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43 lines
1.4 KiB
Python
43 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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Special activation functions.
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"""
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import torch
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from torch.autograd import Function
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from torch.cuda.amp import custom_bwd, custom_fwd
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class _TruncExp(Function): # pylint: disable=abstract-method
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# Implementation from torch-ngp:
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# https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, x): # pylint: disable=arguments-differ
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ctx.save_for_backward(x)
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return torch.exp(x)
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@staticmethod
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@custom_bwd
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def backward(ctx, g): # pylint: disable=arguments-differ
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x = ctx.saved_tensors[0]
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return g * torch.exp(x.clamp(-15, 15))
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trunc_exp = _TruncExp.apply
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"""Same as torch.exp, but with the backward pass clipped to prevent vanishing/exploding
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gradients."""
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