if FLAGS.mode != "train_and_evaluate" else "epoch",
num_iter=FLAGS.num_iter
if FLAGS.mode != "train_and_evaluate" else 1,
warmup_steps=FLAGS.warmup_steps,
batch_size=FLAGS.batch_size,
log_every_n_steps=FLAGS.display_every,
is_benchmark=FLAGS.mode == 'inference_benchmark',
export_dir=FLAGS.export_dir,
quantize=FLAGS.quantize,
symmetric=FLAGS.symmetric,
use_final_conv=FLAGS.use_final_conv,
use_qdq=FLAGS.use_qdq)
if FLAGS.mode == 'predict':
if FLAGS.to_predict is None:
raise ValueError("No data to predict on.")
if not os.path.isfile(FLAGS.to_predict):
raise ValueError("Only prediction on single images is supported!")
if hvd_utils.is_using_hvd():
raise NotImplementedError(
"Only single GPU inference is implemented.")
elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
runner.predict(FLAGS.to_predict,
quantize=FLAGS.quantize,
symmetric=FLAGS.symmetric,
use_qdq=FLAGS.use_qdq,
use_final_conv=FLAGS.use_final_conv)
if FLAGS.mode == 'inference_benchmark' and hvd_utils.is_using_hvd():
raise NotImplementedError(
"Only single GPU inference is implemented.")
elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
runner.evaluate(iter_unit=FLAGS.iter_unit
if FLAGS.mode != "train_and_evaluate" else "epoch",
num_iter=FLAGS.num_iter
if FLAGS.mode != "train_and_evaluate" else 1,
warmup_steps=FLAGS.warmup_steps,
batch_size=FLAGS.batch_size,
log_every_n_steps=FLAGS.display_every,
is_benchmark=FLAGS.mode == 'inference_benchmark',
export_dir=FLAGS.export_dir)
if FLAGS.mode == 'predict':
if FLAGS.to_predict is None:
raise ValueError("No data to predict on.")
if not os.path.isfile(FLAGS.to_predict):
raise ValueError("Only prediction on single images is supported!")
if hvd_utils.is_using_hvd():
raise NotImplementedError(
"Only single GPU inference is implemented.")
elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
runner.predict(FLAGS.to_predict)
StdOutBackend(verbosity=Verbosity.DEFAULT)
]
DLLogger.init(backends=backends)
DLLogger.log(data=vars(FLAGS), step='PARAMETER')
runner = Runner(FLAGS, DLLogger)
if FLAGS.mode in ["train", "train_and_eval", "training_benchmark"]:
runner.train()
if FLAGS.mode in ['eval', 'evaluate', 'inference_benchmark']:
if FLAGS.mode == 'inference_benchmark' and hvd_utils.is_using_hvd():
raise NotImplementedError("Only single GPU inference is implemented.")
elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
runner.evaluate()
if FLAGS.mode == 'predict':
if FLAGS.to_predict is None:
raise ValueError("No data to predict on.")
if not os.path.isdir(FLAGS.to_predict):
raise ValueError("Provide directory with images to infer!")
if hvd_utils.is_using_hvd():
raise NotImplementedError("Only single GPU inference is implemented.")
elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
runner.predict(FLAGS.to_predict, FLAGS.inference_checkpoint)
if FLAGS.mode == 'savemodel_as_backbone':
runner.savemodel_as_backbone()
DLLogger.log(data=vars(config), step='PARAMETER')
#========== initialize the runner
runner = Runner(config, DLLogger)
#========== determine the operation mode of the runner (tr,eval,predict)
if config.mode in ["train", "train_and_eval", "training_benchmark"]:
runner.train()
if config.mode in ['eval', 'evaluate', 'inference_benchmark']:
if config.mode == 'inference_benchmark' and hvd_utils.is_using_hvd():
raise NotImplementedError(
"Only single GPU inference is implemented.")
elif hvd_utils.is_using_hvd():
raise NotImplementedError(
"Only single GPU evaluation is implemented.")
else:
runner.evaluate()
if config.mode == 'predict':
if config.predict_img_dir is None:
raise ValueError("No data to predict on.")
if not os.path.isdir(config.predict_img_dir):
raise ValueError("Provide directory with images to infer!")
if hvd_utils.is_using_hvd():
raise NotImplementedError(
"Only single GPU inference is implemented.")
elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
runner.predict(config.predict_img_dir, config.predict_ckpt)