(),
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(checkpoint, os.path.join(output_path, 'checkpoint.pth'))
plt.figure()
plt.plot(range(epochs), average_loss_train, lw=0.3, c='g')
plt.plot(range(epochs), average_loss_test, lw=0.3, c='r')
plt.legend(['train loss', 'test_loss'])
plt.xlabel('#Epoch')
plt.ylabel('Loss')
plt.savefig(jpath(output_path, 'loss.png'))
plt.figure()
plt.plot(range(epochs), accuracy_train, lw=0.3, c='g')
plt.plot(range(epochs), accuracy_test, lw=0.3, c='r')
plt.legend(['train_acc', 'test_acc'])
plt.xlabel('#Epoch')
plt.ylabel('Accuracy')
plt.savefig(jpath(output_path, 'accuracy.png'))
