refactor: making the code actually work

Former-commit-id: 302f77ef109ca44eef8c4ce6c5b3f59ba2891884 [formerly 5ae3d84ea691440283f73b5f12e2c85b7d95b191]
Former-commit-id: 33cbfef93d9be0cefd526e1cdbd7c0435db4c613

src/data/__init__.py

@@ -1 +1 @@
-from .dataloader import SyntheticSphere
+from .dataloader import Spheres

src/data/dataloader.py

@@ -1,6 +1,6 @@
 import albumentations as A
 import pytorch_lightning as pl
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, Subset
 
 import wandb
 from utils import RandomPaste
@@ -8,7 +8,7 @@ from utils import RandomPaste
 from .dataset import LabeledDataset, SyntheticDataset
 
 
-class SyntheticSphere(pl.LightningDataModule):
+class Spheres(pl.LightningDataModule):
     def __init__(self):
         super().__init__()
 
@@ -25,7 +25,7 @@ class SyntheticSphere(pl.LightningDataModule):
         )
 
         dataset = SyntheticDataset(image_dir=wandb.config.DIR_TRAIN_IMG, transform=transform)
-        # ds_train = torch.utils.data.Subset(ds_train, list(range(0, len(ds_train), len(ds_train) // 10000)))
+        dataset = Subset(dataset, list(range(0, len(dataset), len(dataset) // 10000 + 1)))
 
         return DataLoader(
             dataset,
@@ -37,6 +37,7 @@ class SyntheticSphere(pl.LightningDataModule):
 
     def val_dataloader(self):
         dataset = LabeledDataset(image_dir=wandb.config.DIR_VALID_IMG)
+        dataset = Subset(dataset, list(range(0, len(dataset), len(dataset) // 100 + 1)))
 
         return DataLoader(
             dataset,

src/data/dataset.py

@@ -63,7 +63,7 @@ class LabeledDataset(Dataset):
         # convert image & mask to Tensor float in [0, 1]
         post_process = A.Compose(
             [
-                # A.SmallestMaxSize(1024),
+                A.SmallestMaxSize(1024),
                 A.ToFloat(max_value=255),
                 ToTensorV2(),
             ],

src/train.py

@@ -1,11 +1,12 @@
 import logging
 
 import pytorch_lightning as pl
-from pytorch_lightning.callbacks import RichProgressBar
+from pytorch_lightning.callbacks import ModelCheckpoint, RichProgressBar
 from pytorch_lightning.loggers import WandbLogger
 
 import wandb
-from unet import UNet
+from data import Spheres
+from unet import UNetModule
 
 CONFIG = {
     "DIR_TRAIN_IMG": "/home/lilian/data_disk/lfainsin/train/",
@@ -18,9 +19,9 @@ CONFIG = {
     "PIN_MEMORY": True,
     "BENCHMARK": True,
     "DEVICE": "gpu",
-    "WORKERS": 10,
+    "WORKERS": 14,
     "EPOCHS": 1,
-    "BATCH_SIZE": 32,
+    "BATCH_SIZE": 16 * 3,
     "LEARNING_RATE": 1e-4,
     "WEIGHT_DECAY": 1e-8,
     "MOMENTUM": 0.9,
@@ -45,7 +46,7 @@ if __name__ == "__main__":
     pl.seed_everything(69420, workers=True)
 
     # Create network
-    net = UNet(
+    model = UNetModule(
         n_channels=CONFIG["N_CHANNELS"],
         n_classes=CONFIG["N_CLASSES"],
         batch_size=CONFIG["BATCH_SIZE"],
@@ -54,27 +55,31 @@ if __name__ == "__main__":
     )
 
     # log gradients and weights regularly
-    logger.watch(net, log="all")
+    logger.watch(model, log="all")
 
     # create checkpoint callback
-    checkpoint_callback = pl.ModelCheckpoint(
+    checkpoint_callback = ModelCheckpoint(
         dirpath="checkpoints",
         monitor="val/dice",
     )
 
+    # Create the dataloaders
+    datamodule = Spheres()
+
     # Create the trainer
     trainer = pl.Trainer(
         max_epochs=CONFIG["EPOCHS"],
         accelerator=CONFIG["DEVICE"],
-        # precision=16,
         benchmark=CONFIG["BENCHMARK"],
-        val_check_interval=100,
+        # profiler="simple",
-        callbacks=RichProgressBar(),
+        # precision=16,
         logger=logger,
         log_every_n_steps=1,
+        val_check_interval=100,
+        callbacks=RichProgressBar(),
     )
 
-    trainer.fit(model=net)
+    trainer.fit(model=model, datamodule=datamodule)
 
     # stop wandb
     wandb.run.finish()

src/unet/model.py

@@ -29,8 +29,6 @@ class UNet(nn.Module):
     def forward(self, x):
         skips = []
 
-        x = x.to(self.device)
-
         x = self.inc(x)
 
         for down in self.downs:

src/unet/module.py

@@ -73,7 +73,7 @@ class UNetModule(pl.LightningModule):
             columns = ["ID", "image", "ground truth", "prediction", "dice", "dice_bin"]
             for i, (img, mask, pred, pred_bin) in enumerate(
                 zip(  # TODO: use comprehension list to zip the dictionnary
-                    tensors["images"].cpu(),
+                    tensors["data"].cpu(),
                     tensors["ground_truth"].cpu(),
                     tensors["prediction"].cpu(),
                     tensors["binary"]
@@ -121,7 +121,7 @@ class UNetModule(pl.LightningModule):
         if batch_idx % 50 == 0 or metrics["dice"] > 0.9:
             for i, (img, mask, pred, pred_bin) in enumerate(
                 zip(  # TODO: use comprehension list to zip the dictionnary
-                    tensors["images"].cpu(),
+                    tensors["data"].cpu(),
                     tensors["ground_truth"].cpu(),
                     tensors["prediction"].cpu(),
                     tensors["binary"]
@@ -150,11 +150,12 @@ class UNetModule(pl.LightningModule):
                     ]
                 )
 
-        return metrics
+        return metrics, rows
 
     def validation_epoch_end(self, validation_outputs):
         # unpacking
-        metricss, rowss = validation_outputs
+        metricss = [v[0] for v in validation_outputs]
+        rowss = [v[1] for v in validation_outputs]
 
         # metrics flattening
         metrics = {
@@ -162,7 +163,7 @@ class UNetModule(pl.LightningModule):
             "dice_bin": torch.stack([d["dice_bin"] for d in metricss]).mean(),
             "bce": torch.stack([d["bce"] for d in metricss]).mean(),
             "mae": torch.stack([d["mae"] for d in metricss]).mean(),
-            "accuracy": torch.stack([d["accuracy"] for d in validation_outputs]).mean(),
+            "accuracy": torch.stack([d["accuracy"] for d in metricss]).mean(),
         }
 
         # log metrics