Revision of BYOL module and tests

pl_bolts/models/self_supervised/byol/byol_module.py

@@ -4,36 +4,39 @@
 
 import torch
 from pytorch_lightning import LightningModule, Trainer, seed_everything
+from torch import Tensor
 from torch.nn import functional as F
 from torch.optim import Adam
 
 from pl_bolts.callbacks.byol_updates import BYOLMAWeightUpdate
-from pl_bolts.models.self_supervised.byol.models import SiameseArm
+from pl_bolts.models.self_supervised.byol.models import MLP, SiameseArm
 from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
-from pl_bolts.utils.stability import under_review
 
 
-@under_review()
 class BYOL(LightningModule):
     """PyTorch Lightning implementation of Bootstrap Your Own Latent (BYOL_)_
 
     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.
 
+    Args:
+        learning_rate (float, optional): optimizer learning rate. Defaults to 0.2.
+        weight_decay (float, optional): optimizer weight decay. Defaults to 1.5e-6.
+        warmup_epochs (int, optional): number of epochs for scheduler warmup. Defaults to 10.
+        max_epochs (int, optional): maximum number of epochs for scheduler. Defaults to 1000.
+        base_encoder (Union[str, torch.nn.Module], optional): base encoder architecture. Defaults to "resnet50".
+        encoder_out_dim (int, optional): base encoder output dimension. Defaults to 2048.
+        projector_hidden_dim (int, optional): projector MLP hidden dimension. Defaults to 4096.
+        projector_out_dim (int, optional): projector MLP output dimension. Defaults to 256.
+        initial_tau (float, optional): initial value of target decay rate used. Defaults to 0.996.
+
     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::
 
-        model = BYOL(num_classes=10)
+        model = BYOL()
 
         dm = CIFAR10DataModule(num_workers=0)
         dm.train_transforms = SimCLRTrainDataTransform(32)
@@ -42,11 +45,6 @@ class BYOL(LightningModule):
         trainer = pl.Trainer()
         trainer.fit(model, datamodule=dm)
 
-    Train::
-
-        trainer = Trainer()
-        trainer.fit(model)
-
     CLI command::
 
         # cifar10
@@ -56,7 +54,6 @@ class BYOL(LightningModule):
         python byol_module.py
             --gpus 8
             --dataset imagenet2012
-            --data_dir /path/to/imagenet/
             --meta_dir /path/to/folder/with/meta.bin/
             --batch_size 32
 
@@ -65,87 +62,82 @@ class BYOL(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,
         base_encoder: Union[str, torch.nn.Module] = "resnet50",
         encoder_out_dim: int = 2048,
-        projector_hidden_size: int = 4096,
+        projector_hidden_dim: int = 4096,
         projector_out_dim: int = 256,
-        **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
-            base_encoder: the base encoder module or resnet name
-            encoder_out_dim: output dimension of base_encoder
-            projector_hidden_size: hidden layer size of projector MLP
-            projector_out_dim: output size of projector MLP
-        """
+        initial_tau: float = 0.996,
+        **kwargs: Any,
+    ) -> None:
+
         super().__init__()
         self.save_hyperparameters(ignore="base_encoder")
 
-        self.online_network = SiameseArm(base_encoder, encoder_out_dim, projector_hidden_size, projector_out_dim)
+        self.online_network = SiameseArm(base_encoder, encoder_out_dim, projector_hidden_dim, projector_out_dim)
         self.target_network = deepcopy(self.online_network)
-        self.weight_callback = BYOLMAWeightUpdate()
+        self.predictor = MLP(projector_out_dim, projector_hidden_dim, projector_out_dim)
 
-    def on_train_batch_end(self, outputs, batch: Any, batch_idx: int) -> None:
-        # Add callback for user automatically since it's key to BYOL weight update
+        self.weight_callback = BYOLMAWeightUpdate(initial_tau=initial_tau)
+
+    def on_train_batch_end(self, outputs: Any, batch: Any, batch_idx: int) -> None:
+        """Add callback to perform exponential moving average weight update on target network."""
         self.weight_callback.on_train_batch_end(self.trainer, self, outputs, batch, batch_idx)
 
-    def forward(self, x):
-        y, _, _ = self.online_network(x)
-        return y
+    def forward(self, x: Tensor) -> Tensor:
+        """Returns the encoded representation of a view.
 
-    def shared_step(self, batch, batch_idx):
-        imgs, y = batch
-        img_1, img_2 = imgs[:2]
+        Args:
+            x (Tensor): sample to be encoded
+        """
+        return self.online_network.encode(x)
 
-        # 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 * F.cosine_similarity(h1, z2).mean()
+    def training_step(self, batch: Any, batch_idx: int) -> Tensor:
+        """Complete training loop."""
+        return self._shared_step(batch, batch_idx, "train")
 
-        # 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 * F.cosine_similarity(h1, z2).mean()
+    def validation_step(self, batch: Any, batch_idx: int) -> Tensor:
+        """Complete validation loop."""
+        return self._shared_step(batch, batch_idx, "val")
 
-        # Final loss
-        total_loss = loss_a + loss_b
+    def _shared_step(self, batch: Any, batch_idx: int, step: str) -> Tensor:
+        """Shared evaluation step for training and validation loop."""
+        imgs, _ = batch
+        img1, img2 = imgs[:2]
 
-        return loss_a, loss_b, total_loss
+        # Calculate similarity loss in each direction
+        loss_12 = self.calculate_loss(img1, img2)
+        loss_21 = self.calculate_loss(img2, img1)
 
