msg = ','.join(['{}={:.3f}'.format(*metric.get()) for metric in metrics])
logger.info('[Epoch {}] Training cost: {:.3f}, {}'.format(
epoch, (time.time() - tic), msg))
if not (epoch + 1) % args.val_interval:
# consider reduce the frequency of validation to save time
map_name, mean_ap = validate(net, val_data, ctx, eval_metric, args)
val_msg = '\n'.join(['{}={}'.format(k, v) for k, v in zip(map_name, mean_ap)])
logger.info('[Epoch {}] Validation: \n{}'.format(epoch, val_msg))
current_map = float(mean_ap[-1])
else:
current_map = 0.
save_params(net, logger, best_map, current_map, epoch, args.save_interval,
args.save_prefix)
executor.__del__()
if __name__ == '__main__':
import sys
sys.setrecursionlimit(1100)
args = parse_args()
# fix seed for mxnet, numpy and python builtin random generator.
gutils.random.seed(args.seed)
if args.amp:
amp.init()
# training contexts
if args.horovod:
ctx = [mx.gpu(hvd.local_rank())]
else:
def train(net, train_data, val_data, eval_metric, batch_size, ctx, args):
"""Training pipeline"""
kv = mx.kvstore.create(args.kv_store)
net.collect_params().setattr('grad_req', 'null')
net.collect_train_params().setattr('grad_req', 'write')
optimizer_params = {
'learning_rate': args.lr,
'wd': args.wd,
'momentum': args.momentum
}
if args.horovod:
hvd.broadcast_parameters(net.collect_params(), root_rank=0)
trainer = hvd.DistributedTrainer(
net.collect_train_params(
), # fix batchnorm, fix first stage, etc...
'sgd',
optimizer_params)
else:
trainer = gluon.Trainer(
net.collect_train_params(
), # fix batchnorm, fix first stage, etc...
'sgd',
optimizer_params,
update_on_kvstore=(False if args.amp else None),
kvstore=kv)
if args.amp:
amp.init_trainer(trainer)
# lr decay policy
lr_decay = float(args.lr_decay)
lr_steps = sorted(
[float(ls) for ls in args.lr_decay_epoch.split(',') if ls.strip()])
lr_warmup = float(args.lr_warmup) # avoid int division
# TODO(zhreshold) losses?
rpn_cls_loss = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss(
from_sigmoid=False)
rpn_box_loss = mx.gluon.loss.HuberLoss(rho=1 / 9.) # == smoothl1
rcnn_cls_loss = mx.gluon.loss.SoftmaxCrossEntropyLoss()
rcnn_box_loss = mx.gluon.loss.HuberLoss() # == smoothl1
metrics = [
mx.metric.Loss('RPN_Conf'),
mx.metric.Loss('RPN_SmoothL1'),
mx.metric.Loss('RCNN_CrossEntropy'),
mx.metric.Loss('RCNN_SmoothL1'),
]
rpn_acc_metric = RPNAccMetric()
rpn_bbox_metric = RPNL1LossMetric()
rcnn_acc_metric = RCNNAccMetric()
rcnn_bbox_metric = RCNNL1LossMetric()
metrics2 = [
rpn_acc_metric, rpn_bbox_metric, rcnn_acc_metric, rcnn_bbox_metric
]
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
log_file_path = args.save_prefix + '_train.log'
log_dir = os.path.dirname(log_file_path)
if log_dir and not os.path.exists(log_dir):
os.makedirs(log_dir)
fh = logging.FileHandler(log_file_path)
logger.addHandler(fh)
logger.info(args)
if args.verbose:
logger.info('Trainable parameters:')
logger.info(net.collect_train_params().keys())
logger.info('Start training from [Epoch {}]'.format(args.start_epoch))
best_map = [0]
for epoch in range(args.start_epoch, args.epochs):
mix_ratio = 1.0
if not args.disable_hybridization:
net.hybridize(static_alloc=args.static_alloc)
rcnn_task = ForwardBackwardTask(net,
trainer,
rpn_cls_loss,
rpn_box_loss,
