data_source

Summary:
Move dataset_args and dataloader_args from ExperimentConfig into a new member called datasource so that it can contain replaceables.

Also add enum Task for task type.

Reviewed By: shapovalov

Differential Revision: D36201719

fbshipit-source-id: 47d6967bfea3b7b146b6bbd1572e0457c9365871

projects/implicitron_trainer/README.md

@@ -66,7 +66,8 @@ If you have a custom `experiment.py` script (as in the Option 2 above), replace
 To run training, pass a yaml config file, followed by a list of overridden arguments.
 For example, to train NeRF on the first skateboard sequence from CO3D dataset, you can run:
 ```shell
-pytorch3d_implicitron_runner --config-path ./configs/ --config-name repro_singleseq_nerf dataset_args.dataset_root=<DATASET_ROOT> dataset_args.category='skateboard' dataset_args.test_restrict_sequence_id=0 test_when_finished=True exp_dir=<CHECKPOINT_DIR>
+dataset_args=data_source_args.dataset_args
+pytorch3d_implicitron_runner --config-path ./configs/ --config-name repro_singleseq_nerf $dataset_args.dataset_root=<DATASET_ROOT> $dataset_args.category='skateboard' $dataset_args.test_restrict_sequence_id=0 test_when_finished=True exp_dir=<CHECKPOINT_DIR>
 ```
 
 Here, `--config-path` points to the config path relative to `pytorch3d_implicitron_runner` location;
@@ -84,7 +85,8 @@ To run evaluation on the latest checkpoint after (or during) training, simply ad
 
 E.g. for executing the evaluation on the NeRF skateboard sequence, you can run:
 ```shell
-pytorch3d_implicitron_runner --config-path ./configs/ --config-name repro_singleseq_nerf dataset_args.dataset_root=<CO3D_DATASET_ROOT> dataset_args.category='skateboard' dataset_args.test_restrict_sequence_id=0 exp_dir=<CHECKPOINT_DIR> eval_only=True
+dataset_args=data_source_args.dataset_args
+pytorch3d_implicitron_runner --config-path ./configs/ --config-name repro_singleseq_nerf $dataset_args.dataset_root=<CO3D_DATASET_ROOT> $dataset_args.category='skateboard' $dataset_args.test_restrict_sequence_id=0 exp_dir=<CHECKPOINT_DIR> eval_only=True
 ```
 Evaluation prints the metrics to `stdout` and dumps them to a json file in `exp_dir`.
 
@@ -202,7 +204,7 @@ to replace the implementation and potentially override the parameters.
 # Code and config structure
 
 As per above, the config structure is parsed automatically from the module hierarchy.
-In particular, model parameters are contained in `generic_model_args` node, and dataset parameters in `dataset_args` node.
+In particular, model parameters are contained in `generic_model_args` node, and dataset parameters in `data_source_args` node.
 
 Here is the class structure (single-line edges show aggregation, while double lines show available implementations):
 ```
@@ -233,8 +235,9 @@ generic_model_args: GenericModel
     ╘== AngleWeightedReductionFeatureAggregator
     ╘== ReductionFeatureAggregator
 solver_args: init_optimizer
-dataset_args: dataset_zoo
+data_source_args: ImplicitronDataSource
-dataloader_args: dataloader_zoo
+└-- dataset_args
+└-- dataloader_args
 ```
 
 Please look at the annotations of the respective classes or functions for the lists of hyperparameters.

projects/implicitron_trainer/configs/repro_base.yaml

@@ -5,7 +5,8 @@ exp_dir: ./data/exps/base/
 architecture: generic
 visualize_interval: 0
 visdom_port: 8097
-dataloader_args:
+data_source_args:
+  dataloader_args:
     batch_size: 10
     dataset_len: 1000
     dataset_len_val: 1
@@ -20,7 +21,7 @@ dataloader_args:
     - 8
     - 9
     - 10
-dataset_args:
+  dataset_args:
     dataset_root: ${oc.env:CO3D_DATASET_ROOT}
     load_point_clouds: false
     mask_depths: false

