NeRF/encoding.py

import torch
import torch.nn as nn
import torch.nn.functional as F


def get_encoder(encoding, input_dim=3, 

                multires=6, 

                degree=4,

                num_levels=16, level_dim=2, base_resolution=16, log2_hashmap_size=19, desired_resolution=2048, align_corners=False,

                **kwargs):

    if encoding == 'None':
        return lambda x, **kwargs: x, input_dim
    
    elif encoding == 'frequency':
        from freqencoder import FreqEncoder
        encoder = FreqEncoder(input_dim=input_dim, degree=multires)

    elif encoding == 'spherical_harmonics':
        from shencoder import SHEncoder
        encoder = SHEncoder(input_dim=input_dim, degree=degree)

    elif encoding == 'hashgrid':
        from gridencoder import GridEncoder
        encoder = GridEncoder(input_dim=input_dim, num_levels=num_levels, level_dim=level_dim, base_resolution=base_resolution, log2_hashmap_size=log2_hashmap_size, desired_resolution=desired_resolution, gridtype='hash', align_corners=align_corners)
    
    elif encoding == 'tiledgrid':
        from gridencoder import GridEncoder
        encoder = GridEncoder(input_dim=input_dim, num_levels=num_levels, level_dim=level_dim, base_resolution=base_resolution, log2_hashmap_size=log2_hashmap_size, desired_resolution=desired_resolution, gridtype='tiled', align_corners=align_corners)
    
    elif encoding == 'ash':
        from ashencoder import AshEncoder
        encoder = AshEncoder(input_dim=input_dim, output_dim=16, log2_hashmap_size=log2_hashmap_size, resolution=desired_resolution)

    else:
        raise NotImplementedError('Unknown encoding mode, choose from [None, frequency, spherical_harmonics, hashgrid, tiledgrid]')

    return encoder, encoder.output_dim