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1b8d86a104
Summary: As suggested in #802. By not persisting the _xy_grid buffer, we can allow (in some cases) a model with one image_size to be loaded from a saved model which was trained at a different resolution. Also avoid persisting _frequencies in HarmonicEmbedding for similar reasons. BC-break: This will cause load_state_dict, in strict mode, to complain if you try to load an old model with the new code. Reviewed By: patricklabatut Differential Revision: D30349234 fbshipit-source-id: d6061d1e51c9f79a78d61a9f732c9a5dfadbbb47
89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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class HarmonicEmbedding(torch.nn.Module):
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def __init__(
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self,
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n_harmonic_functions: int = 6,
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omega0: float = 1.0,
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logspace: bool = True,
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include_input: bool = True,
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) -> None:
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"""
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Given an input tensor `x` of shape [minibatch, ... , dim],
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the harmonic embedding layer converts each feature
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in `x` into a series of harmonic features `embedding`,
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where for each i in range(dim) the following are present
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in embedding[...]:
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```
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[
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sin(x[..., i]),
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sin(f_1*x[..., i]),
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sin(f_2*x[..., i]),
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...
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sin(f_N * x[..., i]),
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cos(x[..., i]),
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cos(f_1*x[..., i]),
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cos(f_2*x[..., i]),
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...
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cos(f_N * x[..., i]),
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x[..., i] # only present if include_input is True.
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]
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```
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where N corresponds to `n_harmonic_functions`, and f_i is a scalar
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denoting the i-th frequency of the harmonic embedding.
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The shape of the output is [minibatch, ... , dim * (2 * N + 1)] if
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include_input is True, otherwise [minibatch, ... , dim * (2 * N)].
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If `logspace==True`, the frequencies `[f_1, ..., f_N]` are
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powers of 2:
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`f_1 = 1, ..., f_N = 2**torch.arange(n_harmonic_functions)`
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If `logspace==False`, frequencies are linearly spaced between
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`1.0` and `2**(n_harmonic_functions-1)`:
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`f_1, ..., f_N = torch.linspace(
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1.0, 2**(n_harmonic_functions-1), n_harmonic_functions
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)`
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Note that `x` is also premultiplied by the base frequency `omega0`
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before evaluating the harmonic functions.
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"""
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super().__init__()
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if logspace:
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frequencies = 2.0 ** torch.arange(
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n_harmonic_functions,
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dtype=torch.float32,
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)
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else:
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frequencies = torch.linspace(
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1.0,
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2.0 ** (n_harmonic_functions - 1),
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n_harmonic_functions,
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dtype=torch.float32,
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)
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self.register_buffer("_frequencies", omega0 * frequencies, persistent=False)
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self.include_input = include_input
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x: tensor of shape [..., dim]
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Returns:
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embedding: a harmonic embedding of `x` of shape
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[..., dim * (n_harmonic_functions * 2 + T)] where
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T is 1 if include_input is True and 0 otherwise.
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"""
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embed = (x[..., None] * self._frequencies).view(*x.shape[:-1], -1)
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if self.include_input:
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return torch.cat((embed.sin(), embed.cos(), x), dim=-1)
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else:
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return torch.cat((embed.sin(), embed.cos()), dim=-1)
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