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# 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 numpy as np
import random
import torch
import torch.nn.functional as F
from typing import Optional, Tuple
EPS = 1e-6
def smart_cat(tensor1, tensor2, dim):
if tensor1 is None:
return tensor2
return torch.cat([tensor1, tensor2], dim=dim)
def get_uniformly_sampled_pts(
size: int,
num_frames: int,
extent: Tuple[float, ...],
device: Optional[torch.device] = torch.device("cpu"),
):
time_points = torch.randint(low=0, high=num_frames, size=(size, 1), device=device)
space_points = torch.rand(size, 2, device=device) * torch.tensor(
[extent[1], extent[0]], device=device
)
points = torch.cat((time_points, space_points), dim=1)
return points[None]
def get_superpoint_sampled_pts(
video,
size: int,
num_frames: int,
extent: Tuple[float, ...],
device: Optional[torch.device] = torch.device("cpu"),
):
extractor = SuperPoint(max_num_keypoints=48).eval().cuda()
points = list()
for _ in range(8):
frame_num = random.randint(0, int(num_frames * 0.25))
key_points = extractor.extract(
video[0, frame_num, :, :, :] / 255.0, resize=None
)["keypoints"]
frame_tensor = torch.full((1, key_points.shape[1], 1), frame_num).cuda()
points.append(torch.cat([frame_tensor.cuda(), key_points], dim=2))
return torch.cat(points, dim=1)[:, :size, :]
def get_sift_sampled_pts(
video,
size: int,
num_frames: int,
extent: Tuple[float, ...],
device: Optional[torch.device] = torch.device("cpu"),
num_sampled_frames: int = 8,
sampling_length_percent: float = 0.25,
):
import cv2
# assert size == 384, "hardcoded for experiment"
sift = cv2.SIFT_create(nfeatures=size // num_sampled_frames)
points = list()
for _ in range(num_sampled_frames):
frame_num = random.randint(0, int(num_frames * sampling_length_percent))
key_points, _ = sift.detectAndCompute(
video[0, frame_num, :, :, :]
.cpu()
.permute(1, 2, 0)
.numpy()
.astype(np.uint8),
None,
)
for kp in key_points:
points.append([frame_num, int(kp.pt[0]), int(kp.pt[1])])
return torch.tensor(points[:size], device=device)[None]
def get_points_on_a_grid(
size: int,
extent: Tuple[float, ...],
center: Optional[Tuple[float, ...]] = None,
device: Optional[torch.device] = torch.device("cpu"),
):
r"""Get a grid of points covering a rectangular region
`get_points_on_a_grid(size, extent)` generates a :attr:`size` by
:attr:`size` grid fo points distributed to cover a rectangular area
specified by `extent`.
The `extent` is a pair of integer :math:`(H,W)` specifying the height
and width of the rectangle.
Optionally, the :attr:`center` can be specified as a pair :math:`(c_y,c_x)`
specifying the vertical and horizontal center coordinates. The center
defaults to the middle of the extent.
Points are distributed uniformly within the rectangle leaving a margin
:math:`m=W/64` from the border.
It returns a :math:`(1, \text{size} \times \text{size}, 2)` tensor of
points :math:`P_{ij}=(x_i, y_i)` where
.. math::
P_{ij} = \left(
c_x + m -\frac{W}{2} + \frac{W - 2m}{\text{size} - 1}\, j,~
c_y + m -\frac{H}{2} + \frac{H - 2m}{\text{size} - 1}\, i
\right)
Points are returned in row-major order.
Args:
size (int): grid size.
extent (tuple): height and with of the grid extent.
center (tuple, optional): grid center.
device (str, optional): Defaults to `"cpu"`.
Returns:
Tensor: grid.
