update train loop eval logic

TODO.md

@@ -0,0 +1,11 @@
+# Long-term
+- Implement a dataloader in-house, with a clear, lightweight mechanism for
+  collection-of-structures to structure-of-collections. For multi-proc handling
+  (happens in torch's dataloader, as well as the BatchedDataset for two
+  different purposes), we should rely on (a hopefully more stable) `execlib`.
+- `Domains` may be externalized (`co3` or `convlib`)
+- Up next: CLI, fully JSON-ification of model selection + train.
+- Consider a "multi-train" alternative (or arg support in `train()`) for
+  training many "rollouts" from the same base estimator (basically forks under
+  different seeds). For architecture benchmarking above all, seeing average
+  training behavior. Consider corresponding `Plotter` methods (error bars)

example/example.json

@@ -0,0 +1,29 @@
+{
+    "estimator_name": "mlp",
+    "dataset_name": "random_xy_dataset",
+    "dataloader_name": "supervised_data_loader",
+    "estimator_kwargs": {
+        "input_dim": 4,
+        "output_dim": 2
+    },
+    "dataset_kwargs": {
+        "num_samples": 100000,
+        "preload": true,
+        "input_dim": 4,
+        "output_dim": 2
+    },
+    "dataset_split_fracs": {
+        "train": 0.4,
+        "val": 0.3,
+        "aux": [0.3]
+    },
+    "dataloader_kwargs": {
+        "batch_size": 16
+    },
+    "train_kwargs": {
+        "summarize_every": 20,
+        "max_epochs": 100,
+        "stop_after_epochs": 100
+    },
+    "load_only": false
+}

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "trainlib"
-version = "0.1.2"
+version = "0.3.1"
 description = "Minimal framework for ML modeling. Supports advanced dataset operations and streamlined training."
 requires-python = ">=3.13"
 authors = [
@@ -30,6 +30,7 @@ dependencies = [
     "numpy>=2.4.1",
     "tensorboard>=2.20.0",
     "tqdm>=4.67.1",
+    "setuptools<=81.0.0",  # here currently b/c tensorboard breaks @ v82.0.0
 ]
 
 [project.scripts]

trainlib/__main__.py

@@ -0,0 +1,18 @@
+import logging
+
+from trainlib.cli import create_parser
+
+
+def main() -> None:
+    parser = create_parser()
+    args = parser.parse_args()
+
+    # skim off log level to handle higher-level option
+    if hasattr(args, "log_level") and args.log_level is not None:
+        logging.basicConfig(level=args.log_level)
+
+    args.func(args) if "func" in args else parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

trainlib/cli/__init__.py

@@ -0,0 +1,26 @@
+import logging
+import argparse
+
+from trainlib.cli import train
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+def create_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="trainlib cli",
+        # formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument(
+        "--log-level",
+        type=int,
+        metavar="int",
+        choices=[10, 20, 30, 40, 50],
+        help="Log level: 10=DEBUG, 20=INFO, 30=WARNING, 40=ERROR, 50=CRITICAL",
+    )
+
+    subparsers = parser.add_subparsers(help="subcommand help")
+    train.register_parser(subparsers)
+
+    return parser
+

trainlib/cli/train.py

@@ -0,0 +1,164 @@
+import gc
+import json
+import argparse
+from typing import Any
+from argparse import _SubParsersAction
+
+import torch
+
+from trainlib.trainer import Trainer
+from trainlib.datasets import dataset_map
+from trainlib.estimator import Estimator
+from trainlib.estimators import estimator_map
+from trainlib.dataloaders import dataloader_map
+
+
+def prepare_run() -> None:
+    # prepare cuda memory
+    memory_allocated = torch.cuda.memory_allocated() / 1024**3  # GB
+    print(f"CUDA allocated: {memory_allocated}GB")
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+
+def run(
+    estimator_name: str,
+    dataset_name: str,
+    dataloader_name: str,
+    estimator_kwargs: dict[str, Any] | None = None,
+    dataset_kwargs: dict[str, Any] | None = None,
+    dataset_split_fracs: dict[str, Any] | None = None,
+    dataset_split_kwargs: dict[str, Any] | None = None,
+    dataloader_kwargs: dict[str, Any] | None = None,
+    trainer_kwargs: dict[str, Any] | None = None,
+    train_kwargs: dict[str, Any] | None = None,
+    load_only: bool = False,
+) -> Trainer | Estimator:
+
+    try:
+        estimator_cls = estimator_map[estimator_name]
+    except KeyError as err:
+        raise ValueError(
+            f"Invalid estimator name '{estimator_name}',"
+            f"must be one of {estimator_map.keys()}"
+        ) from err
+
+    try:
+        dataset_cls = dataset_map[dataset_name]
+    except KeyError as err:
+        raise ValueError(
+            f"Invalid dataset name '{dataset_name}',"
+            f"must be one of {dataset_map.keys()}"
+        ) from err
+
+    try:
+        dataloader_cls = dataloader_map[dataloader_name]
+    except KeyError as err:
+        raise ValueError(
+            f"Invalid dataloader name '{dataloader_name}',"
+            f"must be one of {dataloader_map.keys()}"
+        ) from err
+
+    estimator_kwargs = estimator_kwargs or {}
+    dataset_kwargs = dataset_kwargs or {}
+    dataset_split_fracs = dataset_split_fracs or {}
+    dataset_split_kwargs = dataset_split_kwargs or {}
+    dataloader_kwargs = dataloader_kwargs or {}
+    trainer_kwargs = trainer_kwargs or {}
+    train_kwargs = train_kwargs or {}
+
+    default_estimator_kwargs = {}
+    default_dataset_kwargs = {}
+    default_dataset_split_kwargs = {}
+    default_dataset_split_fracs = {"train": 1.0, "val": 0.0, "aux": []}
+    default_dataloader_kwargs = {}
+    default_trainer_kwargs = {}
+    default_train_kwargs = {}
+
+    estimator_kwargs = {**default_estimator_kwargs, **estimator_kwargs}
+    dataset_kwargs = {**default_dataset_kwargs, **dataset_kwargs}
+    dataset_split_kwargs = {**default_dataset_split_kwargs, **dataset_split_kwargs}
+    dataset_split_fracs = {**default_dataset_split_fracs, **dataset_split_fracs}
+    dataloader_kwargs = {**default_dataloader_kwargs, **dataloader_kwargs}
+    trainer_kwargs = {**default_trainer_kwargs, **trainer_kwargs}
+    train_kwargs = {**default_train_kwargs, **train_kwargs}
+
+    estimator = estimator_cls(**estimator_kwargs)
+    dataset = dataset_cls(**dataset_kwargs)
+    train_dataset, val_dataset, *aux_datasets = dataset.split(
+        fracs=[
+            dataset_split_fracs["train"],
+            dataset_split_fracs["val"],
+            *dataset_split_fracs["aux"]
+        ],
+        **dataset_split_kwargs
+    )
+    train_loader = dataloader_cls(train_dataset, **dataloader_kwargs)
+    val_loader = dataloader_cls(val_dataset, **dataloader_kwargs)
+    aux_loaders = [
+        dataloader_cls(aux_dataset, **dataloader_kwargs)
+        for aux_dataset in aux_datasets
+    ]
+
+    trainer = Trainer(
+        estimator,
+        **trainer_kwargs,
+    )
+
+    if load_only:
+        return trainer
+
+    return trainer.train(
+        train_loader=train_loader,
+        val_loader=val_loader,
+        aux_loaders=aux_loaders,
+        **train_kwargs,
+    )
+
+
+def run_from_json(
+    parameters_json: str | None = None,
+    parameters_file: str | None = None,
+) -> Trainer | Estimator:
+    if not (parameters_json or parameters_file):
+        raise ValueError("parameter json or file required")
+
+    parameters: dict[str, Any]
+    if parameters_json:
+        try:
+            parameters = json.loads(parameters_json)
+        except json.JSONDecodeError as e:
+            raise ValueError(f"Invalid JSON format: {e}") from e
+        except Exception as e:
+            raise ValueError(f"Error loading JSON parameters: {e}") from e
+
+    elif parameters_file:
+        try:
+            with open(parameters_file, encoding="utf-8") as f:
+                parameters = json.load(f)
+        except FileNotFoundError as e:
+            raise ValueError(f"JSON file not found: {parameters_file}") from e
+        except Exception as e:
+            raise ValueError(f"Error loading JSON parameters: {e}") from e
+
+    return run(**parameters)
+
+
+def handle_train(args: argparse.Namespace) -> None:
+    run_from_json(args.parameters_json, args.parameters_file)
+
+
+def register_parser(subparsers: _SubParsersAction) -> None:
+    parser = subparsers.add_parser("train", help="run training loop")
+    parser.add_argument(
+        "--parameters-json",
+        type=str,
+        help="Raw JSON string with train parameters",
+    )
+    parser.add_argument(
+        "--parameters-file",
+        type=str,
+        help="Path to JSON file with train parameters",
+    )
+    parser.set_defaults(func=handle_train)

