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# 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.
# Modified from https://github.com/facebookresearch/PoseDiffusion
# and https://github.com/facebookresearch/co-tracker/tree/main
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Union
from einops import rearrange, repeat
from pytorch3d.renderer import HarmonicEmbedding
# from pytorch3d.transforms.rotation_conversions import matrix_to_quaternion, quaternion_to_matrix
EPS = 1e-9
from pytorch3d.transforms.rotation_conversions import matrix_to_quaternion, quaternion_to_matrix
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# pyre-unsafe
from typing import Optional
def pose_encoding_to_camera(
pose_encoding,
pose_encoding_type="vgg",
log_focal_length_bias=1.,
min_focal_length=0.1,
max_focal_length=30,
return_dict=False,
):
"""
Args:
pose_encoding: A tensor of shape `BxNxC`, containing a batch of
`BxN` `C`-dimensional pose encodings.
pose_encoding_type: The type of pose encoding,
"""
pose_encoding_reshaped = pose_encoding#.reshape(-1, pose_encoding.shape[-1]) # Reshape to BNxC
if pose_encoding_type == "train":
# 3 for absT, 4 for quaR, 2 for absFL
abs_T = pose_encoding_reshaped[..., :3]
quaternion_R = pose_encoding_reshaped[..., 3:7]
R = quaternion_to_matrix(quaternion_R)
log_focal_length = pose_encoding_reshaped[..., 7:9]
# log_focal_length_bias was the hyperparameter
# to ensure the mean of logFL close to 0 during training
# Now converted back
m = nn.Softplus()
log_focal_length = m(log_focal_length)
# clamp to avoid weird fl values
focal_length = torch.clamp(log_focal_length, min=min_focal_length, max=max_focal_length)
elif pose_encoding_type == "train_fusion":
# 3 for absT, 4 for quaR, 2 for absF
abs_T = pose_encoding_reshaped[:, :3]
quaternion_R = pose_encoding_reshaped[:, 3:7]
R = quaternion_to_matrix(quaternion_R)
scale = pose_encoding_reshaped[:, 7:8]
# log_focal_length_bias was the hyperparameter
# to ensure the mean of logFL close to 0 during training
# Now converted back
focal_length = softplus(scale)
# clamp to avoid weird fl values
# focal_length = torch.clamp(focal_length, min=min_focal_length, max=max_focal_length)
elif pose_encoding_type == "absT_quaR_logFL":
# 3 for absT, 4 for quaR, 2 for absFL
abs_T = pose_encoding_reshaped[:, :3]
quaternion_R = pose_encoding_reshaped[:, 3:7]
R = quaternion_to_matrix(quaternion_R)
log_focal_length = pose_encoding_reshaped[:, 7:9]
# log_focal_length_bias was the hyperparameter
# to ensure the mean of logFL close to 0 during training
# Now converted back
focal_length = (log_focal_length + log_focal_length_bias).exp()
# clamp to avoid weird fl values
focal_length = torch.clamp(focal_length, min=min_focal_length, max=max_focal_length)
elif pose_encoding_type == "absT_quaR_OneFL":
# 3 for absT, 4 for quaR, 1 for absFL
# [absolute translation, quaternion rotation, normalized focal length]
abs_T = pose_encoding_reshaped[:, :3]
quaternion_R = pose_encoding_reshaped[:, 3:7]
R = quaternion_to_matrix(quaternion_R)
focal_length = pose_encoding_reshaped[:, 7:8]
focal_length = torch.clamp(focal_length, min=min_focal_length, max=max_focal_length)
else:
raise ValueError(f"Unknown pose encoding {pose_encoding_type}")
# if return_dict:
return {"focal_length": focal_length, "R": R, "T": abs_T, "quaternion_R": quaternion_R}
# pred_cameras = PerspectiveCameras(focal_length=focal_length, R=R, T=abs_T, device=R.device)
# return pred_cameras
def camera_to_pose_encoding(
camera, pose_encoding_type="absT_quaR_logFL", log_focal_length_bias=1.8, min_focal_length=0.1, max_focal_length=30
):
"""
Inverse to pose_encoding_to_camera
"""
if pose_encoding_type == "absT_quaR_logFL":
