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