fixed mnist downloading to temporary directory for surrogate testing

tests/test_surrogate.py

@@ -1,6 +1,8 @@
 import logging
 import os
 import random
+from functools import partial
+from pathlib import Path
 from typing import Dict, Generator, Tuple, Union
 
 import pytest
@@ -134,7 +136,7 @@ def configure_optimizers(self) -> torch.optim.SGD:
         return torch.optim.SGD(self.parameters(), lr=self.lr)
 
 
-def get_data_loaders(batch_size: int) -> Tuple[DataLoader, DataLoader]:
+def get_data_loaders(batch_size: int, root=Path) -> Tuple[DataLoader, DataLoader]:
     """
     Get MNIST train and validation dataloaders.
 
@@ -162,7 +164,7 @@ def get_data_loaders(batch_size: int) -> Tuple[DataLoader, DataLoader]:
     if MPI.COMM_WORLD.Get_rank() == 0:  # Only root downloads data.
         train_loader = DataLoader(
             dataset=MNIST(
-                download=True, root=".", transform=data_transform, train=True
+                download=True, root=root, transform=data_transform, train=True
             ),  # Use MNIST training dataset.
             batch_size=batch_size,  # Batch size
             shuffle=True,  # Shuffle data.
@@ -176,14 +178,14 @@ def get_data_loaders(batch_size: int) -> Tuple[DataLoader, DataLoader]:
     if MPI.COMM_WORLD.Get_rank() != 0:
         train_loader = DataLoader(
             dataset=MNIST(
-                download=False, root=".", transform=data_transform, train=True
+                download=False, root=root, transform=data_transform, train=True
             ),  # Use MNIST training dataset.
             batch_size=batch_size,  # Batch size
             shuffle=True,  # Shuffle data.
         )
     val_loader = DataLoader(
         dataset=MNIST(
-            download=False, root=".", transform=data_transform, train=False
+            download=False, root=root, transform=data_transform, train=False
         ),  # Use MNIST testing dataset.
         batch_size=1,  # Batch size
         shuffle=False,  # Do not shuffle data.
@@ -192,7 +194,7 @@ def get_data_loaders(batch_size: int) -> Tuple[DataLoader, DataLoader]:
 
 
 def ind_loss(
-    params: Dict[str, Union[int, float, str]],
+    params: Dict[str, Union[int, float, str]], root: Path
 ) -> Generator[float, None, None]:
     """
     Loss function for evolutionary optimization with Propulate. Minimize the model's negative validation accuracy.
@@ -243,7 +245,7 @@ def ind_loss(
     model.best_accuracy = 0.0  # Initialize the model's best validation accuracy.
 
     train_loader, val_loader = get_data_loaders(
-        batch_size=8
+        batch_size=8, root=root
     )  # Get training and validation data loaders.
 
     # Configure optimizer.
@@ -327,7 +329,7 @@ def test_mnist_static(mpi_tmp_path):
         rng=rng,  # Random number generator for evolutionary optimizer
     )
     islands = Islands(  # Set up island model.
-        loss_fn=ind_loss,  # Loss function to optimize
+        loss_fn=partial(ind_loss, root=mpi_tmp_path),  # Loss function to optimize
         propagator=propagator,  # Evolutionary operator
         rng=rng,  # Random number generator
         generations=num_generations,  # Number of generations per worker
@@ -365,7 +367,7 @@ def test_mnist_dynamic(mpi_tmp_path):
         rng=rng,  # Random number generator for evolutionary optimizer
     )
     islands = Islands(  # Set up island model.
-        loss_fn=ind_loss,  # Loss function to optimize
+        loss_fn=partial(ind_loss, root=mpi_tmp_path),  # Loss function to optimize
         propagator=propagator,  # Evolutionary operator
         rng=rng,  # Random number generator
         generations=num_generations,  # Number of generations per worker