"""
if size == 1:
return torch.tensor([extent[1] / 2, extent[0] / 2], device=device)[None, None]
if center is None:
center = [extent[0] / 2, extent[1] / 2]
margin = extent[1] / 64
range_y = (margin - extent[0] / 2 + center[0], extent[0] / 2 + center[0] - margin)
range_x = (margin - extent[1] / 2 + center[1], extent[1] / 2 + center[1] - margin)
grid_y, grid_x = torch.meshgrid(
torch.linspace(*range_y, size, device=device),
torch.linspace(*range_x, size, device=device),
indexing="ij",
)
return torch.stack([grid_x, grid_y], dim=-1).reshape(1, -1, 2)
def reduce_masked_mean(input, mask, dim=None, keepdim=False):
r"""Masked mean
`reduce_masked_mean(x, mask)` computes the mean of a tensor :attr:`input`
over a mask :attr:`mask`, returning
.. math::
\text{output} =
\frac
{\sum_{i=1}^N \text{input}_i \cdot \text{mask}_i}
{\epsilon + \sum_{i=1}^N \text{mask}_i}
where :math:`N` is the number of elements in :attr:`input` and
:attr:`mask`, and :math:`\epsilon` is a small constant to avoid
division by zero.
`reduced_masked_mean(x, mask, dim)` computes the mean of a tensor
:attr:`input` over a mask :attr:`mask` along a dimension :attr:`dim`.
Optionally, the dimension can be kept in the output by setting
:attr:`keepdim` to `True`. Tensor :attr:`mask` must be broadcastable to
the same dimension as :attr:`input`.
The interface is similar to `torch.mean()`.
Args:
inout (Tensor): input tensor.
mask (Tensor): mask.
dim (int, optional): Dimension to sum over. Defaults to None.
keepdim (bool, optional): Keep the summed dimension. Defaults to False.
Returns:
Tensor: mean tensor.
"""
mask = mask.expand_as(input)
prod = input * mask
if dim is None:
numer = torch.sum(prod)
denom = torch.sum(mask)
else:
numer = torch.sum(prod, dim=dim, keepdim=keepdim)
denom = torch.sum(mask, dim=dim, keepdim=keepdim)
mean = numer / (EPS + denom)
return mean
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
def sample_features5d(input, coords):
r"""Sample spatio-temporal features
`sample_features5d(input, coords)` works in the same way as
:func:`sample_features4d` but for spatio-temporal features and points:
:attr:`input` is a 5D tensor :math:`(B, T, C, H, W)`, :attr:`coords` is
a :math:`(B, R1, R2, 3)` tensor of spatio-temporal point :math:`(t_i,
x_i, y_i)`. The output tensor has shape :math:`(B, R1, R2, C)`.
Args:
input (Tensor): spatio-temporal features.
coords (Tensor): spatio-temporal points.
Returns:
Tensor: sampled features.
"""
B, T, _, _, _ = input.shape
# B T C H W -> B C T H W
input = input.permute(0, 2, 1, 3, 4)
# B R1 R2 3 -> B R1 R2 1 3
coords = coords.unsqueeze(3)
# B C R1 R2 1
feats = bilinear_sampler(input, coords)
return feats.permute(0, 2, 3, 1, 4).view(
B, feats.shape[2], feats.shape[3], feats.shape[1]
) # B C R1 R2 1 -> B R1 R2 C
def get_grid(
height,
width,
shape=None,
dtype="torch",
device="cpu",
align_corners=True,
normalize=True,
):
H, W = height, width
S = shape if shape else []
if align_corners:
x = torch.linspace(0, 1, W, device=device)
y = torch.linspace(0, 1, H, device=device)
if not normalize:
x = x * (W - 1)
y = y * (H - 1)
else:
x = torch.linspace(0.5 / W, 1.0 - 0.5 / W, W, device=device)
y = torch.linspace(0.5 / H, 1.0 - 0.5 / H, H, device=device)
if not normalize:
x = x * W
y = y * H
x_view, y_view, exp = [1 for _ in S] + [1, -1], [1 for _ in S] + [-1, 1], S + [H, W]
x = x.view(*x_view).expand(*exp)
y = y.view(*y_view).expand(*exp)
grid = torch.stack([x, y], dim=-1)
if dtype == "numpy":
grid = grid.numpy()
return grid
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 round_to_multiple_of_4(n):
return round(n / 4) * 4
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