nerfstudio/nerfactory/dataloaders/eval.py

# Copyright 2022 The Plenoptix Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Data loader.
"""

import random
from abc import abstractmethod
from typing import Dict, Optional, Tuple, Union

import torch

from nerfactory.cameras.cameras import Camera, get_camera
from nerfactory.cameras.rays import RayBundle
from nerfactory.dataloaders.image_dataset import ImageDataset
from nerfactory.utils.misc import get_dict_to_torch


class EvalDataloader:  # pylint: disable=too-few-public-methods
    """Evaluation dataloader base class"""

    def __init__(
        self,
        image_dataset: ImageDataset,
        intrinsics,
        camera_to_world,
        num_rays_per_chunk: int,
        device: Union[torch.device, str] = "cpu",
        **kwargs,
    ):
        super().__init__()
        self.image_dataset = image_dataset
        self.intrinsics = intrinsics
        self.camera_to_world = camera_to_world
        self.num_rays_per_chunk = num_rays_per_chunk
        self.device = device
        self.kwargs = kwargs

    @abstractmethod
    def __iter__(self):
        """Iterates over the dataset"""
        return self

    @abstractmethod
    def __next__(self) -> Tuple[RayBundle, Dict]:
        """Returns the next batch of data"""

    def get_camera(self, image_idx) -> Camera:
        """Get camera for the given image index"""
        intrinsics = self.intrinsics[image_idx].to(self.device)
        camera_to_world = self.camera_to_world[image_idx].to(self.device)
        camera = get_camera(intrinsics, camera_to_world, camera_index=image_idx)
        return camera

    def get_data_from_image_idx(self, image_idx) -> Tuple[RayBundle, Dict]:
        """Returns the data for a specific image index."""
        camera = self.get_camera(image_idx)
        ray_bundle = camera.get_camera_ray_bundle(device=self.device)
        ray_bundle.num_rays_per_chunk = self.num_rays_per_chunk
        batch = self.image_dataset[image_idx]
        batch = get_dict_to_torch(batch, device=self.device)
        return ray_bundle, batch


class FixedIndicesEvalDataloader(EvalDataloader):
    """Dataloader that returns a fixed set of indices."""

    def __init__(
        self,
        image_dataset: ImageDataset,
        intrinsics,
        camera_to_world,
        num_rays_per_chunk: int,
        image_indices: Optional[Tuple[int]] = None,
        device: Union[torch.device, str] = "cpu",
        **kwargs,
    ):
        """
        Args:
            image_dataset: ImageDataset to load data from
            image_indices: List of image indices to load data from. If None, then use all images.
        """
        super().__init__(image_dataset, intrinsics, camera_to_world, num_rays_per_chunk, device, **kwargs)
        if image_indices is None:
            self.image_indices = list(range(len(image_dataset)))
        else:
            self.image_indices = image_indices
        self.num_rays_per_chunk = num_rays_per_chunk
        self.count = 0

    def __iter__(self):
        self.count = 0
        return self

    def __next__(self):
        if self.count < len(self.image_indices):
            image_idx = self.image_indices[self.count]
            ray_bundle, batch = self.get_data_from_image_idx(image_idx)
            ray_bundle.num_rays_per_chunk = self.num_rays_per_chunk
            self.count += 1
            return ray_bundle, batch
        raise StopIteration


class RandIndicesEvalDataloader(EvalDataloader):
    """Dataloader that returns random images."""

    def __init__(
        self,
        image_dataset: ImageDataset,
        intrinsics,
        camera_to_world,
        num_rays_per_chunk: int,
        device: Union[torch.device, str] = "cpu",
        **kwargs,
    ):
        super().__init__(image_dataset, intrinsics, camera_to_world, num_rays_per_chunk, device, **kwargs)
        self.count = 0

    def __iter__(self):
        self.count = 0
        return self

    def __next__(self):
        if self.count < 1:
            image_indices = range(len(self.camera_to_world))
            image_idx = random.choice(image_indices)
            ray_bundle, batch = self.get_data_from_image_idx(image_idx)
            self.count += 1
            return ray_bundle, batch
        raise StopIteration