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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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@@ -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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