projects/implicitron_trainer/configs/repro_multiseq_base.yaml

@@ -1,7 +1,8 @@
 defaults:
 - repro_base.yaml
 - _self_
-dataloader_args:
+data_source_args:
+  dataloader_args:
     batch_size: 10
     dataset_len: 1000
     dataset_len_val: 1
@@ -16,7 +17,7 @@ dataloader_args:
     - 8
     - 9
     - 10
-dataset_args:
+  dataset_args:
     assert_single_seq: false
     dataset_name: co3d_multisequence
     load_point_clouds: false

projects/implicitron_trainer/configs/repro_singleseq_base.yaml

@@ -1,14 +1,15 @@
 defaults:
 - repro_base
 - _self_
-dataloader_args:
+data_source_args:
+  dataloader_args:
     batch_size: 1
     dataset_len: 1000
     dataset_len_val: 1
     num_workers: 8
     images_per_seq_options:
     - 2
-dataset_args:
+  dataset_args:
     dataset_name: co3d_singlesequence
     assert_single_seq: true
     n_frames_per_sequence: -1

projects/implicitron_trainer/configs/repro_singleseq_wce_base.yaml

@@ -1,7 +1,8 @@
 defaults:
 - repro_singleseq_base
 - _self_
-dataloader_args:
+data_source_args:
+  dataloader_args:
     batch_size: 10
     dataset_len: 1000
     dataset_len_val: 1

projects/implicitron_trainer/experiment.py

@@ -64,8 +64,9 @@ import tqdm
 from omegaconf import DictConfig, OmegaConf
 from packaging import version
 from pytorch3d.implicitron.dataset import utils as ds_utils
-from pytorch3d.implicitron.dataset.dataloader_zoo import dataloader_zoo, Dataloaders
+from pytorch3d.implicitron.dataset.data_source import ImplicitronDataSource, Task
-from pytorch3d.implicitron.dataset.dataset_zoo import dataset_zoo, Datasets
+from pytorch3d.implicitron.dataset.dataloader_zoo import Dataloaders
+from pytorch3d.implicitron.dataset.dataset_zoo import Datasets
 from pytorch3d.implicitron.dataset.implicitron_dataset import (
     FrameData,
     ImplicitronDataset,
@@ -428,7 +429,7 @@ def trainvalidate(
             optimizer.step()
 
 
-def run_training(cfg: DictConfig, device: str = "cpu"):
+def run_training(cfg: DictConfig, device: str = "cpu") -> None:
     """
     Entry point to run the training and validation loops
     based on the specified config file.
@@ -452,8 +453,9 @@ def run_training(cfg: DictConfig, device: str = "cpu"):
         warnings.warn("Cant dump config due to insufficient permissions!")
 
     # setup datasets
-    datasets = dataset_zoo(**cfg.dataset_args)
+    datasource = ImplicitronDataSource(**cfg.data_source_args)
-    dataloaders = dataloader_zoo(datasets, **cfg.dataloader_args)
+    datasets, dataloaders = datasource.get_datasets_and_dataloaders()
+    task = datasource.get_task()
 
     # init the model
     model, stats, optimizer_state = init_model(cfg)
@@ -464,7 +466,7 @@ def run_training(cfg: DictConfig, device: str = "cpu"):
 
     # only run evaluation on the test dataloader
     if cfg.eval_only:
-        _eval_and_dump(cfg, datasets, dataloaders, model, stats, device=device)
+        _eval_and_dump(cfg, task, datasets, dataloaders, model, stats, device=device)
         return
 
     # init the optimizer
@@ -526,7 +528,7 @@ def run_training(cfg: DictConfig, device: str = "cpu"):
                 and cfg.test_interval > 0
                 and epoch % cfg.test_interval == 0
             ):
-                run_eval(cfg, model, stats, dataloaders.test, device=device)
+                _run_eval(model, stats, dataloaders.test, task, device=device)
 
             assert stats.epoch == epoch, "inconsistent stats!"
 
@@ -546,11 +548,17 @@ def run_training(cfg: DictConfig, device: str = "cpu"):
                 logger.info(f"LR change! {cur_lr} -> {new_lr}")
 
     if cfg.test_when_finished:
-        _eval_and_dump(cfg, datasets, dataloaders, model, stats, device=device)
+        _eval_and_dump(cfg, task, datasets, dataloaders, model, stats, device=device)
 
 
 def _eval_and_dump(
-    cfg, datasets: Datasets, dataloaders: Dataloaders, model, stats, device
+    cfg,
+    task: Task,
+    datasets: Datasets,
+    dataloaders: Dataloaders,
+    model,
+    stats,
+    device,
 ) -> None:
     """
     Run the evaluation loop with the test data loader and
@@ -562,16 +570,13 @@ def _eval_and_dump(
     if dataloader is None:
         raise ValueError('Dataloaders have to contain the "test" entry for eval!')
 
