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import argparse
import yaml
import os
import shutil
import numpy as np
import random
import math
# torch
import torch
import torch.nn as nn
import torch.optim as optim
# torchlight
import torchlight.torchlight as torchlight
from torchlight.torchlight import str2bool
from torchlight.torchlight import DictAction
from torchlight.torchlight import import_class
from .io import IO
from tensorboardX import SummaryWriter
def init_seed(seed=1):
torch.cuda.manual_seed_all(seed)
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
# torch.backends.cudnn.enabled = False
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
class Processor(IO):
"""
Base Processor
"""
def __init__(self, argv=None):
self.load_arg(argv)
self.init_environment()
if self.arg.phase == 'train':
if os.path.isdir(self.arg.work_dir + '/train'):
print('log_dir: ', self.arg.work_dir, 'already exist')
shutil.rmtree(self.arg.work_dir + '/train')
shutil.rmtree(self.arg.work_dir + '/val')
print('Dir removed: ', self.arg.work_dir + '/train')
print('Dir removed: ', self.arg.work_dir + '/val')
self.train_writer = SummaryWriter(os.path.join(self.arg.work_dir, 'train'), 'train')
self.val_writer = SummaryWriter(os.path.join(self.arg.work_dir, 'val'), 'val')
else:
self.train_writer = self.val_writer = SummaryWriter(os.path.join(self.arg.work_dir, 'test'), 'test')
self.load_model()
self.load_weights()
self.gpu()
self.load_data()
self.load_optimizer()
self.global_step = 0
def train_log_writer(self, epoch):
self.train_writer.add_scalar('batch_loss', self.iter_info['loss'], self.global_step)
self.train_writer.add_scalar('lr', self.lr, self.global_step)
self.train_writer.add_scalar('epoch', epoch, self.global_step)
def eval_log_writer(self, epoch):
self.val_writer.add_scalar('eval_loss', self.eval_info['eval_mean_loss'], epoch)
self.val_writer.add_scalar('current_result', self.current_result, epoch)
self.val_writer.add_scalar('best_result', self.best_result, epoch)
def init_environment(self):
super().init_environment()
self.result = dict()
self.iter_info = dict()
self.epoch_info = dict()
self.eval_info = dict()
self.meta_info = dict(epoch=0, iter=0)
def load_optimizer(self):
pass
def load_data(self):
self.data_loader = dict()
if self.arg.train_feeder_args:
train_feeder = import_class(self.arg.train_feeder)
self.data_loader['train'] = torch.utils.data.DataLoader(
dataset=train_feeder(**self.arg.train_feeder_args),
batch_size=self.arg.batch_size,
shuffle=True,
pin_memory=False, # set True when memory is abundant
num_workers=self.arg.num_worker * torchlight.ngpu(
self.arg.device),
drop_last=True,
worker_init_fn=init_seed)
if self.arg.test_feeder_args:
test_feeder = import_class(self.arg.test_feeder)
self.data_loader['test'] = torch.utils.data.DataLoader(
dataset=test_feeder(**self.arg.test_feeder_args),
batch_size=self.arg.test_batch_size,
shuffle=False,
pin_memory=False,
num_workers=self.arg.num_worker * torchlight.ngpu(
self.arg.device),
drop_last=False,
worker_init_fn=init_seed)
def show_epoch_info(self):
for k, v in self.epoch_info.items():
self.io.print_log('\t{}: {}'.format(k, v))
if self.arg.pavi_log:
self.io.log('train', self.meta_info['iter'], self.epoch_info)
def show_eval_info(self):
for k, v in self.eval_info.items():
self.io.print_log('\t{}: {}'.format(k, v))
if self.arg.pavi_log:
self.io.log('eval', self.meta_info['iter'], self.eval_info)
def show_iter_info(self):
if self.meta_info['iter'] % self.arg.log_interval == 0:
info ='\tIter {} Done.'.format(self.meta_info['iter'])
for k, v in self.iter_info.items():
if isinstance(v, float):
info = info + ' | {}: {:.4f}'.format(k, v)
else:
info = info + ' | {}: {}'.format(k, v)
self.io.print_log(info)
if self.arg.pavi_log:
self.io.log('train', self.meta_info['iter'], self.iter_info)
def train(self):
for _ in range(100):
self.iter_info['loss'] = 0
self.show_iter_info()
self.meta_info['iter'] += 1
self.epoch_info['train_mean_loss'] = 0
self.show_epoch_info()
def test(self):
for _ in range(100):
self.iter_info['loss'] = 1
self.show_iter_info()
self.eval_info['test_mean_loss'] = 1
self.show_eval_info()
def print_networks(self, net, print_flag=False):
self.io.print_log('---------- Networks initialized -------------')
num_params = 0
for param in net.parameters():
#print(param.size())
num_params += param.numel()
if print_flag:
self.io.print_log(net)
self.io.print_log('[Network] Total number of parameters : %.3f M' % (num_params / 1e6))
self.io.print_log('-----------------------------------------------')
def start(self):
self.io.print_log('Parameters:\n{}\n'.format(str(vars(self.arg))))
self.print_networks(self.model)
# training phase
if self.arg.phase == 'train':
self.global_step = self.arg.start_epoch * len(self.data_loader['train'])
