test.py

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=False)
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().cuda()
x = torch.rand(40, 3, 128, 128).cuda()
base_model.cuda()
print(base_model(x).shape)
output = model(x)
print(output.shape)
triplet_loss = criterion(output, labels)