CO3Dv2 trainer configs

Summary:
Adds yaml configs to train selected methods on CO3Dv2.

Few more updates:
1) moved some fields to base classes so that we can check is_multisequence in experiment.py
2) skip loading all train cameras for multisequence datasets, without this, co3d-fewview is untrainable
3) fix bug in json index dataset provider v2

Reviewed By: kjchalup

Differential Revision: D38952755

fbshipit-source-id: 3edac6fc8e20775aa70400bd73a0e6d52b091e0c

projects/implicitron_trainer/configs/repro_multiseq_co3dv2_base.yaml

+data_source_ImplicitronDataSource_args:
+  dataset_map_provider_class_type: JsonIndexDatasetMapProviderV2
+  dataset_map_provider_JsonIndexDatasetMapProviderV2_args:
+    category: teddybear
+    subset_name: fewview_dev
+training_loop_ImplicitronTrainingLoop_args:
+  evaluator_ImplicitronEvaluator_args:
+    is_multisequence: true

projects/implicitron_trainer/configs/repro_multiseq_v2_nerf_wce.yaml

+defaults:
+- repro_multiseq_nerf_wce.yaml
+- repro_multiseq_co3dv2_base.yaml
+- _self_

projects/implicitron_trainer/configs/repro_multiseq_v2_nerformer.yaml

+defaults:
+- repro_multiseq_nerformer.yaml
+- repro_multiseq_co3dv2_base.yaml
+- _self_

projects/implicitron_trainer/configs/repro_multiseq_v2_srn_ad_hypernet.yaml

+defaults:
+- repro_multiseq_srn_ad_hypernet.yaml
+- repro_multiseq_co3dv2_base.yaml
+- _self_

projects/implicitron_trainer/configs/repro_multiseq_v2_srn_wce.yaml

+defaults:
+- repro_multiseq_srn_wce.yaml
+- repro_multiseq_co3dv2_base.yaml
+- _self_

projects/implicitron_trainer/configs/repro_singleseq_co3dv2_base.yaml

+data_source_ImplicitronDataSource_args:
+  dataset_map_provider_class_type: JsonIndexDatasetMapProviderV2
+  dataset_map_provider_JsonIndexDatasetMapProviderV2_args:
+    category: teddybear
+    subset_name: manyview_dev_0
+training_loop_ImplicitronTrainingLoop_args:
+  evaluator_ImplicitronEvaluator_args:
+    is_multisequence: false

projects/implicitron_trainer/configs/repro_singleseq_v2_idr.yaml

+defaults:
+- repro_singleseq_idr.yaml
+- repro_singleseq_co3dv2_base.yaml
+- _self_

projects/implicitron_trainer/configs/repro_singleseq_v2_nerf.yaml

+defaults:
+- repro_singleseq_nerf.yaml
+- repro_singleseq_co3dv2_base.yaml
+- _self_

projects/implicitron_trainer/configs/repro_singleseq_v2_nerformer.yaml

+defaults:
+- repro_singleseq_nerformer.yaml
+- repro_singleseq_co3dv2_base.yaml
+- _self_

projects/implicitron_trainer/configs/repro_singleseq_v2_srn_noharm.yaml

+defaults:
+- repro_singleseq_srn_noharm.yaml
+- repro_singleseq_co3dv2_base.yaml
+- _self_

projects/implicitron_trainer/experiment.py

@@ -207,7 +207,10 @@ class Experiment(Configurable):  # pyre-ignore: 13
                 val_loader,
             ) = accelerator.prepare(model, optimizer, train_loader, val_loader)
 
-        all_train_cameras = self.data_source.all_train_cameras
+        if not self.training_loop.evaluator.is_multisequence:
+            all_train_cameras = self.data_source.all_train_cameras
+        else:
+            all_train_cameras = None
 
         # Enter the main training loop.
         self.training_loop.run(

projects/implicitron_trainer/impl/training_loop.py

@@ -30,6 +30,14 @@ logger = logging.getLogger(__name__)
 
 
 class TrainingLoopBase(ReplaceableBase):
+    """
+    Members:
+        evaluator: An EvaluatorBase instance, used to evaluate training results.
+    """
+
+    evaluator: Optional[EvaluatorBase]
+    evaluator_class_type: Optional[str] = "ImplicitronEvaluator"
+
     def run(
         self,
         train_loader: DataLoader,
@@ -58,7 +66,6 @@ class ImplicitronTrainingLoop(TrainingLoopBase):  # pyre-ignore [13]
     """
     Members:
         eval_only: If True, only run evaluation using the test dataloader.
-        evaluator: An EvaluatorBase instance, used to evaluate training results.
         max_epochs: Train for this many epochs. Note that if the model was
             loaded from a checkpoint, we will restart training at the appropriate
             epoch and run for (max_epochs - checkpoint_epoch) epochs.
@@ -82,8 +89,6 @@ class ImplicitronTrainingLoop(TrainingLoopBase):  # pyre-ignore [13]
 
     # Parameters of the outer training loop.
     eval_only: bool = False
-    evaluator: EvaluatorBase
-    evaluator_class_type: str = "ImplicitronEvaluator"
     max_epochs: int = 1000
     store_checkpoints: bool = True
     store_checkpoints_purge: int = 1

projects/implicitron_trainer/tests/experiment.yaml

@@ -406,8 +406,13 @@ optimizer_factory_ImplicitronOptimizerFactory_args:
   linear_exponential_lr_milestone: 200
   linear_exponential_start_gamma: 0.1
 training_loop_ImplicitronTrainingLoop_args:
-  eval_only: false
   evaluator_class_type: ImplicitronEvaluator
+  evaluator_ImplicitronEvaluator_args:
+    is_multisequence: false
+    camera_difficulty_bin_breaks:
+    - 0.97
+    - 0.98
+  eval_only: false
   max_epochs: 1000
   store_checkpoints: true
   store_checkpoints_purge: 1
@@ -420,8 +425,3 @@ training_loop_ImplicitronTrainingLoop_args:
   visdom_env: ''
   visdom_port: 8097
   visdom_server: http://127.0.0.1
-  evaluator_ImplicitronEvaluator_args:
-    camera_difficulty_bin_breaks:
-    - 0.97
-    - 0.98
-    is_multisequence: false

projects/implicitron_trainer/tests/test_experiment.py

@@ -190,6 +190,34 @@ class TestNerfRepro(unittest.TestCase):
             experiment.dump_cfg(cfg)
             experiment_runner.run()
 