rcnn_cls_loss,
rcnn_box_loss,
mix_ratio=1.0)
executor = Parallel(args.executor_threads,
rcnn_task) if not args.horovod else None
if args.mixup:
# TODO(zhreshold) only support evenly mixup now, target generator needs to be modified otherwise
train_data._dataset._data.set_mixup(np.random.uniform, 0.5, 0.5)
mix_ratio = 0.5
if epoch >= args.epochs - args.no_mixup_epochs:
train_data._dataset._data.set_mixup(None)
mix_ratio = 1.0
while lr_steps and epoch >= lr_steps[0]:
new_lr = trainer.learning_rate * lr_decay
lr_steps.pop(0)
trainer.set_learning_rate(new_lr)
logger.info("[Epoch {}] Set learning rate to {}".format(
epoch, new_lr))
for metric in metrics:
metric.reset()
tic = time.time()
btic = time.time()
base_lr = trainer.learning_rate
rcnn_task.mix_ratio = mix_ratio
print(len(train_data))
for i, batch in enumerate(train_data):
if epoch == 0 and i <= lr_warmup:
# adjust based on real percentage
new_lr = base_lr * get_lr_at_iter(i / lr_warmup,
args.lr_warmup_factor)
if new_lr != trainer.learning_rate:
if i % args.log_interval == 0:
logger.info(
'[Epoch 0 Iteration {}] Set learning rate to {}'.
format(i, new_lr))
trainer.set_learning_rate(new_lr)
batch = split_and_load(batch, ctx_list=ctx)
metric_losses = [[] for _ in metrics]
add_losses = [[] for _ in metrics2]
if executor is not None:
for data in zip(*batch):
executor.put(data)
for j in range(len(ctx)):
if executor is not None:
result = executor.get()
else:
result = rcnn_task.forward_backward(list(zip(*batch))[0])
if (not args.horovod) or hvd.rank() == 0:
for k in range(len(metric_losses)):
metric_losses[k].append(result[k])
for k in range(len(add_losses)):
add_losses[k].append(result[len(metric_losses) + k])
for metric, record in zip(metrics, metric_losses):
metric.update(0, record)
for metric, records in zip(metrics2, add_losses):
for pred in records:
metric.update(pred[0], pred[1])
trainer.step(batch_size)
# update metrics
if (not args.horovod or hvd.rank() == 0) and args.log_interval \
and not (i + 1) % args.log_interval:
msg = ','.join([
'{}={:.3f}'.format(*metric.get())
for metric in metrics + metrics2
])
logger.info(
'[Epoch {}][Batch {}], Speed: {:.3f} samples/sec, {}'.
format(
epoch, i, args.log_interval * args.batch_size /
(time.time() - btic), msg))
btic = time.time()
if (not args.horovod) or hvd.rank() == 0:
msg = ','.join(
['{}={:.3f}'.format(*metric.get()) for metric in metrics])
logger.info('[Epoch {}] Training cost: {:.3f}, {}'.format(
epoch, (time.time() - tic), msg))
if not (epoch + 1) % args.val_interval:
# consider reduce the frequency of validation to save time
map_name, mean_ap = validate(net, val_data, ctx, eval_metric,
args)
map_name_train, mean_ap_train = validate(
net, train_data, ctx, eval_metric, args)
if isinstance(map_name, list):
val_msg = '\n'.join([
'{}={}'.format(k, v)
for k, v in zip(map_name, mean_ap)
])
train_msg = '\n'.join([
'{}={}'.format(k, v)
for k, v in zip(map_name_train, mean_ap_train)
])
current_map = float(mean_ap[-1])
else:
val_msg = '{}={}'.format(map_name, mean_ap)
train_msg = '{}={}'.format(map_name_train, mean_ap_train)
current_map = mean_ap
logger.info('[Epoch {}] Validation: {}'.format(epoch, val_msg))
logger.info('[Epoch {}] Train: {}'.format(epoch, train_msg))
else:
current_map = 0.
