def test_byol_ma_weight_update_callback(tmpdir):
a = nn.Linear(100, 10)
b = deepcopy(a)
a_original = deepcopy(a)
b_original = deepcopy(b)
# make sure a params and b params are the same
assert torch.equal(
next(iter(a.parameters()))[0],
next(iter(b.parameters()))[0])
# fake weight update
opt = torch.optim.SGD(a.parameters(), lr=0.1)
y = a(torch.randn(3, 100))
loss = y.sum()
loss.backward()
opt.step()
opt.zero_grad()
# make sure a did in fact update
assert not torch.equal(
next(iter(a_original.parameters()))[0],
next(iter(a.parameters()))[0])
# do update via callback
cb = BYOLMAWeightUpdate(0.8)
cb.update_weights(a, b)
assert not torch.equal(
next(iter(b_original.parameters()))[0],
next(iter(b.parameters()))[0])
def __init__(self,
num_classes,
learning_rate: float = 0.2,
weight_decay: float = 1.5e-6,
input_height: int = 32,
batch_size: int = 32,
num_workers: int = 0,
warmup_epochs: int = 10,
max_epochs: int = 1000,
**kwargs):
"""
Args:
datamodule: The datamodule
learning_rate: the learning rate
weight_decay: optimizer weight decay
input_height: image input height
batch_size: the batch size
num_workers: number of workers
warmup_epochs: num of epochs for scheduler warm up
max_epochs: max epochs for scheduler
"""
super().__init__()
self.save_hyperparameters()
self.online_network = SiameseArm()
self.target_network = deepcopy(self.online_network)
self.weight_callback = BYOLMAWeightUpdate()
def __init__(self,
lr: float = 0.2,
weight_decay: float = 15e-6,
input_height: int = 32,
batch_size: int = 32,
num_workers: int = 0,
warmup_epochs: int = 10,
max_epochs: int = 1000,
**kwargs):
super().__init__()
self.save_hyperparameters()
self.online_network = SiameseArm()
self.target_network = deepcopy(self.online_network)
self.weight_callback = BYOLMAWeightUpdate()
def __init__(self,
num_classes,
learning_rate: float = 0.2,
weight_decay: float = 1.5e-6,
input_height: int = 32,
batch_size: int = 32,
num_workers: int = 0,
warmup_epochs: int = 10,
max_epochs: int = 1000,
**kwargs):
"""
PyTorch Lightning implementation of `Bootstrap Your Own Latent (BYOL)
<https://arxiv.org/pdf/2006.07733.pdf>`_
Paper authors: Jean-Bastien Grill ,Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, \
Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, \
Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko.
Model implemented by:
- `Annika Brundyn <https://github.com/annikabrundyn>`_
.. warning:: Work in progress. This implementation is still being verified.
TODOs:
- verify on CIFAR-10
- verify on STL-10
- pre-train on imagenet
Example::
import pytorch_lightning as pl
from pl_bolts.models.self_supervised import BYOL
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.models.self_supervised.simclr.transforms import (
SimCLREvalDataTransform, SimCLRTrainDataTransform)
# model
model = BYOL(num_classes=10)
# data
dm = CIFAR10DataModule(num_workers=0)
dm.train_transforms = SimCLRTrainDataTransform(32)
dm.val_transforms = SimCLREvalDataTransform(32)
trainer = pl.Trainer()
trainer.fit(model, dm)
Train::
trainer = Trainer()
trainer.fit(model)
CLI command::
# cifar10
python byol_module.py --gpus 1
# imagenet
python byol_module.py
--gpus 8
--dataset imagenet2012
--data_dir /path/to/imagenet/
--meta_dir /path/to/folder/with/meta.bin/
--batch_size 32
Args:
datamodule: The datamodule
learning_rate: the learning rate
weight_decay: optimizer weight decay
input_height: image input height
batch_size: the batch size
num_workers: number of workers
warmup_epochs: num of epochs for scheduler warm up
max_epochs: max epochs for scheduler
"""
super().__init__()
self.save_hyperparameters()
self.online_network = SiameseArm()
self.target_network = deepcopy(self.online_network)
self.weight_callback = BYOLMAWeightUpdate()
class BYOL(pl.LightningModule):
def __init__(self,
num_classes,
learning_rate: float = 0.2,
weight_decay: float = 1.5e-6,
input_height: int = 32,
batch_size: int = 32,
num_workers: int = 0,
warmup_epochs: int = 10,
max_epochs: int = 1000,
**kwargs):
"""
PyTorch Lightning implementation of `Bootstrap Your Own Latent (BYOL)
<https://arxiv.org/pdf/2006.07733.pdf>`_
Paper authors: Jean-Bastien Grill ,Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, \
Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, \
Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko.
