mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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0b042be956
* Model class done, pipeline class still needs more visualization stuffs * Renaming the BaseDataContainer to DataloaderOutputs and adding documentation * Pushing more documentation, removing some trivial functions * Updating dict typing * Removing config dicts for collider * Passing tests * Pushing shell new dataloader * Saving work, need to fix NGP model's get_loss_dict and Pipeline * V1 of the ngp pipeline should be done. Need train script and debugging * Updating with latest instant-ngp code * Syntax updates from latest ngp graph code * Linter, removing unneeded code in new NGP graph code * Style * Should be good now. Linter is happy and the code is verbatum current instant-ngp * Everything done except novel view testing code * Fixing some nitpicks * Debugging dataloader * merged master * WOP. integrating pipeline with configs and notebook demo * removing model folder * a lot of name changing from Graph to Model * in progress... changing trainer to use pipeline... broken right now * pipeline code runs * switched to val dataset * makes run_eval.py work * updated configs for pipeline change * fixing test_run_train script * fixing black and linting * fix docs * small updates to trainer Co-authored-by: Jake Austin <jake-austin@berkeley.edu>
117 lines
4.1 KiB
Python
117 lines
4.1 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Code for sampling images from a dataset of images.
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"""
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import random
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from abc import abstractmethod
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from typing import Union
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import torch
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from torch.utils.data import IterableDataset, default_collate
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from nerfactory.dataloaders.image_dataset import ImageDataset
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from nerfactory.utils.misc import get_dict_to_torch
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class ImageSampler(IterableDataset):
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"""Samples image_batch's.
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Attributes:
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dataset (torch.utils.data.Dataset): Dataset to sample from.
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"""
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# dataset: ImageDataset
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def __init__(self, dataset: ImageDataset):
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super().__init__()
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self.dataset = dataset
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@abstractmethod
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def __getitem__(self, idx):
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raise NotImplementedError()
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@abstractmethod
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def __iter__(self):
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raise NotImplementedError()
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class CacheImageSampler(ImageSampler):
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"""Collated image dataset that implements caching of images.
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Creates batches of the ImageDataset return type.
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Args:
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dataset (torch.utils.data.Dataset): Dataset to sample from.
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num_samples_to_collate (int, optional): How many images to sample rays for each batch. Defaults to all images.
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num_times_to_repeat_images (int): How often to collate new images. Defaults to every iteration.
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device (Union[torch.device, str]): Device to perform computation. Defaults to "cpu".
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"""
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def __init__(
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self,
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dataset: ImageDataset,
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num_images_to_sample_from: int = -1,
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num_times_to_repeat_images: int = 0,
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device: Union[torch.device, str] = "cpu",
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):
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super().__init__(dataset=dataset)
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self.dataset = dataset
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self.cache_all_images = num_images_to_sample_from == -1
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self.num_images_to_sample_from = len(self.dataset) if self.cache_all_images else num_images_to_sample_from
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self.num_times_to_repeat_images = num_times_to_repeat_images
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self.device = device
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self.num_repeated = self.num_times_to_repeat_images # starting value
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self.first_time = True
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self.cached_collated_batch = None
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if self.cache_all_images:
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self.cached_collated_batch = self._get_collated_batch()
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def __getitem__(self, idx):
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return self.dataset.__getitem__(idx)
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def _get_batch_list(self):
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"""Returns a list of batches from the dataset attribute."""
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indices = random.sample(range(len(self.dataset)), k=self.num_images_to_sample_from)
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batch_list = [self.dataset.__getitem__(idx) for idx in indices]
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return batch_list
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def _get_collated_batch(self):
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"""Returns a collated batch."""
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batch_list = self._get_batch_list()
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collated_batch = default_collate(batch_list)
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collated_batch = get_dict_to_torch(collated_batch, device=self.device)
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return collated_batch
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def __iter__(self):
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while True:
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if self.cache_all_images:
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collated_batch = self.cached_collated_batch
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elif self.first_time or (
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self.num_times_to_repeat_images != -1 and self.num_repeated >= self.num_times_to_repeat_images
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):
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# trigger a reset
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self.num_repeated = 0
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collated_batch = self._get_collated_batch()
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# possibly save a cached item
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self.cached_collated_batch = collated_batch if self.num_times_to_repeat_images != 0 else None
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self.first_time = False
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else:
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collated_batch = self.cached_collated_batch
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self.num_repeated += 1
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yield collated_batch
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