mirror of
https://github.com/facebookresearch/pytorch3d.git
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c83ec3555d
Summary: LLFF (and most/all non-synth datasets) will have no background/foreground distinction. Add support for data with no fg mask. Also, we had a bug in stats loading, like this: * Load stats * One of the stats has a history of length 0 * That's fine, e.g. maybe it's fg_error but the dataset has no notion of fg/bg. So leave it as len 0 * Check whether all the stats have the same history length as an arbitrarily chosen "reference-stat" * Ooops the reference-stat happened to be the stat with length 0 * assert (legit_stat_len == reference_stat_len (=0)) ---> failed assert Also some minor fixes (from Jeremy's other diff) to support LLFF Reviewed By: davnov134 Differential Revision: D38475272 fbshipit-source-id: 5b35ac86d1d5239759f537621f41a3aa4eb3bd68
48 lines
1.6 KiB
YAML
48 lines
1.6 KiB
YAML
defaults:
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- repro_singleseq_base
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- _self_
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exp_dir: "./data/nerf_blender_repro/${oc.env:BLENDER_SINGLESEQ_CLASS}"
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data_source_ImplicitronDataSource_args:
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data_loader_map_provider_SequenceDataLoaderMapProvider_args:
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dataset_length_train: 100
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dataset_map_provider_class_type: BlenderDatasetMapProvider
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dataset_map_provider_BlenderDatasetMapProvider_args:
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base_dir: ${oc.env:BLENDER_DATASET_ROOT}
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n_known_frames_for_test: null
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object_name: ${oc.env:BLENDER_SINGLESEQ_CLASS}
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path_manager_factory_class_type: PathManagerFactory
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path_manager_factory_PathManagerFactory_args:
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silence_logs: true
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model_factory_ImplicitronModelFactory_args:
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model_GenericModel_args:
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mask_images: false
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raysampler_class_type: NearFarRaySampler
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raysampler_NearFarRaySampler_args:
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n_rays_per_image_sampled_from_mask: 4096
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min_depth: 2
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max_depth: 6
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renderer_MultiPassEmissionAbsorptionRenderer_args:
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density_noise_std_train: 1.0
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n_pts_per_ray_fine_training: 128
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n_pts_per_ray_fine_evaluation: 128
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raymarcher_EmissionAbsorptionRaymarcher_args:
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blend_output: false
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loss_weights:
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loss_rgb_mse: 1.0
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loss_prev_stage_rgb_mse: 1.0
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loss_mask_bce: 0.0
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loss_prev_stage_mask_bce: 0.0
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loss_autodecoder_norm: 0.00
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optimizer_factory_ImplicitronOptimizerFactory_args:
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exponential_lr_step_size: 2500
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lr_policy: Exponential
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training_loop_ImplicitronTrainingLoop_args:
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max_epochs: 2000
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metric_print_interval: 100
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store_checkpoints_purge: 3
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test_when_finished: true
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validation_interval: 100
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