trainlib/dataloader.py

@@ -0,0 +1,102 @@
+"""
+This class took me a long time to really settle into. It's a connector, and it
+feels redundant in many ways, so I've nearly deleted it several times while
+talking through the design. But in total, I think it serves a clear purpose.
+Reasons:
+
+- Need a typed dataloader, even if I know the type of my attached transform
+- Need a new scope that uses the same base dataset without interfering with the
+  transform attribute; a design that sets or relies on that is subject to
+  conflict
+
+- Why not just use vanilla DataLoaders?
+
+  I'd like to, but the two reasons above make it clear why this is challenging:
+  I don't get static checks on the structures returned during iteration, and
+  while you can control ad hoc data transforms via dataset ``post_transforms``,
+  things can get messy if you need to do that for many transforms using the
+  same dataset (without copying). Simplest way around this is just a new scope
+  with the same underlying dataset instance and a transform wrapper around the
+  iterator; no interference with object attributes.
+
+This is really just meant as the minimum viable logic needed to accomplish the
+above - it's a very lightweight wrapper on the base ``DataLoader`` object.
+There's an explicit type upper bound ``Kw: EstimatorKwargs``, but it is
+otherwise a completely general transform over dataloader batches, highlighting
+that it's *mostly* here to place nice with type checks.
+"""
+
+from typing import Unpack
+from collections.abc import Iterator
+
+from torch.utils.data import DataLoader
+
+from trainlib.dataset import BatchedDataset
+from trainlib.estimator import EstimatorKwargs
+from trainlib.utils.type import LoaderKwargs
+
+
+class EstimatorDataLoader[B, Kw: EstimatorKwargs]:
+    """
+    Data loaders for estimators.
+
+    This class exists to connect batched data from datasets to the expected
+    representation for estimator methods. Datasets may be developed
+    independently from a given model structures, and models should be trainable
+    under any such data. We need a way to ensure the batched groups of items we
+    get from dataloaders match on a type level, i.e., can be reshaped into the
+    expected ``Kw`` signature.
+
+    Note: batch structure ``B`` cannot be directly inferred from type variables
+    exposed by ``BatchedDatasets`` (namely ``R`` and ``I``). What's returned by
+    a data loader wrapping any such dataset can be arbitrary (depending on the
+    ``collate_fn``), with default behavior being fairly consistent under nested
+    collections but challenging to accurately type.
+
+    .. todo::
+
+        To log (have changed for Trainer):
+
+        - New compact eval pipeline for train/val/auxiliary dataloaders.
+          Somewhat annoying logic, but handled consistently
+        - Convergence tracker will dynamically use training loss (early
+          stopping) when a validation set isn't provided. Same mechanics for
+          stagnant epochs (although early stopping is generally a little more
+          nuanced, having a rate-based stopper, b/c train loss generally quite
+          monotonic). So that's to be updated, plus room for possible model
+          selection strategies later.
+        - Logging happens at each batch, but we append to an epoch-indexed list
+          and later average. There was a bug in the last round of testing that
+          I didn't pick up where I was just overwriting summaries using the
+          last seen batch.
+        - Reworked general dataset/dataloader handling for main train loop, now
+          accepting objects of this class to bridge estimator and dataset
+          communication. This cleans up the batch mapping model.
+        - TODO: implement a version of this that canonically works with the
+          device passing plus EstimatorKwargs input; this is the last fuzzy bit
+          I think.
+    """
+
+    def __init__(
+        self, 
+        dataset: BatchedDataset,
+        **dataloader_kwargs: Unpack[LoaderKwargs],
+    ) -> None:
+        self._dataloader = DataLoader(dataset, **dataloader_kwargs)
+
+    def batch_to_est_kwargs(self, batch_data: B) -> Kw:
+        """
+        .. note::
+
+            Even if we have a concrete shape for the output kwarg dict for base
+            estimators (requiring a tensor "inputs" attribute), we don't
+            presuppose how a given batch object will map into this dict
+            structure.
+
+            return EstimatorKwargs({"inputs":0})
+        """
+
+        raise NotImplementedError
+
+    def __iter__(self) -> Iterator[Kw]:
+        return map(self.batch_to_est_kwargs, self._dataloader)

trainlib/dataloaders/__init__.py

@@ -0,0 +1,12 @@
+from trainlib.dataloader import EstimatorDataLoader
+from trainlib.utils.text import camel_to_snake
+from trainlib.dataloaders.memory import SupervisedDataLoader
+
+_dataloaders = [
+    SupervisedDataLoader,
+]
+dataloader_map: dict[str, type[EstimatorDataLoader]] = {
+    camel_to_snake(_dataloader.__name__): _dataloader
+    for _dataloader in _dataloaders
+}
+

trainlib/dataloaders/memory.py

@@ -0,0 +1,17 @@
+from torch import Tensor
+
+from trainlib.estimator import SupervisedKwargs
+from trainlib.dataloader import EstimatorDataLoader
+
+
+class SupervisedDataLoader(
+    EstimatorDataLoader[tuple[Tensor, Tensor], SupervisedKwargs]
+):
+    def batch_to_est_kwargs(
+        self,
+        batch_data: tuple[Tensor, Tensor]
+    ) -> SupervisedKwargs:
+        return SupervisedKwargs(
+            inputs=batch_data[0],
+            labels=batch_data[1],
+        )

trainlib/datasets/__init__.py

@@ -0,0 +1,12 @@
+from trainlib.dataset import BatchedDataset
+from trainlib.utils.text import camel_to_snake
+from trainlib.datasets.memory import RandomXYDataset
+
+_datasets = [
+    RandomXYDataset,
+]
+dataset_map: dict[str, type[BatchedDataset]] = {
+    camel_to_snake(_dataset.__name__): _dataset
+    for _dataset in _datasets
+}
+

trainlib/datasets/memory.py

@@ -73,13 +73,33 @@ class RecordDataset[T: NamedTuple](HomogenousDataset[int, T, T]):
 
 from typing import Unpack
 
+import torch
 import torch.nn.functional as F
 from torch import Tensor
+from torch.utils.data import TensorDataset
 
 from trainlib.domain import SequenceDomain
 from trainlib.dataset import TupleDataset, DatasetKwargs
 
 
+class RandomXYDataset(TupleDataset[Tensor]):
+    def __init__(
+        self,
+        num_samples: int,
+        input_dim: int,
+        output_dim: int,
+        **kwargs: Unpack[DatasetKwargs],
+    ) -> None:
+        domain = SequenceDomain[tuple[Tensor, Tensor]](
+            TensorDataset(
+                torch.randn((num_samples, input_dim)),
+                torch.randn((num_samples, output_dim))
+            ),
+        )
+
+        super().__init__(domain, **kwargs)
+
+
 class SlidingWindowDataset(TupleDataset[Tensor]):
     def __init__(
         self,
@@ -88,12 +108,26 @@ class SlidingWindowDataset(TupleDataset[Tensor]):
         offset: int = 0,
         lookahead: int = 1,
         num_windows: int = 1,
+        pad_mode: str = "constant",
+        #fill_with: str = "zero",
         **kwargs: Unpack[DatasetKwargs],
     ) -> None:
+        """
+        Parameters:
+            TODO: implement options for `fill_with`; currently just passing
+            through a `pad_mode` the Functional call, which does the job
+
+            fill_with: strategy to use for padding values in windows
+              - `zero`: fill with zeros
+              - `left`: use nearest window column (repeat leftmost)
+              - `mean`: fill with the window mean
+        """
+
         self.lookback = lookback
         self.offset = offset
         self.lookahead = lookahead
         self.num_windows = num_windows
+        self.pad_mode = pad_mode
 
         super().__init__(domain, **kwargs)
 
@@ -103,7 +137,7 @@ class SlidingWindowDataset(TupleDataset[Tensor]):
         batch_index: int,
     ) -> list[tuple[Tensor, ...]]:
         """
-        Backward pads first sequence over (lookback-1) length, and steps the
+        Backward pads window sequences over (lookback-1) length, and steps the
         remaining items forward by the lookahead.
 
         Batch data:
@@ -146,7 +180,7 @@ class SlidingWindowDataset(TupleDataset[Tensor]):
           exceeds the offset.
 
         To get windows starting with the first index at the left: we first set
-        out window size (call it L), determined by `lookback`. Then the
+        our window size (call it L), determined by `lookback`. Then the
         rightmost index we want will be `L-1`, which determines our `offset`
         setting.
 
@@ -173,7 +207,7 @@ class SlidingWindowDataset(TupleDataset[Tensor]):
             # for window sized `lb`, we pad with `lb-1` zeros. We then take off
             # the amount of our offset, which in the extreme cases does no
             # padding.
-            xip = F.pad(t, ((lb-1) - off, 0))
+            xip = F.pad(t, ((lb-1) - off, 0), mode=self.pad_mode)
 
             # extract sliding windows over the padded tensor
             # unfold(-1, lb, 1) slides over the last dim, 1 step at a time, for

trainlib/estimator.py

@@ -35,6 +35,10 @@ class EstimatorKwargs(TypedDict):
     inputs: Tensor
 
 
+class SupervisedKwargs(EstimatorKwargs):
+    labels: Tensor
+
+
 class Estimator[Kw: EstimatorKwargs](nn.Module):
     """
     Estimator base class.
@@ -164,7 +168,7 @@ class Estimator[Kw: EstimatorKwargs](nn.Module):
         self,
         writer: SummaryWriter,
         step: int | None = None,
-        val: bool = False,
+        group: str | None = None,
         **kwargs: Unpack[Kw],
     ) -> None:
         """

trainlib/estimators/__init__.py

@@ -0,0 +1,15 @@
+from trainlib.estimator import Estimator
+from trainlib.utils.text import camel_to_snake
+from trainlib.estimators.mlp import MLP
+from trainlib.estimators.rnn import LSTM, ConvGRU, MultiheadLSTM
+
+_estimators: list[type[Estimator]] = [
+    MLP,
+    LSTM,
+    MultiheadLSTM,
+    ConvGRU,
+]
+estimator_map: dict[str, type[Estimator]] = {
+    camel_to_snake(_estimator.__name__): _estimator
+    for _estimator in _estimators
+}

trainlib/estimators/mlp.py

@@ -21,7 +21,7 @@ class MLPKwargs(EstimatorKwargs):
     labels: NotRequired[Tensor]
 
