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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
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data_source_ImplicitronDataSource_args:
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dataset_map_provider_class_type: JsonIndexDatasetMapProviderV2
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dataset_map_provider_JsonIndexDatasetMapProviderV2_args:
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category: teddybear
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subset_name: fewview_dev
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training_loop_ImplicitronTrainingLoop_args:
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evaluator_ImplicitronEvaluator_args:
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is_multisequence: true
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defaults:
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- repro_multiseq_nerf_wce.yaml
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- repro_multiseq_co3dv2_base.yaml
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- _self_
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defaults:
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- repro_multiseq_nerformer.yaml
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- repro_multiseq_co3dv2_base.yaml
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- _self_
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defaults:
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- repro_multiseq_srn_ad_hypernet.yaml
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- repro_multiseq_co3dv2_base.yaml
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- _self_
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defaults:
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- repro_multiseq_srn_wce.yaml
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- repro_multiseq_co3dv2_base.yaml
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- _self_
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data_source_ImplicitronDataSource_args:
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dataset_map_provider_class_type: JsonIndexDatasetMapProviderV2
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dataset_map_provider_JsonIndexDatasetMapProviderV2_args:
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category: teddybear
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subset_name: manyview_dev_0
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training_loop_ImplicitronTrainingLoop_args:
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evaluator_ImplicitronEvaluator_args:
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is_multisequence: false
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defaults:
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- repro_singleseq_idr.yaml
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- repro_singleseq_co3dv2_base.yaml
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- _self_
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defaults:
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- repro_singleseq_nerf.yaml
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- repro_singleseq_co3dv2_base.yaml
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- _self_
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defaults:
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- repro_singleseq_nerformer.yaml
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- repro_singleseq_co3dv2_base.yaml
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- _self_
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defaults:
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- repro_singleseq_srn_noharm.yaml
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- repro_singleseq_co3dv2_base.yaml
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- _self_
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@ -207,7 +207,10 @@ class Experiment(Configurable): # pyre-ignore: 13
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val_loader,
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) = accelerator.prepare(model, optimizer, train_loader, val_loader)
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all_train_cameras = self.data_source.all_train_cameras
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if not self.training_loop.evaluator.is_multisequence:
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all_train_cameras = self.data_source.all_train_cameras
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else:
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all_train_cameras = None
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# Enter the main training loop.
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self.training_loop.run(
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@ -30,6 +30,14 @@ logger = logging.getLogger(__name__)
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class TrainingLoopBase(ReplaceableBase):
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"""
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Members:
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evaluator: An EvaluatorBase instance, used to evaluate training results.
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"""
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evaluator: Optional[EvaluatorBase]
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evaluator_class_type: Optional[str] = "ImplicitronEvaluator"
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def run(
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self,
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train_loader: DataLoader,
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@ -58,7 +66,6 @@ class ImplicitronTrainingLoop(TrainingLoopBase): # pyre-ignore [13]
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"""
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Members:
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eval_only: If True, only run evaluation using the test dataloader.
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evaluator: An EvaluatorBase instance, used to evaluate training results.
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max_epochs: Train for this many epochs. Note that if the model was
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loaded from a checkpoint, we will restart training at the appropriate
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epoch and run for (max_epochs - checkpoint_epoch) epochs.
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@ -82,8 +89,6 @@ class ImplicitronTrainingLoop(TrainingLoopBase): # pyre-ignore [13]
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# Parameters of the outer training loop.
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eval_only: bool = False
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evaluator: EvaluatorBase
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evaluator_class_type: str = "ImplicitronEvaluator"
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max_epochs: int = 1000
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store_checkpoints: bool = True
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store_checkpoints_purge: int = 1
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@ -406,8 +406,13 @@ optimizer_factory_ImplicitronOptimizerFactory_args:
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linear_exponential_lr_milestone: 200
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linear_exponential_start_gamma: 0.1
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training_loop_ImplicitronTrainingLoop_args:
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eval_only: false
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evaluator_class_type: ImplicitronEvaluator
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evaluator_ImplicitronEvaluator_args:
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is_multisequence: false
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camera_difficulty_bin_breaks:
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- 0.97
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- 0.98
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eval_only: false
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max_epochs: 1000
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store_checkpoints: true
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store_checkpoints_purge: 1
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@ -420,8 +425,3 @@ training_loop_ImplicitronTrainingLoop_args:
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visdom_env: ''
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visdom_port: 8097
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visdom_server: http://127.0.0.1
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evaluator_ImplicitronEvaluator_args:
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camera_difficulty_bin_breaks:
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- 0.97
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- 0.98
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is_multisequence: false
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@ -190,6 +190,34 @@ class TestNerfRepro(unittest.TestCase):
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experiment.dump_cfg(cfg)
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experiment_runner.run()
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@unittest.skip("This test runs nerf training on co3d v2 - manyview.")