-    eval_task = cfg.dataset_args["dataset_name"].split("_")[-1]
+    if task == Task.SINGLE_SEQUENCE:
-    if eval_task == "singlesequence":
         if datasets.train is None:
             raise ValueError("train dataset must be provided")
         all_source_cameras = _get_all_source_cameras(datasets.train)
     else:
         all_source_cameras = None
-    results = run_eval(
+    results = _run_eval(model, all_source_cameras, dataloader, task, device=device)
-        cfg, model, all_source_cameras, dataloader, eval_task, device=device
-    )
 
     # add the evaluation epoch to the results
     for r in results:
@@ -598,7 +603,7 @@ def _get_eval_frame_data(frame_data):
     return frame_data_for_eval
 
 
-def run_eval(cfg, model, all_source_cameras, loader, task, device):
+def _run_eval(model, all_source_cameras, loader, task: Task, device):
     """
     Run the evaluation loop on the test dataloader
     """
@@ -672,8 +677,7 @@ def _seed_all_random_engines(seed: int):
 class ExperimentConfig:
     generic_model_args: DictConfig = get_default_args_field(GenericModel)
     solver_args: DictConfig = get_default_args_field(init_optimizer)
-    dataset_args: DictConfig = get_default_args_field(dataset_zoo)
+    data_source_args: DictConfig = get_default_args_field(ImplicitronDataSource)
-    dataloader_args: DictConfig = get_default_args_field(dataloader_zoo)
     architecture: str = "generic"
     detect_anomaly: bool = False
     eval_only: bool = False

projects/implicitron_trainer/visualize_reconstruction.py

@@ -23,6 +23,7 @@ import torch
 import torch.nn.functional as Fu
 from experiment import init_model
 from omegaconf import OmegaConf
+from pytorch3d.implicitron.dataset.data_source import ImplicitronDataSource
 from pytorch3d.implicitron.dataset.dataset_zoo import dataset_zoo
 from pytorch3d.implicitron.dataset.implicitron_dataset import (
     FrameData,
@@ -326,12 +327,14 @@ def export_scenes(
     config.gpu_idx = gpu_idx
     config.exp_dir = exp_dir
     # important so that the CO3D dataset gets loaded in full
-    config.dataset_args.test_on_train = False
+    config.data_source_args.dataset_args.test_on_train = False
     # Set the rendering image size
     config.generic_model_args.render_image_width = render_size[0]
     config.generic_model_args.render_image_height = render_size[1]
     if restrict_sequence_name is not None:
-        config.dataset_args.restrict_sequence_name = restrict_sequence_name
+        config.data_source_args.dataset_args.restrict_sequence_name = (
+            restrict_sequence_name
+        )
 
     # Set up the CUDA env for the visualization
     os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
@@ -343,7 +346,8 @@ def export_scenes(
     model.eval()
 
     # Setup the dataset
-    datasets = dataset_zoo(**config.dataset_args)
+    datasource = ImplicitronDataSource(**config.data_source_args)
+    datasets = dataset_zoo(**datasource.dataset_args)
     dataset: Optional[ImplicitronDatasetBase] = getattr(datasets, split, None)
     if dataset is None:
         raise ValueError(f"{split} dataset not provided")