 
-class MLP[K: MLPKwargs](Estimator[K]):
+class MLP[Kw: MLPKwargs](Estimator[Kw]):
     """
     Base MLP architecture.
     """
@@ -82,19 +82,19 @@ class MLP[K: MLPKwargs](Estimator[K]):
             max=1.0,
         )
 
-    def forward(self, **kwargs: Unpack[K]) -> tuple[Tensor, ...]:
+    def forward(self, **kwargs: Unpack[Kw]) -> tuple[Tensor, ...]:
         inputs = kwargs["inputs"]
         x = self._net(inputs)
 
         return (x,)
 
-    def loss(self, **kwargs: Unpack[K]) -> Generator[Tensor]:
+    def loss(self, **kwargs: Unpack[Kw]) -> Generator[Tensor]:
         predictions = self(**kwargs)[0]
         labels = kwargs["labels"]
 
         yield F.mse_loss(predictions, labels)
 
-    def metrics(self, **kwargs: Unpack[K]) -> dict[str, float]:
+    def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
         with torch.no_grad():
             loss = next(self.loss(**kwargs)).item()
 
@@ -103,8 +103,7 @@ class MLP[K: MLPKwargs](Estimator[K]):
             mae = F.l1_loss(predictions, labels).item()
 
         return {
-            "loss": loss,
+            # "mse": loss,
-            "mse": loss,
             "mae": mae,
             "grad_norm": get_grad_norm(self)
         }
@@ -136,8 +135,8 @@ class MLP[K: MLPKwargs](Estimator[K]):
         self,
         writer: SummaryWriter,
         step: int | None = None,
-        val: bool = False,
+        group: str | None = None,
-        **kwargs: Unpack[K],
+        **kwargs: Unpack[Kw],
     ) -> None:
         return None
 

trainlib/estimators/rnn.py

@@ -8,6 +8,7 @@ from torch import nn, Tensor
 from torch.optim import Optimizer
 from torch.utils.tensorboard import SummaryWriter
 
+from trainlib.utils import op
 from trainlib.estimator import Estimator, EstimatorKwargs
 from trainlib.utils.type import OptimizerKwargs
 from trainlib.utils.module import get_grad_norm
@@ -21,7 +22,7 @@ class RNNKwargs(EstimatorKwargs):
     labels: NotRequired[Tensor]
 
 
-class LSTM[K: RNNKwargs](Estimator[K]):
+class LSTM[Kw: RNNKwargs](Estimator[Kw]):
     """
     Base RNN architecture.
     """
@@ -85,7 +86,7 @@ class LSTM[K: RNNKwargs](Estimator[K]):
             max=1.0,
         )
 
-    def forward(self, **kwargs: Unpack[K]) -> tuple[Tensor, ...]:
+    def forward(self, **kwargs: Unpack[Kw]) -> tuple[Tensor, ...]:
         inputs = kwargs["inputs"]
 
         # data shaped (B, C, T); map to (B, T, C)
@@ -97,24 +98,29 @@ class LSTM[K: RNNKwargs](Estimator[K]):
 
         return z[:, -1, :], hidden
 
-    def loss(self, **kwargs: Unpack[K]) -> Generator[Tensor]:
+    def loss(self, **kwargs: Unpack[Kw]) -> Generator[Tensor]:
         predictions = self(**kwargs)[0]
         labels = kwargs["labels"]
 
         yield F.mse_loss(predictions, labels)
+        #yield F.l1_loss(predictions, labels)
 
-    def metrics(self, **kwargs: Unpack[K]) -> dict[str, float]:
+    def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
         with torch.no_grad():
             loss = next(self.loss(**kwargs)).item()
 
             predictions = self(**kwargs)[0]
             labels = kwargs["labels"]
+
+            mse = F.mse_loss(predictions, labels).item()
             mae = F.l1_loss(predictions, labels).item()
+            r2 = op.r2_score(predictions, labels).item()
 
         return {
             "loss": loss,
-            "mse": loss,
+            "mse": mse,
             "mae": mae,
+            "r2": r2,
             "grad_norm": get_grad_norm(self)
         }
 
@@ -145,8 +151,8 @@ class LSTM[K: RNNKwargs](Estimator[K]):
         self,
         writer: SummaryWriter,
         step: int | None = None,
-        val: bool = False,
+        group: str | None = None,
-        **kwargs: Unpack[K],
+        **kwargs: Unpack[Kw],
     ) -> None:
         return None
 
@@ -165,7 +171,7 @@ class MultiheadLSTMKwargs(EstimatorKwargs):
     auxiliary: NotRequired[Tensor]
 
 
-class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
+class MultiheadLSTM[Kw: MultiheadLSTMKwargs](Estimator[Kw]):
     def __init__(
         self,
         input_dim: int,
@@ -223,7 +229,7 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
             max=1.0,
         )
 
-    def forward(self, **kwargs: Unpack[K]) -> tuple[Tensor, ...]:
+    def forward(self, **kwargs: Unpack[Kw]) -> tuple[Tensor, ...]:
         inputs = kwargs["inputs"]
 
         # data shaped (B, C, T); map to (B, T, C)
@@ -237,7 +243,7 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
 
         return z[:, -1, :], zs[:, -1, :]
 
-    def loss(self, **kwargs: Unpack[K]) -> Generator[Tensor]:
+    def loss(self, **kwargs: Unpack[Kw]) -> Generator[Tensor]:
         pred, pred_aux = self(**kwargs)
         labels = kwargs["labels"]
         aux_labels = kwargs.get("auxiliary")
@@ -247,12 +253,8 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
         else:
             yield F.mse_loss(pred, labels) + F.mse_loss(pred_aux, aux_labels)
 
-    def metrics(self, **kwargs: Unpack[K]) -> dict[str, float]:
+    def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
-        with torch.no_grad():
-            loss = next(self.loss(**kwargs)).item()
-
         return {
-            "loss": loss,
             "grad_norm": get_grad_norm(self)
         }
 
@@ -283,8 +285,8 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
         self,
         writer: SummaryWriter,
         step: int | None = None,
-        val: bool = False,
+        group: str | None = None,
-        **kwargs: Unpack[K],
+        **kwargs: Unpack[Kw],
     ) -> None:
         return None
 
@@ -297,7 +299,7 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
         logger.info(f"| > {self.output_dim=}")
 
 
-class ConvGRU[K: RNNKwargs](Estimator[K]):
+class ConvGRU[Kw: RNNKwargs](Estimator[Kw]):
     """
     Base recurrent convolutional architecture.
 
@@ -381,7 +383,8 @@ class ConvGRU[K: RNNKwargs](Estimator[K]):
         # will be (B, T, C), applies indep at each time step across channels
         # self.dense_z = nn.Linear(layer_in_dim, self.output_dim)
 
-        # will be (B, C, T), applies indep at each time step across channels
+        # will be (B, Co, 1), applies indep at each channel across temporal dim
+        # size time steps
         self.dense_z = TDNNLayer(
             layer_in_dim,
             self.output_dim,
@@ -408,7 +411,7 @@ class ConvGRU[K: RNNKwargs](Estimator[K]):
             max=1.0,
         )
 
-    def forward(self, **kwargs: Unpack[K]) -> tuple[Tensor, ...]:
+    def forward(self, **kwargs: Unpack[Kw]) -> tuple[Tensor, ...]:
         inputs = kwargs["inputs"]
 
         # embedding shaped (B, C, T)
@@ -434,7 +437,7 @@ class ConvGRU[K: RNNKwargs](Estimator[K]):
 
         return (z,)
 
-    def loss(self, **kwargs: Unpack[K]) -> Generator[Tensor]:
+    def loss(self, **kwargs: Unpack[Kw]) -> Generator[Tensor]:
         predictions = self(**kwargs)[0]
         labels = kwargs["labels"]
 
@@ -442,19 +445,24 @@ class ConvGRU[K: RNNKwargs](Estimator[K]):
         predictions = predictions.squeeze(-1)
 
         yield F.mse_loss(predictions, labels, reduction="mean")
+        #yield F.l1_loss(predictions, labels)
 
-    def metrics(self, **kwargs: Unpack[K]) -> dict[str, float]:
+    def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
         with torch.no_grad():
             loss = next(self.loss(**kwargs)).item()
 
             predictions = self(**kwargs)[0].squeeze(-1)
             labels = kwargs["labels"]
+
+            mse = F.mse_loss(predictions, labels).item()
             mae = F.l1_loss(predictions, labels).item()
+            r2 = op.r2_score(predictions, labels).item()
 
         return {
             "loss": loss,
-            "mse": loss,
+            "mse": mse,
             "mae": mae,
+            "r2": r2,
             "grad_norm": get_grad_norm(self)
         }
 
