# 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