self.meta_info['iter'] = self.global_step
self.best_result = 0.0
for epoch in range(self.arg.start_epoch, self.arg.num_epoch):
self.meta_info['epoch'] = epoch + 1
# training
self.io.print_log('Training epoch: {}'.format(epoch + 1))
self.train(epoch + 1)
# save model
if self.arg.save_interval == -1:
pass
elif ((epoch + 1) % self.arg.save_interval == 0) or (
epoch + 1 == self.arg.num_epoch):
filename = 'epoch{}_model.pt'.format(epoch + 1)
self.io.save_model(self.model, filename)
# evaluation
if self.arg.eval_interval == -1:
pass
elif ((epoch + 1) % self.arg.eval_interval == 0) or (
epoch + 1 == self.arg.num_epoch):
self.io.print_log('Eval epoch: {}'.format(epoch + 1))
self.test(epoch + 1)
self.io.print_log("current %.2f%%, best %.2f%%" %
(self.current_result, self.best_result))
# save best model
filename = 'epoch%.3d_acc%.2f_model.pt' % (epoch + 1, self.current_result)
self.io.save_model(self.model, filename)
if self.current_result >= self.best_result:
filename = 'best_model.pt'
self.io.save_model(self.model, filename)
# save the output of model
# test phase
elif self.arg.phase == 'test':
# the path of weights must be appointed
if self.arg.weights is None:
raise ValueError('Please appoint --weights.')
self.io.print_log('Model: {}.'.format(self.arg.model))
self.io.print_log('Weights: {}.'.format(self.arg.weights))
self.best_result = 0.0
# evaluation
self.io.print_log('Evaluation Start:')
self.test(1)
self.io.print_log('Done.\n')
# save the output of model
if self.arg.save_result:
result_dict = dict(
zip(self.data_loader['test'].dataset.sample_name,
self.result))
self.io.save_pkl(result_dict, 'test_result_%.3d.pkl'% (epoch + 1))
@staticmethod
def get_parser(add_help=False):
# parameter priority: command line > config > default
parser = argparse.ArgumentParser( add_help=add_help, description='Base Processor')
parser.add_argument('-w', '--work_dir', default='./work_dir/tmp', help='the work folder for storing results')
parser.add_argument('-c', '--config', default=None, help='path to the configuration file')
# processor
parser.add_argument('--phase', default='train', help='must be train or test')
parser.add_argument('--save_result', type=str2bool, default=True, help='if ture, the output of the model will be stored')
parser.add_argument('--start_epoch', type=int, default=0, help='start training from which epoch')
parser.add_argument('--num_epoch', type=int, default=80, help='stop training in which epoch')
parser.add_argument('--use_gpu', type=str2bool, default=True, help='use GPUs or not')
parser.add_argument('--device', type=int, default=0, nargs='+', help='the indexes of GPUs for training or testing')
# visualize and debug
parser.add_argument('--log_interval', type=int, default=100, help='the interval for printing messages (#iteration)')
parser.add_argument('--save_interval', type=int, default=10, help='the interval for storing models (#epoch)')
parser.add_argument('--eval_interval', type=int, default=5, help='the interval for evaluating models (#epoch)')
parser.add_argument('--save_log', type=str2bool, default=True, help='save logging or not')
parser.add_argument('--print_log', type=str2bool, default=True, help='print logging or not')
parser.add_argument('--pavi_log', type=str2bool, default=False, help='logging on pavi or not')
# feeder
parser.add_argument('--train_feeder', default='feeder.feeder', help='train data loader will be used')
parser.add_argument('--test_feeder', default='feeder.feeder', help='test data loader will be used')
parser.add_argument('--num_worker', type=int, default=8, help='the number of worker per gpu for data loader')
parser.add_argument('--train_feeder_args', action=DictAction, default=dict(), help='the arguments of data loader for training')
parser.add_argument('--test_feeder_args', action=DictAction, default=dict(), help='the arguments of data loader for test')
parser.add_argument('--batch_size', type=int, default=256, help='training batch size')
parser.add_argument('--test_batch_size', type=int, default=256, help='test batch size')
parser.add_argument('--debug', action="store_true", help='less data, faster loading')
parser.add_argument('--rename_weights', type=str, default=[], nargs='+', help='the name of weights which will be ignored in the initialization')
# model
parser.add_argument('--model', default=None, help='the model will be used')
parser.add_argument('--model_args', action=DictAction, default=dict(), help='the arguments of model')
parser.add_argument('--weights', default=None, help='the weights for network initialization')
parser.add_argument('--ignore_weights', type=str, default=[], nargs='+', help='the name of weights which will be ignored in the initialization')
return parser
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