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https://github.com/facebookresearch/pytorch3d.git
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34bbb3ad32
Summary: Applies new import merging and sorting from µsort v1.0. When merging imports, µsort will make a best-effort to move associated comments to match merged elements, but there are known limitations due to the diynamic nature of Python and developer tooling. These changes should not produce any dangerous runtime changes, but may require touch-ups to satisfy linters and other tooling. Note that µsort uses case-insensitive, lexicographical sorting, which results in a different ordering compared to isort. This provides a more consistent sorting order, matching the case-insensitive order used when sorting import statements by module name, and ensures that "frog", "FROG", and "Frog" always sort next to each other. For details on µsort's sorting and merging semantics, see the user guide: https://usort.readthedocs.io/en/stable/guide.html#sorting Reviewed By: bottler Differential Revision: D35553814 fbshipit-source-id: be49bdb6a4c25264ff8d4db3a601f18736d17be1
96 lines
3.1 KiB
Python
96 lines
3.1 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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from typing import cast, Optional, Tuple
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import torch
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from pytorch3d.implicitron.tools.point_cloud_utils import get_rgbd_point_cloud
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from pytorch3d.structures import Pointclouds
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from .implicitron_dataset import FrameData, ImplicitronDataset
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def get_implicitron_sequence_pointcloud(
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dataset: ImplicitronDataset,
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sequence_name: Optional[str] = None,
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mask_points: bool = True,
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max_frames: int = -1,
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num_workers: int = 0,
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load_dataset_point_cloud: bool = False,
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) -> Tuple[Pointclouds, FrameData]:
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"""
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Make a point cloud by sampling random points from each frame the dataset.
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"""
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if len(dataset) == 0:
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raise ValueError("The dataset is empty.")
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if not dataset.load_depths:
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raise ValueError("The dataset has to load depths (dataset.load_depths=True).")
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if mask_points and not dataset.load_masks:
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raise ValueError(
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"For mask_points=True, the dataset has to load masks"
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+ " (dataset.load_masks=True)."
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)
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# setup the indices of frames loaded from the dataset db
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sequence_entries = list(range(len(dataset)))
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if sequence_name is not None:
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sequence_entries = [
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ei
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for ei in sequence_entries
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if dataset.frame_annots[ei]["frame_annotation"].sequence_name
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== sequence_name
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]
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if len(sequence_entries) == 0:
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raise ValueError(
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f'There are no dataset entries for sequence name "{sequence_name}".'
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)
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# subsample loaded frames if needed
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if (max_frames > 0) and (len(sequence_entries) > max_frames):
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sequence_entries = [
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sequence_entries[i]
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for i in torch.randperm(len(sequence_entries))[:max_frames].sort().values
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]
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# take only the part of the dataset corresponding to the sequence entries
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sequence_dataset = torch.utils.data.Subset(dataset, sequence_entries)
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# load the required part of the dataset
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loader = torch.utils.data.DataLoader(
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sequence_dataset,
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batch_size=len(sequence_dataset),
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shuffle=False,
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num_workers=num_workers,
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collate_fn=FrameData.collate,
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)
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frame_data = next(iter(loader)) # there's only one batch
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# scene point cloud
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if load_dataset_point_cloud:
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if not dataset.load_point_clouds:
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raise ValueError(
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"For load_dataset_point_cloud=True, the dataset has to"
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+ " load point clouds (dataset.load_point_clouds=True)."
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)
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point_cloud = frame_data.sequence_point_cloud
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else:
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point_cloud = get_rgbd_point_cloud(
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frame_data.camera,
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frame_data.image_rgb,
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frame_data.depth_map,
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(cast(torch.Tensor, frame_data.fg_probability) > 0.5).float()
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if frame_data.fg_probability is not None
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else None,
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mask_points=mask_points,
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)
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return point_cloud, frame_data
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