New VQVAE

1 files changed, 46 insertions, 23 deletions

diff --git a/training/callbacks/wandb_callbacks.py b/training/callbacks/wandb_callbacks.py
index 906531f..c750e4b 100644
--- a/training/callbacks/wandb_callbacks.py
+++ b/training/callbacks/wandb_callbacks.py
@@ -5,6 +5,7 @@ import wandb
 from pytorch_lightning import Callback, LightningModule, Trainer
 from pytorch_lightning.loggers import LoggerCollection, WandbLogger
 from pytorch_lightning.utilities import rank_zero_only
+from torch.utils.data import DataLoader
 
 
 def get_wandb_logger(trainer: Trainer) -> WandbLogger:
@@ -86,7 +87,11 @@ class LogTextPredictions(Callback):
         self.ready = False
 
     def _log_predictions(
-        self, stage: str, trainer: Trainer, pl_module: LightningModule
+        self,
+        stage: str,
+        trainer: Trainer,
+        pl_module: LightningModule,
+        dataloader: DataLoader,
     ) -> None:
         """Logs the predicted text contained in the images."""
         if not self.ready:
@@ -96,22 +101,20 @@ class LogTextPredictions(Callback):
         experiment = logger.experiment
 
         # Get a validation batch from the validation dataloader.
-        samples = next(iter(trainer.datamodule.val_dataloader()))
+        samples = next(iter(dataloader))
         imgs, labels = samples
 
         imgs = imgs.to(device=pl_module.device)
         logits = pl_module(imgs)
 
         mapping = pl_module.mapping
-        columns = ["id", "image", "prediction", "truth"]
+        columns = ["image", "prediction", "truth"]
         data = [
-            [id, wandb.Image(img), mapping.get_text(pred), mapping.get_text(label)]
-            for id, (img, pred, label) in enumerate(
-                zip(
-                    imgs[: self.num_samples],
-                    logits[: self.num_samples],
-                    labels[: self.num_samples],
-                )
+            [wandb.Image(img), mapping.get_text(pred), mapping.get_text(label)]
+            for img, pred, label in zip(
+                imgs[: self.num_samples],
+                logits[: self.num_samples],
+                labels[: self.num_samples],
             )
         ]
 
@@ -133,11 +136,17 @@ class LogTextPredictions(Callback):
         self, trainer: Trainer, pl_module: LightningModule
     ) -> None:
         """Logs predictions on validation epoch end."""
-        self._log_predictions(stage="val", trainer=trainer, pl_module=pl_module)
+        dataloader = trainer.datamodule.val_dataloader()
+        self._log_predictions(
+            stage="val", trainer=trainer, pl_module=pl_module, dataloader=dataloader
+        )
 
     def on_test_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
         """Logs predictions on train epoch end."""
-        self._log_predictions(stage="test", trainer=trainer, pl_module=pl_module)
+        dataloader = trainer.datamodule.test_dataloader()
+        self._log_predictions(
+            stage="test", trainer=trainer, pl_module=pl_module, dataloader=dataloader
+        )
 
 
 class LogReconstuctedImages(Callback):
@@ -148,7 +157,11 @@ class LogReconstuctedImages(Callback):
         self.ready = False
 
     def _log_reconstruction(
-        self, stage: str, trainer: Trainer, pl_module: LightningModule
+        self,
+        stage: str,
+        trainer: Trainer,
+        pl_module: LightningModule,
+        dataloader: DataLoader,
     ) -> None:
         """Logs the reconstructions."""
         if not self.ready:
@@ -158,20 +171,24 @@ class LogReconstuctedImages(Callback):
         experiment = logger.experiment
 
         # Get a validation batch from the validation dataloader.
-        samples = next(iter(trainer.datamodule.val_dataloader()))
+        samples = next(iter(dataloader))
         imgs, _ = samples
 
+        colums = ["input", "reconstruction"]
         imgs = imgs.to(device=pl_module.device)
-        reconstructions = pl_module(imgs)
+        reconstructions = pl_module(imgs)[0]
+        data = [
+            [wandb.Image(img), wandb.Image(rec)]
+            for img, rec in zip(
+                imgs[: self.num_samples], reconstructions[: self.num_samples]
+            )
+        ]
 
         experiment.log(
             {
-                f"Reconstructions/{experiment.name}/{stage}": [
-                    [wandb.Image(img), wandb.Image(rec),]
-                    for img, rec in zip(
-                        imgs[: self.num_samples], reconstructions[: self.num_samples],
-                    )
-                ]
+                f"Reconstructions/{experiment.name}/{stage}": wandb.Table(
+                    data=data, columns=colums
+                )
             }
         )
 
@@ -189,8 +206,14 @@ class LogReconstuctedImages(Callback):
         self, trainer: Trainer, pl_module: LightningModule
     ) -> None:
         """Logs predictions on validation epoch end."""
-        self._log_reconstruction(stage="val", trainer=trainer, pl_module=pl_module)
+        dataloader = trainer.datamodule.val_dataloader()
+        self._log_reconstruction(
+            stage="val", trainer=trainer, pl_module=pl_module, dataloader=dataloader
+        )
 
     def on_test_epoch_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
         """Logs predictions on train epoch end."""
-        self._log_reconstruction(stage="test", trainer=trainer, pl_module=pl_module)
+        dataloader = trainer.datamodule.test_dataloader()
+        self._log_reconstruction(
+            stage="test", trainer=trainer, pl_module=pl_module, dataloader=dataloader
+        )