-    def training_step(self, batch, batch_idx):
-        loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)
+        # Calculate total loss
+        total_loss = loss_12 + loss_21
 
-        # log results
-        self.log_dict({"1_2_loss": loss_a, "2_1_loss": loss_b, "train_loss": total_loss})
+        # Log losses
+        if step == "train":
+            self.log_dict({"train_loss_12": loss_12, "train_loss_21": loss_21, "train_loss": total_loss})
+        elif step == "val":
+            self.log_dict({"val_loss_12": loss_12, "val_loss_21": loss_21, "val_loss": total_loss})
+        else:
+            raise ValueError(f"Step '{step}' is invalid. Must be 'train' or 'val'.")
 
         return total_loss
 
-    def validation_step(self, batch, batch_idx):
-        loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)
+    def calculate_loss(self, v_online: Tensor, v_target: Tensor) -> Tensor:
+        """Calculates similarity loss between the online network prediction of target network projection.
 
-        # log results
-        self.log_dict({"1_2_loss": loss_a, "2_1_loss": loss_b, "val_loss": total_loss})
-
-        return total_loss
+        Args:
+            v_online (Tensor): Online network view
+            v_target (Tensor): Target network view
+        """
+        _, z1 = self.online_network(v_online)
+        h1 = self.predictor(z1)
+        with torch.no_grad():
+            _, z2 = self.target_network(v_target)
+        loss = -2 * F.cosine_similarity(h1, z2).mean()
+        return loss
 
     def configure_optimizers(self):
         optimizer = Adam(self.parameters(), lr=self.hparams.learning_rate, weight_decay=self.hparams.weight_decay)
@@ -155,30 +147,23 @@ def configure_optimizers(self):
         return [optimizer], [scheduler]
 
     @staticmethod
-    def add_model_specific_args(parent_parser):
+    def add_model_specific_args(parent_parser: ArgumentParser) -> ArgumentParser:
         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", choices=["cifar10", "imagenet2012", "stl10"])
-
-        (args, _) = parser.parse_known_args()
+        args = parser.parse_args([])
 
-        # Data
-        parser.add_argument("--data_dir", type=str, default=".")
-        parser.add_argument("--num_workers", default=8, type=int)
+        if "max_epochs" in args:
+            parser.set_defaults(max_epochs=1000)
+        else:
+            parser.add_argument("--max_epochs", type=int, default=1000)
 
-        # optim
-        parser.add_argument("--batch_size", type=int, default=256)
-        parser.add_argument("--learning_rate", type=float, default=1e-3)
+        parser.add_argument("--learning_rate", type=float, default=0.2)
         parser.add_argument("--weight_decay", type=float, default=1.5e-6)
-        parser.add_argument("--warmup_epochs", type=float, default=10)
-
-        # Model
+        parser.add_argument("--warmup_epochs", type=int, default=10)
         parser.add_argument("--meta_dir", default=".", type=str, help="path to meta.bin for imagenet")
 
         return parser
 
 
-@under_review()
 def cli_main():
     from pl_bolts.callbacks.ssl_online import SSLOnlineEvaluator
     from pl_bolts.datamodules import CIFAR10DataModule, ImagenetDataModule, STL10DataModule
@@ -188,23 +173,19 @@ def cli_main():
 
     parser = ArgumentParser()
 
-    # trainer args
     parser = Trainer.add_argparse_args(parser)
-
-    # model args
     parser = BYOL.add_model_specific_args(parser)
-    args = parser.parse_args()
+    parser = CIFAR10DataModule.add_dataset_specific_args(parser)
+    parser.add_argument("--dataset", type=str, default="cifar10", choices=["cifar10", "imagenet2012", "stl10"])
 
-    # pick data
-    dm = None
+    args = parser.parse_args()
 
-    # init default datamodule
+    # Initialize datamodule
     if args.dataset == "cifar10":
         dm = CIFAR10DataModule.from_argparse_args(args)
         dm.train_transforms = SimCLRTrainDataTransform(32)
         dm.val_transforms = SimCLREvalDataTransform(32)
         args.num_classes = dm.num_classes
-
     elif args.dataset == "stl10":
         dm = STL10DataModule.from_argparse_args(args)
         dm.train_dataloader = dm.train_dataloader_mixed
@@ -214,20 +195,24 @@ def cli_main():
         dm.train_transforms = SimCLRTrainDataTransform(h)
         dm.val_transforms = SimCLREvalDataTransform(h)
         args.num_classes = dm.num_classes
-
     elif args.dataset == "imagenet2012":
         dm = ImagenetDataModule.from_argparse_args(args, image_size=196)
         (c, h, w) = dm.dims
         dm.train_transforms = SimCLRTrainDataTransform(h)
         dm.val_transforms = SimCLREvalDataTransform(h)
         args.num_classes = dm.num_classes
+    else:
+        raise ValueError(
+            f"{args.dataset} is not a valid dataset. Dataset must be 'cifar10', 'stl10', or 'imagenet2012'."
+        )
 
-    model = BYOL(**args.__dict__)
+    # Initialize BYOL module
+    model = BYOL(**vars(args))
 
     # finetune in real-time
     online_eval = SSLOnlineEvaluator(dataset=args.dataset, z_dim=2048, num_classes=dm.num_classes)
 
-    trainer = Trainer.from_argparse_args(args, max_steps=300000, callbacks=[online_eval])
+    trainer = Trainer.from_argparse_args(args, callbacks=[online_eval])
 
     trainer.fit(model, datamodule=dm)