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>
85 lines
3.0 KiB
Python
85 lines
3.0 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 pixels.
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"""
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import random
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import torch
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def collate_image_dataset_batch(batch, num_rays_per_batch, keep_full_image: bool = False):
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"""
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Operates on a batch of images and samples pixels to use for generating rays.
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Returns a collated batch which is input to the Graph.
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It will sample only within the valid 'mask' if it's specified.
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"""
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device = batch["image"].device
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num_images, image_height, image_width, _ = batch["image"].shape
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# only sample within the mask, if the mask is in the batch
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if "mask" in batch:
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nonzero_indices = torch.nonzero(batch["mask"][..., 0], as_tuple=False)
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chosen_indices = random.sample(range(len(nonzero_indices)), k=num_rays_per_batch)
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indices = nonzero_indices[chosen_indices]
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else:
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indices = torch.floor(
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torch.rand((num_rays_per_batch, 3), device=device)
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* torch.tensor([num_images, image_height, image_width], device=device)
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).long()
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c, y, x = (i.flatten() for i in torch.split(indices, 1, dim=-1))
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image = batch["image"][c, y, x]
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mask, semantics = None, None
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if "mask" in batch:
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mask = batch["mask"][c, y, x]
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if "semantics" in batch:
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semantics = batch["semantics"][c, y, x]
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assert image.shape == (num_rays_per_batch, 3), image.shape
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# Needed to correct the random indices to their actual camera idx locations.
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local_indices = indices.clone()
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indices[:, 0] = batch["image_idx"][c]
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collated_batch = {
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"local_indices": local_indices, # local to the batch returned
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"indices": indices, # with the abs camera indices
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"image": image,
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}
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if mask is not None:
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collated_batch["mask"] = mask
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if semantics is not None:
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collated_batch["semantics"] = semantics
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if keep_full_image:
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collated_batch["full_image"] = batch["image"]
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return collated_batch
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class PixelSampler: # pylint: disable=too-few-public-methods
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"""Samples 'pixel_batch's from 'image_batch's."""
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def __init__(self, num_rays_per_batch, keep_full_image=False) -> None:
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self.num_rays_per_batch = num_rays_per_batch
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self.keep_full_image = keep_full_image
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def sample(self, image_batch):
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"""Sample an image batch and return a pixel batch."""
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pixel_batch = collate_image_dataset_batch(
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image_batch, self.num_rays_per_batch, keep_full_image=self.keep_full_image
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)
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return pixel_batch
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