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https://github.com/facebookresearch/pytorch3d.git
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771cf8a328
Summary: Add facilities for dataloading non-sequential scenes. Reviewed By: shapovalov Differential Revision: D37291277 fbshipit-source-id: 0a33e3727b44c4f0cba3a2abe9b12f40d2a20447
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import os
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import unittest
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from pathlib import Path
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import experiment
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import torch
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from hydra import compose, initialize_config_dir
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from omegaconf import OmegaConf
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def interactive_testing_requested() -> bool:
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"""
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Certain tests are only useful when run interactively, and so are not regularly run.
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These are activated by this funciton returning True, which the user requests by
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setting the environment variable `PYTORCH3D_INTERACTIVE_TESTING` to 1.
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"""
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return os.environ.get("PYTORCH3D_INTERACTIVE_TESTING", "") == "1"
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DATA_DIR = Path(__file__).resolve().parent
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IMPLICITRON_CONFIGS_DIR = Path(__file__).resolve().parent.parent / "configs"
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DEBUG: bool = False
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# TODO:
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# - add enough files to skateboard_first_5 that this works on RE.
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# - share common code with PyTorch3D tests?
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# - deal with the temporary output files this test creates
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class TestExperiment(unittest.TestCase):
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def setUp(self):
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self.maxDiff = None
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def test_from_defaults(self):
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# Test making minimal changes to the dataclass defaults.
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if not interactive_testing_requested():
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return
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cfg = OmegaConf.structured(experiment.ExperimentConfig)
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cfg.data_source_args.dataset_map_provider_class_type = (
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"JsonIndexDatasetMapProvider"
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)
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dataset_args = (
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cfg.data_source_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
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)
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dataloader_args = (
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cfg.data_source_args.data_loader_map_provider_SequenceDataLoaderMapProvider_args
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)
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dataset_args.category = "skateboard"
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dataset_args.test_restrict_sequence_id = 0
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dataset_args.dataset_root = "manifold://co3d/tree/extracted"
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dataset_args.dataset_JsonIndexDataset_args.limit_sequences_to = 5
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dataloader_args.dataset_length_train = 1
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cfg.solver_args.max_epochs = 2
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device = torch.device("cuda:0")
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experiment.run_training(cfg, device)
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def test_yaml_contents(self):
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cfg = OmegaConf.structured(experiment.ExperimentConfig)
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yaml = OmegaConf.to_yaml(cfg, sort_keys=False)
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if DEBUG:
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(DATA_DIR / "experiment.yaml").write_text(yaml)
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self.assertEqual(yaml, (DATA_DIR / "experiment.yaml").read_text())
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def test_load_configs(self):
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config_files = []
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for pattern in ("repro_singleseq*.yaml", "repro_multiseq*.yaml"):
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config_files.extend(
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[
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f
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for f in IMPLICITRON_CONFIGS_DIR.glob(pattern)
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if not f.name.endswith("_base.yaml")
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]
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)
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for file in config_files:
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with self.subTest(file.name):
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with initialize_config_dir(config_dir=str(IMPLICITRON_CONFIGS_DIR)):
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compose(file.name)
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