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import time
import logging
from tqdm import tqdm
import pickle as cPickle
import torch
import torch.optim as optim
import gorilla
from gorilla.solver.build import build_optimizer, build_lr_scheduler
from tensorboardX import SummaryWriter
class Solver(gorilla.solver.BaseSolver):
def __init__(self, model, loss, dataloaders, logger, cfg, coarse_model=None):
super(Solver, self).__init__(
model=model,
dataloaders=dataloaders,
cfg=cfg,
)
self.loss = loss
self.logger = logger
self.logger.propagate = 0
self.coarse_model = coarse_model.eval() if coarse_model is not None else coarse_model
tb_writer_ = tools_writer(
dir_project=cfg.log_dir, num_counter=2, get_sum=False)
tb_writer_.writer = self.tb_writer
self.tb_writer = tb_writer_
self.iters_to_print = cfg.iters_to_print
if cfg.checkpoint_iter != -1:
logger.info("=> loading checkpoint from iter {} ...".format(cfg.checkpoint_iter))
checkpoint = os.path.join(cfg.log_dir, 'checkpoint_iter' + str(cfg.checkpoint_iter).zfill(6) + '.pth')
checkpoint_file = gorilla.solver.resume(model=model, filename=checkpoint, optimizer=self.optimizer, scheduler=self.lr_scheduler)
start_epoch = checkpoint_file['epoch']+1
start_iter = checkpoint_file['iter']
del checkpoint_file
else:
start_epoch = 1
start_iter = 0
self.epoch = start_epoch
self.iter = start_iter
if hasattr(cfg, 'warmup_iter') and cfg.warmup_iter > 0:
self.warmup_optimizer = build_optimizer(model, cfg.warmup_optimizer)
self.warmup_scheduler = build_lr_scheduler(self.warmup_optimizer, cfg.warmup_lr_scheduler)
def solve(self):
while self.epoch <= self.cfg.training_epoch:
self.logger.info('\nEpoch {} :'.format(self.epoch))
end = time.time()
dict_info_train = self.train()
train_time = time.time()-end
dict_info = {'train_time(min)': train_time/60.0}
for key, value in dict_info_train.items():
if 'loss' in key:
dict_info['train_'+key] = value
ckpt_path = os.path.join(
self.cfg.log_dir, 'checkpoint_iter'+ str(self.iter).zfill(6) +'.pth')
gorilla.solver.save_checkpoint(
model=self.model, filename=ckpt_path, optimizer=self.optimizer, scheduler=self.lr_scheduler, meta={'iter': self.iter, "epoch": self.epoch})
prefix = 'Epoch {} - '.format(self.epoch)
write_info = self.get_logger_info(prefix, dict_info=dict_info)
self.logger.warning(write_info)
self.epoch += 1
def train(self):
mode = 'train'
self.model.train()
end = time.time()
self.dataloaders["train"].dataset.reset()
for i, data in enumerate(self.dataloaders["train"]):
torch.cuda.synchronize()
data_time = time.time()-end
if hasattr(self.cfg, 'warmup_iter') and self.cfg.warmup_iter > 0:
if self.iter >= self.cfg.warmup_iter:
optimizer = self.optimizer
lr_scheduler = self.lr_scheduler
else:
optimizer = self.warmup_optimizer
lr_scheduler = self.warmup_scheduler
else:
optimizer = self.optimizer
lr_scheduler = self.lr_scheduler
optimizer.zero_grad()
loss, dict_info_step = self.step(data, mode)
dict_info_step['lr'] = lr_scheduler.get_last_lr()[0]
forward_time = time.time()-end-data_time
loss.backward()
optimizer.step()
backward_time = time.time() - end - forward_time-data_time
dict_info_step.update({
'T_data': data_time,
'T_forward': forward_time,
'T_back': backward_time,
})
self.log_buffer.update(dict_info_step)
self.iter += 1
if i % self.iters_to_print == 0:
# ipdb.set_trace()
self.log_buffer.average(self.iters_to_print)
prefix = 'Iter {} Train - '.format(str(self.iter).zfill(6))
write_info = self.get_logger_info(
prefix, dict_info=self.log_buffer._output)
self.logger.info(write_info)
self.write_summary(self.log_buffer._output, mode)
end = time.time()
lr_scheduler.step()
dict_info_epoch = self.log_buffer.avg
self.log_buffer.clear()
return dict_info_epoch
def evaluate(self):
mode = 'eval'
self.model.eval()
for i, data in enumerate(self.dataloaders["eval"]):
with torch.no_grad():
_, dict_info_step = self.step(data, mode)
self.log_buffer.update(dict_info_step)
if i % self.iters_to_print == 0:
self.log_buffer.average(self.iters_to_print)
prefix = '[{}/{}][{}/{}] Test - '.format(
self.epoch, self.cfg.max_epoch, i, len(self.dataloaders["eval"]))
write_info = self.get_logger_info(
prefix, dict_info=self.log_buffer._output)
self.logger.info(write_info)
self.write_summary(self.log_buffer._output, mode)
dict_info_epoch = self.log_buffer.avg
self.log_buffer.clear()
return dict_info_epoch
def step(self, data, mode):
torch.cuda.synchronize()
for key in data:
data[key] = data[key].cuda()
if self.coarse_model is not None:
with torch.no_grad():
data = self.coarse_model(data)
end_points = self.model(data)
dict_info = self.loss(end_points)
loss_all = dict_info['loss']
for key in dict_info:
dict_info[key] = float(dict_info[key].item())
return loss_all, dict_info
def get_logger_info(self, prefix, dict_info):
info = prefix
for key, value in dict_info.items():
if 'T_' in key:
info = info + '{}: {:.3f}\t'.format(key, value)
elif 'lr' in key:
info = info + '{}: {:.6f}\t'.format(key, value)
else:
info = info + '{}: {:.5f}\t'.format(key, value)
return info
def write_summary(self, dict_info, mode):
keys = list(dict_info.keys())
values = list(dict_info.values())
if mode == "train":
self.tb_writer.update_scalar(
list_name=keys, list_value=values, index_counter=0, prefix="train_")
elif mode == "eval":
self.tb_writer.update_scalar(
list_name=keys, list_value=values, index_counter=1, prefix="eval_")
else:
assert False
class tools_writer():
def __init__(self, dir_project, num_counter, get_sum):
if not os.path.isdir(dir_project):
os.makedirs(dir_project)
if get_sum:
writer = SummaryWriter(dir_project)
else:
writer = None
# writer = SummaryWriter(dir_project)
self.writer = writer
self.num_counter = num_counter
self.list_couter = []
for i in range(num_counter):
self.list_couter.append(0)
def update_scalar(self, list_name, list_value, index_counter, prefix):
for name, value in zip(list_name, list_value):
self.writer.add_scalar(prefix+name, float(value), self.list_couter[index_counter])
self.list_couter[index_counter] += 1
def refresh(self):
for i in range(self.num_counter):
self.list_couter[i] = 0
def get_logger(level_print, level_save, path_file, name_logger = "logger"):
# level: logging.INFO / logging.WARN
logger = logging.getLogger(name_logger)
logger.setLevel(level = logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(message)s')
# set file handler
handler_file = logging.FileHandler(path_file)
handler_file.setLevel(level_save)
handler_file.setFormatter(formatter)
logger.addHandler(handler_file)
# set console holder
handler_view = logging.StreamHandler()
handler_view.setFormatter(formatter)
handler_view.setLevel(level_print)
logger.addHandler(handler_view)
return logger |