@@ -485,8 +493,8 @@ class ConvGRU[K: RNNKwargs](Estimator[K]):
         self,
         writer: SummaryWriter,
         step: int | None = None,
-        val: bool = False,
+        group: str | None = None,
-        **kwargs: Unpack[K],
+        **kwargs: Unpack[Kw],
     ) -> None:
         return None
 

trainlib/plotter.py

@@ -0,0 +1,531 @@
+from typing import Any
+from collections.abc import Callable
+
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+from torch import Tensor
+
+from trainlib.trainer import Trainer
+from trainlib.estimator import EstimatorKwargs
+from trainlib.dataloader import EstimatorDataLoader
+from trainlib.utils.type import AxesArray, SubplotsKwargs
+
+type SubplotFn = Callable[[plt.Axes, int, Tensor, Tensor], None]
+type ContextFn = Callable[[plt.Axes, str], None]
+
+
+class Plotter[Kw: EstimatorKwargs]:
+    """
+    TODOs:
+
+    - best fit lines for plots and residuals (compare to ideal lines in each
+      case)
+    - show val options across columns; preview how val is changing across
+      natural training, and what the best will look (so plot like uniform
+      intervals broken over the training epochs at 0, 50, 100, 150, ... and
+      highlight the best one, even if that's not actually the single best
+      epoch)
+    - Implement data and dimension limits; in the instance dataloaders have
+      huge numbers of samples or labels are high-dimensional
+    """
+
+    def __init__(
+        self,
+        trainer: Trainer[Any, Kw],
+        dataloaders: list[EstimatorDataLoader[Any, Kw]],
+        kw_to_actual: Callable[[Kw], Tensor],
+        dataloader_labels: list[str] | None = None,
+    ) -> None:
+        self.trainer = trainer
+        self.dataloaders = dataloaders
+        self.dataloader_labels = (
+            dataloader_labels or list(map(str, range(1, len(dataloaders)+1)))
+        )
+        self.kw_to_actual = kw_to_actual
+
+        self._outputs: list[list[Tensor]] | None = None
+        self._metrics: list[list[dict[str, float]]] | None = None
+
+        self._data_tuples = None
+
+    @property
+    def data_tuples(self) -> list[tuple[Tensor, Tensor, str]]:
+        """
+        Produce data items; to be cached. Zip later with axes
+        """
+
+        self.trainer.estimator.eval()
+
+        if self._data_tuples is not None:
+            return self._data_tuples
+
+        data_tuples = []
+        for i, loader in enumerate(self.dataloaders):
+            label = self.dataloader_labels[i]
+
+            actual = [
+                self.kw_to_actual(batch_kwargs).detach().cpu()
+                for batch_kwargs in loader
+            ]
+            actual = torch.cat([ai.reshape(*([*ai.shape]+[1])[:2]) for ai in actual])
+
+            output = [
+                self.trainer.estimator(**batch_kwargs)[0].detach().cpu()
+                for batch_kwargs in loader
+            ]
+            output = torch.cat([oi.reshape(*([*oi.shape]+[1])[:2]) for oi in output])
+
+            data_tuples.append((actual, output, label))
+
+        self._data_tuples = data_tuples
+
+        return self._data_tuples
+
+    def get_transposed_handles_labels(
+        self,
+        ax: plt.Axes
+    ) -> tuple[list, list]:
+        # transpose legend layout for more natural view
+        handles, labels = ax.get_legend_handles_labels()
+        handles = handles[::2] + handles[1::2]
+        labels = labels[::2] + labels[1::2]
+
+        return handles, labels
+
+    def _lstsq_dim(
+        self,
+        dim_actual: Tensor,
+        dim_output: Tensor
+    ) -> tuple[Tensor, Tensor]:
+        A = torch.stack(
+            [dim_actual, torch.ones_like(dim_actual)],
+            dim=1
+        )
+        m, b = torch.linalg.lstsq(A, dim_output).solution
+
+        return m, b
+
+    def _prepare_figure_kwargs(
+        self,
+        rows: int,
+        cols: int,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> SubplotsKwargs:
+        """
+        """
+
+        default_figure_kwargs = {
+            "sharex": True,
+            "figsize": (col_size*cols, row_size*rows),
+        }
+        figure_kwargs = {
+            **default_figure_kwargs,
+            **(figure_kwargs or {}),
+        }
+
+        return figure_kwargs
+
+    def _prepare_subplot_kwargs(
+        self,
+        subplot_kwargs: dict | None = None,
+    ) -> dict:
+        """
+        """
+
+        default_subplot_kwargs = {}
+        subplot_kwargs = {
+            **default_subplot_kwargs,
+            **(subplot_kwargs or {}),
+        }
+
+        return subplot_kwargs
+
+    def _prepare_gof_kwargs(
+        self,
+        gof_kwargs: dict | None = None,
+    ) -> dict:
+        """
+        """
+
+        default_gof_kwargs = {
+            "alpha": 0.5
+        }
+        gof_kwargs = {
+            **default_gof_kwargs,
+            **(gof_kwargs or {}),
+        }
+
+        return gof_kwargs
+
+    def _create_subplots(
+        self,
+        rows: int,
+        cols: int,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        """
+
+        figure_kwargs: SubplotsKwargs = self._prepare_figure_kwargs(
+            rows,
+            cols,
+            row_size=row_size,
+            col_size=col_size,
+            figure_kwargs=figure_kwargs,
+        )
+        fig, axes = plt.subplots(
+            rows,
+            cols,
+            squeeze=False,
+            **figure_kwargs,
+        )  # ty:ignore[no-matching-overload]
+
+        return fig, axes
+
+    def _plot_base(
+        self,
+        subplot_fn: SubplotFn,
+        context_fn: ContextFn,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Note: transform samples in dataloader definitions beforehand if you
+        want to change data
+
+        .. todo::
+
+            Merge in logic from general diagnostics, allowing collapse from
+            either dim and transposing.
+
+            Later: multi-trial error bars, or at least the ability to pass that
+            downstream
+
+        Parameters:
+            row_size:
+            col_size:
+            figure_kwargs:
+            subplot_kwargs:
+            gof_kwargs:
+        """
+        
+        ndims = self.data_tuples[0][0].size(-1)
+        fig, axes = self._create_subplots(
+            rows=len(self.dataloaders),
+            cols=1 if (norm_samples or combine_dims) else ndims,
+            row_size=row_size,
+            col_size=col_size,
+            figure_kwargs=figure_kwargs,
+        )
+
+        for axes_row, data_tuple in zip(axes, self.data_tuples, strict=True):
+            actual, output, loader_label = data_tuple
+
+            if norm_samples:
+                actual = actual.norm(dim=1, keepdim=True)
+                output = output.norm(dim=1, keepdim=True)
+
+            for dim in range(ndims):
+                ax = axes_row[0 if combine_dims else dim]
+                subplot_fn(ax, dim, actual, output)
+
+                add_plot_context = (
+                    norm_samples  # dim=0 implicit b/c we break
+                    or not combine_dims  # add to every subplot across grid
+                    or combine_dims and dim == ndims-1  # wait for last dim
+                )
+
+                # always exec plot logic if not combining, o/w exec just once
+                if add_plot_context:
+                    context_fn(ax, loader_label)
+
+                    # transpose legend layout for more natural view
+                    if norm_samples or not combine_dims:
+                        ax.legend()
+                    else:
+                        handles, labels = self.get_transposed_handles_labels(ax)
+                        ax.legend(handles, labels, ncols=2)
+
+                # break dimension loop if collapsed by norm
+                if norm_samples:
+                    break
+
+        return fig, axes
+
+    # def plot_ordered(...): ...
+    #     """
+    #     Simple ordered view of output dimensions, with actual and output
+    #     overlaid.
+    #     """
+
+    def plot_actual_output(
+        self,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+        subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Plot residual distribution.
+        """
+
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+        def subplot_fn(
+            ax: plt.Axes, dim: int, actual: Tensor, output: Tensor
+        ) -> None:
+            dim_color = colors[dim % len(colors)]
+            group_name = "norm" if norm_samples else f"$d_{dim}$"
+
+            dim_actual = actual[:, dim]
+            dim_output = output[:, dim]
+
+            ax.scatter(
+                dim_actual, dim_output,
+                color=dim_color,
+                label=group_name,
+                **subplot_kwargs,
+            )
+
+            # plot goodness of fit line
+            m, b = self._lstsq_dim(dim_actual, dim_output)
+            ax.plot(
+                dim_actual, m * dim_actual + b,
+                color=dim_color,
+                label=f"GoF {group_name}",
+                **gof_kwargs,
+            )
+
+        def context_fn(ax: plt.Axes, loader_label: str) -> None:
+            # plot perfect prediction reference line, y=x
+            ax.plot(
+                [0, 1], [0, 1],
+                transform=ax.transAxes,
+                c="black",
+                alpha=0.2,
+            )
+
+            ax.set_title(
+                f"[{loader_label}] True labels vs Predictions"
+            )
+            ax.set_xlabel("actual")
+            ax.set_ylabel("output")
+
+        return self._plot_base(
+            subplot_fn, context_fn,
+            row_size=row_size,
+            col_size=col_size,
+            norm_samples=norm_samples,
+            combine_dims=combine_dims,
+            figure_kwargs=figure_kwargs,
+        )
+
+    def plot_actual_output_residual(
+        self,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        order_residuals: bool = False,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+        subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Plot prediction residuals.
+        """
+
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+        def subplot_fn(
+            ax: plt.Axes, dim: int, actual: Tensor, output: Tensor
+        ) -> None:
+            dim_color = colors[dim % len(colors)]
+            group_name = "norm" if norm_samples else f"$d_{dim}$"
+
+            dim_actual = actual[:, dim]
+            dim_output = output[:, dim]
+
+            residuals = dim_actual - dim_output
+            X, Y = dim_actual, residuals
+
+            if order_residuals:
+                X = range(1, residuals.size(0)+1)
+                Y = residuals[residuals.argsort()]
+
+            ax.scatter(
+                X, Y,
+                color=dim_color,
+                label=group_name,
+                **subplot_kwargs,
+            )
+
+            # plot goodness of fit line
+            if not order_residuals:
+                m, b = self._lstsq_dim(dim_actual, residuals)
+                ax.plot(
+                    dim_actual, m * dim_actual + b,
+                    color=dim_color,
+                    label=f"GoF {group_name}",
+                    **gof_kwargs,
+                )
+
+        def context_fn(ax: plt.Axes, loader_label: str) -> None:
+            # compare residuals to y=0
+            ax.axhline(y=0, c="black", alpha=0.2)
+
+            ax.set_title(f"[{loader_label}] Prediction residuals")
+            ax.set_xlabel("actual")
+            ax.set_ylabel("residual")
+
+        return self._plot_base(
+            subplot_fn, context_fn,
+            row_size=row_size,
+            col_size=col_size,
+            norm_samples=norm_samples,
+            combine_dims=combine_dims,
+            figure_kwargs=figure_kwargs,
+        )
+ 
+    def plot_actual_output_residual_dist(
+        self,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+        subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Plot residual distribution.
+        """
+
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+        def subplot_fn(
+            ax: plt.Axes, dim: int, actual: Tensor, output: Tensor
+        ) -> None:
+            dim_color = colors[dim % len(colors)]
+            group_name = "norm" if norm_samples else f"$d_{dim}$"
+
+            dim_actual = actual[:, dim]
+            dim_output = output[:, dim]
+
+            N = dim_actual.size(0)
+            residuals = dim_actual - dim_output
+
+            _, _, patches = ax.hist(
+                residuals.abs(),
+                bins=int(np.sqrt(N)),
+                density=True,
+                alpha=0.3,
+                color=dim_color,
+                label=group_name,
+                **subplot_kwargs
+            )
+
+            mu = residuals.abs().mean().item()
+            ax.axvline(mu, linestyle=":", c=dim_color, label=f"$\\mu_{dim}$")
+
+        def context_fn(ax: plt.Axes, loader_label: str) -> None:
+            ax.set_title(f"[{loader_label}] Residual distribution")
+            ax.set_xlabel("actual")
+            ax.set_ylabel("residual (density)")
+
+        return self._plot_base(
+            subplot_fn, context_fn,
+            row_size=row_size,
+            col_size=col_size,
+            norm_samples=norm_samples,
+            combine_dims=combine_dims,
+            figure_kwargs=figure_kwargs,
+        )
+
+    def estimator_diagnostic(
+        self,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        session_name: str | None = None,
+        combine_groups: bool = False,
+        combine_metrics: bool = False,
+        transpose_layout: bool = False,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        session_map = self.trainer._event_log
+        session_name = session_name or next(iter(session_map))
+        groups = session_map[session_name]
+        num_metrics = len(groups[next(iter(groups))])
+        # colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+        rows = 1 if combine_groups else len(groups)
+        cols = 1 if combine_metrics else num_metrics
+        if transpose_layout:
+            rows, cols = cols, rows
+
+        fig, axes = self._create_subplots(
+            rows=rows,
+            cols=cols,
+            row_size=row_size,
+            col_size=col_size,
+            figure_kwargs=figure_kwargs,
+        )
+        if transpose_layout:
+            axes = axes.T
+
+        for i, group_name in enumerate(groups):
+            axes_row = axes[0 if combine_groups else i]
+            group_metrics = groups[group_name]
+
+            for j, metric_name in enumerate(group_metrics):
+                ax = axes_row[0 if combine_metrics else j]
+
+                metric_dict = group_metrics[metric_name]
+                metric_data = np.array([
+                    (k, np.mean(v)) for k, v in metric_dict.items()
+                ])
+
+                if combine_groups and combine_metrics:
+                    label = f"{group_name}-{metric_name}"
+                    title_prefix = "all"
+                elif combine_groups:
+                    label = group_name
+                    title_prefix = metric_name
+                # elif combine_metrics:
+                else:
+                    label = metric_name
+                    title_prefix = group_name
+                # else:
+                #     label = ""
+                #     title_prefix = f"{group_name},{metric_name}"
+
+                ax.plot(
+                    metric_data[:, 0],
+                    metric_data[:, 1],
+                    label=label,
+                    # color=colors[j],
+                )
+
+                ax.set_title(f"[{title_prefix}] Metrics over epochs")
+                ax.set_xlabel("epoch")
+                ax.set_ylabel("value")
+                ax.legend()
+
+        return fig, axes

