Tweak datamodule configs to use torchvision dir

project/configs/datamodule/__init__.py

@@ -1,21 +1,9 @@
 from logging import getLogger as get_logger
-from pathlib import Path
 
 from hydra_zen import store
 
-from project.utils.env_vars import NETWORK_DIR
-
 logger = get_logger(__name__)
 
-torchvision_dir: Path | None = None
-"""Network directory with torchvision datasets."""
-if (
-    NETWORK_DIR
-    and (_torchvision_dir := NETWORK_DIR / "datasets/torchvision").exists()
-    and _torchvision_dir.is_dir()
-):
-    torchvision_dir = _torchvision_dir
-
 
 # TODO: Make it possible to extend a structured base via yaml files as well as adding new fields
 # (for example, ImagetNet32DataModule has a new constructor argument which can't be set atm in the

project/configs/datamodule/cifar10.yaml

@@ -1,6 +1,7 @@
 defaults:
 - vision
 _target_: project.datamodules.CIFAR10DataModule
+data_dir: ${constant:torchvision_dir,DATA_DIR}
 batch_size: 128
 train_transforms:
   _target_: project.datamodules.image_classification.cifar10.cifar10_train_transforms

project/configs/datamodule/mnist.yaml

@@ -1,6 +1,7 @@
 defaults:
 - vision
 _target_: project.datamodules.MNISTDataModule
+data_dir: ${constant:torchvision_dir,DATA_DIR}
 normalize: True
 batch_size: 128
 train_transforms:

project/configs/experiment/example.yaml

@@ -9,18 +9,14 @@ defaults:
   - override /network: resnet18
   - override /trainer: default
   - override /trainer/callbacks: default
+  - override /trainer/logger: wandb
 
 # all parameters below will be merged with parameters from default configurations set above
 # this allows you to overwrite only specified parameters
+name: example
 
 seed: ${oc.env:SLURM_PROCID,12345}
 
-trainer:
-  min_epochs: 1
-  max_epochs: 10
-  gradient_clip_val: 0.5
-
-
 algorithm:
   hp:
     optimizer:
@@ -29,4 +25,21 @@ algorithm:
 datamodule:
   batch_size: 64
 
-name: example
+
+trainer:
+  min_epochs: 1
+  max_epochs: 10
+  gradient_clip_val: 0.5
+  logger:
+    wandb:
+      project: "ResearchTemplate"
+      name: ${oc.env:SLURM_JOB_ID}_${oc.env:SLURM_PROCID}
+      save_dir: "${hydra:runtime.output_dir}"
+      offline: False # set True to store all logs only locally
+      id: ${oc.env:SLURM_JOB_ID}_${oc.env:SLURM_PROCID} # pass correct id to resume experiment!
+      # entity: ""  # set to name of your wandb team
+      log_model: False
+      prefix: ""
+      job_type: "train"
+      group: ${oc.env:SLURM_JOB_ID}
+      tags: ["${name}"]

project/utils/env_vars.py

@@ -1,8 +1,13 @@
+import importlib
 import os
+from logging import getLogger as get_logger
 from pathlib import Path
 
 import torch
 
+logger = get_logger(__name__)
+
+
 SLURM_JOB_ID: int | None = (
     int(os.environ["SLURM_JOB_ID"]) if "SLURM_JOB_ID" in os.environ else None
 )
@@ -69,9 +74,37 @@
 """Local Directory where datasets should be extracted on this machine."""
 
 
-def get_constant(name: str):
+torchvision_dir: Path | None = None
+"""Network directory with torchvision datasets."""
+if (
+    NETWORK_DIR
+    and (_torchvision_dir := NETWORK_DIR / "datasets/torchvision").exists()
+    and _torchvision_dir.is_dir()
+):
+    torchvision_dir = _torchvision_dir
+
+
+def get_constant(*names: str):
     """Resolver for Hydra to get the value of a constant in this file."""
-    return globals()[name]
+    assert names
+    for name in names:
+        if name in globals():
+            obj = globals()[name]
+            if obj is None:
+                logger.debug(f"Value of {name} is None, moving on to the next value.")
+                continue
+            return obj
+        parts = name.split(".")
+        obj = importlib.import_module(parts[0])
+        for part in parts[1:]:
+            obj = getattr(obj, part)
+        if obj is not None:
+            return obj
+        logger.debug(f"Value of {name} is None, moving on to the next value.")
+
+    if len(names) == 1:
+        raise RuntimeError(f"Could not find non-None value for name {names[0]}")
+    raise RuntimeError(f"Could not find non-None value for names {names}")
 
 
 NUM_WORKERS = int(