pytorch3d/implicitron/dataset/data_source.py

@@ -0,0 +1,48 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from typing import Tuple
+
+from omegaconf import DictConfig
+from pytorch3d.implicitron.tools.config import get_default_args_field, ReplaceableBase
+
+from .dataloader_zoo import dataloader_zoo, Dataloaders
+from .dataset_zoo import dataset_zoo, Datasets
+
+
+class Task(Enum):
+    SINGLE_SEQUENCE = "singlesequence"
+    MULTI_SEQUENCE = "multisequence"
+
+
+class DataSourceBase(ReplaceableBase):
+    """
+    Base class for a data source in Implicitron. It encapsulates Dataset
+    and DataLoader configuration.
+    """
+
+    def get_datasets_and_dataloaders(self) -> Tuple[Datasets, Dataloaders]:
+        raise NotImplementedError()
+
+
+class ImplicitronDataSource(DataSourceBase):
+    """
+    Represents the data used in Implicitron. This is the only implementation
+    of DataSourceBase provided.
+    """
+
+    dataset_args: DictConfig = get_default_args_field(dataset_zoo)
+    dataloader_args: DictConfig = get_default_args_field(dataloader_zoo)
+
+    def get_datasets_and_dataloaders(self) -> Tuple[Datasets, Dataloaders]:
+        datasets = dataset_zoo(**self.dataset_args)
+        dataloaders = dataloader_zoo(datasets, **self.dataloader_args)
+        return datasets, dataloaders
+
+    def get_task(self) -> Task:
+        eval_task = self.dataset_args["dataset_name"].split("_")[-1]
+        return Task(eval_task)

pytorch3d/implicitron/eval_demo.py

@@ -7,11 +7,12 @@
 
 import dataclasses
 import os
-from typing import cast, Optional, Tuple
+from typing import Any, cast, Dict, List, Optional, Tuple
 
 import lpips
 import torch
 from iopath.common.file_io import PathManager
+from pytorch3d.implicitron.dataset.data_source import Task
 from pytorch3d.implicitron.dataset.dataloader_zoo import dataloader_zoo
 from pytorch3d.implicitron.dataset.dataset_zoo import CO3D_CATEGORIES, dataset_zoo
 from pytorch3d.implicitron.dataset.implicitron_dataset import (
@@ -47,10 +48,12 @@ def main() -> None:
     """
 
     task_results = {}
-    for task in ("singlesequence", "multisequence"):
+    for task in (Task.SINGLE_SEQUENCE, Task.MULTI_SEQUENCE):
         task_results[task] = []
-        for category in CO3D_CATEGORIES[: (20 if task == "singlesequence" else 10)]:
+        for category in CO3D_CATEGORIES[: (20 if task == Task.SINGLE_SEQUENCE else 10)]:
-            for single_sequence_id in (0, 1) if task == "singlesequence" else (None,):
+            for single_sequence_id in (
+                (0, 1) if task == Task.SINGLE_SEQUENCE else (None,)
+            ):
                 category_result = evaluate_dbir_for_category(
                     category, task=task, single_sequence_id=single_sequence_id
                 )
@@ -74,9 +77,9 @@ def main() -> None:
 
 
 def evaluate_dbir_for_category(
-    category: str = "apple",
+    category: str,
+    task: Task,
     bg_color: Tuple[float, float, float] = (0.0, 0.0, 0.0),
-    task: str = "singlesequence",
     single_sequence_id: Optional[int] = None,
     num_workers: int = 16,
     path_manager: Optional[PathManager] = None,
@@ -101,14 +104,16 @@ def evaluate_dbir_for_category(
 
     torch.manual_seed(42)
 
-    if task not in ["multisequence", "singlesequence"]:
+    dataset_name = {
-        raise ValueError("'task' has to be either 'multisequence' or 'singlesequence'")
+        Task.SINGLE_SEQUENCE: "co3d_singlesequence",
+        Task.MULTI_SEQUENCE: "co3d_multisequence",
+    }[task]
 
     datasets = dataset_zoo(
         category=category,
         dataset_root=os.environ["CO3D_DATASET_ROOT"],
-        assert_single_seq=task == "singlesequence",
+        assert_single_seq=task == Task.SINGLE_SEQUENCE,
-        dataset_name=f"co3d_{task}",
+        dataset_name=dataset_name,
         test_on_train=False,
         load_point_clouds=True,
         test_restrict_sequence_id=single_sequence_id,
@@ -122,7 +127,7 @@ def evaluate_dbir_for_category(
     if test_dataset is None or test_dataloader is None:
         raise ValueError("must have a test dataset.")
 