trainlib/trainer.py

@@ -1,5 +1,15 @@
 """
 Core interface for training ``Estimators`` with ``Datasets``
+
+.. admonition:: Design of preview ``get_dataloaders()``
+
+
+    Note how much this method is doing, and the positivity in letting that be
+    more explicit elsewhere. The assignment of transforms to datasets before
+    wrapping as loaders is chief among these items, alongside the balancing and
+    splitting; I think those are hamfisted here to make it work with the old
+    setup, but I generally it's not consistent with the name "get dataloaders"
+    (i.e., and also balance and split and set transforms)
 """
 
 import os
@@ -7,48 +17,42 @@ import time
 import logging
 from io import BytesIO
 from copy import deepcopy
-from typing import Any, Self
+from typing import Any
 from pathlib import Path
 from collections import defaultdict
-from collections.abc import Callable
 
 import torch
 from tqdm import tqdm
 from torch import cuda, Tensor
 from torch.optim import Optimizer
 from torch.nn.utils import clip_grad_norm_
-from torch.utils.data import Dataset, DataLoader
 from torch.utils.tensorboard import SummaryWriter
 
-from trainlib.dataset import BatchedDataset
 from trainlib.estimator import Estimator, EstimatorKwargs
-from trainlib.transform import Transform
+from trainlib.utils.map import nested_defaultdict
-from trainlib.utils.type import (
+from trainlib.dataloader import EstimatorDataLoader
-    SplitKwargs,
-    LoaderKwargs,
-    BalanceKwargs,
-)
 from trainlib.utils.module import ModelWrapper
+from trainlib.utils.session import ensure_same_device
 
 logger: logging.Logger = logging.getLogger(__name__)
 
 
-class Trainer[I, K: EstimatorKwargs]:
+class Trainer[I, Kw: EstimatorKwargs]:
     """
     Training interface for optimizing parameters of ``Estimators`` with
     ``Datasets``.
 
     This class is generic to a dataset item type ``I`` and an estimator kwarg
-    type ``K``. These are the two primary components ``Trainer`` objects need
+    type ``Kw``. These are the two primary components ``Trainer`` objects need
     to coordinate: they ultimately rely on a provided map to ensure data items
     (type ``I``) from a dataset are appropriately routed as inputs to key
     estimator methods (like ``forward()`` and ``loss()``), which accept inputs
-    of type ``K``.
+    of type ``Kw``.
     """
 
     def __init__(
         self,
-        estimator: Estimator[K],
+        estimator: Estimator[Kw],
         device: str | None = None,
         chkpt_dir: str = "chkpt/",
         tblog_dir: str = "tblog/",
@@ -93,75 +97,66 @@ class Trainer[I, K: EstimatorKwargs]:
 
         self.estimator = estimator
         self.estimator.to(self.device)
+        self._event_log = nested_defaultdict(4, list)
 
         self.chkpt_dir = Path(chkpt_dir).resolve()
         self.tblog_dir = Path(tblog_dir).resolve()
 
         self.reset()
 
-    def reset(self) -> None:
+    def reset(self, resume: bool = False) -> None:
         """
         Set initial tracking parameters for the primary training loop.
+
+        Parameters:
+            resume: if ``True``, just resets the stagnant epoch counter, with
+                the aims of continuing any existing training state under
+                resumed ``train()`` call. This should likely only be set when
+                training is continued on the same dataset and the goal is to
+                resume convergence loss-based scoring for a fresh set of
+                epochs. If even that element of the training loop should resume
+                (which should only happen if a training loop was interrupted or
+                a max epoch limit was reached), then this method shouldn't be
+                called at all between ``train()`` invocations.
         """
 
-        self._step: int = 0
-        self._epoch: int = 0
-        self._summary: dict[str, list[tuple[float, int]]] = defaultdict(list)
-
-        self._val_loss = float("inf")
-        self._best_val_loss = float("inf")
         self._stagnant_epochs = 0
+
+        if not resume:
+            self._epoch: int = 0
+            self._summary = defaultdict(lambda: defaultdict(list))
+
+            self._conv_loss = float("inf")
+            self._best_conv_loss = float("inf")
+            self._best_conv_epoch = 0
             self._best_model_state_dict: dict[str, Any] = {}
 
     def _train_epoch(
         self,
-        train_loader: DataLoader,
+        loader: EstimatorDataLoader[Any, Kw],
-        batch_estimator_map: Callable[[I, Self], K],
         optimizers: tuple[Optimizer, ...],
-        writer: SummaryWriter,
         max_grad_norm: float | None = None,
+        progress_bar: tqdm | None = None,
     ) -> list[float]:
         """
         Train the estimator for a single epoch.
         """
 
-        train_loss_sums = []
+        loss_sums = []
         self.estimator.train()
-        with tqdm(train_loader, unit="batch") as train_epoch:
+        for i, batch_kwargs in enumerate(loader):
-            for i, batch_data in enumerate(train_epoch):
+            batch_kwargs = ensure_same_device(batch_kwargs, self.device)
-                est_kwargs = batch_estimator_map(batch_data, self)
+            losses = self.estimator.loss(**batch_kwargs)
-                inputs = est_kwargs["inputs"]
 
-                # one-time logging
+            for o_idx, (loss, optimizer) in enumerate(
-                if self._step == 0:
+                zip(losses, optimizers, strict=True)
-                    writer.add_graph(
+            ):
-                        ModelWrapper(self.estimator),
+                if len(loss_sums) <= o_idx:
-                        est_kwargs
+                    loss_sums.append(0.0)
-                    )
+                loss_sums[o_idx] += loss.item()
 
-                # once-per-epoch logging
-                if i == 0:
-                    self.estimator.epoch_write(
-                        writer,
-                        step=self._step,
-                        val=False,
-                        **est_kwargs
-                    )
-
-                train_losses = self.estimator.loss(**est_kwargs)
-                train_loss_items = []
-                for o_idx, optimizer in enumerate(optimizers):
                 optimizer.zero_grad()
-                    train_loss = next(train_losses)
+                loss.backward()
-
-                    if len(train_loss_sums) <= o_idx:
-                        train_loss_sums.append(0.0)
-
-                    train_loss_item = train_loss.item()
-                    train_loss_sums[o_idx] += train_loss_item
-                    train_loss_items.append(train_loss_item)
-
-                    train_loss.backward()
 
