Spaces:
Sleeping
Sleeping
File size: 8,693 Bytes
bcec54e |
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 |
# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Shariq Farooq Bhat
import torch
import torch.nn as nn
@torch.jit.script
def exp_attractor(dx, alpha: float = 300, gamma: int = 2):
"""Exponential attractor: dc = exp(-alpha*|dx|^gamma) * dx , where dx = a - c, a = attractor point, c = bin center, dc = shift in bin centermmary for exp_attractor
Args:
dx (torch.Tensor): The difference tensor dx = Ai - Cj, where Ai is the attractor point and Cj is the bin center.
alpha (float, optional): Proportional Attractor strength. Determines the absolute strength. Lower alpha = greater attraction. Defaults to 300.
gamma (int, optional): Exponential Attractor strength. Determines the "region of influence" and indirectly number of bin centers affected. Lower gamma = farther reach. Defaults to 2.
Returns:
torch.Tensor : Delta shifts - dc; New bin centers = Old bin centers + dc
"""
return torch.exp(-alpha*(torch.abs(dx)**gamma)) * (dx)
@torch.jit.script
def inv_attractor(dx, alpha: float = 300, gamma: int = 2):
"""Inverse attractor: dc = dx / (1 + alpha*dx^gamma), where dx = a - c, a = attractor point, c = bin center, dc = shift in bin center
This is the default one according to the accompanying paper.
Args:
dx (torch.Tensor): The difference tensor dx = Ai - Cj, where Ai is the attractor point and Cj is the bin center.
alpha (float, optional): Proportional Attractor strength. Determines the absolute strength. Lower alpha = greater attraction. Defaults to 300.
gamma (int, optional): Exponential Attractor strength. Determines the "region of influence" and indirectly number of bin centers affected. Lower gamma = farther reach. Defaults to 2.
Returns:
torch.Tensor: Delta shifts - dc; New bin centers = Old bin centers + dc
"""
return dx.div(1+alpha*dx.pow(gamma))
class AttractorLayer(nn.Module):
def __init__(self, in_features, n_bins, n_attractors=16, mlp_dim=128, min_depth=1e-3, max_depth=10,
alpha=300, gamma=2, kind='sum', attractor_type='exp', memory_efficient=False):
"""
Attractor layer for bin centers. Bin centers are bounded on the interval (min_depth, max_depth)
"""
super().__init__()
self.n_attractors = n_attractors
self.n_bins = n_bins
self.min_depth = min_depth
self.max_depth = max_depth
self.alpha = alpha
self.gamma = gamma
self.kind = kind
self.attractor_type = attractor_type
self.memory_efficient = memory_efficient
self._net = nn.Sequential(
nn.Conv2d(in_features, mlp_dim, 1, 1, 0),
nn.ReLU(inplace=True),
nn.Conv2d(mlp_dim, n_attractors*2, 1, 1, 0), # x2 for linear norm
nn.ReLU(inplace=True)
)
def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False):
"""
Args:
x (torch.Tensor) : feature block; shape - n, c, h, w
b_prev (torch.Tensor) : previous bin centers normed; shape - n, prev_nbins, h, w
Returns:
tuple(torch.Tensor,torch.Tensor) : new bin centers normed and scaled; shape - n, nbins, h, w
"""
if prev_b_embedding is not None:
if interpolate:
prev_b_embedding = nn.functional.interpolate(
prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True)
x = x + prev_b_embedding
A = self._net(x)
eps = 1e-3
A = A + eps
n, c, h, w = A.shape
A = A.view(n, self.n_attractors, 2, h, w)
A_normed = A / A.sum(dim=2, keepdim=True) # n, a, 2, h, w
A_normed = A[:, :, 0, ...] # n, na, h, w
b_prev = nn.functional.interpolate(
b_prev, (h, w), mode='bilinear', align_corners=True)
b_centers = b_prev
if self.attractor_type == 'exp':
dist = exp_attractor
else:
dist = inv_attractor
if not self.memory_efficient:
func = {'mean': torch.mean, 'sum': torch.sum}[self.kind]
# .shape N, nbins, h, w
delta_c = func(dist(A_normed.unsqueeze(
2) - b_centers.unsqueeze(1)), dim=1)
else:
delta_c = torch.zeros_like(b_centers, device=b_centers.device)
for i in range(self.n_attractors):
# .shape N, nbins, h, w
delta_c += dist(A_normed[:, i, ...].unsqueeze(1) - b_centers)
if self.kind == 'mean':
delta_c = delta_c / self.n_attractors
b_new_centers = b_centers + delta_c
B_centers = (self.max_depth - self.min_depth) * \
b_new_centers + self.min_depth
B_centers, _ = torch.sort(B_centers, dim=1)
B_centers = torch.clip(B_centers, self.min_depth, self.max_depth)
return b_new_centers, B_centers
class AttractorLayerUnnormed(nn.Module):
def __init__(self, in_features, n_bins, n_attractors=16, mlp_dim=128, min_depth=1e-3, max_depth=10,
alpha=300, gamma=2, kind='sum', attractor_type='exp', memory_efficient=False):
"""
Attractor layer for bin centers. Bin centers are unbounded
"""
super().__init__()
self.n_attractors = n_attractors
self.n_bins = n_bins
self.min_depth = min_depth
self.max_depth = max_depth
self.alpha = alpha
self.gamma = gamma
self.kind = kind
self.attractor_type = attractor_type
self.memory_efficient = memory_efficient
self._net = nn.Sequential(
nn.Conv2d(in_features, mlp_dim, 1, 1, 0),
nn.ReLU(inplace=True),
nn.Conv2d(mlp_dim, n_attractors, 1, 1, 0),
nn.Softplus()
)
def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False):
"""
Args:
x (torch.Tensor) : feature block; shape - n, c, h, w
b_prev (torch.Tensor) : previous bin centers normed; shape - n, prev_nbins, h, w
Returns:
tuple(torch.Tensor,torch.Tensor) : new bin centers unbounded; shape - n, nbins, h, w. Two outputs just to keep the API consistent with the normed version
"""
if prev_b_embedding is not None:
if interpolate:
prev_b_embedding = nn.functional.interpolate(
prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True)
x = x + prev_b_embedding
A = self._net(x)
n, c, h, w = A.shape
b_prev = nn.functional.interpolate(
b_prev, (h, w), mode='bilinear', align_corners=True)
b_centers = b_prev
if self.attractor_type == 'exp':
dist = exp_attractor
else:
dist = inv_attractor
if not self.memory_efficient:
func = {'mean': torch.mean, 'sum': torch.sum}[self.kind]
# .shape N, nbins, h, w
delta_c = func(
dist(A.unsqueeze(2) - b_centers.unsqueeze(1)), dim=1)
else:
delta_c = torch.zeros_like(b_centers, device=b_centers.device)
for i in range(self.n_attractors):
delta_c += dist(A[:, i, ...].unsqueeze(1) -
b_centers) # .shape N, nbins, h, w
if self.kind == 'mean':
delta_c = delta_c / self.n_attractors
b_new_centers = b_centers + delta_c
B_centers = b_new_centers
return b_new_centers, B_centers
|