Spaces:
Running on Zero
Running on Zero
File size: 7,453 Bytes
41ff959 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | # 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 torch
import torch.nn as nn
import torch.nn.functional as F
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
)
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_fn="group", stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes,
planes,
kernel_size=3,
padding=1,
stride=stride,
padding_mode="zeros",
)
self.conv2 = nn.Conv2d(
planes, planes, kernel_size=3, padding=1, padding_mode="zeros"
)
self.relu = nn.ReLU(inplace=True)
num_groups = planes // 8
if norm_fn == "group":
self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
if not stride == 1:
self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
elif norm_fn == "batch":
self.norm1 = nn.BatchNorm2d(planes)
self.norm2 = nn.BatchNorm2d(planes)
if not stride == 1:
self.norm3 = nn.BatchNorm2d(planes)
elif norm_fn == "instance":
self.norm1 = nn.InstanceNorm2d(planes)
self.norm2 = nn.InstanceNorm2d(planes)
if not stride == 1:
self.norm3 = nn.InstanceNorm2d(planes)
elif norm_fn == "none":
self.norm1 = nn.Sequential()
self.norm2 = nn.Sequential()
if not stride == 1:
self.norm3 = nn.Sequential()
if stride == 1:
self.downsample = None
else:
self.downsample = nn.Sequential(
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3
)
def forward(self, x):
y = x
y = self.relu(self.norm1(self.conv1(y)))
y = self.relu(self.norm2(self.conv2(y)))
if self.downsample is not None:
x = self.downsample(x)
return self.relu(x + y)
class BasicEncoder(nn.Module):
def __init__(self, input_dim=3, output_dim=128, stride=4):
super(BasicEncoder, self).__init__()
self.stride = stride
self.norm_fn = "instance"
self.in_planes = output_dim // 2
self.norm1 = nn.InstanceNorm2d(self.in_planes)
self.norm2 = nn.InstanceNorm2d(output_dim * 2)
self.conv1 = nn.Conv2d(
input_dim,
self.in_planes,
kernel_size=7,
stride=2,
padding=3,
padding_mode="zeros",
)
self.relu1 = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(output_dim // 2, stride=1)
self.layer2 = self._make_layer(output_dim // 4 * 3, stride=2)
self.layer3 = self._make_layer(output_dim, stride=2)
self.layer4 = self._make_layer(output_dim, stride=2)
self.conv2 = nn.Conv2d(
output_dim * 3 + output_dim // 4,
output_dim * 2,
kernel_size=3,
padding=1,
padding_mode="zeros",
)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(output_dim * 2, output_dim, kernel_size=1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, (nn.InstanceNorm2d)):
if m.weight is not None:
nn.init.constant_(m.weight, 1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def _make_layer(self, dim, stride=1):
layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
layers = (layer1, layer2)
self.in_planes = dim
return nn.Sequential(*layers)
def forward(self, x):
_, _, H, W = x.shape
x = self.conv1(x)
x = self.norm1(x)
x = self.relu1(x)
a = self.layer1(x)
b = self.layer2(a)
c = self.layer3(b)
d = self.layer4(c)
def _bilinear_intepolate(x):
return F.interpolate(
x,
(H // self.stride, W // self.stride),
mode="bilinear",
align_corners=True,
)
a = _bilinear_intepolate(a)
b = _bilinear_intepolate(b)
c = _bilinear_intepolate(c)
d = _bilinear_intepolate(d)
x = self.conv2(torch.cat([a, b, c, d], dim=1))
x = self.norm2(x)
x = self.relu2(x)
x = self.conv3(x)
return x
|