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trainer.py
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trainer.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tranier."""
__author__ = 'Chong Guo <armourcy@gmail.com>'
__copyright__ = 'Copyright 2018, Chong Guo'
__license__ = 'MIT'
import os
import math
import argparse
import numpy as np
import os.path as osp
import json
import torch
import torch.nn as nn
import torch.cuda as cuda
from torch.autograd import Variable
from utils.logger import Logger
class Trainer():
def __init__(self, net, train_loader, test_loader, optimizer, start_epoch=0,
best_accuracy=0, best_epoch=0, base_lr=0.1, criterion=nn.CrossEntropyLoss(),
lr_decay_interval=50, use_cuda=True, save_dir='checkpoint'):
self.net = net
self.train_loader = train_loader
self.test_loader = test_loader
self.optimizer = optimizer
self.base_lr = base_lr
self.criterion = criterion
self.lr_decay_interval = lr_decay_interval
self.use_cuda = use_cuda
self.best_accuracy = best_accuracy
self.best_epoch = best_epoch
self.start_epoch = start_epoch
self.save_dir = save_dir
self.tflog_writer = None
try:
from tools.logger import Logger
except ImportError as e:
print("fail to import tensorboard: {} ".format(e))
else:
self.tflog_writer = Logger(self.save_dir, restart=True)
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
self.jsonlog_writer_train = open(osp.join(self.save_dir, "train.log"), 'w+')
self.jsonlog_writer_test = open(osp.join(self.save_dir, "test.log"), 'w+')
def __del__(self):
self.jsonlog_writer_train.close()
self.jsonlog_writer_test.close()
def train(self, epoch):
""" Traning epoch """
print('==> Training Epoch: %d' % epoch)
self.net.train()
total_train_loss = 0
total_correct = 0
total_size = 0
n_train = len(self.train_loader.dataset)
for batch_idx, (inputs, targets) in enumerate(self.train_loader):
if self.use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
self.optimizer.zero_grad()
outputs = self.net(inputs)
loss = self.criterion(outputs, targets)
loss.backward()
self.optimizer.step()
total_train_loss += loss.data[0]
_, predicted = torch.max(outputs.data, 1)
batch_correct = predicted.eq(targets.data).cpu().sum()
total_correct += batch_correct
error_rate = 100. * (1 - batch_correct / len(inputs))
total_size += targets.size(0)
partial_epoch = epoch + batch_idx / len(self.train_loader)
print('Epoch: [{}]\tTrain:[{}/{} ({:.0f}%)]\tLoss: {:.6f}\tError: {:.6f}'.format(
epoch, total_size, n_train, 100. * batch_idx / len(self.train_loader),
loss.data[0], error_rate))
info = {
'epoch': partial_epoch,
'train-loss': loss.data[0],
'train-top1-error': error_rate
}
self.jsonlog_writer_train.write(json.dumps(info) + "\n")
if self.tflog_writer is not None:
info.pop('epoch', None)
for tag, value in info.items():
self.tflog_writer.scalar_summary(tag, value, partial_epoch)
print('Epoch: [{}]\tTotal training loss: [{:.6f}]\tTotal training error rate: [{:.6f}]'.format(
epoch, total_train_loss, (total_size - total_correct) / total_size * 100))
def test(self, epoch):
""" Testing epoch """
print('==> Testing Epoch: %d' % epoch)
self.net.eval()
total_test_loss = 0
total_correct = 0
total_size = 0
n_train = len(self.test_loader.dataset)
for batch_idx, (inputs, targets) in enumerate(self.test_loader):
if self.use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs, volatile=True), Variable(targets)
outputs = self.net(inputs)
loss = self.criterion(outputs, targets)
total_test_loss += loss.data[0]
_, predicted = torch.max(outputs.data, 1)
batch_correct = predicted.eq(targets.data).cpu().sum()
total_correct += batch_correct
total_size += targets.size(0)
error_rate = 100. * (1 - batch_correct / len(inputs))
partial_epoch = epoch + batch_idx / len(self.train_loader)
print('Epoch: [{}]\tTest: [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tError: {:.6f}'.format(
epoch, total_size, n_train, 100. * batch_idx / len(self.train_loader),
loss.data[0], error_rate))
print('Epoch: [{}]\tTotal testing loss: [{:.6f}]\tTotal testing error rate: [{:.6f}]'.format(
epoch, total_test_loss, (total_size - total_correct) / total_size * 100))
accuracy = total_correct / total_size * 100
loss = total_test_loss / total_size
# writing logs into files
info = {
'epoch': epoch,
'test-loss': loss,
'test-top1-error': 100 - accuracy
}
self.jsonlog_writer_test.write(json.dumps(info) + "\n")
if self.tflog_writer is not None:
info.pop('epoch', None)
for tag, value in info.items():
self.tflog_writer.scalar_summary(tag, value, partial_epoch)
return accuracy, loss
def adjust_learning_rate(self, epoch):
""" Sets the learning rate to the initial learning rate decayed by 10 every args.lr_decay_interval epochs """
learning_rate = self.base_lr * (0.1 ** (epoch // self.lr_decay_interval))
print('==> Set learning rate: %f' % learning_rate)
for param_group in self.optimizer.param_groups:
param_group['lr'] = learning_rate
def execute(self, end_epoch):
for epoch in range(self.start_epoch, end_epoch):
self.adjust_learning_rate(epoch)
self.train(epoch)
accuracy, loss = self.test(epoch)
# Save checkpoint.
if accuracy > self.best_accuracy:
print('==> Saving checkpoint..')
self.best_accuracy = accuracy
self.best_epoch = epoch
state = {
'start_epoch': epoch,
'best_epoch': self.best_epoch,
'best_accuracy': self.best_accuracy,
'state_dict': self.net.state_dict(),
}
torch.save(state, osp.join(self.save_dir, 'ckpt.t7'))
print('Epoch [%d], Best accuracy : %.2f from Epoch [%d]' % (
epoch, self.best_accuracy, self.best_epoch))