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* add licensing headers * add licensing to run actions * rerun headers file * remove trailing white spaces
133 lines
4.5 KiB
Python
133 lines
4.5 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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Data loader.
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"""
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import random
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from typing import Dict, List, Tuple, Union
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from omegaconf import ListConfig
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from torchtyping import TensorType
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from pyrad.cameras.cameras import get_camera
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from pyrad.cameras.rays import RayBundle
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from pyrad.data.image_dataset import ImageDataset
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from pyrad.data.image_sampler import ImageSampler
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from pyrad.data.pixel_sampler import PixelSampler
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from pyrad.utils.misc import get_dict_to_torch
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class TrainDataloader:
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"""Training dataloader base class."""
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def __init__(self, image_sampler: ImageSampler, pixel_sampler: PixelSampler):
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self.image_sampler = image_sampler
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self.pixel_sampler = pixel_sampler
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self.iter_image_sampler = iter(self.image_sampler)
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self.count = 0
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def __iter__(self):
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self.count = 0
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return self
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def __next__(self) -> Tuple[TensorType["num_rays", 3], Dict]:
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self.count += 1
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image_batch = next(self.iter_image_sampler)
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batch = self.pixel_sampler.sample(image_batch)
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ray_indices = batch["indices"]
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return ray_indices, batch
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class EvalDataloader: # pylint: disable=too-few-public-methods
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"""Evaluation dataloader base class"""
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def __init__(
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self, image_dataset: ImageDataset, intrinsics, camera_to_world, num_rays_per_chunk: int, device="cpu", **kwargs
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):
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super().__init__()
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self.image_dataset = image_dataset
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self.intrinsics = intrinsics
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self.camera_to_world = camera_to_world
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self.num_rays_per_chunk = num_rays_per_chunk
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self.device = device
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self.kwargs = kwargs
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def get_data_from_image_idx(self, image_idx) -> Tuple[RayBundle, Dict]:
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"""Returns the data for a specific image index."""
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intrinsics = self.intrinsics[image_idx].to(self.device)
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camera_to_world = self.camera_to_world[image_idx].to(self.device)
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camera = get_camera(intrinsics, camera_to_world, camera_index=image_idx)
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ray_bundle = camera.get_camera_ray_bundle(device=self.device)
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ray_bundle.num_rays_per_chunk = self.num_rays_per_chunk
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batch = self.image_dataset[image_idx]
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batch = get_dict_to_torch(batch, device=self.device)
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return ray_bundle, batch
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class FixedIndicesEvalDataloader(EvalDataloader):
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"""Dataloader that returns a fixed set of indices."""
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def __init__(
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self,
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image_dataset: ImageDataset,
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intrinsics,
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camera_to_world,
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num_rays_per_chunk: int,
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image_indices: Union[List[int], ListConfig],
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device="cpu",
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**kwargs
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):
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super().__init__(image_dataset, intrinsics, camera_to_world, num_rays_per_chunk, device, **kwargs)
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self.image_indices = image_indices
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self.num_rays_per_chunk = num_rays_per_chunk
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self.count = 0
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def __iter__(self):
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self.count = 0
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return self
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def __next__(self):
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if self.count < len(self.image_indices):
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image_idx = self.image_indices[self.count]
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ray_bundle, batch = self.get_data_from_image_idx(image_idx)
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ray_bundle.num_rays_per_chunk = self.num_rays_per_chunk
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self.count += 1
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return ray_bundle, batch
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raise StopIteration
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class RandIndicesEvalDataloader(EvalDataloader):
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"""Dataloader that returns random images."""
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def __init__(
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self, image_dataset: ImageDataset, intrinsics, camera_to_world, num_rays_per_chunk: int, device="cpu", **kwargs
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):
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super().__init__(image_dataset, intrinsics, camera_to_world, num_rays_per_chunk, device, **kwargs)
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self.count = 0
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def __iter__(self):
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self.count = 0
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return self
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def __next__(self):
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if self.count < 1:
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image_indices = range(len(self.camera_to_world))
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image_idx = random.choice(image_indices)
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ray_bundle, batch = self.get_data_from_image_idx(image_idx)
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self.count += 1
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return ray_bundle, batch
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raise StopIteration
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