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base_trainer.py
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base_trainer.py
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import os
import time
import yaml
import torch
import datetime
from torch.utils.tensorboard import SummaryWriter
import torch.utils.data
import numpy as np
import glob
import shutil
from utils.net_util import to_cuda
def worker_init_fn(worker_id): # set numpy's random seed
seed = torch.initial_seed()
seed = seed % (2 ** 32)
np.random.seed(seed + worker_id)
class BaseTrainer:
def __init__(self, opt):
self.opt = opt
self.dataset = None
self.network = None
self.net_dict = {}
self.optm_dict = {}
self.update_keys = None
self.lr_schedule_dict = {}
self.iter_idx = 0
self.loss_weight = self.opt['train']['loss_weight']
@staticmethod
def load_pretrained(path, dict_):
data = torch.load(path)
for k in dict_:
if k in data:
print('# Loading %s...' % k)
dict_[k].load_state_dict(data[k])
else:
print('# %s not found!' % k)
def load_ckpt(self, path, load_optm = True):
self.load_pretrained(path + '/net.pt', self.net_dict)
if load_optm:
if os.path.exists(path + '/optm.pt'):
self.load_pretrained(path + '/optm.pt', self.optm_dict)
else:
print('# Optimizer not found!')
@staticmethod
def save_trained(path, dict_):
data = {}
for k in dict_:
data[k] = dict_[k].state_dict()
torch.save(data, path)
def save_ckpt(self, path, save_optm = True):
self.save_trained(path + '/net.pt', self.net_dict)
if save_optm:
self.save_trained(path + '/optm.pt', self.optm_dict)
def zero_grad(self):
if self.update_keys is None:
update_keys = self.optm_dict.keys()
else:
update_keys = self.update_keys
for k in update_keys:
self.optm_dict[k].zero_grad()
def step(self):
if self.update_keys is None:
update_keys = self.optm_dict.keys()
else:
update_keys = self.update_keys
for k in update_keys:
self.optm_dict[k].step()
def update_lr(self, iter_idx):
lr_dict = {}
if self.update_keys is None:
update_keys = self.optm_dict.keys()
else:
update_keys = self.update_keys
for k in update_keys:
lr = self.lr_schedule_dict[k].get_learning_rate(iter_idx)
for param_group in self.optm_dict[k].param_groups:
param_group['lr'] = lr
lr_dict[k] = lr
return lr_dict
def set_dataset(self, dataset):
self.dataset = dataset
def set_network(self, network):
self.network = network
def set_net_dict(self, net_dict):
self.net_dict = net_dict
def set_optm_dict(self, optm_dict):
self.optm_dict = optm_dict
def set_update_keys(self, update_keys):
self.update_keys = update_keys
def set_lr_schedule_dict(self, lr_schedule_dict):
self.lr_schedule_dict = lr_schedule_dict
def set_train(self, flag = True):
if flag:
for k, net in self.net_dict.items():
if k in self.update_keys:
net.train()
else:
net.eval()
else:
for k, net in self.net_dict.items():
net.eval()
def train(self):
# log
os.makedirs(self.opt['train']['net_ckpt_dir'], exist_ok = True)
log_dir = self.opt['train']['net_ckpt_dir'] + '/' + datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
os.makedirs(log_dir, exist_ok = True)
writer = SummaryWriter(log_dir)
yaml.dump(self.opt, open(log_dir + '/config_bk.yaml', 'w'), sort_keys = False)
self.set_train()
self.dataset.training = True
batch_size = self.opt['train'].get('batch_size', 1)
num_workers = self.opt['train'].get('num_workers', 0)
dataloader = torch.utils.data.DataLoader(self.dataset,
batch_size = batch_size,
shuffle = True,
num_workers = num_workers,
worker_init_fn = worker_init_fn,
drop_last = True)
batch_num = len(self.dataset) // batch_size
if self.opt['train'].get('save_init_ckpt', False) and self.opt['train'].get('start_epoch', 0) == 0:
init_folder = self.opt['train']['net_ckpt_dir'] + '/init_ckpt'
if not os.path.exists(init_folder) or self.opt['train']['start_epoch'] == 0:
os.makedirs(init_folder, exist_ok = True)
self.save_ckpt(init_folder, False)
else:
print('# Init checkpoint has been saved!')
if self.opt['train']['prev_ckpt'] is not None:
self.load_ckpt(self.opt['train']['prev_ckpt'])
start_epoch = self.opt['train'].get('start_epoch', 0)
end_epoch = self.opt['train'].get('end_epoch', 999)
for epoch_idx in range(start_epoch, end_epoch):
self.update_config_before_epoch(epoch_idx)
epoch_losses = dict()
for batch_idx, items in enumerate(dataloader):
time0 = time.time()
iter_idx = batch_idx + batch_num * epoch_idx
self.iter_idx = iter_idx
lr_dict = self.update_lr(iter_idx)
items = to_cuda(items)
self.zero_grad()
loss, batch_losses = self.forward_one_pass(items)
loss.backward()
self.step()
# record batch loss
log_info = 'epoch %d, batch %d, ' % (epoch_idx, batch_idx)
log_info += 'lr: '
for k in lr_dict.keys():
log_info += '%s %e, ' % (k, lr_dict[k])
for key in batch_losses.keys():
log_info = log_info + ('%s: %f, ' % (key, batch_losses[key]))
writer.add_scalar('%s/Batch' % key, batch_losses[key], iter_idx)
if key in epoch_losses:
epoch_losses[key] += batch_losses[key]
else:
epoch_losses[key] = batch_losses[key]
print(log_info)
with open(os.path.join(log_dir, 'loss.txt'), 'a') as fp:
# record loss weight
if batch_idx == 0:
loss_weights_info = ''
for k in self.opt['train']['loss_weight'].keys():
loss_weights_info += '%s: %f, ' % (k, self.opt['train']['loss_weight'][k])
fp.write('# Loss weights: \n' + loss_weights_info + '\n')
fp.write(log_info + '\n')
if iter_idx % self.opt['train']['ckpt_interval']['batch'] == 0 and iter_idx != 0:
for folder in glob.glob(self.opt['train']['net_ckpt_dir'] + '/batch_*'):
shutil.rmtree(folder)
model_folder = self.opt['train']['net_ckpt_dir'] + '/batch_%d' % iter_idx
os.makedirs(model_folder, exist_ok = True)
self.save_ckpt(model_folder, save_optm = False)
if iter_idx % self.opt['train']['eval_interval'] == 0 and iter_idx != 0:
# if True:
self.mini_test()
time1 = time.time()
print('One iteration costs %f secs' % (time1 - time0))
""" EPOCH """
# record epoch loss
for key in epoch_losses.keys():
epoch_losses[key] /= batch_num
writer.add_scalar('%s/Epoch' % key, epoch_losses[key], epoch_idx)
if epoch_idx % self.opt['train']['ckpt_interval']['epoch'] == 0:
model_folder = self.opt['train']['net_ckpt_dir'] + '/epoch_%d' % epoch_idx
os.makedirs(model_folder, exist_ok = True)
self.save_ckpt(model_folder)
if batch_num > 50:
latest_folder = self.opt['train']['net_ckpt_dir'] + '/epoch_latest'
os.makedirs(latest_folder, exist_ok = True)
self.save_ckpt(latest_folder)
writer.close()
@torch.no_grad()
def mini_test(self):
""" Test during training """
pass
def forward_one_pass(self, items):
raise NotImplementedError('"forward_one_pass" method is not implemented!')
def update_config_before_epoch(self, epoch_idx):
pass