INNER CODE UNIT · Python

HashEmbedder

yashbhalgat/HashNeRF-pytorch · hash_encoding.py:10

class HashEmbedder(nn.Module):
    def __init__(self, bounding_box, n_levels=16, n_features_per_level=2,\
                log2_hashmap_size=19, base_resolution=16, finest_resolution=512):
        super(HashEmbedder, self).__init__()
        self.bounding_box = bounding_box
        self.n_levels = n_levels
        self.n_features_per_level = n_features_per_level
        self.log2_hashmap_size = log2_hashmap_size
        self.base_resolution = torch.tensor(base_resolution)
        self.finest_resolution = torch.tensor(finest_resolution)
        self.out_dim = self.n_levels * self.n_features_per_level

        self.b = torch.exp((torch.log(self.finest_resolution)-torch.log(self.base_resolution))/(n_levels-1))

        self.embeddings = nn.ModuleList([nn.Embedding(2**self.log2_hashmap_size, \
                                        self.n_features_per_level) for i in range(n_levels)])
        # custom uniform initialization
        for i in range(n_levels):

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