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47 lines
1.4 KiB
Python
47 lines
1.4 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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Code for embeddings.
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"""
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import torch
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from torchtyping import TensorType
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from nerfactory.fields.modules.base import FieldModule
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class Embedding(FieldModule):
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"""Index into embeddings.
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# TODO: add different types of initializations
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Args:
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in_dim (int): Number of embeddings
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out_dim (int): Dimension of the embedding vectors
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"""
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def __init__(self, in_dim: int, out_dim: int) -> None:
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super().__init__()
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self.in_dim = in_dim
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self.out_dim = out_dim
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self.build_nn_modules()
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def build_nn_modules(self) -> None:
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self.embedding = torch.nn.Embedding(self.in_dim, self.out_dim)
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def forward(self, in_tensor: TensorType[..., "input_dim"]) -> TensorType[..., "output_dim"]:
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"""Call forward"""
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return self.embedding(in_tensor)
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