save_params(net, logger, best_map, current_map, epoch,
args.save_interval,
os.path.join(args.model_dir, 'fastrcnn'))
executor.__del__()
def train(net, train_data, val_data, eval_metric, ctx, args):
net.collect_params.reset_ctx(ctx)
kv = mx.kvstore.create(args.kv_store)
net.collect_params().setattr('grad_req', 'null')
net.collect_train_params.setattr('grad_req', 'write')
if args.horovod:
hvd.broadcast_parameters(net.collect_params(), root_rank=0)
trainer = hvd.DistributedTrainer(net.collect_train_params(), 'sgd',
{'learning_rate': args.lr, 'wd': args.wd, 'momentum': args.momentum})
else:
trainer = gluon.Trainer(net.collect_train_params(), 'sgd',
{'learning_rate': args.lr, 'wd': args.wd, 'momentum': args.momentum},
update_on_kvstore=(False if args.amp else None), kvstore=kv)
if args.amp:
amp.init_trainer(trainer)
lr_decay = float(args.lr_decay)
lr_steps = sorted([float(ls) for ls in args.lr_decay_epoch.split(',') if ls.strip()])
lr_warmup = float(args.lr_warmup)
# losses, 以下4个loss是rcnn_task 里面要用到
rpn_cls_loss = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=False)
rpn_box_loss = mx.gluon.loss.HuberLoss(rho=1 / 9.)
rcnn_cls_loss = mx.gluon.loss.SoftmaxCrossEntropyLoss()
rcnn_box_loss = mx.gluon.loss.HuberLoss()
metrics = [mx.metric.Loss('RPN_Conf'),
mx.metric.Loss('RPN_SmoothL1'),
mx.metric.Loss('RCNN_CrossEntropy'),
mx.metric.Loss('RCNN_SmoothL1')]
# metrics: [rpn_cls_loss, rpn_box_loss, rcnn_cls_loss, rcnn_box_loss]
# metric_losses: [[rpn_cls_loss], [rpn_box_loss], [rcnn_cls_loss], [rcnn_box_loss]]
# metric.update(0, record)
rpn_acc_metric = RPNAccMetric()
rpn_bbox_metric = RPNL1LossMetric()
rcnn_acc_metric = RCNNAccMetric()
rcnn_bbox_metric = RCNNL1LossMetric()
metrics2 = [rpn_acc_metric, rpn_bbox_metric, rcnn_acc_metric, rcnn_bbox_metric]
# logger set_up
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
log_file_path = args.save_prefix + '_train.log'
log_dir = os.path.dirname(log_file_path)
if log_dir and not os.path.exists(log_dir):
os.makedirs(log_dir)
fh = logging.FileHandler(log_file_path)
logger.addHandler(fh)
logger.info(args)
if args.verbose:
logger.info('Trainable parameters:')
logger.info(net.collect_train_params().keys())
logger.info("Start training from [Epoch {}]".format(args.start_epoch))
best_map = [0]
for epoch in range(args.start_epoch, args.epochs):
mix_ratio = 1.0
if not args.disable_hybridization:
net.hybridize(static_alloc=args.static_alloc)
rcnn_task = ForwardBackwardTask(net, trainer, rpn_cls_loss, rpn_box_loss,
rcnn_cls_loss, rcnn_box_loss, mix_ratio=1.0)
executor = Parallel(1 if args.horovod else args.executor_threads, rcnn_task)
# executor 这一句什么意思
if args.mixup:
train_data._dataset._data.set_mixup(np.random.uniform, 0.5, 0.5)
mix_ratio =0.5
if epoch >= args.epochs - args.no_mixup_epochs:
train_data._dataset._data.set_mixup(None)
mix_ratio = 1.0
# 调整学习率
while lr_steps and epoch >= lr_steps[0]:
new_lr = trainer.learning_rate * lr_decay
lr_steps.pop(0)
trainer.set_learning_rate(new_lr)