# Convert rotation matrix to quaternion
quaternion_R = matrix_to_quaternion(camera.R)
# Calculate log_focal_length
log_focal_length = (
torch.log(torch.clamp(camera.focal_length, min=min_focal_length, max=max_focal_length))
- log_focal_length_bias
)
# Concatenate to form pose_encoding
pose_encoding = torch.cat([camera.T, quaternion_R, log_focal_length], dim=-1)
elif pose_encoding_type == "absT_quaR_OneFL":
# [absolute translation, quaternion rotation, normalized focal length]
quaternion_R = matrix_to_quaternion(camera.R)
focal_length = (torch.clamp(camera.focal_length, min=min_focal_length, max=max_focal_length))[..., 0:1]
pose_encoding = torch.cat([camera.T, quaternion_R, focal_length], dim=-1)
else:
raise ValueError(f"Unknown pose encoding {pose_encoding_type}")
return pose_encoding
class PoseEmbedding(nn.Module):
def __init__(self, target_dim, n_harmonic_functions=10, append_input=True):
super().__init__()
self._emb_pose = HarmonicEmbedding(n_harmonic_functions=n_harmonic_functions, append_input=append_input)
self.out_dim = self._emb_pose.get_output_dim(target_dim)
def forward(self, pose_encoding):
e_pose_encoding = self._emb_pose(pose_encoding)
return e_pose_encoding
def get_2d_sincos_pos_embed(embed_dim: int, pos_encoding, return_grid=False) -> torch.Tensor:
"""
This function initializes a grid and generates a 2D positional embedding using sine and cosine functions.
It is a wrapper of get_2d_sincos_pos_embed_from_grid.
Args:
- embed_dim: The embedding dimension.
- grid_size: The grid size.
Returns:
- pos_embed: The generated 2D positional embedding.
"""
# if isinstance(grid_size, tuple):
# grid_size_h, grid_size_w = grid_size
# else:
# grid_size_h = grid_size_w = grid_size
# grid_h = torch.arange(grid_size_h, dtype=torch.float)
# grid_w = torch.arange(grid_size_w, dtype=torch.float)
# grid = torch.meshgrid(grid_w, grid_h, indexing="xy")
grid = pos_encoding # grid.reshape([..., 2])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
return pos_embed.reshape(pos_encoding.shape[0], pos_encoding.shape[1], -1)
def get_2d_sincos_pos_embed_from_grid(embed_dim: int, grid: torch.Tensor) -> torch.Tensor:
"""
This function generates a 2D positional embedding from a given grid using sine and cosine functions.
Args:
- embed_dim: The embedding dimension.
- grid: The grid to generate the embedding from.
Returns:
- emb: The generated 2D positional embedding.
"""
assert embed_dim % 2 == 0
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[..., 0].detach().cpu()) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[..., 1].detach().cpu()) # (H*W, D/2)
emb = torch.cat([emb_h, emb_w], dim=2) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim: int, pos: torch.Tensor) -> torch.Tensor:
"""
This function generates a 1D positional embedding from a given grid using sine and cosine functions.
Args:
- embed_dim: The embedding dimension.
- pos: The position to generate the embedding from.
Returns:
- emb: The generated 1D positional embedding.
"""
assert embed_dim % 2 == 0
omega = torch.arange(embed_dim // 2, dtype=torch.double)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = torch.sin(out) # (M, D/2)
emb_cos = torch.cos(out) # (M, D/2)
emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D)
return emb[None].float()
def get_2d_embedding(xy: torch.Tensor, C: int, cat_coords: bool = True) -> torch.Tensor:
"""
This function generates a 2D positional embedding from given coordinates using sine and cosine functions.
Args:
- xy: The coordinates to generate the embedding from.
- C: The size of the embedding.
- cat_coords: A flag to indicate whether to concatenate the original coordinates to the embedding.
Returns:
- pe: The generated 2D positional embedding.
