def _eval(self):
logdir = args.logdir
if cfg.TRAINER == 'replicated':
all_results = multithread_eval_coco(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 = eval_coco(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.global_step))
with open(output_file, 'w') as f:
json.dump(all_results, f)
try:
scores = print_coco_metrics(output_file)
for k, v in scores.items():
self.trainer.monitors.put_scalar(k, v)
except Exception:
logger.exception("Exception in COCO evaluation.")
def offline_evaluate(pred_config, output_file):
num_gpu = cfg.TRAIN.NUM_GPUS
graph_funcs = MultiTowerOfflinePredictor(pred_config, list(
range(num_gpu))).get_predictors()
predictors = []
dataflows = []
for k in range(num_gpu):
predictors.append(
lambda img, pred=graph_funcs[k]: detect_one_image(img, pred))
dataflows.append(get_eval_dataflow(shard=k, num_shards=num_gpu))
if num_gpu > 1:
all_results = multithread_eval_coco(dataflows, predictors)
else:
all_results = eval_coco(dataflows[0], predictors[0])
with open(output_file, 'w') as f:
json.dump(all_results, f)
print_coco_metrics(output_file)
def offline_evaluate(pred_config, output_file):
num_gpu = cfg.TRAIN.NUM_GPUS
graph_funcs = MultiTowerOfflinePredictor(pred_config, list(
range(num_gpu))).get_predictors()
predictors = []
for k in range(num_gpu):
predictors.append(
lambda img, pred=graph_funcs[k]: detect_one_image(img, pred))
for dataset in cfg.DATA.VAL:
logger.info("Evaluating {} ...".format(dataset))
dataflows = [
get_eval_dataflow(dataset, shard=k, num_shards=num_gpu)
for k in range(num_gpu)
]
if num_gpu > 1:
all_results = multithread_eval_coco(dataflows, predictors)
else:
all_results = eval_coco(dataflows[0], predictors[0])
output = output_file + '-' + dataset
with open(output, 'w') as f:
json.dump(all_results, f)
print_coco_metrics(dataset, output)
