def _eval(self):
logdir = self._output_dir
if cfg.TRAINER == 'replicated':
all_results = multithread_predict_dataflow(self.dataflows, self.predictors)
else:
filenames = [os.path.join(
logdir, 'outputs{}-part{}.json'.format(self.global_step, rank)
) for rank in range(hvd.local_size())]
if self._horovod_run_eval:
local_results = predict_dataflow(self.dataflow, self.predictor)
fname = filenames[hvd.local_rank()]
with open(fname, 'w') as f:
json.dump(local_results, f)
self.barrier.eval()
if hvd.rank() > 0:
return
all_results = []
for fname in filenames:
with open(fname, 'r') as f:
obj = json.load(f)
all_results.extend(obj)
os.unlink(fname)
output_file = os.path.join(
logdir, '{}-outputs{}.json'.format(self._eval_dataset, self.global_step))
scores = DetectionDataset().eval_or_save_inference_results(
all_results, self._eval_dataset, output_file)
for k, v in scores.items():
self.trainer.monitors.put_scalar(k, v)
def _eval(self):
logdir = self._output_dir
if cfg.TRAINER == 'replicated':
all_results = multithread_predict_dataflow(self.dataflows,
self.predictors)
else:
if self.batched:
local_results = predict_dataflow_batch(self.dataflow,
self.predictor)
else:
local_results = predict_dataflow(self.dataflow, self.predictor)
results = gather_result_from_all_processes(local_results)
if hvd.rank() > 0:
return
all_results = []
for item in results:
if item is not None:
all_results.extend(item)
output_file = os.path.join(
logdir, '{}-outputs{}'.format(self._eval_dataset,
self.global_step))
scores = DetectionDataset().eval_or_save_inference_results(
all_results, self._eval_dataset, output_file)
for k, v in scores.items():
self.trainer.monitors.put_scalar(k, v)
def background_coco(all_results):
output_file = os.path.join(
logdir, '{}-outputs{}'.format(self._eval_dataset,
self.global_step))
scores = DetectionDataset().eval_or_save_inference_results(
all_results, self._eval_dataset, output_file)
cfg.TRAIN.SHOULD_STOP = scores[
'mAP(bbox)/IoU=0.5:0.95'] >= cfg.TEST.BOX_TARGET and scores[
'mAP(segm)/IoU=0.5:0.95'] >= cfg.TEST.MASK_TARGET
for k, v in scores.items():
self.trainer.monitors.put_scalar(k, v)
return
