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217 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: train.py
import argparse
import six
assert six.PY3, "This example requires Python 3!"
from tensorpack import *
from tensorpack.tfutils import collect_env_info
from tensorpack.tfutils.common import get_tf_version_tuple
from mot.object_detection.dataset import register_mot
from mot.object_detection.config import config as cfg
from mot.object_detection.config import finalize_configs
from mot.object_detection.data import get_train_dataflow
from mot.object_detection.eval import EvalCallback
from mot.object_detection.modeling.generalized_rcnn import ResNetC4Model, ResNetFPNModel
try:
import horovod.tensorflow as hvd
except ImportError:
pass
if __name__ == '__main__':
# "spawn/forkserver" is safer than the default "fork" method and
# produce more deterministic behavior & memory saving
# However its limitation is you cannot pass a lambda function to subprocesses.
import multiprocessing as mp
mp.set_start_method('spawn')
parser = argparse.ArgumentParser()
parser.add_argument('--load', help='Load a model to start training from. It overwrites BACKBONE.WEIGHTS')
parser.add_argument('--logdir', help='Log directory. Will remove the old one if already exists.',
default='train_log/maskrcnn')
parser.add_argument('--config', help="A list of KEY=VALUE to overwrite those defined in config.py", nargs='+')
if get_tf_version_tuple() < (1, 6):
# https://github.com/tensorflow/tensorflow/issues/14657
logger.warn("TF<1.6 has a bug which may lead to crash in FasterRCNN if you're unlucky.")
args = parser.parse_args()
if args.config:
cfg.update_args(args.config)
register_mot(cfg.DATA.BASEDIR) # add the mot datasets to the registry
# Setup logging ...
is_horovod = cfg.TRAINER == 'horovod'
if is_horovod:
hvd.init()
if not is_horovod or hvd.rank() == 0:
logger.set_logger_dir(args.logdir, 'd')
logger.info("Environment Information:\n" + collect_env_info())
finalize_configs(is_training=True)
# Create model
MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model()
# Compute the training schedule from the number of GPUs ...
stepnum = cfg.TRAIN.STEPS_PER_EPOCH
# warmup is step based, lr is epoch based
init_lr = cfg.TRAIN.WARMUP_INIT_LR * min(8. / cfg.TRAIN.NUM_GPUS, 1.)
warmup_schedule = [(0, init_lr), (cfg.TRAIN.WARMUP, cfg.TRAIN.BASE_LR)]
warmup_end_epoch = cfg.TRAIN.WARMUP * 1. / stepnum
lr_schedule = [(int(warmup_end_epoch + 0.5), cfg.TRAIN.BASE_LR)]
factor = 8. / cfg.TRAIN.NUM_GPUS
for idx, steps in enumerate(cfg.TRAIN.LR_SCHEDULE[:-1]):
mult = 0.1 ** (idx + 1)
lr_schedule.append(
(steps * factor // stepnum, cfg.TRAIN.BASE_LR * mult))
logger.info("Warm Up Schedule (steps, value): " + str(warmup_schedule))
logger.info("LR Schedule (epochs, value): " + str(lr_schedule))
train_dataflow = get_train_dataflow()
# This is what's commonly referred to as "epochs"
total_passes = cfg.TRAIN.LR_SCHEDULE[-1] * 8 / train_dataflow.size()
logger.info("Total passes of the training set is: {:.5g}".format(total_passes))
# Create callbacks ...
callbacks = [
PeriodicCallback(
ModelSaver(max_to_keep=10, keep_checkpoint_every_n_hours=1),
every_k_epochs=cfg.TRAIN.CHECKPOINT_PERIOD),
# linear warmup
ScheduledHyperParamSetter(
'learning_rate', warmup_schedule, interp='linear', step_based=True),
ScheduledHyperParamSetter('learning_rate', lr_schedule),
GPUMemoryTracker(),
HostMemoryTracker(),
ThroughputTracker(samples_per_step=cfg.TRAIN.NUM_GPUS),
EstimatedTimeLeft(median=True),
SessionRunTimeout(60000), # 1 minute timeout
GPUUtilizationTracker()
]
if cfg.TRAIN.EVAL_PERIOD > 0:
callbacks.extend([
EvalCallback(dataset, *MODEL.get_inference_tensor_names(), args.logdir)
for dataset in cfg.DATA.VAL
])
if is_horovod and hvd.rank() > 0:
session_init = None
else:
if args.load:
# ignore mismatched values, so you can `--load` a model for fine-tuning
session_init = SmartInit(args.load, ignore_mismatch=True)
else:
session_init = SmartInit(cfg.BACKBONE.WEIGHTS)
traincfg = TrainConfig(
model=MODEL,
data=QueueInput(train_dataflow),
callbacks=callbacks,
steps_per_epoch=stepnum,
max_epoch=cfg.TRAIN.LR_SCHEDULE[-1] * factor // stepnum,
session_init=session_init,
starting_epoch=cfg.TRAIN.STARTING_EPOCH
)
if is_horovod:
trainer = HorovodTrainer(average=False)
else:
# nccl mode appears faster than cpu mode
trainer = SyncMultiGPUTrainerReplicated(cfg.TRAIN.NUM_GPUS, average=False, mode='nccl')
launch_train_with_config(traincfg, trainer)
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