First functional training loop

.gitignore

@@ -162,3 +162,4 @@ cython_debug/
 #.idea/
 
 datasets/
+lightning_logs/

symbolic_nn_tests/__main__.py

@@ -1,5 +1,4 @@
-def main():
-    print("Working")
+from .ffnn import main
 
 
 if __name__ == "__main__":

symbolic_nn_tests/dataloader.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 from torchvision.datasets import Caltech256
-from torchvision.transforms import ToTensor
+from torchvision.transforms import Compose, Lambda, ToTensor
 from torch.utils.data import random_split
 from torch.utils.data import BatchSampler
 
@@ -15,7 +15,9 @@ def get_dataset(
     drop_last: bool = False,
     **kwargs,
 ):
-    _kwargs = {"transform": ToTensor()}
+    _kwargs = {
+        "transform": ToTensor(),
+    }
     _kwargs.update(kwargs)
     ds = dataset(PROJECT_ROOT / "datasets/", download=True, **_kwargs)
     train, test, val = (

symbolic_nn_tests/ffnn.py

@@ -1,9 +1,33 @@
-from .dataloader import get_dataset
+from torch import nn
+
+
+model = nn.Sequential(
+    nn.Flatten(1, -1),
+    nn.Linear(784, 128),
+    nn.ReLU(),
+    nn.Linear(128, 128),
+    nn.ReLU(),
+    nn.Linear(128, 128),
+    nn.ReLU(),
+    nn.Linear(128, 128),
+    nn.ReLU(),
+    nn.Linear(128, 64),
+    nn.ReLU(),
+    nn.Linear(64, 32),
+    nn.ReLU(),
+    nn.Linear(32, 10),
+    nn.Softmax(dim=1),
+)
 
 
 def main():
-    pass
+    from torchvision.datasets import QMNIST
+    import lightning as L
 
+    from .dataloader import get_dataset
+    from .trainer import Trainer
 
-if __name__ == "__main__":
-    main()
+    train, test, val = get_dataset(dataset=QMNIST)
+    training_model = Trainer(model)
+    trainer = L.Trainer(max_epochs=10)
+    trainer.fit(model=training_model, train_dataloaders=train, val_dataloaders=val)

symbolic_nn_tests/trainer.py

@@ -1,19 +1,37 @@
+import torch
 from torch import nn, optim
 import lightning as L
 
 
-class Trainer(L.LighningModule):
-    def __init__(self, model):
+def collate_batch(batch):
+    x, y = zip(*batch)
+    x = [i[0] for i in x]
+    y = [torch.tensor(i) for i in y]
+    x = torch.stack(x).to("cuda")
+    y = torch.tensor(y).to("cuda")
+    return x, y
+
+
+class Trainer(L.LightningModule):
+    def __init__(self, model, loss_func=nn.functional.cross_entropy):
         super().__init__()
         self.model = model
+        self.loss_func = loss_func
 
     def training_step(self, batch, batch_idx):
-        x, y = batch
+        x, y = collate_batch(batch)
         y_pred = self.model(x)
-        loss = nn.functional.mse_loss(y_pred, y)
+        loss = self.loss_func(y_pred, y)
         self.log("train_loss", loss)
         return loss
 
-    def configure_optimizers(self):
-        optimizer = optim.Adam(self.parameters(), lr=1e-3)
-        return optimizer
+    def validation_step(self, batch, batch_idx):
+        x, y = collate_batch(batch)
+        y_pred = self.model(x)
+        loss = self.loss_func(y_pred, y)
+        self.log("val_loss", loss)
+        return loss
+
+    def configure_optimizers(self, optimizer=optim.Adam, *args, **kwargs):
+        _optimizer = optimizer(self.parameters(), *args, **kwargs)
+        return _optimizer