Simplified autograd mode specification.

examples/homography_estimation.py

@@ -21,7 +21,7 @@
 from torch.utils.data import DataLoader, Dataset
 
 import theseus as th
-from theseus.core.cost_function import AutogradMode, ErrFnType
+from theseus.core.cost_function import ErrFnType
 from theseus.third_party.easyaug import GeoAugParam, RandomGeoAug, RandomPhotoAug
 from theseus.third_party.utils import grid_sample
 
@@ -286,7 +286,7 @@ def run(
     outer_lr: float = 1e-4,
     max_iterations: int = 50,
     step_size: float = 0.1,
-    autograd_mode: AutogradMode = AutogradMode.VMAP,
+    autograd_mode: str = "vmap",
 ):
     logger.info(
         "==============================================================="
@@ -479,18 +479,12 @@ def run(
 
 @hydra.main(config_path="./configs/", config_name="homography_estimation")
 def main(cfg):
-    autograd_modes = {
-        "dense": AutogradMode.DENSE,
-        "loop_batch": AutogradMode.LOOP_BATCH,
-        "vmap": AutogradMode.VMAP,
-    }
-
     num_epochs: int = cfg.outer_optim.num_epochs
     batch_size: int = cfg.outer_optim.batch_size
     outer_lr: float = cfg.outer_optim.lr
     max_iterations: int = cfg.inner_optim.max_iters
     step_size: float = cfg.inner_optim.step_size
-    autograd_mode = autograd_modes[cfg.autograd_mode]
+    autograd_mode = cfg.autograd_mode
 
     run(
         batch_size=batch_size,

theseus/core/cost_function.py

@@ -4,7 +4,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import abc
-from typing import Callable, List, Optional, Sequence, Tuple, cast
+from typing import Callable, List, Optional, Sequence, Tuple, Union, cast
 from enum import Enum
 
 import torch
@@ -101,6 +101,22 @@ class AutogradMode(Enum):
     LOOP_BATCH = 1
     VMAP = 2
 
+    @staticmethod
+    def resolve(key: Union[str, "AutogradMode"]) -> "AutogradMode":
+        if isinstance(key, AutogradMode):
+            return key
+        if not isinstance(key, str):
+            raise ValueError("Autograd mode must be of type th.AutogradMode or string.")
+
+        try:
+            mode = AutogradMode[key.upper()]
+        except KeyError:
+            raise ValueError(
+                f"Invalid autograd mode {key}. "
+                "Valid options are dense, loop_batch, and vmap."
+            )
+        return mode
+
 
 # The error function is assumed to receive variables in the format
 #    err_fn(
@@ -120,7 +136,7 @@ def __init__(
         name: Optional[str] = None,
         autograd_strict: bool = False,
         autograd_vectorize: bool = False,
-        autograd_mode: AutogradMode = AutogradMode.DENSE,
+        autograd_mode: Union[str, AutogradMode] = AutogradMode.DENSE,
     ):
         if cost_weight is None:
             cost_weight = ScaleCostWeight(1.0)
@@ -147,7 +163,7 @@ def __init__(
         self._tmp_optim_vars_for_loop = None
         self._tmp_aux_vars_for_loop = None
 
-        self._autograd_mode = autograd_mode
+        self._autograd_mode = AutogradMode.resolve(autograd_mode)
 
         if self._autograd_mode == AutogradMode.LOOP_BATCH:
             self._tmp_optim_vars_for_loop = tuple(v.copy() for v in optim_vars)

theseus/core/tests/test_cost_function.py

@@ -57,9 +57,8 @@ def test_default_name_and_ids():
     assert len(seen_ids) == reps
 
 
-@pytest.mark.parametrize(
-    "autograd_mode", [AutogradMode.DENSE, AutogradMode.LOOP_BATCH, AutogradMode.VMAP]
-)
+# Adding three formatting options to include coverage for autograd mode resolution
+@pytest.mark.parametrize("autograd_mode", ["DENSE", "loop_batch", AutogradMode.VMAP])
 def test_autodiff_cost_function_error_and_jacobians_shape(autograd_mode):
     rng = torch.Generator()
     rng.manual_seed(0)