def trainer(epoch, model, train_loader, valid_loader, criterion, optimizer,
lr_scheduler, path, dataset):
maximum = float(-np.inf)
best_epoch = 0
result_list = defaultdict()
for i in range(epoch):
train_loss, train_gt, train_probs = batch_trainer(
epoch=i,
model=model,
train_loader=train_loader,
criterion=criterion,
optimizer=optimizer,
)
valid_loss, valid_gt, valid_probs = valid_trainer(
epoch=i,
model=model,
valid_loader=valid_loader,
criterion=criterion,
)
lr_scheduler.step(metrics=valid_loss)
train_result = get_pedestrian_metrics(train_gt, train_probs)
valid_result = get_pedestrian_metrics(valid_gt, valid_probs)
print(
f'Evaluation on test set, \n',
'ma: {:.4f}, pos_recall: {:.4f} , neg_recall: {:.4f} \n'.format(
valid_result.ma, np.mean(valid_result.label_pos_recall),
np.mean(valid_result.label_neg_recall)),
'Acc: {:.4f}, Prec: {:.4f}, Rec: {:.4f}, F1: {:.4f}'.format(
valid_result.instance_acc, valid_result.instance_prec,
valid_result.instance_recall, valid_result.instance_f1))
# print label metrics ma
attr_name_list = return_attr_name_list(dataset)
for attr_name, _ma in zip(attr_name_list, valid_result.label_ma):
print(f'{attr_name}: {_ma}')
print(f'{time_str()}')
print('-' * 60)
cur_metric = valid_result.ma
if cur_metric > maximum:
maximum = cur_metric
best_epoch = i
save_ckpt(model, path, i, maximum)
result_list[i] = [train_result, valid_result]
torch.save(result_list,
os.path.join(os.path.dirname(path), 'metric_log.pkl'))
return maximum, best_epoch
def trainer(epoch, model, train_loader, valid_loader, criterion, optimizer,
lr_scheduler, path, dataset):
maximum = float(-np.inf)
best_epoch = 0
result_list = defaultdict()
for i in range(epoch):
#####
train_loss, train_gt, train_probs = batch_trainer(
epoch=i,
model=model,
train_loader=train_loader,
criterion=criterion,
optimizer=optimizer,
)
##eval in train set
# test_alm(train_loader, model, attr_num=dataset.attr_num, description=dataset.attr_id, set = 'train')
# eval in test set
test_alm(valid_loader,
model,
attr_num=dataset.attr_num,
description=dataset.attr_id,
set='test',
threshold=0.5)
valid_loss, valid_gt, valid_probs = valid_trainer(
model=model,
valid_loader=valid_loader,
criterion=criterion,
)
lr_scheduler.step(metrics=valid_loss, epoch=i)
train_result = get_pedestrian_metrics(train_gt,
train_probs,
threshold=0.5)
valid_result = get_pedestrian_metrics(valid_gt,
valid_probs,
threshold=0.5)
print(
f'Evaluation on test set, \n',
'ma: {:.4f}, pos_recall: {:.4f} , neg_recall: {:.4f} \n'.format(
valid_result.ma, np.mean(valid_result.label_pos_recall),
np.mean(valid_result.label_neg_recall)),
'Acc: {:.4f}, Prec: {:.4f}, Rec: {:.4f}, F1: {:.4f}'.format(
valid_result.instance_acc, valid_result.instance_prec,
valid_result.instance_recall, valid_result.instance_f1))
print(f'{time_str()}')
print('-' * 60)
cur_metric = valid_result.ma
if cur_metric > maximum:
maximum = cur_metric
best_epoch = i
save_ckpt(model, path, i, maximum)
result_list[i] = [train_result, valid_result]
torch.save(result_list,
os.path.join(os.path.dirname(path), 'metric_log.pkl'))
return maximum, best_epoch
def trainer(cfg, args, epoch, model, model_ema, train_loader, valid_loader,
criterion, optimizer, lr_scheduler, path, loss_w, viz, tb_writer):
maximum = float(-np.inf)
maximum_ema = float(-np.inf)
best_epoch = 0
result_list = defaultdict()
for e in range(epoch):
if args.distributed:
train_loader.sampler.set_epoch(epoch)
lr = optimizer.param_groups[1]['lr']
train_loss, train_gt, train_probs, train_imgs = batch_trainer(
cfg,
