first working transformer training!!

configs/data/upgrade_energy.yaml

@@ -13,10 +13,12 @@ input_cols:
   - pmt_dir_x
   - pmt_dir_y
   - pmt_dir_z
-target_cols: energy
+target_cols: 
+  - energy
 truth_table: truth
-max_token_count: 1024
+max_token_count: 16384
 num_workers: 16
+multi_processing_reading_service_num_workers: 4
 # data_dir: ${paths.data_dir}
 # batch_size: 256
 # # train_val_test_split: [879064,109884,109884] #[0.8, 0.1, 0.1]

lightning_no_config_test.py

@@ -0,0 +1,146 @@
+import torch
+import torch.nn.functional as F
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+from torch import nn, optim
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from torch.utils.data import random_split
+import pytorch_lightning as pl 
+import torchmetrics 
+from torchmetrics import Metric
+
+
+class MyAccuracy(Metric):
+    def __init__(self):
+        super().__init__()
+        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
+        self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
+
+    def update(self, preds, target):
+        preds = torch.argmax(preds, dim=1)
+        assert preds.shape == target.shape
+        self.correct += torch.sum(preds == target)
+        self.total += target.numel()
+
+    def compute(self):
+        return self.correct.float() / self.total.float()
+
+
+class NN(pl.LightningModule):
+    def __init__(self, input_size, num_classes):
+        super().__init__()
+        self.fc1 = nn.Linear(input_size, 50)
+        self.fc2 = nn.Linear(50, num_classes)
+        self.loss_fn = nn.CrossEntropyLoss()
+        self.accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
+        self.my_accuracy = MyAccuracy()
+        self.f1_score = torchmetrics.F1Score(task="multiclass", num_classes=num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+    def training_step(self, batch, batch_idx):
+        loss, scores, y = self._common_step(batch, batch_idx)
+        accuracy = self.my_accuracy(scores, y) 
+        f1_score = self.f1_score(scores, y)
+        self.log_dict({'train_loss': loss, 'train_accuracy': accuracy, 'train_f1_score': f1_score},
+                      on_step=False, on_epoch=True, prog_bar=True)
+        return {'loss': loss, "scores": scores, "y": y}
+
+    def validation_step(self, batch, batch_idx):
+        loss, scores, y = self._common_step(batch, batch_idx)
+        self.log('val_loss', loss)
+        return loss
+
+    def test_step(self, batch, batch_idx):
+        loss, scores, y = self._common_step(batch, batch_idx)
+        self.log('test_loss', loss)
+        return loss
+
+    def _common_step(self, batch, batch_idx):
+        x, y = batch 
+        x = x.reshape(x.size(0), -1)
+        scores = self.forward(x)
+        loss = self.loss_fn(scores, y)
+        return loss, scores, y
+
+    def predict_step(self, batch, batch_idx):
+        x, y = batch 
+        x = x.reshape(x.size(0), -1)
+        scores = self.forward(x)
+        preds = torch.argmax(scores, dim=1)
+        return preds
+
+    def configure_optimizers(self):
+        return optim.Adam(self.parameters(), lr=0.001)
+
+
+class MnistDataModule(pl.LightningDataModule):
+    def __init__(self, data_dir, batch_size, num_workers):
+        super().__init__()
+        self.data_dir = data_dir 
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+
+    def prepare_data(self):
+        datasets.MNIST(self.data_dir, train=True, download=True)
+        datasets.MNIST(self.data_dir, train=False, download=True)
+
+    def setup(self, stage):
+        entire_dataset = datasets.MNIST(
+            root=self.data_dir, 
+            train=True,
+            transform=transforms.ToTensor(),
+            download=False,
+        )
+        self.train_ds, self.val_ds = random_split(entire_dataset, [50000, 10000])
+        self.test_ds = datasets.MNIST(
+            root=self.data_dir,
+            train=False,
+            transform=transforms.ToTensor(),
+            download=False,
+        )
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_ds,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=True,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.val_ds,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=False,
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            self.test_ds,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=False,
+        )
+
+# Set device cuda for GPU if it's available otherwise run on the CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Hyperparameters
+input_size = 784
+num_classes = 10
+learning_rate = 0.001
+batch_size = 64
+num_epochs = 3
+
+model = NN(input_size=input_size, num_classes=num_classes)
+dm = MnistDataModule(data_dir="dataset/", batch_size=batch_size, num_workers=4)
+trainer = pl.Trainer(accelerator="gpu", devices=1, min_epochs=1, max_epochs=3, precision=16)
+trainer.fit(model, dm)
+trainer.validate(model, dm)
+trainer.test(model, dm)