logger.info('[Epoch {}] Set learning rate to {}'.format(epoch, new_lr))
for metric in metrics:
metric.reset()
tic = time.time() # 记录一次循环的时间
btic = time.time() # 记录每一个batch的时间
base_lr = trainer.learning_rate
rcnn_task.mix_ratio = mix_ratio
for i, batch in enumerate(train_data):
if epoch == 0 and i <= lr_warmup:
new_lr = base_lr * get_lr_at_iter(i / lr_warmup)
if new_lr != trainer.learning_rate:
if i % args.log_interval == 0:
logger.info('[Epoch 0 Iteration {}] Set learning rate to {}'.format(i, new_lr))
trainer.set_learning_rate(new_lr)
batch = split_and_load(batch, ctx_list=ctx)
batch_size = len(batch[0])
metric_losses = [[] for _ in metrics] # metrics: [rpn_cls_loss, rpn_box_loss, rcnn_cls_loss, rcnn_box_loss]
add_losses = [[] for _ in metrics2] # metrics2 : [rpn_acc_metric, rpn_bbox_metric, rcnn_acc_metric, rcnn_bbox_metric]
for data in zip(*batch):
executor.put(data)
for j in range(len(ctx)):
result = executor.get()
if (not args.horovod) or hvd.rank() == 0:
for k in range(len(metric_losses)):
metric_losses[k].append(result[k])
for k in range(len(add_losses)):
add_losses[k].append(result[len(metric_losses) + k])
for metric, record in zip(metrics, metric_losses):
# metrics: [rpn_cls_loss, rpn_box_loss, rcnn_cls_loss, rcnn_box_loss]
# metric_losses: [[rpn_cls_loss], [rpn_box_loss], [rcnn_cls_loss], [rcnn_box_loss]]
metric.update(0, record)
for metric, records in zip(metrics2, add_losses):
# metrics2 = [rpn_acc_metric, rpn_bbox_metric, rcnn_acc_metric, rcnn_bbox_metric]
# add_losses: [[rpn_acc_metric], [rpn_bbox_metric], [rcnn_acc_metric], [rcnn_bbox_metric]]
# rpn_acc_metric: [[rpn_label, rpn_weight], [rpn_cls_logits]]
for pred in records:
# update(label, preds)
# label: [rpn_label, rpn_weight]
# preds: [rpn_cls_logits]
metric.update(pred[0], pred[1])
trainer.step(batch_size)
if (not args.horovod or hvd.rank() == 0) and args.log_interval and not (i + 1) % args.log_interval:
msg = ','.join(['{} ={:.3f}'.format(*metric.get()) for metric in metrics + metrics2])
logger.info('[Epoch {}][Batch {}], Speed: {:.3f} samples/sec, {}'.format(
epoch, i, args.batch_size * args.log_interval / (time.time() - btic), msg))
btic = time.time()
if (not args.horovod) or hvd.rank() == 0:
msg = ','.join(['{} ={:.3f}'.format(*metric.get()) for metric in metrics])
logger.info('[Epoch {}] Training cost: {:.3f}, {}'.format(
epoch, (time.time() - tic), msg))
if (epoch % args.val_interval == 0) or (args.save_interval and epoch % args.save_interval == 0):
# 每循环args.val_interval或者args.save_interval次
# 就需要使用验证集来测试一次，得到current_map
map_name, mean_ap = validate(net, val_data, ctx, eval_metric, args)
val_msg = "\n".join('{}={}'.format(k, v) for k, v in zip(map_name, mean_ap))
logger.info('[Epoch {}] Validation: \n{}'.format(epoch, val_msg))
current_map = float(mean_ap[-1]) # mean_ap的最后一个数据就是mAP
else:
current_map = 0
save_params(net, logger, best_map, current_map, epoch, args.save_interval, args.save_prefix)
executor.__del__()