args=args,
epoch=e,
model=model,
model_ema=model_ema,
train_loader=train_loader,
criterion=criterion,
optimizer=optimizer,
loss_w=loss_w,
scheduler=lr_scheduler,
)
if args.distributed:
if args.local_rank == 0:
print("Distributing BatchNorm running means and vars")
distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
# valid_loss, valid_gt, valid_probs, valid_imgs = valid_trainer(
# args=args,
# model=model,
# valid_loader=valid_loader,
# criterion=criterion,
# loss_w=loss_w
# )
if model_ema is not None: # and not cfg.TRAIN.EMA.FORCE_CPU:
if args.local_rank == 0:
print('using model_ema to validate')
if args.distributed:
distribute_bn(model_ema, args.world_size,
args.dist_bn == 'reduce')
valid_loss, valid_gt, valid_probs, valid_probs_ema, valid_imgs = valid_trainer(
args=args,
model=model,
ema_model=model_ema.module,
valid_loader=valid_loader,
criterion=criterion,
loss_w=loss_w)
# if cfg.TRAIN.LR_SCHEDULER.TYPE == 'plateau':
# lr_scheduler.step(metrics=valid_loss)
# elif cfg.TRAIN.LR_SCHEDULER.TYPE == 'warmup_cosine':
# lr_scheduler.step(epoch=e + 1)
# else:
# lr_scheduler.step()
if cfg.METRIC.TYPE == 'multi_label':
train_metric = get_multilabel_metrics(train_gt, train_probs)
valid_metric = get_multilabel_metrics(valid_gt, valid_probs)
if model_ema is not None: # and not cfg.TRAIN.EMA.FORCE_CPU:
valid_metric_ema = get_multilabel_metrics(
valid_gt, valid_probs_ema)
if args.local_rank == 0:
print(
'Performance : mAP: {:.4f}, OP: {:.4f}, OR: {:.4f}, OF1: {:.4f} CP: {:.4f}, CR: {:.4f}, '
'CF1: {:.4f}'.format(valid_metric.map, valid_metric.OP,
valid_metric.OR, valid_metric.OF1,
valid_metric.CP, valid_metric.CR,
valid_metric.CF1))
print(
'EMA Performance : mAP: {:.4f}, OP: {:.4f}, OR: {:.4f}, OF1: {:.4f} CP: {:.4f}, CR: {:.4f}, '
'CF1: {:.4f}'.format(
valid_metric_ema.map, valid_metric_ema.OP,
valid_metric_ema.OR, valid_metric_ema.OF1,
valid_metric_ema.CP, valid_metric_ema.CR,
valid_metric_ema.CF1))
print(f'{time_str()}')
print('-' * 60)
tb_writer.add_scalars('train/lr', {'lr': lr}, e)
tb_writer.add_scalars('train/loss', {
'train': train_loss,
'test': valid_loss
}, e)
tb_writer.add_scalars(
'train/perf', {
'mAP': train_metric.map,
'OP': train_metric.OP,
'OR': train_metric.OR,
'OF1': train_metric.OF1,
'CP': train_metric.CP,
'CR': train_metric.CR,
'CF1': train_metric.CF1
}, e)
tb_writer.add_scalars(
'test/perf', {
'mAP': valid_metric.map,
'OP': valid_metric.OP,
'OR': valid_metric.OR,
'OF1': valid_metric.OF1,
'CP': valid_metric.CP,
'CR': valid_metric.CR,
'CF1': valid_metric.CF1
}, e)
tb_writer.add_scalars(
'test/ema_perf', {
'mAP': valid_metric_ema.map,
'OP': valid_metric_ema.OP,
'OR': valid_metric_ema.OR,
'OF1': valid_metric_ema.OF1,
'CP': valid_metric_ema.CP,
'CR': valid_metric_ema.CR,
'CF1': valid_metric_ema.CF1
}, e)
cur_metric = valid_metric.map
if cur_metric > maximum:
maximum = cur_metric
best_epoch = e
save_ckpt(model, path, e, maximum)
cur_metric = valid_metric_ema.map
if cur_metric > maximum_ema:
maximum_ema = cur_metric
best_epoch = e
save_ckpt(model, path, e, maximum_ema)
result_list[e] = {
'train_result': train_metric,
'valid_result': valid_metric,
'train_gt': train_gt,
'train_probs': train_probs,
'valid_gt': valid_gt,
'valid_probs': valid_probs
}
else:
assert False, f'{cfg.METRIC.TYPE} is unavailable'
with open(os.path.join(os.path.dirname(path), 'metric_log.pkl'),
'wb') as f:
pickle.dump(result_list, f)
return maximum, best_epoch
def trainer(epoch, model, train_loader, valid_loader, criterion, optimizer, lr_scheduler,
path):
maximum = float(-np.inf)
