mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 18:44:21 +08:00
Ethan WeberandJake Austin
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>
70 lines
2.7 KiB
Python
70 lines
2.7 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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Ray generator.
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"""
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from torch import nn
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from torchtyping import TensorType
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from nerfactory.cameras.cameras import get_camera_model
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from nerfactory.cameras.rays import RayBundle
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class RayGenerator(nn.Module):
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"""torch.nn Module for generating rays.
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This class will store the intrinsics and extrinsics parameters of the cameras.
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Args:
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intrinsics: The intrinsics parameters.
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camera_to_world: Camera to world transformation matrix.
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"""
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def __init__(
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self,
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intrinsics: TensorType["num_cameras", "num_intrinsics_params"],
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camera_to_world: TensorType["num_cameras", 3, 4],
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) -> None:
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super().__init__()
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self.num_cameras, self.num_intrinsics_params = intrinsics.shape
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assert self.num_cameras >= 0
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self.intrinsics = nn.Parameter(intrinsics, requires_grad=False)
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self.camera_to_world = nn.Parameter(camera_to_world, requires_grad=False)
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# TODO(ethan): add learnable parameters that are deltas on the intrinsics and camera_to_world parameters
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# NOTE(ethan): we currently assume all images have the same height and width
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camera_index = 0
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self.camera_class = get_camera_model(self.num_intrinsics_params)
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camera = self.camera_class(*self.intrinsics[camera_index].tolist())
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self.image_coords = nn.Parameter(camera.get_image_coords(), requires_grad=False)
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def forward(self, ray_indices: TensorType["num_rays", 3]) -> RayBundle:
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"""Index into the cameras to generate the rays.
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Args:
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ray_indices: Contains camera, row, and col indicies for target rays.
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"""
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c = ray_indices[:, 0] # camera indices
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y = ray_indices[:, 1] # row indices
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x = ray_indices[:, 2] # col indices
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intrinsics = self.intrinsics[c]
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camera_to_world = self.camera_to_world[c]
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coords = self.image_coords[y, x]
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ray_bundle = self.camera_class.generate_rays(
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intrinsics=intrinsics, camera_to_world=camera_to_world, coords=coords
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)
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ray_bundle.camera_indices = c[..., None] # ["num_rays",1]
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return ray_bundle
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