def offline_evaluate(pred_func, output_file):
df = get_eval_dataflow()
all_results = eval_coco(
df, lambda img: detect_one_image(img, pred_func))
with open(output_file, 'w') as f:
json.dump(all_results, f)
print_evaluation_scores(output_file)
def _eval(self):
all_results = eval_coco(self.df, lambda img: detect_one_image(img, self.pred))
output_file = os.path.join(
logger.get_logger_dir(), 'outputs{}.json'.format(self.global_step))
with open(output_file, 'w') as f:
json.dump(all_results, f)
scores = print_evaluation_scores(output_file)
for k, v in scores.items():
self.trainer.monitors.put_scalar(k, v)
def _eval(self):
logdir = args.logdir
if cfg.TRAINER == 'replicated':
with ThreadPoolExecutor(max_workers=self.num_predictor, thread_name_prefix='EvalWorker') as executor, \
tqdm.tqdm(total=sum([df.size() for df in self.dataflows])) as pbar:
futures = []
for dataflow, pred in zip(self.dataflows, self.predictors):
futures.append(executor.submit(eval_coco, dataflow, pred, pbar))
all_results = list(itertools.chain(*[fut.result() for fut in futures]))
else:
if self._horovod_run_eval:
local_results = eval_coco(self.dataflow, self.predictor)
output_partial = os.path.join(
logdir, 'outputs{}-part{}.json'.format(self.global_step, hvd.local_rank()))
with open(output_partial, 'w') as f:
json.dump(local_results, f)
self.barrier.eval()
if hvd.rank() > 0:
return
all_results = []
for k in range(hvd.local_size()):
output_partial = os.path.join(
logdir, 'outputs{}-part{}.json'.format(self.global_step, k))
with open(output_partial, 'r') as f:
obj = json.load(f)
all_results.extend(obj)
os.unlink(output_partial)
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_evaluation_scores(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_func, output_file):
df = get_eval_dataflow()
all_results = eval_coco(df, lambda img: detect_one_image(img, pred_func))
with open(output_file, 'w') as f:
json.dump(all_results, f)
print_evaluation_scores(output_file)
if args.evaluate:
batch_size = cfg.PREPROC.EVAL_BATCH_SIZE
if args.predict:
batch_size = 1
MODEL = PredModel(batch_size, input_shape)
pred_config = PredictConfig( \
model=MODEL, \
session_init=get_model_loader(args.load), \
input_names=MODEL.get_inference_tensor_names()[0], \
output_names=MODEL.get_inference_tensor_names()[1])
if args.evaluate:
assert args.evaluate.endswith('.json') or args.evaluate.endswith(
'.npz'), args.evaluate
if args.evalfromjson:
ret = print_evaluation_scores(args.evaluate)
else:
ret = do_evaluate(pred_config, args.evaluate, batch_size)
print('mIoU = {:.3f}'.format(ret['miou']))
else:
pred = OfflinePredictor(pred_config)
if args.export_graph:
from tensorflow.python.framework import graph_io
export_path, export_name = os.path.split(args.export_graph)
graph_io.write_graph(pred.sess.graph,
export_path,
export_name + 'txt',
as_text=True)
graph_io.write_graph(pred.sess.graph,
export_path,
export_name,