                 # clip gradients for optimizer's parameters
                 if max_grad_norm is not None:
@@ -172,117 +167,194 @@ class Trainer[I, K: EstimatorKwargs]:
 
                 optimizer.step()
 
-                self._step += len(inputs)
+            # set loop loss to running average (reducing if multi-loss)
+            loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
 
-                for train_loss_item, train_loss_sum in zip(
+            if progress_bar:
-                    train_loss_items,
+                progress_bar.update(1)
-                    train_loss_sums,
+                progress_bar.set_postfix(
-                    strict=True,
+                    epoch=self._epoch,
-                ):
+                    mode="opt",
-                    train_epoch.set_postfix(loss=f"{train_loss_sum/(i+1):8.2f}")
+                    data="train",
-                    self._add_summary_item("train_loss", train_loss_item)
+                    loss=f"{loss_avg:8.2f}",
-
-                estimator_metrics = self.estimator.metrics(**est_kwargs)
-                for metric_name, metric_value in estimator_metrics.items():
-                    self._add_summary_item(
-                        f"train_{metric_name}",
-                        metric_value
                 )
 
+        # step estimator hyperparam schedules
         self.estimator.epoch_step()
 
-            for li, train_loss_sum in enumerate(train_loss_sums):
+        return loss_sums
-                self._add_summary_item(
-                    f"train_loss{li}_epoch", train_loss_sum / len(train_loader)
-                )
 
-        return train_loss_sums
+    def _eval_epoch(
-
-    def _val_epoch(
         self,
-        val_loader: DataLoader,
+        loader: EstimatorDataLoader[Any, Kw],
-        batch_estimator_map: Callable[[I, Self], K],
+        label: str,
-        optimizers: tuple[Optimizer, ...],
+        progress_bar: tqdm | None = None,
-        writer: SummaryWriter,
     ) -> list[float]:
         """
         Perform and record validation scores for a single epoch.
+
+        .. On summary writers::
+
+            See the similarly titled note for ``_train_epoch()`` for general
+            remarks about optimizers and how we're recording losses/metrics.
+            The same mostly applies here in the validation setting, but we
+            crucially aren't stepping forward ``_step`` between batches. This
+            means that, even though we're writing losses and metrics once for
+            each val batch, those values are simply piling up under the same
+            summary item name. This is consistent with how we report training
+            items: the model isn't changing across val batches, so these don't
+            get plotted at different step points (which might be interpreted as
+            performance along model progression when it's actually variation
+            across batches). Currently, this "piling" means we actually write
+            batch values to the same event name at the same step rather than
+            manually averaging beforehand; we defer the handling to TB, and
+            although that may technically be discouraged, the val plots render
+            collections effectively as a vertical line between the min and max
+            value (which I find to be a satisfactory way to view cross-batch
+            variation during each val epoch).
         """
 
-        val_loss_sums = []
+        loss_sums = []
         self.estimator.eval()
-        with tqdm(val_loader, unit="batch") as val_epoch:
+        for i, batch_kwargs in enumerate(loader):
-            for i, batch_data in enumerate(val_epoch):
+            batch_kwargs = ensure_same_device(batch_kwargs, self.device)
-                est_kwargs = batch_estimator_map(batch_data, self)
+            losses = self.estimator.loss(**batch_kwargs)
 
+            # once-per-session logging
+            if self._epoch == 0 and i == 0:
+                self._writer.add_graph(
+                    ModelWrapper(self.estimator), batch_kwargs
+                )
             # once-per-epoch logging
             if i == 0:
                 self.estimator.epoch_write(
-                        writer,
+                    self._writer,
-                        step=self._step,
+                    step=self._epoch,
-                        val=True,
+                    group=label,
-                        **est_kwargs
+                    **batch_kwargs
                 )
 
-                val_losses = self.estimator.loss(**est_kwargs)
+            loss_items = []
-                val_loss_items = []
+            for o_idx, loss in enumerate(losses):
-                for o_idx in range(len(optimizers)):
+                if len(loss_sums) <= o_idx:
-                    val_loss = next(val_losses)
+                    loss_sums.append(0.0)
 
-                    if len(val_loss_sums) <= o_idx:
+                loss_item = loss.item()
-                        val_loss_sums.append(0.0)
+                loss_sums[o_idx] += loss_item
+                loss_items.append(loss_item)
 
-                    val_loss_item = val_loss.item()
+            # set loop loss to running average (reducing if multi-loss)
-                    val_loss_sums[o_idx] += val_loss_item
+            loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
-                    val_loss_items.append(val_loss_item)
 
-                for val_loss_item, val_loss_sum in zip(
+            if progress_bar:
-                    val_loss_items,
+                progress_bar.update(1)
-                    val_loss_sums,
+                progress_bar.set_postfix(
-                    strict=True,
+                    epoch=self._epoch,
-                ):
+                    mode="eval",
-                    val_epoch.set_postfix(loss=f"{val_loss_sum/(i+1):8.2f}")
+                    data=label,
-                    self._add_summary_item("val_loss", val_loss_item)
+                    loss=f"{loss_avg:8.2f}",
+                )
 
-                estimator_metrics = self.estimator.metrics(**est_kwargs)
+            # log individual loss terms after each batch
+            for o_idx, loss_item in enumerate(loss_items):
+                self._log_event(label, f"loss_{o_idx}", loss_item)
+
+            # log metrics for batch
+            estimator_metrics = self.estimator.metrics(**batch_kwargs)
             for metric_name, metric_value in estimator_metrics.items():
-                    self._add_summary_item(f"val_{metric_name}", metric_value)
+                self._log_event(label, metric_name, metric_value)
 
-            for li, val_loss_sum in enumerate(val_loss_sums):
+        avg_losses = [loss_sum / (i+1) for loss_sum in loss_sums]
-                self._add_summary_item(
-                    f"val_loss{li}_epoch", val_loss_sum / len(val_loader)
-                )
 
-            # convergence of multiple losses may be ambiguous
+        return avg_losses
-            self._val_loss = sum(val_loss_sums) / len(val_loader)
 
-        return val_loss_sums
+    def _eval_loaders(
-
-    def train[B](
         self,
-        dataset: BatchedDataset[..., ..., I],
+        train_loader: EstimatorDataLoader[Any, Kw],
-        batch_estimator_map: Callable[[B, Self], K],
+        val_loader: EstimatorDataLoader[Any, Kw] | None = None,
+        aux_loaders: list[EstimatorDataLoader[Any, Kw]] | None = None,
+        progress_bar: tqdm | None = None,
+    ) -> tuple[list[float], list[float] | None, *list[float]]:
+        """
+        Evaluate estimator over each provided dataloader.
+
+        This streamlines the general train/val/etc evaluation pipeline during
+        training. This triggers logging of summary items, TensorBoard writes,
+        etc, and is therefore exclusively intended for use inside the primary
+        ``train()`` loop.
+
+        If looking to use this method publicly, you should instead work with
+        individual dataloaders and call helper methods like
+        ``get_batch_outputs()`` or ``get_batch_metrics()`` while iterating over
+        batches. This will have no internal side effects and provides much more
+        information (just aggregated losses are provided here).
+
+        .. admonition:: On epoch counting
+
+            Epoch counts start at 0 to allow for a sensible place to benchmark
+            the initial (potentially untrained/pre-trained) model before any
+            training data is seen. In the train loop, we increment the epoch
+            immediately, and all logging happens under the epoch value that's
+            set at the start of the iteration (rather than incrementing at the
+            end). Before beginning an additional training iteration, the
+            convergence condition in the ``while`` is effectively checking what
+            happened during the last epoch (the counter has not yet been
+            incremented); if no convergence, we begin again. (This is only
+            being noted because the epoch counting was previously quite
+            different: indexing started at ``1``, we incremented at the end of
+            the loop, and we didn't evaluate the model before the loop began.
+            This affects how we interpret plots and TensorBoard records, for
+            instance, so it's useful to spell out the approach clearly
+            somewhere given the many possible design choices here.)
+        """
+
+        train_loss = self._eval_epoch(train_loader, "train", progress_bar)
+
+        val_loss = None
+        if val_loader is not None:
+            val_loss = self._eval_epoch(val_loader, "val", progress_bar) 
+
+        aux_loaders = aux_loaders or []
+        aux_losses = [
+            self._eval_epoch(aux_loader, f"aux{i}", progress_bar)
+            for i, aux_loader in enumerate(aux_loaders)
+        ]
+        
+        return train_loss, val_loss, *aux_losses
+
+    def train(
+        self,
+        train_loader: EstimatorDataLoader[Any, Kw],
+        val_loader: EstimatorDataLoader[Any, Kw] | None = None,
+        aux_loaders: list[EstimatorDataLoader[Any, Kw]] | None = None,
+        *,
         lr: float = 1e-3,
         eps: float = 1e-8,
         max_grad_norm: float | None = None,
         max_epochs: int = 10,
         stop_after_epochs: int = 5,
-        batch_size: int = 256,
-        val_frac: float = 0.1,
-        train_transform: Transform | None = None,
-        val_transform: Transform | None = None,
-        dataset_split_kwargs: SplitKwargs | None = None,
-        dataset_balance_kwargs: BalanceKwargs | None = None,
-        dataloader_kwargs: LoaderKwargs | None = None,
         summarize_every: int = 1,
         chkpt_every: int = 1,
-        resume_latest: bool = False,
+        session_name: str | None = None,
         summary_writer: SummaryWriter | None = None,
     ) -> Estimator:
         """
-        TODO: consider making the dataloader ``collate_fn`` an explicit
+        .. todo::
-        parameter with a type signature that reflects ``B``, connecting the
+
-        ``batch_estimator_map`` somewhere
+            - consider making the dataloader ``collate_fn`` an explicit
+              parameter with a type signature that reflects ``B``, connecting
+              the ``batch_estimator_map`` somewhere. Might also re-type a
+              ``DataLoader`` in-house to allow a generic around ``B``
+            - Rework the validation specification. Accept something like a
+              "validate_with" parameter, or perhaps just move entirely to
+              accepting a dataloader list, label list. You might then also need
+              a "train_with," and you could set up sensible defaults so you
+              basically have the same interaction as now. The "problem" is you
+              always need a train set, and there's some clearly dependent logic
+              on a val set, but you don't *need* val, so this should be
+              slightly reworked (and the more general, *probably* the better in
+              this case, given I want to plug into the Plotter with possibly
+              several purely eval sets over the model training lifetime).
 
