# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
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# 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