Model implemented by:
- `Annika Brundyn <https://github.com/annikabrundyn>`_
.. warning:: Work in progress. This implementation is still being verified.
TODOs:
- verify on CIFAR-10
- verify on STL-10
- pre-train on imagenet
Example::
import pytorch_lightning as pl
from pl_bolts.models.self_supervised import BYOL
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.models.self_supervised.simclr.transforms import (
SimCLREvalDataTransform, SimCLRTrainDataTransform)
# model
model = BYOL(num_classes=10)
# data
dm = CIFAR10DataModule(num_workers=0)
dm.train_transforms = SimCLRTrainDataTransform(32)
dm.val_transforms = SimCLREvalDataTransform(32)
trainer = pl.Trainer()
trainer.fit(model, dm)
Train::
trainer = Trainer()
trainer.fit(model)
CLI command::
# cifar10
python byol_module.py --gpus 1
# imagenet
python byol_module.py
--gpus 8
--dataset imagenet2012
--data_dir /path/to/imagenet/
--meta_dir /path/to/folder/with/meta.bin/
--batch_size 32
Args:
datamodule: The datamodule
learning_rate: the learning rate
weight_decay: optimizer weight decay
input_height: image input height
batch_size: the batch size
num_workers: number of workers
warmup_epochs: num of epochs for scheduler warm up
max_epochs: max epochs for scheduler
"""
super().__init__()
self.save_hyperparameters()
self.online_network = SiameseArm()
self.target_network = deepcopy(self.online_network)
self.weight_callback = BYOLMAWeightUpdate()
def on_train_batch_end(self, batch: Any, batch_idx: int,
dataloader_idx: int) -> None:
# Add callback for user automatically since it's key to BYOL weight update
self.weight_callback.on_train_batch_end(self.trainer, self, batch,
batch_idx, dataloader_idx)
def forward(self, x):
y, _, _ = self.online_network(x)
return y
def cosine_similarity(self, a, b):
a = F.normalize(a, dim=-1)
b = F.normalize(b, dim=-1)
sim = (a * b).sum(-1).mean()
return sim
def shared_step(self, batch, batch_idx):
(img_1, img_2), y = batch
# Image 1 to image 2 loss
y1, z1, h1 = self.online_network(img_1)
with torch.no_grad():
y2, z2, h2 = self.target_network(img_2)
loss_a = -2 * self.cosine_similarity(h1, z2)
# Image 2 to image 1 loss
y1, z1, h1 = self.online_network(img_2)
with torch.no_grad():
y2, z2, h2 = self.target_network(img_1)
# L2 normalize
loss_b = -2 * self.cosine_similarity(h1, z2)
# Final loss
total_loss = loss_a + loss_b
return loss_a, loss_b, total_loss
def training_step(self, batch, batch_idx):
loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)
# log results
result = pl.TrainResult(minimize=total_loss)
result.log_dict({
'1_2_loss': loss_a,
'2_1_loss': loss_b,
'train_loss': total_loss
})
return result
def validation_step(self, batch, batch_idx):
loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)
# log results
result = pl.EvalResult(early_stop_on=total_loss,
checkpoint_on=total_loss)
result.log_dict({
'1_2_loss': loss_a,
'2_1_loss': loss_b,
'train_loss': total_loss
})
return result
def configure_optimizers(self):
optimizer = Adam(self.parameters(),
lr=self.hparams.learning_rate,
weight_decay=self.hparams.weight_decay)
optimizer = LARSWrapper(optimizer)
scheduler = LinearWarmupCosineAnnealingLR(
optimizer,
warmup_epochs=self.hparams.warmup_epochs,
max_epochs=self.hparams.max_epochs)
return [optimizer], [scheduler]
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
parser.add_argument('--online_ft',
action='store_true',
help='run online finetuner')
parser.add_argument('--dataset',
type=str,
default='cifar10',
help='cifar10, imagenet2012, stl10')
(args, _) = parser.parse_known_args()
# Data
parser.add_argument('--data_dir', type=str, default='.')