-    if task == "singlesequence":
+    if task == Task.SINGLE_SEQUENCE:
         # all_source_cameras are needed for evaluation of the
         # target camera difficulty
         # pyre-fixme[16]: `ImplicitronDataset` has no attribute `frame_annots`.
@@ -173,7 +178,9 @@ def evaluate_dbir_for_category(
     return category_result["results"]
 
 
-def _print_aggregate_results(task, task_results) -> None:
+def _print_aggregate_results(
+    task: Task, task_results: Dict[Task, List[List[Dict[str, Any]]]]
+) -> None:
     """
     Prints the aggregate metrics for a given task.
     """

pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py

@@ -14,6 +14,7 @@ from typing import Any, Dict, List, Optional, Sequence, Union
 import numpy as np
 import torch
 import torch.nn.functional as F
+from pytorch3d.implicitron.dataset.data_source import Task
 from pytorch3d.implicitron.dataset.implicitron_dataset import FrameData
 from pytorch3d.implicitron.dataset.utils import is_known_frame, is_train_frame
 from pytorch3d.implicitron.models.base_model import ImplicitronRender
@@ -317,7 +318,7 @@ def eval_batch(
             if visualize:
                 visualizer.show_depth(abs_.mean().item(), name_postfix, loss_mask_now)
                 if break_after_visualising:
-                    import pdb
+                    import pdb  # noqa: B602
 
                     pdb.set_trace()
 
@@ -411,16 +412,16 @@ def _reduce_camera_iou_overlap(ious: torch.Tensor, topk: int = 2) -> torch.Tenso
     return ious.topk(k=min(topk, len(ious) - 1)).values.mean()
 
 
-def get_camera_difficulty_bin_edges(task: str):
+def _get_camera_difficulty_bin_edges(task: Task):
     """
     Get the edges of camera difficulty bins.
     """
     _eps = 1e-5
-    if task == "multisequence":
+    if task == Task.MULTI_SEQUENCE:
         # TODO: extract those to constants
         diff_bin_edges = torch.linspace(0.5, 1.0 + _eps, 4)
         diff_bin_edges[0] = 0.0 - _eps
-    elif task == "singlesequence":
+    elif task == Task.SINGLE_SEQUENCE:
         diff_bin_edges = torch.tensor([0.0 - _eps, 0.97, 0.98, 1.0 + _eps]).float()
     else:
         raise ValueError(f"No such eval task {task}.")
@@ -430,7 +431,7 @@ def get_camera_difficulty_bin_edges(task: str):
 
 def summarize_nvs_eval_results(
     per_batch_eval_results: List[Dict[str, Any]],
-    task: str = "singlesequence",
+    task: Task,
 ):
     """
     Compile the per-batch evaluation results `per_batch_eval_results` into
@@ -439,7 +440,6 @@ def summarize_nvs_eval_results(
     Args:
         per_batch_eval_results: Metrics of each per-batch evaluation.
         task: The type of the new-view synthesis task.
-            Either 'singlesequence' or 'multisequence'.
 
     Returns:
         nvs_results_flat: A flattened dict of all aggregate metrics.
@@ -447,10 +447,10 @@ def summarize_nvs_eval_results(
     """
     n_batches = len(per_batch_eval_results)
     eval_sets: List[Optional[str]] = []
-    if task == "singlesequence":
+    if task == Task.SINGLE_SEQUENCE:
         eval_sets = [None]
         # assert n_batches==100
-    elif task == "multisequence":
+    elif task == Task.MULTI_SEQUENCE:
         eval_sets = ["train", "test"]
         # assert n_batches==1000
     else:
@@ -466,17 +466,17 @@ def summarize_nvs_eval_results(
     # init the result database dict
     results = []
 
-    diff_bin_edges, diff_bin_names = get_camera_difficulty_bin_edges(task)
+    diff_bin_edges, diff_bin_names = _get_camera_difficulty_bin_edges(task)
     n_diff_edges = diff_bin_edges.numel()
 
     # add per set averages
     for SET in eval_sets:
         if SET is None:
-            # task=='singlesequence'
+            assert task == Task.SINGLE_SEQUENCE
             ok_set = torch.ones(n_batches, dtype=torch.bool)
             set_name = "test"
         else:
-            # task=='multisequence'
+            assert task == Task.MULTI_SEQUENCE
             ok_set = is_train == int(SET == "train")
             set_name = SET
 
@@ -501,7 +501,7 @@ def summarize_nvs_eval_results(
                 }
             )
 
-        if task == "multisequence":
+        if task == Task.MULTI_SEQUENCE:
             # split based on n_src_views
             n_src_views = batch_sizes - 1
             for n_src in EVAL_N_SRC_VIEWS: