# NetVLAD-pytorch

**Repository Path**: wind_x/NetVLAD-pytorch

## Basic Information

- **Project Name**: NetVLAD-pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-19
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# NetVLAD-pytorch
Pytorch implementation of NetVLAD & Online Hardest Triplet Loss.
In NetVLAD, broadcasting is used to calculate residuals of clusters and it makes whole calculation time much faster. 

NetVLAD: https://arxiv.org/abs/1511.07247

In Defense of the Triplet Loss for Person Re-Identification: https://arxiv.org/abs/1703.07737 https://omoindrot.github.io/triplet-loss

## Usage
```
import torch
import torch.nn as nn
from torch.autograd import Variable

from netvlad import NetVLAD
from netvlad import EmbedNet
from hard_triplet_loss import HardTripletLoss
from torchvision.models import resnet18


# Discard layers at the end of base network
encoder = resnet18(pretrained=True)
base_model = nn.Sequential(
    encoder.conv1,
    encoder.bn1,
    encoder.relu,
    encoder.maxpool,
    encoder.layer1,
    encoder.layer2,
    encoder.layer3,
    encoder.layer4,
])
dim = list(base_model.parameters())[-1].shape[0]  # last channels (512)

# Define model for embedding
net_vlad = NetVLAD(num_clusters=32, dim=dim, alpha=1.0)
model = EmbedNet(base_model, net_vlad).cuda()

# Define loss
criterion = HardTripletLoss(margin=0.1).cuda()

# This is just toy example. Typically, the number of samples in each classes are 4.
labels = torch.randint(0, 10, (40, )).long()
x = torch.rand(40, 3, 128, 128).cuda()
output = model(x)

triplet_loss = criterion(output, labels)
```