         Note: this method attempts to implement a general scheme for passing
         needed items to the estimator's loss function from the dataloader. The
@@ -324,10 +396,47 @@ class Trainer[I, K: EstimatorKwargs]:
             This function should map from batches - which *may* be item
             shaped, i.e., have an ``I`` skeleton, even if stacked items may be
             different on the inside - into estimator keyword arguments (type
-            ``K``). Collation behavior from a DataLoader (which can be
+            ``Kw``). Collation behavior from a DataLoader (which can be
             customized) doesn't consistently yield a known type shape, however,
             so it's not appropriate to use ``I`` as the callable param type.
 
+        .. admonition:: On session management
+
+            This method works around an implicit notion of training sessions.
+            Estimators are set during instantiation and effectively coupled
+            with ``Trainer`` instances, but datasets can be supplied
+            dynamically here. One can, for instance, run under one condition
+            (specific dataset, number of epochs, etc), then resume later under
+            another. Relevant details persist across calls: the estimator is
+            still attached, best val scores stowed, current epoch tracked. By
+            default, new ``session_names`` are always generated, but you can
+            write to the same TB location if you using the same
+            ``session_name`` across calls; that's about as close to a direct
+            training resume as you could want.
+
+            If restarting training on new datasets, including short
+            fine-tuning on training-plus-validation data, it's often sensible
+            to call ``.reset()`` between ``.train()`` calls. While the same
+            estimator will be used, tracked variables will be wiped; subsequent
+            model updates take place under a fresh epoch, no val losses, and be
+            logged under a separate TB session. This is the general approach to
+            "piecemeal" training, i.e., incremental model updates under varying
+            conditions (often just data changes).
+
+        .. warning::
+
+            Validation convergence when there are multiple losses may be
+            ambiguous. These are cases where certain parameter sets are
+            optimized independently; the sum over these losses may not reflect
+            expected or consistent behavior. For instance, we may record a low
+            cumulative loss early with a small 1st loss and moderate 2nd loss,
+            while later encountering a moderate 1st lost and small 2nd loss. We
+            might prefer the latter case, while ``_converged()`` will stick to
+            the former -- we need to consider possible weighting across losses,
+            or storing possibly several best models (e.g., for each loss, the
+            model that scores best, plus the one scoring best cumulatively,
+            etc).
+
         Parameters:
             dataset: dataset to train the estimator
             batch_estimator_map: function mapping from batch data to expected
@@ -348,90 +457,98 @@ class Trainer[I, K: EstimatorKwargs]:
                 dataset
             val_split_frac: fraction of dataset to use for validation
             chkpt_every: how often model checkpoints should be saved
-            resume_latest: resume training from the latest available checkpoint
-                in the `chkpt_dir`
         """
 
         logger.info("> Begin train loop:")
         logger.info(f"| > {lr=}")
         logger.info(f"| > {eps=}")
         logger.info(f"| > {max_epochs=}")
-        logger.info(f"| > {batch_size=}")
-        logger.info(f"| > {val_frac=}")
         logger.info(f"| > {chkpt_every=}")
-        logger.info(f"| > {resume_latest=}")
         logger.info(f"| > with device: {self.device}")
         logger.info(f"| > core count: {os.cpu_count()}")
 
-        writer: SummaryWriter
+        self._session_name = session_name or str(int(time.time()))
-        dir_prefix = str(int(time.time()))
+        tblog_path = Path(self.tblog_dir, self._session_name)
-        if summary_writer is None:
+        self._writer = summary_writer or SummaryWriter(f"{tblog_path}")
-            writer = SummaryWriter(f"{self.tblog_dir}")
+        progress_bar = tqdm(train_loader, unit="batch")
-        else:
-            writer = summary_writer
 
-        train_loader, val_loader = self.get_dataloaders(
+        # evaluate model on dataloaders once before training starts
-            dataset,
+        train_loss, val_loss, *_ = self._eval_loaders(
-            batch_size,
+            train_loader, val_loader, aux_loaders, progress_bar
-            val_frac=val_frac,
-            train_transform=train_transform,
-            val_transform=val_transform,
-            dataset_split_kwargs=dataset_split_kwargs,
-            dataset_balance_kwargs=dataset_balance_kwargs,
-            dataloader_kwargs=dataloader_kwargs,
         )
+        conv_loss = val_loss if val_loss else train_loss
+        self._conv_loss = sum(conv_loss) / len(conv_loss)
 
         optimizers = self.estimator.optimizers(lr=lr, eps=eps)
 
-        self._step = 0
+        while self._epoch < max_epochs and not self._converged(stop_after_epochs):
-        self._epoch = 1  # start from 1 for logging convenience
+            self._epoch += 1
-        while self._epoch <= max_epochs and not self._converged(
+            #train_frac = f"{self._epoch}/{max_epochs}"
-            self._epoch, stop_after_epochs
+            #stag_frac = f"{self._stagnant_epochs}/{stop_after_epochs}"
-        ):
+            #print(f"Training epoch {train_frac}...")
-            train_frac = f"{self._epoch}/{max_epochs}"
+            #print(f"Stagnant epochs {stag_frac}...")
-            stag_frac = f"{self._stagnant_epochs}/{stop_after_epochs}"
-            print(f"Training epoch {train_frac}...")
-            print(f"Stagnant epochs {stag_frac}...")
 
             epoch_start_time = time.time()
             self._train_epoch(
                 train_loader,
-                batch_estimator_map,
                 optimizers,
-                writer,
+                max_grad_norm,
-                max_grad_norm
+                progress_bar=progress_bar
             )
+            epoch_end_time = time.time() - epoch_start_time
+            self._log_event("train", "epoch_duration", epoch_end_time)
 
-            if val_frac > 0:
+            train_loss, val_loss, *_ = self._eval_loaders(
-                self._val_epoch(
+                train_loader, val_loader, aux_loaders, progress_bar
-                    val_loader,
-                    batch_estimator_map,
-                    optimizers,
-                    writer,
-                )
-
-            self._add_summary_item(
-                "epoch_time_sec",
-                time.time() - epoch_start_time
             )
+            # determine loss to use for measuring convergence
+            conv_loss = val_loss if val_loss else train_loss
+            self._conv_loss = sum(conv_loss) / len(conv_loss)
 
             if self._epoch % summarize_every == 0:
-                self._summarize(writer, self._epoch)
+                self._summarize()
-
-            # save checkpoint
             if self._epoch % chkpt_every == 0:
-                self.save_model(self._epoch, self.chkpt_dir, dir_prefix)
+                self.save_model()
-
-            self._epoch += 1
 
         return self.estimator
 
-    def _converged(self, epoch: int, stop_after_epochs: int) -> bool:
+    def _converged(self, stop_after_epochs: int) -> bool:
+        """
+        Check if model has converged.
+
+        This method looks at the current "convergence loss" (validation-based
+        if a val set is provided to ``train()``, otherwise the training loss is
+        used), checking if it's the best yet recorded, incrementing the
+        stagnancy count if not. Convergence is asserted only if the number of
+        stagnant epochs exceeds ``stop_after_epochs``.
+
+        .. admonition:: Evaluation order
+
+            Convergence losses are recorded before the first training update,
+            so initial model states are appropriately benchmarked by the time
+            ``_converged()`` is invoked.
+
+            If resuming training on the same dataset, one might expect only to
+            reset the stagnant epoch counter: you'll resume from the last
+            epoch, estimator state, and best seen loss, while allowed
+            ``stop_after_epochs`` more chances for better validation.
+
+            If picking up training on a new dataset, even a training+validation
+            setting, resetting the best seen loss and best model state is
+            needed: you can't reliably compare the existing stats under new
+            data. It's somewhat ambiguous whether ``epoch`` absolutely must be
+            reset; you could continue logging metrics under the same named
+            session. But best practices would suggest restarting the epoch
+            count and have events logged under a new session heading when data
+            change.
+        """
+
         converged = False
 
-        if epoch == 1 or self._val_loss < self._best_val_loss:
+        if self._conv_loss < self._best_conv_loss:
-            self._best_val_loss = self._val_loss
             self._stagnant_epochs = 0
+            self._best_conv_loss = self._conv_loss
+            self._best_conv_epoch = self._epoch
             self._best_model_state_dict = deepcopy(self.estimator.state_dict())
         else:
             self._stagnant_epochs += 1
@@ -442,94 +559,25 @@ class Trainer[I, K: EstimatorKwargs]:
 
         return converged
 
-    @staticmethod
+    def _summarize(self) -> None:
-    def get_dataloaders(
-        dataset: BatchedDataset,
-        batch_size: int,
-        val_frac: float = 0.1,
-        train_transform: Transform | None = None,
-        val_transform: Transform | None = None,
-        dataset_split_kwargs: SplitKwargs | None = None,
-        dataset_balance_kwargs: BalanceKwargs | None = None,
-        dataloader_kwargs: LoaderKwargs | None = None,
-    ) -> tuple[DataLoader, DataLoader]:
         """
-        Create training and validation dataloaders for the provided dataset.
+        Flush the training summary to the TensorBoard summary writer and print
+        metrics for the current epoch.
         """
 
