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VDSH.py
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VDSH.py
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import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
from utils.utils import *
from utils.datasets import *
def get_config():
config = {
"device": torch.device("cuda:0"),
"dataset": "ng20.tfidf",
"bit": 8,
"dropout": 0.1,
"batch_size": 64,
"epoch": 300,
"optimizer": {"type": optim.Adam, "optim_params": {"lr": 0.001}},
"stop_iter": 10
}
return config
class VDSH(nn.Module):
def __init__(self, dataset, vocabSize, latentDim, device, dropoutProb=0.):
super(VDSH, self).__init__()
self.dataset = dataset
self.hidden_dim = 1000
self.vocabSize = vocabSize
self.latentDim = latentDim
self.dropoutProb = dropoutProb
self.device = device
self.encoder = nn.Sequential(nn.Linear(self.vocabSize, self.hidden_dim),
nn.ReLU(inplace=True),
nn.Linear(self.hidden_dim, self.hidden_dim),
nn.ReLU(inplace=True),
nn.Dropout(p=dropoutProb))
self.h_to_mu = nn.Linear(self.hidden_dim, self.latentDim)
self.h_to_logvar = nn.Sequential(nn.Linear(self.hidden_dim, self.latentDim),
nn.Sigmoid())
self.decoder = nn.Sequential(nn.Linear(self.latentDim, self.vocabSize),
nn.LogSoftmax(dim=1))
def encode(self, doc_mat):
h = self.encoder(doc_mat)
z_mu = self.h_to_mu(h)
z_logvar = self.h_to_logvar(h)
return z_mu, z_logvar
def reparametrize(self, mu, logvar):
std = torch.sqrt(torch.exp(logvar))
eps = torch.cuda.FloatTensor(std.size()).normal_()
eps = Variable(eps)
return eps.mul(std).add_(mu)
def forward(self, document_mat):
mu, logvar = self.encode(document_mat)
z = self.reparametrize(mu, logvar)
prob_w = self.decoder(z)
return prob_w, mu, logvar
def get_name(self):
return "VDSH"
@staticmethod
def calculate_KL_loss(mu, logvar):
KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)
KLD = torch.sum(KLD_element, dim=1)
KLD = torch.mean(KLD).mul_(-0.5)
return KLD
@staticmethod
def compute_reconstr_loss(logprob_word, doc_mat):
return -torch.mean(torch.sum(logprob_word * doc_mat, dim=1))
def get_binary_code(self, train, test):
train_zy = [(self.encode(xb.to(self.device))[0], yb) for xb, yb in train]
train_z, train_y = zip(*train_zy)
train_z = torch.cat(train_z, dim=0)
train_y = torch.cat(train_y, dim=0)
test_zy = [(self.encode(xb.to(self.device))[0], yb) for xb, yb in test]
test_z, test_y = zip(*test_zy)
test_z = torch.cat(test_z, dim=0)
test_y = torch.cat(test_y, dim=0)
mid_val, _ = torch.median(train_z, dim=0)
train_b = (train_z > mid_val).type(torch.cuda.ByteTensor)
test_b = (test_z > mid_val).type(torch.cuda.ByteTensor)
del train_z
del test_z
return train_b, test_b, train_y, test_y
def train_val(config):
bit = config["bit"]
device = config["device"]
dataset, data_fmt = config["dataset"].split('.')
batch_size = config["batch_size"]
if dataset in ['reuters', 'tmc', 'rcv1']:
single_label_flag = False
else:
single_label_flag = True
if single_label_flag:
train_set = SingleLabelTextDataset('datasets/{}'.format(dataset), subset='train', bow_format=data_fmt, download=True)
test_set = SingleLabelTextDataset('datasets/{}'.format(dataset), subset='test', bow_format=data_fmt, download=True)
else:
train_set = MultiLabelTextDataset('datasets/{}'.format(dataset), subset='train', bow_format=data_fmt, download=True)
test_set = MultiLabelTextDataset('datasets/{}'.format(dataset), subset='test', bow_format=data_fmt, download=True)
train_loader = torch.utils.data.DataLoader(dataset=train_set, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_set, batch_size=batch_size, shuffle=True)
num_features = train_set[0][0].size(0)
model = VDSH(dataset, num_features, bit, dropoutProb=0.1, device=device)
model.to(device)
optimizer = config["optimizer"]["type"](model.parameters(), lr=config["optimizer"]["optim_params"]["lr"])
kl_weight = 0.
kl_step = 1 / 5000.
best_precision = 0
prec = 0
best_precision_epoch = 0
step_count = 0
for epoch in tqdm(range(config["epoch"])):
avg_loss = []
for step, (xb, yb) in enumerate(train_loader):
xb = xb.to(device)
yb = yb.to(device)
logprob_w, mu, logvar = model(xb)
kl_loss = VDSH.calculate_KL_loss(mu, logvar)
reconstr_loss = VDSH.compute_reconstr_loss(logprob_w, xb)
loss = reconstr_loss + kl_weight * kl_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
kl_weight = min(kl_weight + kl_step, 1.)
avg_loss.append(loss.item())
with torch.no_grad():
train_b, test_b, train_y, test_y = model.get_binary_code(train_loader, test_loader)
retrieved_indices = retrieve_topk(test_b.to(device), train_b.to(device), topK=100)
prec = compute_precision_at_k(retrieved_indices, test_y.to(device), train_y.to(device), topK=100, is_single_label=single_label_flag)
if prec.item() > best_precision:
best_precision = prec.item()
best_precision_epoch = epoch + 1
step_count = 0
else:
step_count += 1
if step_count >= config["stop_iter"]:
break
tqdm.write(f'Epoch {epoch+1}/{config["epoch"]} - Current Precision: {prec.item():.4f} - Best Precision: {best_precision:.4f} [{best_precision_epoch}]')
if __name__ == "__main__":
config = get_config()
train_val(config)