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PRIMA: Boosting Animal Mesh Recovery with Biological Priors and Test-Time Adaptation
Official implementation of the paper:
"PRIMA: Boosting Animal Mesh Recovery with Biological Priors and Test-Time Adaptation"
by Xiaohang Yu, Ti Wang, and Mackenzie Weygandt Mathis
Licensed under a modified MIT license
"""
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Any, Optional, Tuple
import numpy as np
import torch
from torch import nn
# Rotary Positional Encoding, adapted from:
# 1. https://github.com/meta-llama/codellama/blob/main/llama/model.py
# 2. https://github.com/naver-ai/rope-vit
# 3. https://github.com/lucidrains/rotary-embedding-torch
def init_t_xy(end_x: int, end_y: int):
t = torch.arange(end_x * end_y, dtype=torch.float32)
t_x = (t % end_x).float()
t_y = torch.div(t, end_x, rounding_mode="floor").float()
return t_x, t_y
def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0):
freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
t_x, t_y = init_t_xy(end_x, end_y)
freqs_x = torch.outer(t_x, freqs_x)
freqs_y = torch.outer(t_y, freqs_y)
freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)
freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)
return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
ndim = x.ndim
assert 0 <= 1 < ndim
assert freqs_cis.shape == (x.shape[-2], x.shape[-1])
shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]
return freqs_cis.view(*shape)
def apply_rotary_enc(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cis: torch.Tensor,
repeat_freqs_k: bool = False,
):
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
xk_ = (
torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
if xk.shape[-2] != 0
else None
)
freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
if xk_ is None:
# no keys to rotate, due to dropout
return xq_out.type_as(xq).to(xq.device), xk
# repeat freqs along seq_len dim to match k seq_len
if repeat_freqs_k:
r = xk_.shape[-2] // xq_.shape[-2]
if freqs_cis.is_cuda:
freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)
else:
# torch.repeat on complex numbers may not be supported on non-CUDA devices
# (freqs_cis has 4 dims and we repeat on dim 2) so we use expand + flatten
freqs_cis = freqs_cis.unsqueeze(2).expand(-1, -1, r, -1, -1).flatten(2, 3)
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)
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