-        if dataset_split_kwargs is None:
+        print(f"==== Epoch [{self._epoch}] summary ====")
-            dataset_split_kwargs = {}
+        for (group, name), epoch_map in self._summary.items():
-
+            for epoch, values in epoch_map.items():
-        if dataset_balance_kwargs is not None:
+                # compute average over batch items recorded for the epoch
-            dataset.balance(**dataset_balance_kwargs)
+                mean = torch.tensor(values).mean().item()
-
+                self._writer.add_scalar(f"{group}-{name}", mean, epoch)
-        if val_frac <= 0:
+                if epoch == self._epoch:
-            dataset.post_transform = train_transform
+                    print(
-            train_loader_kwargs: LoaderKwargs = {
+                        f"> ({len(values)}) [{group}] {name} :: {mean:.2f}"
-                "batch_size": min(batch_size, len(dataset)),
-                "num_workers": 0,
-                "shuffle": True,
-            }
-            if dataloader_kwargs is not None:
-                train_loader_kwargs: LoaderKwargs = {
-                    **train_loader_kwargs,
-                    **dataloader_kwargs
-                }
-
-            return (
-                DataLoader(dataset, **train_loader_kwargs),
-                DataLoader(Dataset())
                     )
 
-        train_dataset, val_dataset = dataset.split(
+        self._writer.flush()
-            [1 - val_frac, val_frac],
+        self._summary = defaultdict(lambda: defaultdict(list))
-            **dataset_split_kwargs,
-        )
-
-        # Dataset.split() returns light Subset objects of shallow copies of the
-        # underlying dataset; can change the transform attribute of both splits
-        # w/o overwriting
-        train_dataset.post_transform = train_transform
-        val_dataset.post_transform = val_transform
-
-        train_loader_kwargs: LoaderKwargs = {
-            "batch_size": min(batch_size, len(train_dataset)),
-            "num_workers": 0,
-            "shuffle": True,
-        }
-        val_loader_kwargs: LoaderKwargs = {
-            "batch_size": min(batch_size, len(val_dataset)),
-            "num_workers": 0,
-            "shuffle": True,  # shuffle to prevent homogeneous val batches
-        }
-
-        if dataloader_kwargs is not None:
-            train_loader_kwargs = {**train_loader_kwargs, **dataloader_kwargs}
-            val_loader_kwargs = {**val_loader_kwargs, **dataloader_kwargs}
-
-        train_loader = DataLoader(train_dataset, **train_loader_kwargs)
-        val_loader = DataLoader(val_dataset, **val_loader_kwargs)
-
-        return train_loader, val_loader
-
-    def _summarize(self, writer: SummaryWriter, epoch: int) -> None:
-        """
-        Flush the training summary to the TensorBoard summary writer.
-        """
-
-        summary_values = defaultdict(list)
-        for name, records in self._summary.items():
-            for value, step in records:
-                writer.add_scalar(name, value, step)
-                summary_values[name].append(value)
-
-        print(f"==== Epoch [{epoch}] summary ====")
-        for name, values in summary_values.items():
-            mean_value = torch.tensor(values).mean().item()
-            print(f"> ({len(values)}) {name} :: {mean_value:.2f}")
-
-        writer.flush()
-        self._summary = defaultdict(list)
 
     def _get_optimizer_parameters(
         self,
@@ -542,15 +590,13 @@ class Trainer[I, K: EstimatorKwargs]:
             if param.grad is not None
         ]
 
-    def _add_summary_item(self, name: str, value: float) -> None:
+    def _log_event(self, group: str, name: str, value: float) -> None:
-        self._summary[name].append((value, self._step))
+        session, epoch = self._session_name, self._epoch
 
-    def save_model(
+        self._summary[group, name][epoch].append(value)
-        self,
+        self._event_log[session][group][name][epoch].append(value)
-        epoch: int,
+
-        chkpt_dir: str | Path,
+    def save_model(self) -> None:
-        dir_prefix: str,
-    ) -> None:
         """
         Save a model checkpoint.
         """
@@ -560,15 +606,15 @@ class Trainer[I, K: EstimatorKwargs]:
         model_buff.seek(0)
 
         model_class = self.estimator.__class__.__name__
-        chkpt_name = f"m_{model_class}-e_{epoch}.pth"
+        chkpt_name = f"m_{model_class}-e_{self._epoch}.pth"
 
-        chkpt_dir = Path(chkpt_dir, dir_prefix)
+        chkpt_dir = Path(self.chkpt_dir, self._session_name)
         chkpt_path = Path(chkpt_dir, chkpt_name)
 
         chkpt_dir.mkdir(parents=True, exist_ok=True)
         chkpt_path.write_bytes(model_buff.getvalue())
 
-    def load_model(self, epoch: int, chkpt_dir: str) -> None:
+    def load_model(self, chkpt_dir: str, epoch: int) -> None:
         """
         Load a model checkpoint from a given epoch.
 

trainlib/utils/custom.mplstyle

@@ -1,6 +1,9 @@
 text.usetex                   : False
 mathtext.default              : regular
 
+# testing to prevent component overlap/clipping
+figure.constrained_layout.use : True
+
 font.family                   : sans-serif
 font.sans-serif               : DejaVu Sans
 font.serif                    : DejaVu Serif
@@ -14,7 +17,7 @@ axes.labelsize       : 9
 xtick.labelsize               : 9
 ytick.labelsize               : 9
 
-#axes.prop_cycle : cycler('color', ['4f7dd5', 'af7031', '55905e', 'd84739', '888348', 'b75e8b', '2f8f99', '9862cb'])
+# monobiome -d 0.45 -l 22
 axes.prop_cycle               : cycler('color', ['5e8de4', 'c38141', '67a771', 'e15344', '9e9858', '41a6b0', 'a46fd7', 'c86d9a'])
 
 image.interpolation           : nearest

trainlib/utils/map.py

@@ -0,0 +1,10 @@
+from collections import defaultdict
+
+
+def nested_defaultdict(
+    depth: int,
+    final: type = dict,
+) -> defaultdict:
+    if depth == 1:
+        return defaultdict(final)
+    return defaultdict(lambda: nested_defaultdict(depth - 1, final))

trainlib/utils/op.py

@@ -0,0 +1,9 @@
+from torch import Tensor
+
+
+def r2_score(y: Tensor, y_hat: Tensor) -> Tensor:
+    ss_res = ((y - y_hat)**2).sum()
+    ss_tot = ((y - y.mean())**2).sum()
+    r2 = 1 - ss_res / ss_tot
+
+    return r2

trainlib/utils/session.py

@@ -3,6 +3,7 @@ import random
 import numpy as np
 import torch
 from torch import Tensor
+from torch.utils import _pytree as pytree
 
 
 def seed_all_backends(seed: int | Tensor | None = None) -> None:
@@ -19,3 +20,9 @@ def seed_all_backends(seed: int | Tensor | None = None) -> None:
     torch.cuda.manual_seed(seed)
     torch.backends.cudnn.deterministic = True
     torch.backends.cudnn.benchmark = False
+
+def ensure_same_device[T](tree: T, device: str) -> T:
+    return pytree.tree_map(
+        lambda x: x.to(device) if isinstance(x, torch.Tensor) else x,
+        tree,
+    )

trainlib/utils/text.py

@@ -1,8 +1,14 @@
+import re
 from typing import Any
 
 from colorama import Style
 
+camel2snake_regex: re.Pattern[str] = re.compile(
+    r"(?<!^)(?=[A-Z][a-z])|(?<=[a-z])(?=[A-Z])"
+)
+
+def camel_to_snake(text: str) -> str:
+    return camel2snake_regex.sub("_", text).lower()
 
 def color_text(text: str, *colorama_args: Any) -> str:
     return f"{''.join(colorama_args)}{text}{Style.RESET_ALL}"
-

trainlib/utils/type.py

@@ -1,14 +1,17 @@
 from typing import Any, TypedDict
-from collections.abc import Callable, Iterable
+from collections.abc import Callable, Iterable, Sequence
 
+import numpy as np
 from torch import Tensor
 from torch.utils.data.sampler import Sampler
 
 from trainlib.dataset import BatchedDataset
 
+# need b/c matplotlib axes are insanely stupid
+type AxesArray = np.ndarray[tuple[int, int], np.dtype[np.object_]]
 
 class LoaderKwargs(TypedDict, total=False):
-    batch_size: int
+    batch_size: int | None
     shuffle: bool
     sampler: Sampler | Iterable | None
     batch_sampler: Sampler[list] | Iterable[list] | None
@@ -50,3 +53,17 @@ class OptimizerKwargs(TypedDict, total=False):
     capturable: bool
     differentiable: bool
     fused: bool | None
+
+
+class SubplotsKwargs(TypedDict, total=False):
+    sharex: bool | str
+    sharey: bool | str
+    squeeze: bool
+    width_ratios: Sequence[float]
+    height_ratios: Sequence[float]
+    subplot_kw: dict[str, Any]
+    gridspec_kw: dict[str, Any]
+    figsize: tuple[float, float]
+    dpi: float
+    layout: str
+    constrained_layout: bool

uv.lock

@@ -128,43 +128,59 @@ wheels = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.4"
+version = "3.4.6"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/7b/60/e3bec1881450851b087e301bedc3daa9377a4d45f1c26aa90b0b235e38aa/charset_normalizer-3.4.6.tar.gz", hash = "sha256:1ae6b62897110aa7c79ea2f5dd38d1abca6db663687c0b1ad9aed6f6bae3d9d6", size = 143363, upload-time = "2026-03-15T18:53:25.478Z" }
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