Source code for towhee.models.layers.netvlad

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# This code is modified by Zilliz.

import torch
import torch.nn.functional as F

from torch import nn


[docs]class NetVLAD(nn.Module): """ NetVLAD layer implementation. Args: num_clusters (`int`): The number of clusters dim (`int`): Dimension of descriptors alpha (`float`): Parameter of initialization. Larger value is harder assignment. normalize_input (`bool`): If true, descriptor-wise L2 normalization is applied to input. """
[docs] def __init__(self, num_clusters: int = 64, dim: int = 128, alpha: float = 100.0, normalize_input: bool = True): super().__init__() self.num_clusters = num_clusters self.alpha = alpha self.normalize_input = normalize_input self.conv = nn.Conv2d(dim, num_clusters, kernel_size=(1, 1), bias=True) self.centroids = nn.Parameter(torch.rand(num_clusters, dim)) self._init_params()
def _init_params(self): self.conv.weight = nn.Parameter( (2.0 * self.alpha * self.centroids).unsqueeze(-1).unsqueeze(-1) ) self.conv.bias = nn.Parameter( - self.alpha * self.centroids.norm(dim=1) )
[docs] def forward(self, x: torch.Tensor): num_sample, in_dim = x.shape[:2] if self.normalize_input: x = F.normalize(x, p=2, dim=1) # across descriptor dim # soft-assignment soft_assign = self.conv(x).view(num_sample, self.num_clusters, -1) soft_assign = F.softmax(soft_assign, dim=1) x_flatten = x.view(num_sample, in_dim, -1) # calculate residuals to each clusters residual = x_flatten.expand(self.num_clusters, -1, -1, -1).permute(1, 0, 2, 3) - \ self.centroids.expand(x_flatten.size(-1), -1, -1).permute(1, 2, 0).unsqueeze(0) residual *= soft_assign.unsqueeze(2) vlad = residual.sum(dim=-1) vlad = F.normalize(vlad, p=2, dim=2) # intra-normalization vlad = vlad.view(x.size(0), -1) # flatten vlad = F.normalize(vlad, p=2, dim=1) # L2 normalize return vlad
[docs]class EmbedNet(nn.Module): """ Embed a base model and the net vlad to a new network. Args: base_model (`int`): The base model that extracts image features net_vlad (`int`): NetVLAD model to extract global features """
[docs] def __init__(self, base_model: torch.nn.Sequential, net_vlad: NetVLAD): super().__init__() self.base_model = base_model self.net_vlad = net_vlad
[docs] def forward(self, x): x = self.base_model(x) embedded_x = self.net_vlad(x) return embedded_x