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run.py
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run.py
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from utils import train_dataset, test_dataset, evaluate, print_result, read_settings
from copy import deepcopy
import time
import torch
import torch.utils.data as data
import torch.optim as optim
import random
import numpy as np
from IPython.display import display, clear_output
def T_annealing(epoch, max_epoch, initial_T, end_T):
new_T = initial_T * ((end_T / initial_T) ** (epoch / max_epoch))
return new_T
def run_base(run_dict, model):
optimizer = optim.Adam(model.parameters(), lr=run_dict['lr'], weight_decay=run_dict['reg'])
train_loader, test_dataset = run_dict['train_loader'], run_dict['test_dataset']
gpu = run_dict['gpu']
early_stop = 0.
history = []
# Begin training
for epoch in range(run_dict['max_epoch']+1):
train_loader.dataset.negative_sampling()
epoch_loss = []
for batch_user, batch_pos_item, batch_neg_item in train_loader:
batch_user = batch_user.to(gpu)
batch_pos_item = batch_pos_item.to(gpu)
batch_neg_item = batch_neg_item.to(gpu)
model.train()
## Base Model
output = model(batch_user, batch_pos_item, batch_neg_item)
base_loss = model.get_loss(output)
batch_loss = base_loss
epoch_loss.append(batch_loss)
## Backward and optimize
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
epoch_loss = float(torch.mean(torch.stack(epoch_loss)))
# Evaluation
if epoch % run_dict['eval_period'] == 0:
model.eval()
with torch.no_grad():
eval_results = evaluate(model, gpu, train_loader, test_dataset)
current_R50 = eval_results['test']['R50']
history.append(current_R50)
clear_output()
display('Epoch [%d/%d], Recall@50: %.4f' % (epoch, run_dict['max_epoch'], current_R50))
clear_output()
print("Train Done!, Recall@50: %.4f" % (current_R50))
return history
def run_FTD(run_dict, model):
optimizer = optim.Adam(model.parameters(), lr=run_dict['lr'], weight_decay=run_dict['reg'])
train_loader, test_dataset = run_dict['train_loader'], run_dict['test_dataset']
gpu = run_dict['gpu']
early_stop = 0.
history = []
# Begin training
for epoch in range(run_dict['max_epoch']+1):
train_loader.dataset.negative_sampling()
epoch_loss = []
for batch_user, batch_pos_item, batch_neg_item in train_loader:
batch_user = batch_user.to(gpu)
batch_pos_item = batch_pos_item.to(gpu)
batch_neg_item = batch_neg_item.to(gpu)
model.train()
## Base Model
output = model(batch_user, batch_pos_item, batch_neg_item)
base_loss = model.get_loss(output)
## Topology Distillation
TD_loss = model.get_TD_loss(batch_user.unique(), torch.cat([batch_pos_item, batch_neg_item], 0).unique())
batch_loss = base_loss + TD_loss * run_dict['lmbda_TD']
epoch_loss.append(batch_loss)
## Backward and optimize
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
epoch_loss = float(torch.mean(torch.stack(epoch_loss)))
# Evaluation
if epoch % run_dict['eval_period'] == 0:
model.eval()
with torch.no_grad():
eval_results = evaluate(model, gpu, train_loader, test_dataset)
current_R50 = eval_results['test']['R50']
history.append(current_R50)
clear_output()
display('Epoch [%d/%d], Recall@50: %.4f' % (epoch, run_dict['max_epoch'], current_R50))
clear_output()
print("Train Done!, Recall@50: %.4f" % (current_R50))
return history
def run_HTD(run_dict, model):
optimizer = optim.Adam(model.parameters(), lr=run_dict['lr'], weight_decay=run_dict['reg'])
train_loader, test_dataset = run_dict['train_loader'], run_dict['test_dataset']
gpu = run_dict['gpu']
early_stop = 0.
current_T = 1.
history = []
# Begin training
for epoch in range(run_dict['max_epoch']+1):
train_loader.dataset.negative_sampling()
epoch_loss = []
model.T = current_T
for batch_user, batch_pos_item, batch_neg_item in train_loader:
batch_user = batch_user.to(gpu)
batch_pos_item = batch_pos_item.to(gpu)
batch_neg_item = batch_neg_item.to(gpu)
model.train()
## Base Model
output = model(batch_user, batch_pos_item, batch_neg_item)
base_loss = model.get_loss(output)
## Group Assignment
GA_loss_user = model.get_GA_loss(batch_user.unique(), is_user=True)
GA_loss_item = model.get_GA_loss(torch.cat([batch_pos_item, batch_neg_item], 0).unique(), is_user=False)
GA_loss = GA_loss_user + GA_loss_item
## Topology Distillation
TD_loss = model.get_TD_loss(batch_user.unique(), torch.cat([batch_pos_item, batch_neg_item], 0).unique())
HTD_loss = TD_loss * run_dict['alpha'] + GA_loss * (1 - run_dict['alpha'])
batch_loss = base_loss + HTD_loss * run_dict['lmbda_TD']
epoch_loss.append(batch_loss)
## Backward and optimize
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
epoch_loss = float(torch.mean(torch.stack(epoch_loss)))
# Evaluation
if epoch % run_dict['eval_period'] == 0:
model.eval()
with torch.no_grad():
eval_results = evaluate(model, gpu, train_loader, test_dataset)
current_R50 = eval_results['test']['R50']
history.append(current_R50)
clear_output()
display('Epoch [%d/%d], Recall@50: %.4f' % (epoch, run_dict['max_epoch'], current_R50))
# T annealing (optional)
current_T = T_annealing(epoch, run_dict['max_epoch'], 1, 1e-10)
if current_T < 1e-10:
current_T = 1e-10
clear_output()
print("Train Done!, Recall@50: %.4f" % (current_R50))
return history