"""
B, N, D = xy.shape
assert D == 2
x = xy[:, :, 0:1]
y = xy[:, :, 1:2]
div_term = (torch.arange(0, C, 2, device=xy.device, dtype=torch.float32) * (1000.0 / C)).reshape(1, 1, int(C / 2))
pe_x = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32)
pe_y = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32)
pe_x[:, :, 0::2] = torch.sin(x * div_term)
pe_x[:, :, 1::2] = torch.cos(x * div_term)
pe_y[:, :, 0::2] = torch.sin(y * div_term)
pe_y[:, :, 1::2] = torch.cos(y * div_term)
pe = torch.cat([pe_x, pe_y], dim=2) # (B, N, C*3)
if cat_coords:
pe = torch.cat([xy, pe], dim=2) # (B, N, C*3+3)
return pe
def bilinear_sampler(input, coords, align_corners=True, padding_mode="border"):
r"""Sample a tensor using bilinear interpolation
`bilinear_sampler(input, coords)` samples a tensor :attr:`input` at
coordinates :attr:`coords` using bilinear interpolation. It is the same
as `torch.nn.functional.grid_sample()` but with a different coordinate
convention.
The input tensor is assumed to be of shape :math:`(B, C, H, W)`, where
:math:`B` is the batch size, :math:`C` is the number of channels,
:math:`H` is the height of the image, and :math:`W` is the width of the
image. The tensor :attr:`coords` of shape :math:`(B, H_o, W_o, 2)` is
interpreted as an array of 2D point coordinates :math:`(x_i,y_i)`.
Alternatively, the input tensor can be of size :math:`(B, C, T, H, W)`,
in which case sample points are triplets :math:`(t_i,x_i,y_i)`. Note
that in this case the order of the components is slightly different
from `grid_sample()`, which would expect :math:`(x_i,y_i,t_i)`.
If `align_corners` is `True`, the coordinate :math:`x` is assumed to be
in the range :math:`[0,W-1]`, with 0 corresponding to the center of the
left-most image pixel :math:`W-1` to the center of the right-most
pixel.
If `align_corners` is `False`, the coordinate :math:`x` is assumed to
be in the range :math:`[0,W]`, with 0 corresponding to the left edge of
the left-most pixel :math:`W` to the right edge of the right-most
pixel.
Similar conventions apply to the :math:`y` for the range
:math:`[0,H-1]` and :math:`[0,H]` and to :math:`t` for the range
:math:`[0,T-1]` and :math:`[0,T]`.
Args:
input (Tensor): batch of input images.
coords (Tensor): batch of coordinates.
align_corners (bool, optional): Coordinate convention. Defaults to `True`.
padding_mode (str, optional): Padding mode. Defaults to `"border"`.
Returns:
Tensor: sampled points.
"""
sizes = input.shape[2:]
assert len(sizes) in [2, 3]
if len(sizes) == 3:
# t x y -> x y t to match dimensions T H W in grid_sample
coords = coords[..., [1, 2, 0]]
if align_corners:
coords = coords * torch.tensor([2 / max(size - 1, 1) for size in reversed(sizes)], device=coords.device)
else:
coords = coords * torch.tensor([2 / size for size in reversed(sizes)], device=coords.device)
coords -= 1
return F.grid_sample(input, coords, align_corners=align_corners, padding_mode=padding_mode)
def sample_features4d(input, coords):
r"""Sample spatial features
`sample_features4d(input, coords)` samples the spatial features
:attr:`input` represented by a 4D tensor :math:`(B, C, H, W)`.
The field is sampled at coordinates :attr:`coords` using bilinear
interpolation. :attr:`coords` is assumed to be of shape :math:`(B, R,
3)`, where each sample has the format :math:`(x_i, y_i)`. This uses the
same convention as :func:`bilinear_sampler` with `align_corners=True`.
The output tensor has one feature per point, and has shape :math:`(B,
R, C)`.
Args:
input (Tensor): spatial features.
coords (Tensor): points.
Returns:
Tensor: sampled features.
"""
B, _, _, _ = input.shape
# B R 2 -> B R 1 2
coords = coords.unsqueeze(2)
# B C R 1
feats = bilinear_sampler(input, coords)
return feats.permute(0, 2, 1, 3).view(B, -1, feats.shape[1] * feats.shape[3]) # B C R 1 -> B R C
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