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logger.py
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logger.py
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import datetime
import logging
import os
import shutil
import numpy as np
from tensorboardX import SummaryWriter
import torch
import utils
class Logger:
r"""Writes results of training/testing"""
@classmethod
def initialize(cls, args, training):
logtime = datetime.datetime.now().__format__('_%m%d_%H%M%S')
logpath = args.logpath if training else '_TEST_' + args.load.split('/')[-2].split('.')[0] + logtime
if logpath == '': logpath = logtime
cls.logpath = os.path.join('logs', logpath + '.log')
cls.benchmark = args.benchmark
if os.path.isdir(cls.logpath):
if logpath == 'debug':
shutil.rmtree(cls.logpath)
else:
resp = input("Existing folder. Overwrite? Y/N: ")
if resp in ['Y','y']:
shutil.rmtree(cls.logpath)
os.makedirs(cls.logpath)
logging.basicConfig(filemode='w',
filename=os.path.join(cls.logpath, 'log.txt'),
level=logging.INFO,
format='%(message)s',
datefmt='%m-%d %H:%M:%S')
# Console log config
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter('%(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
# Tensorboard writer
cls.tbd_writer = SummaryWriter(os.path.join(cls.logpath, 'tbd/runs'))
# Log arguments
if training:
logging.info('\n:======== TransforMatcher ========')
for arg_key in args.__dict__:
logging.info('| %20s: %-24s' % (arg_key, str(args.__dict__[arg_key])))
logging.info(':=================================\n')
@classmethod
def info(cls, msg):
r"""Writes message to .txt"""
logging.info(msg)
@classmethod
def save_model_pck(cls, model, epoch, val_pck):
torch.save(model.state_dict(), os.path.join(cls.logpath, 'pck_best_model.pt'))
cls.info('Model saved @%d w/ val. PCK: %5.2f.\n' % (epoch, val_pck))
@classmethod
def save_model_loss(cls, model, epoch, val_loss):
torch.save(model.state_dict(), os.path.join(cls.logpath, 'loss_best_model.pt'))
cls.info('Model saved @%d w/ val. loss: %5.2f.\n' % (epoch, val_loss))
@classmethod
def save_model(cls, model, epoch):
torch.save(model.state_dict(), os.path.join(cls.logpath, 'last_model.pt'))
cls.info('Model saved @%d.\n' % (epoch))
class AverageMeter:
r"""Stores loss, evaluation results, selected layers"""
def __init__(self, benchamrk):
r"""Constructor of AverageMeter"""
if benchamrk == 'caltech':
self.buffer_keys = ['ltacc', 'iou']
else:
self.buffer_keys = ['pck']
self.buffer = {}
for key in self.buffer_keys:
self.buffer[key] = []
self.loss_buffer = []
def update(self, eval_result, loss=None):
for key in self.buffer_keys:
self.buffer[key] += eval_result[key]
if loss is not None:
self.loss_buffer.append(loss)
def write_result(self, split, epoch):
msg = '\n*** %s ' % split
msg += '[@Epoch %02d] ' % epoch
if len(self.loss_buffer) > 0:
msg += 'Loss: %5.2f ' % (sum(self.loss_buffer) / len(self.loss_buffer))
for key in self.buffer_keys:
msg += '%s: %6.2f ' % (key.upper(), sum(self.buffer[key]) / len(self.buffer[key]))
msg += '***\n'
Logger.info(msg)
def write_process(self, batch_idx, datalen, epoch):
msg = '[Epoch: %02d] ' % epoch
msg += '[Batch: %04d/%04d] ' % (batch_idx+1, datalen)
if len(self.loss_buffer) > 0:
msg += 'Loss: %6.2f ' % self.loss_buffer[-1]
msg += 'Avg Loss: %6.5f ' % (sum(self.loss_buffer) / len(self.loss_buffer))
for key in self.buffer_keys:
msg += 'Avg %s: %6.2f ' % (key.upper(), sum(self.buffer[key]) / len(self.buffer[key]))
Logger.info(msg)
def write_test_process(self, batch_idx, datalen):
msg = '[Batch: %04d/%04d] ' % (batch_idx+1, datalen)
for key in self.buffer_keys:
if key == 'pck':
pcks = torch.stack(self.buffer[key]).mean(dim=0) * 100
val = ''
for p in pcks:
val += '%5.2f ' % p.item()
msg += 'Avg %s: %s ' % (key.upper(), val)
else:
msg += 'Avg %s: %6.2f ' % (key.upper(), sum(self.buffer[key]) / len(self.buffer[key]))
Logger.info(msg)
def get_test_result(self):
result = {}
for key in self.buffer_keys:
if key == 'pck':
result[key] = torch.stack(self.buffer[key]).mean(dim=0) * 100
else:
result[key] = sum(self.buffer[key]) / len(self.buffer[key])
return result
class Evaluator:
r"""Computes evaluation metrics of PCK, LT-ACC, IoU"""
@classmethod
def initialize(cls, benchmark, device, alpha=0.1):
if alpha == -1:
cls.eval_func = cls.eval_kps_transfer_test
cls.alpha = torch.tensor([0.05, 0.1, 0.15]).unsqueeze(1).to(device)
else:
cls.eval_func = cls.eval_kps_transfer
cls.alpha = alpha
@classmethod
def evaluate(cls, prd_kps, batch):
r"""Compute evaluation metric"""
return cls.eval_func(prd_kps, batch)
@classmethod
def classify_prd(cls, prd_kps, trg_kps, pckthres):
r"""Compute the number of correctly transferred key-points"""
l2dist = (prd_kps - trg_kps).pow(2).sum(dim=0).pow(0.5)
thres = pckthres.expand_as(l2dist).float() * cls.alpha
correct_pts = torch.le(l2dist, thres)
correct_ids = utils.where(correct_pts == 1)
incorrect_ids = utils.where(correct_pts == 0)
correct_dist = l2dist[correct_pts]
return correct_dist, correct_ids, incorrect_ids
@classmethod
def eval_kps_transfer(cls, prd_kps, batch):
r"""Compute percentage of correct key-points (PCK)"""
pck = []
for idx, (pk, tk) in enumerate(zip(prd_kps, batch['trg_kps'])):
thres = batch['pckthres'][idx].cuda()
npt = batch['n_pts'][idx]
correct_dist, correct_ids, incorrect_ids = cls.classify_prd(pk[:, :npt].cuda(),
tk[:, :npt].cuda(),
thres)
pck.append((len(correct_ids) / npt.item()) * 100)
eval_result = {'pck': pck}
return eval_result
@classmethod
def eval_kps_transfer_test(cls, prd_kps, batch):
r"""Compute percentage of correct key-points (PCK) with multiple alpha {0.05, 0.1, 0.15}"""
pck = []
for idx, (pk, tk) in enumerate(zip(prd_kps, batch['trg_kps'])):
pckthres = batch['pckthres'][idx].cuda()
npt = batch['n_pts'][idx]
prd_kps = pk[:, :npt].cuda()
trg_kps = tk[:, :npt].cuda()
l2dist = (prd_kps - trg_kps).pow(2).sum(dim=0).pow(0.5).unsqueeze(0).repeat(len(cls.alpha), 1)
thres = pckthres.expand_as(l2dist).float() * cls.alpha
pck.append(torch.le(l2dist, thres).sum(dim=1) / float(npt))
eval_result = {'pck': pck}
return eval_result