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CO3Dv2 trainer configs

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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
This commit is contained in:
David Novotny 2022-08-30 13:42:19 -07:00 committed by Facebook GitHub Bot
parent 03562d87f5
commit 1163eaab43
17 changed files with 120 additions and 21 deletions
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8
projects/implicitron_trainer/configs/repro_multiseq_co3dv2_base.yaml Normal file
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@ -0,0 +1,8 @@
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

4
projects/implicitron_trainer/configs/repro_multiseq_v2_nerf_wce.yaml Normal file
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@ -0,0 +1,4 @@
defaults:
- repro_multiseq_nerf_wce.yaml
- repro_multiseq_co3dv2_base.yaml
- _self_

4
projects/implicitron_trainer/configs/repro_multiseq_v2_nerformer.yaml Normal file
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@ -0,0 +1,4 @@
defaults:
- repro_multiseq_nerformer.yaml
- repro_multiseq_co3dv2_base.yaml
- _self_

4
projects/implicitron_trainer/configs/repro_multiseq_v2_srn_ad_hypernet.yaml Normal file
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@ -0,0 +1,4 @@
defaults:
- repro_multiseq_srn_ad_hypernet.yaml
- repro_multiseq_co3dv2_base.yaml
- _self_

4
projects/implicitron_trainer/configs/repro_multiseq_v2_srn_wce.yaml Normal file
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@ -0,0 +1,4 @@
defaults:
- repro_multiseq_srn_wce.yaml
- repro_multiseq_co3dv2_base.yaml
- _self_

8
projects/implicitron_trainer/configs/repro_singleseq_co3dv2_base.yaml Normal file
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@ -0,0 +1,8 @@
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

4
projects/implicitron_trainer/configs/repro_singleseq_v2_idr.yaml Normal file
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@ -0,0 +1,4 @@
defaults:
- repro_singleseq_idr.yaml
- repro_singleseq_co3dv2_base.yaml
- _self_

4
projects/implicitron_trainer/configs/repro_singleseq_v2_nerf.yaml Normal file
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@ -0,0 +1,4 @@
defaults:
- repro_singleseq_nerf.yaml
- repro_singleseq_co3dv2_base.yaml
- _self_

4
projects/implicitron_trainer/configs/repro_singleseq_v2_nerformer.yaml Normal file
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@ -0,0 +1,4 @@
defaults:
- repro_singleseq_nerformer.yaml
- repro_singleseq_co3dv2_base.yaml
- _self_

4
projects/implicitron_trainer/configs/repro_singleseq_v2_srn_noharm.yaml Normal file
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@ -0,0 +1,4 @@
defaults:
- repro_singleseq_srn_noharm.yaml
- repro_singleseq_co3dv2_base.yaml
- _self_

5
projects/implicitron_trainer/experiment.py
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@ -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(

11
projects/implicitron_trainer/impl/training_loop.py
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@ -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

12
projects/implicitron_trainer/tests/experiment.yaml
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@ -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

28
projects/implicitron_trainer/tests/test_experiment.py
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@ -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.

4
pytorch3d/implicitron/dataset/json_index_dataset.py
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@ -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")

30
pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py
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@ -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

3
pytorch3d/implicitron/evaluation/evaluator.py
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@ -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)