parser.add_argument('--num_workers', default=0, type=int)
# optim
parser.add_argument('--batch_size', type=int, default=256)
parser.add_argument('--learning_rate', type=float, default=1e-3)
parser.add_argument('--weight_decay', type=float, default=1.5e-6)
parser.add_argument('--warmup_epochs', type=float, default=10)
# Model
parser.add_argument('--meta_dir',
default='.',
type=str,
help='path to meta.bin for imagenet')
return parser
class BYOL(pl.LightningModule):
def __init__(self,
lr: float = 0.2,
weight_decay: float = 15e-6,
input_height: int = 32,
batch_size: int = 32,
num_workers: int = 0,
warmup_epochs: int = 10,
max_epochs: int = 1000,
**kwargs):
super().__init__()
self.save_hyperparameters()
self.online_network = SiameseArm()
self.target_network = deepcopy(self.online_network)
self.weight_callback = BYOLMAWeightUpdate()
def on_train_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
# Add callback for user automatically since it's key to BYOL weight update
self.weight_callback.on_train_batch_end(self.trainer, self, batch, batch_idx, dataloader_idx)
def forward(self, x):
y, _, _ = self.online_network(x)
return y
def shared_step(self, batch, batch_idx):
img_1, img_2 = batch
# Image 1 to image 2 loss
y1, z1, h1 = self.online_network(img_1)
with torch.no_grad():
y2, z2, h2 = self.target_network(img_2)
# L2 normalize
h1_norm = F.normalize(h1, p=2, dim=1)
z2_norm = F.normalize(z2, p=2, dim=1)
loss_a = F.mse_loss(h1_norm, z2_norm)
# Image 2 to image 1 loss
y1, z1, h1 = self.online_network(img_2)
with torch.no_grad():
y2, z2, h2 = self.target_network(img_1)
# L2 normalize
h1_norm = F.normalize(h1, p=2, dim=1)
z2_norm = F.normalize(z2, p=2, dim=1)
loss_b = F.mse_loss(h1_norm, z2_norm)
# Final loss
total_loss = loss_a + loss_b
return loss_a, loss_b, total_loss
def training_step(self, batch, batch_idx):
loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)
# log results
result = pl.TrainResult(minimize=total_loss)
# result.log_dict({'1_2_loss': loss_a, '2_1_loss': loss_b, 'train_loss': total_loss})
result.log_dict({'train_loss': total_loss})
return result
def validation_step(self, batch, batch_idx):
loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)
# log results
result = pl.EvalResult(early_stop_on=total_loss, checkpoint_on=total_loss)
# result.log_dict({'1_2_loss': loss_a, '2_1_loss': loss_b, 'train_loss': total_loss})
result.log_dict({'valid_loss': total_loss})
return result
def configure_optimizers(self):
optimizer = Adam(self.parameters(), lr=self.hparams.lr, weight_decay=self.hparams.weight_decay)
optimizer = LARSWrapper(optimizer)
scheduler = LinearWarmupCosineAnnealingLR(
optimizer,
warmup_epochs=self.hparams.warmup_epochs,
max_epochs=self.hparams.max_epochs
)
return [optimizer], [scheduler]
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False, conflict_handler='resolve')
(args, _) = parser.parse_known_args()
# Data
parser.add_argument('--data_dir', type=str, default='.')
parser.add_argument('--num_workers', default=10, type=int)
# optim
parser.add_argument('--batch_size', type=int, default=256)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--weight_decay', type=float, default=15e-6)
parser.add_argument('--warmup_epochs', type=float, default=10)
# Model
parser.add_argument('--meta_dir', default='.', type=str)
# Custom
parser.add_argument('--exp', type=str, default='Pretrain')
parser.add_argument('--name', type=str, default='test')
parser.add_argument('--benchmark', type=bool, default=True)
parser.add_argument('--amp_level', type=str, default='O0')
return parser