+    @unittest.skip("This test runs nerf training on co3d v2 - manyview.")
+    def test_nerf_co3dv2_manyview(self):
+        # Train NERF
+        if not interactive_testing_requested():
+            return
+        with initialize_config_dir(config_dir=str(IMPLICITRON_CONFIGS_DIR)):
+            cfg = compose(
+                config_name="repro_singleseq_v2_nerf",
+                overrides=[],
+            )
+            experiment_runner = experiment.Experiment(**cfg)
+            experiment.dump_cfg(cfg)
+            experiment_runner.run()
+
+    @unittest.skip("This test runs nerformer training on co3d v2 - fewview.")
+    def test_nerformer_co3dv2_fewview(self):
+        # Train NeRFormer
+        if not interactive_testing_requested():
+            return
+        with initialize_config_dir(config_dir=str(IMPLICITRON_CONFIGS_DIR)):
+            cfg = compose(
+                config_name="repro_multiseq_v2_nerformer",
+                overrides=[],
+            )
+            experiment_runner = experiment.Experiment(**cfg)
+            experiment.dump_cfg(cfg)
+            experiment_runner.run()
+
     @unittest.skip("This test checks resuming of the NeRF training.")
     def test_nerf_blender_resume(self):
         # Train one train batch of NeRF, then resume for one more batch.

pytorch3d/implicitron/dataset/json_index_dataset.py

@@ -36,6 +36,7 @@ from pytorch3d.io import IO
 from pytorch3d.renderer.camera_utils import join_cameras_as_batch
 from pytorch3d.renderer.cameras import CamerasBase, PerspectiveCameras
 from pytorch3d.structures.pointclouds import Pointclouds
+from tqdm import tqdm
 
 from . import types
 from .dataset_base import DatasetBase, FrameData
@@ -338,9 +339,10 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
         """
         Returns the cameras corresponding to all the known frames.
         """
+        logger.info("Loading all train cameras.")
         cameras = []
         # pyre-ignore[16]
-        for frame_idx, frame_annot in enumerate(self.frame_annots):
+        for frame_idx, frame_annot in enumerate(tqdm(self.frame_annots)):
             frame_type = self._get_frame_type(frame_annot)
             if frame_type is None:
                 raise ValueError("subsets not loaded")

pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py

@@ -14,6 +14,7 @@ from collections import defaultdict
 from typing import Dict, List, Optional, Tuple, Type, Union
 
 import numpy as np
+from iopath.common.file_io import PathManager
 
 from omegaconf import DictConfig
 from pytorch3d.implicitron.dataset.dataset_map_provider import (
@@ -383,12 +384,11 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
         return data
 
     def _get_available_subset_names(self):
-        path_manager = self.path_manager_factory.get()
-        if path_manager is not None:
-            dataset_root = path_manager.get_local_path(self.dataset_root)
-        else:
-            dataset_root = self.dataset_root
-        return get_available_subset_names(dataset_root, self.category)
+        return get_available_subset_names(
+            self.dataset_root,
+            self.category,
+            path_manager=self.path_manager_factory.get(),
+        )
 
     def _extend_test_data_with_known_views(
         self,
@@ -425,18 +425,30 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
         return eval_batch_index_out, list(test_subset_mapping_set)
 
 
-def get_available_subset_names(dataset_root: str, category: str) -> List[str]:
+def get_available_subset_names(
+    dataset_root: str,
+    category: str,
+    path_manager: Optional[PathManager] = None,
+) -> List[str]:
     """
     Get the available subset names for a given category folder inside a root dataset
     folder `dataset_root`.
     """
     category_dir = os.path.join(dataset_root, category)
-    if not os.path.isdir(category_dir):
+    category_dir_exists = (
+        (path_manager is not None) and path_manager.isdir(category_dir)
+    ) or os.path.isdir(category_dir)
+    if not category_dir_exists:
         raise ValueError(
             f"Looking for dataset files in {category_dir}. "
             + "Please specify a correct dataset_root folder."
         )
-    set_list_jsons = os.listdir(os.path.join(category_dir, "set_lists"))
+
+    set_list_dir = os.path.join(category_dir, "set_lists")
+    set_list_jsons = (os.listdir if path_manager is None else path_manager.ls)(
+        set_list_dir
+    )
+
     return [
         json_file.replace("set_lists_", "").replace(".json", "")
         for json_file in set_list_jsons

pytorch3d/implicitron/evaluation/evaluator.py

@@ -36,6 +36,8 @@ class EvaluatorBase(ReplaceableBase):
     names and their values.
     """
 
+    is_multisequence: bool = False
+
     def run(
         self, model: ImplicitronModelBase, dataloader: DataLoader, **kwargs
     ) -> Dict[str, Any]:
@@ -56,7 +58,6 @@ class ImplicitronEvaluator(EvaluatorBase):
     """
 
     camera_difficulty_bin_breaks: Tuple[float, ...] = 0.97, 0.98
-    is_multisequence: bool = False
 
     def __post_init__(self):
         run_auto_creation(self)