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
159 lines
6.3 KiB
Python
159 lines
6.3 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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import torch
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from pytorch3d.implicitron.dataset.blender_dataset_map_provider import (
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BlenderDatasetMapProvider,
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)
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from pytorch3d.implicitron.dataset.data_source import ImplicitronDataSource
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from pytorch3d.implicitron.dataset.dataset_base import FrameData
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from pytorch3d.implicitron.dataset.llff_dataset_map_provider import (
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LlffDatasetMapProvider,
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)
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from pytorch3d.implicitron.tools.config import expand_args_fields, get_default_args
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from pytorch3d.renderer import PerspectiveCameras
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from tests.common_testing import TestCaseMixin
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# These tests are only run internally, where the data is available.
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internal = os.environ.get("FB_TEST", False)
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inside_re_worker = os.environ.get("INSIDE_RE_WORKER", False)
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@unittest.skipUnless(internal, "no data")
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class TestDataLlff(TestCaseMixin, unittest.TestCase):
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def test_synthetic(self):
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if inside_re_worker:
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return
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expand_args_fields(BlenderDatasetMapProvider)
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provider = BlenderDatasetMapProvider(
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base_dir="manifold://co3d/tree/nerf_data/nerf_synthetic/lego",
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object_name="lego",
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)
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dataset_map = provider.get_dataset_map()
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known_matrix = torch.zeros(1, 4, 4)
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known_matrix[0, 0, 0] = 2.7778
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known_matrix[0, 1, 1] = 2.7778
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known_matrix[0, 2, 3] = 1
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known_matrix[0, 3, 2] = 1
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for name, length in [("train", 100), ("val", 100), ("test", 200)]:
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dataset = getattr(dataset_map, name)
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self.assertEqual(len(dataset), length)
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# try getting a value
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value = dataset[0]
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self.assertEqual(value.image_rgb.shape, (3, 800, 800))
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self.assertEqual(value.fg_probability.shape, (1, 800, 800))
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# corner of image is background
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self.assertEqual(value.fg_probability[0, 0, 0], 0)
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self.assertEqual(value.fg_probability.max(), 1.0)
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self.assertIsInstance(value.camera, PerspectiveCameras)
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self.assertEqual(len(value.camera), 1)
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self.assertIsNone(value.camera.K)
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matrix = value.camera.get_projection_transform().get_matrix()
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self.assertClose(matrix, known_matrix, atol=1e-4)
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self.assertIsInstance(value, FrameData)
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def test_llff(self):
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if inside_re_worker:
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return
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expand_args_fields(LlffDatasetMapProvider)
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provider = LlffDatasetMapProvider(
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base_dir="manifold://co3d/tree/nerf_data/nerf_llff_data/fern",
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object_name="fern",
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downscale_factor=8,
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)
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dataset_map = provider.get_dataset_map()
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known_matrix = torch.zeros(1, 4, 4)
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known_matrix[0, 0, 0] = 2.1564
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known_matrix[0, 1, 1] = 2.1564
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known_matrix[0, 2, 3] = 1
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known_matrix[0, 3, 2] = 1
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for name, length, frame_type in [
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("train", 17, "known"),
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("test", 3, "unseen"),
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("val", 3, "unseen"),
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]:
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dataset = getattr(dataset_map, name)
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self.assertEqual(len(dataset), length)
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# try getting a value
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value = dataset[0]
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self.assertIsInstance(value, FrameData)
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self.assertEqual(value.frame_type, frame_type)
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self.assertEqual(value.image_rgb.shape, (3, 378, 504))
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self.assertIsInstance(value.camera, PerspectiveCameras)
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self.assertEqual(len(value.camera), 1)
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self.assertIsNone(value.camera.K)
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matrix = value.camera.get_projection_transform().get_matrix()
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self.assertClose(matrix, known_matrix, atol=1e-4)
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self.assertEqual(len(dataset_map.test.get_eval_batches()), 3)
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for batch in dataset_map.test.get_eval_batches():
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self.assertEqual(len(batch), 1)
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self.assertEqual(dataset_map.test[batch[0]].frame_type, "unseen")
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def test_include_known_frames(self):
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if inside_re_worker:
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return
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expand_args_fields(LlffDatasetMapProvider)
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provider = LlffDatasetMapProvider(
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base_dir="manifold://co3d/tree/nerf_data/nerf_llff_data/fern",
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object_name="fern",
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n_known_frames_for_test=2,
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)
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dataset_map = provider.get_dataset_map()
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for name, types in [
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("train", ["known"] * 17),
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("val", ["unseen"] * 3 + ["known"] * 17),
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("test", ["unseen"] * 3 + ["known"] * 17),
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]:
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dataset = getattr(dataset_map, name)
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self.assertEqual(len(dataset), len(types))
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for i, frame_type in enumerate(types):
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value = dataset[i]
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self.assertEqual(value.frame_type, frame_type)
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self.assertIsNone(value.fg_probability)
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self.assertEqual(len(dataset_map.test.get_eval_batches()), 3)
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for batch in dataset_map.test.get_eval_batches():
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self.assertEqual(len(batch), 3)
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self.assertEqual(dataset_map.test[batch[0]].frame_type, "unseen")
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for i in batch[1:]:
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self.assertEqual(dataset_map.test[i].frame_type, "known")
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def test_loaders(self):
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if inside_re_worker:
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return
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args = get_default_args(ImplicitronDataSource)
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args.dataset_map_provider_class_type = "BlenderDatasetMapProvider"
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dataset_args = args.dataset_map_provider_BlenderDatasetMapProvider_args
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dataset_args.object_name = "lego"
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dataset_args.base_dir = "manifold://co3d/tree/nerf_data/nerf_synthetic/lego"
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data_source = ImplicitronDataSource(**args)
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_, data_loaders = data_source.get_datasets_and_dataloaders()
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for i in data_loaders.train:
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self.assertEqual(i.frame_type, ["known"])
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self.assertEqual(i.image_rgb.shape, (1, 3, 800, 800))
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for i in data_loaders.val:
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self.assertEqual(i.frame_type, ["unseen"])
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self.assertEqual(i.image_rgb.shape, (1, 3, 800, 800))
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for i in data_loaders.test:
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self.assertEqual(i.frame_type, ["unseen"])
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self.assertEqual(i.image_rgb.shape, (1, 3, 800, 800))
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cameras = data_source.all_train_cameras
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self.assertIsInstance(cameras, PerspectiveCameras)
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self.assertEqual(len(cameras), 100)
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