src/data/datapipe_icecube_datamodule.py

@@ -4,7 +4,7 @@
 import pandas as pd
 import torch
 import torch.nn as nn
-from pytorch_lightning import LightningDataModule
+from lightning import LightningDataModule
 from torch.utils.data import ConcatDataset, DataLoader, Dataset, SequentialSampler #random_split
 from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence
 from torch.utils.data import Dataset, DataLoader, random_split
@@ -269,7 +269,8 @@ def __init__(
         target_cols: List[str],
         truth_table: str = "truth",
         max_token_count: int = 64,
-        num_workers: int = 0,
+        num_workers: int = 16,
+        multi_processing_reading_service_num_workers: int = 4,
         pin_memory: bool = False,
     ):
         super().__init__()
@@ -280,18 +281,13 @@ def __init__(
 
         # data transformations here if any
 
-
-        # self.event_no_list = np.genfromtxt(self.hparams.event_no_list_path,dtype=int)
-
         self.datapipe_train: Optional[IterDataPipe] = None
         self.datapipe_val: Optional[IterDataPipe] = None
         self.datapipe_test: Optional[IterDataPipe] = None
 
-        self.rs = MultiProcessingReadingService(num_workers=2)
-
-    # @property
-    # def num_classes(self):
-    #     return 10
+        self.rs = MultiProcessingReadingService(
+            num_workers = self.hparams.multi_processing_reading_service_num_workers
+            )
 
     def prepare_data(self):
         """Download data if needed.
@@ -306,56 +302,44 @@ def setup(self, stage: Optional[str] = None):
         This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
         careful not to execute things like random split twice!
         """
+
         # sampler = SequentialSampler()
         if not self.datapipe_train and not self.datapipe_val and not self.datapipe_test:
             self.datapipe_train, self.datapipe_val, self.datapipe_test = make_train_test_val_datapipe(
-                self.hparams.train_csv_file,
-                self.hparams.test_csv_file,
-                self.hparams.val_csv_file,
-                self.hparams.db_path, 
-                self.hparams.input_cols, 
-                self.hparams.pulsemap, 
-                self.hparams.target_cols, 
-                self.hparams.truth_table, 
-                self.hparams.max_token_count,
-                upgrade_feature_transform,
-                truth_transform = None
+                train_csv_file = self.hparams.train_csv_file,
+                test_csv_file = self.hparams.test_csv_file,
+                val_csv_file = self.hparams.val_csv_file,
+                db_path = self.hparams.db_path, 
+                input_cols = self.hparams.input_cols, 
+                pulsemap = self.hparams.pulsemap, 
+                target_cols = self.hparams.target_cols, 
+                truth_table = self.hparams.truth_table, 
+                max_token_count = self.hparams.max_token_count,
+                feature_transform = upgrade_feature_transform,
+                truth_transform = None,
             )
 
     def train_dataloader(self):
         self.icecube_train_dataloader = DataLoader2(
-            self.datapipe_train,
-            # self.rs
+            datapipe = self.datapipe_train,
+            reading_service = self.rs,
         )
-        print()
-        for i, batch in enumerate(self.datapipe_train):
-
-            xx, y, pad, = batch
-            print(sum([len(x) for x in xx]))
-            print(len(y))
-            print()
-            if i == 2:
-                break
-
-        print()
-        print()
-        print(iter(self.icecube_train_dataloader))
-        print()
         return self.icecube_train_dataloader
 
     def val_dataloader(self):
         self.icecube_val_dataloader = DataLoader2( 
-            self.datapipe_val
+            datapipe = self.datapipe_val,
+            reading_service = self.rs,
             )
         return self.icecube_val_dataloader
 
     def test_dataloader(self):
         self.icecube_test_dataloader = DataLoader2( 
-            self.datapipe_val
+            datapipe = self.datapipe_val,
+            reading_service = self.rs,
             )
         return self.icecube_test_dataloader
 
-
     def teardown(self, stage: Optional[str] = None):
         """Clean up after fit or test."""