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reid-strong-baseline
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Merge pull request #27 from debrekXuHan/fix_log_bug 

fix log bug
pull/51/head
Hao Luo 2019-05-08 22:00:44 +08:00 committed by GitHub
parent b85e7dc6e4 6556baeee1
commit f951d36029
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 15 additions and 6 deletions
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1
data/samplers/triplet_sampler.py
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@ -68,6 +68,7 @@ class RandomIdentitySampler(Sampler):
if len(batch_idxs_dict[pid]) == 0: if len(batch_idxs_dict[pid]) == 0:
avai_pids.remove(pid) avai_pids.remove(pid)
self.length = len(final_idxs)
return iter(final_idxs) return iter(final_idxs)
def __len__(self): def __len__(self):

20
engine/trainer.py
View File

@ -14,6 +14,8 @@ from ignite.metrics import RunningAverage
from utils.reid_metric import R1_mAP from utils.reid_metric import R1_mAP
global ITER
ITER = 0
def create_supervised_trainer(model, optimizer, loss_fn, def create_supervised_trainer(model, optimizer, loss_fn,
device=None): device=None):
@ -173,13 +175,16 @@ def do_train(
@trainer.on(Events.ITERATION_COMPLETED) @trainer.on(Events.ITERATION_COMPLETED)
def log_training_loss(engine): def log_training_loss(engine):
iter = (engine.state.iteration - 1) % len(train_loader) + 1 global ITER
ITER += 1
if iter % log_period == 0: if ITER % log_period == 0:
logger.info("Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}" logger.info("Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}"
.format(engine.state.epoch, iter, len(train_loader), .format(engine.state.epoch, ITER, len(train_loader),
engine.state.metrics['avg_loss'], engine.state.metrics['avg_acc'], engine.state.metrics['avg_loss'], engine.state.metrics['avg_acc'],
scheduler.get_lr()[0])) scheduler.get_lr()[0]))
if len(train_loader) == ITER:
ITER = 0
# adding handlers using `trainer.on` decorator API # adding handlers using `trainer.on` decorator API
@trainer.on(Events.EPOCH_COMPLETED) @trainer.on(Events.EPOCH_COMPLETED)
@ -251,13 +256,16 @@ def do_train_with_center(
@trainer.on(Events.ITERATION_COMPLETED) @trainer.on(Events.ITERATION_COMPLETED)
def log_training_loss(engine): def log_training_loss(engine):
iter = (engine.state.iteration - 1) % len(train_loader) + 1 global ITER
ITER += 1
if iter % log_period == 0: if ITER % log_period == 0:
logger.info("Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}" logger.info("Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}"
.format(engine.state.epoch, iter, len(train_loader), .format(engine.state.epoch, ITER, len(train_loader),
engine.state.metrics['avg_loss'], engine.state.metrics['avg_acc'], engine.state.metrics['avg_loss'], engine.state.metrics['avg_acc'],
scheduler.get_lr()[0])) scheduler.get_lr()[0]))
if len(train_loader) == ITER:
ITER = 0
# adding handlers using `trainer.on` decorator API # adding handlers using `trainer.on` decorator API
@trainer.on(Events.EPOCH_COMPLETED) @trainer.on(Events.EPOCH_COMPLETED)