best_epoch = 0
result_list = defaultdict()
df_metrics = pd.DataFrame(columns=['epoch', 'train_loss', 'train_instance_acc', 'train_instance_prec', 'train_instance_recall', 'train_instance_f1', 'train_ma', 'train_pos_recall', 'train_neg_recall',
'valid_loss', 'valid_instance_acc', 'valid_instance_prec', 'valid_instance_recall', 'valid_instance_f1', 'valid_ma', 'valid_pos_recall', 'valid_neg_recall'])
for i in range(epoch):
train_loss, train_gt, train_probs = batch_trainer(
epoch=i,
model=model,
train_loader=train_loader,
criterion=criterion,
optimizer=optimizer,
)
valid_loss, valid_gt, valid_probs = valid_trainer(
model=model,
valid_loader=valid_loader,
criterion=criterion,
)
lr_scheduler.step(metrics=valid_loss, epoch=i)
train_result = get_pedestrian_metrics(train_gt, train_probs)
valid_result = get_pedestrian_metrics(valid_gt, valid_probs)
# tensorboard added
# writer.add_scalar(tag, function, iteration)
writer_step = i
writer.add_scalars('Loss', {'Train':train_loss, 'Valid':valid_loss}, writer_step)
writer.add_scalars('Accuracy', {'Train':train_result.instance_acc, 'Valid':valid_result.instance_acc}, writer_step)
writer.add_scalars('Precision', {'Train':train_result.instance_prec, 'Valid':valid_result.instance_prec}, writer_step)
writer.add_scalars('Recall', {'Train':train_result.instance_recall, 'Valid':valid_result.instance_recall}, writer_step)
writer.add_scalars('F1', {'Train':train_result.instance_f1, 'Valid':valid_result.instance_f1}, writer_step)
writer.add_scalars('Mean Accuracy', {'Train':train_result.ma, 'Valid':valid_result.ma}, writer_step)
writer.add_scalars('Pos Recall', {'Train':np.mean(train_result.label_pos_recall), 'Valid':np.mean(valid_result.label_pos_recall)}, writer_step)
writer.add_scalars('Neg Recall', {'Train':np.mean(train_result.label_neg_recall), 'Valid':np.mean(valid_result.label_neg_recall)}, writer_step)
print(f'Evaluation on test set, \n',
'ma: {:.4f}, pos_recall: {:.4f} , neg_recall: {:.4f} \n'.format(
valid_result.ma, np.mean(valid_result.label_pos_recall), np.mean(valid_result.label_neg_recall)),
'Acc: {:.4f}, Prec: {:.4f}, Rec: {:.4f}, F1: {:.4f}'.format(
valid_result.instance_acc, valid_result.instance_prec, valid_result.instance_recall,
valid_result.instance_f1))
print(f'{time_str()}')
print('-' * 60)
# create metrics dataframe to save as csv
new_metrics = {
'epoch':i,
'train_loss':train_loss,
'train_instance_acc':train_result.instance_acc,
'train_instance_prec':train_result.instance_prec,
'train_instance_recall':train_result.instance_recall,
'train_instance_f1':train_result.instance_f1,
'train_ma':train_result.ma,
'train_pos_recall':np.mean(train_result.label_pos_recall),
'train_neg_recall':np.mean(train_result.label_neg_recall),
'valid_loss':valid_loss,
'valid_instance_acc':valid_result.instance_acc,
'valid_instance_prec':valid_result.instance_prec,
'valid_instance_recall':valid_result.instance_recall,
'valid_instance_f1':valid_result.instance_f1,
'valid_ma':valid_result.ma,
'valid_pos_recall':np.mean(valid_result.label_pos_recall),
'valid_neg_recall':np.mean(valid_result.label_neg_recall)
}
#append row to the dataframe
df_metrics = df_metrics.append(new_metrics, ignore_index=True)
df_metrics.to_csv(csv_file_name, index=False)
cur_metric = valid_result.ma
if cur_metric > maximum:
maximum = cur_metric
best_epoch = i
save_ckpt(model, path, i, maximum)
result_list[i] = [train_result, valid_result]
writer.close()
torch.save(result_list, os.path.join(os.path.dirname(path), 'metric_log.pkl'))
return maximum, best_epoch