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def test_nerf_co3dv2_manyview(self):
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# Train NERF
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if not interactive_testing_requested():
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return
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with initialize_config_dir(config_dir=str(IMPLICITRON_CONFIGS_DIR)):
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cfg = compose(
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config_name="repro_singleseq_v2_nerf",
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overrides=[],
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)
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experiment_runner = experiment.Experiment(**cfg)
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experiment.dump_cfg(cfg)
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experiment_runner.run()
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@unittest.skip("This test runs nerformer training on co3d v2 - fewview.")
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def test_nerformer_co3dv2_fewview(self):
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# Train NeRFormer
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if not interactive_testing_requested():
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return
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with initialize_config_dir(config_dir=str(IMPLICITRON_CONFIGS_DIR)):
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cfg = compose(
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config_name="repro_multiseq_v2_nerformer",
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overrides=[],
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)
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experiment_runner = experiment.Experiment(**cfg)
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experiment.dump_cfg(cfg)
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experiment_runner.run()
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@unittest.skip("This test checks resuming of the NeRF training.")
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def test_nerf_blender_resume(self):
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# Train one train batch of NeRF, then resume for one more batch.
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@ -36,6 +36,7 @@ from pytorch3d.io import IO
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from pytorch3d.renderer.camera_utils import join_cameras_as_batch
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from pytorch3d.renderer.cameras import CamerasBase, PerspectiveCameras
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from pytorch3d.structures.pointclouds import Pointclouds
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from tqdm import tqdm
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from . import types
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from .dataset_base import DatasetBase, FrameData
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@ -338,9 +339,10 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
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"""
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Returns the cameras corresponding to all the known frames.
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"""
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logger.info("Loading all train cameras.")
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cameras = []
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# pyre-ignore[16]
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for frame_idx, frame_annot in enumerate(self.frame_annots):
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for frame_idx, frame_annot in enumerate(tqdm(self.frame_annots)):
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frame_type = self._get_frame_type(frame_annot)
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if frame_type is None:
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raise ValueError("subsets not loaded")
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@ -14,6 +14,7 @@ from collections import defaultdict
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from typing import Dict, List, Optional, Tuple, Type, Union
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import numpy as np
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from iopath.common.file_io import PathManager
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from omegaconf import DictConfig
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from pytorch3d.implicitron.dataset.dataset_map_provider import (
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@ -383,12 +384,11 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase): # pyre-ignore [13]
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return data
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def _get_available_subset_names(self):
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path_manager = self.path_manager_factory.get()
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if path_manager is not None:
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dataset_root = path_manager.get_local_path(self.dataset_root)
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else:
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dataset_root = self.dataset_root
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return get_available_subset_names(dataset_root, self.category)
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return get_available_subset_names(
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self.dataset_root,
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self.category,
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path_manager=self.path_manager_factory.get(),
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)
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def _extend_test_data_with_known_views(
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self,
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@ -425,18 +425,30 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase): # pyre-ignore [13]
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return eval_batch_index_out, list(test_subset_mapping_set)
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def get_available_subset_names(dataset_root: str, category: str) -> List[str]:
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def get_available_subset_names(
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dataset_root: str,
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category: str,
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path_manager: Optional[PathManager] = None,
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) -> List[str]:
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"""
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Get the available subset names for a given category folder inside a root dataset
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folder `dataset_root`.
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"""
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category_dir = os.path.join(dataset_root, category)
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if not os.path.isdir(category_dir):
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category_dir_exists = (
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(path_manager is not None) and path_manager.isdir(category_dir)
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) or os.path.isdir(category_dir)
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if not category_dir_exists:
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raise ValueError(
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f"Looking for dataset files in {category_dir}. "
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+ "Please specify a correct dataset_root folder."
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)
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set_list_jsons = os.listdir(os.path.join(category_dir, "set_lists"))
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set_list_dir = os.path.join(category_dir, "set_lists")
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set_list_jsons = (os.listdir if path_manager is None else path_manager.ls)(
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set_list_dir
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)
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return [
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json_file.replace("set_lists_", "").replace(".json", "")
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for json_file in set_list_jsons
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names and their values.
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"""
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is_multisequence: bool = False
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def run(
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self, model: ImplicitronModelBase, dataloader: DataLoader, **kwargs
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) -> Dict[str, Any]:
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@ -56,7 +58,6 @@ class ImplicitronEvaluator(EvaluatorBase):
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"""
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camera_difficulty_bin_breaks: Tuple[float, ...] = 0.97, 0.98
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is_multisequence: bool = False
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def __post_init__(self):
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run_auto_creation(self)
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