def evaluate_rcnn(model_name, paper_arxiv_id, cfg_list, model_file):
evaluator = COCOEvaluator(
root=COCO_ROOT, model_name=model_name, paper_arxiv_id=paper_arxiv_id
)
category_id_to_coco_id = {
v: k for k, v in COCODetection.COCO_id_to_category_id.items()
}
cfg.update_config_from_args(cfg_list) # TODO backup/restore config
finalize_configs(False)
MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model()
predcfg = PredictConfig(
model=MODEL,
session_init=SmartInit(model_file),
input_names=MODEL.get_inference_tensor_names()[0],
output_names=MODEL.get_inference_tensor_names()[1],
)
predictor = OfflinePredictor(predcfg)
def xyxy_to_xywh(box):
box[2] -= box[0]
box[3] -= box[1]
return box
df = get_eval_dataflow("coco_val2017")
df.reset_state()
for img, img_id in tqdm.tqdm(df, total=len(df)):
results = predict_image(img, predictor)
res = [
{
"image_id": img_id,
"category_id": category_id_to_coco_id.get(
int(r.class_id), int(r.class_id)
),
"bbox": xyxy_to_xywh([round(float(x), 4) for x in r.box]),
"score": round(float(r.score), 3),
}
for r in results
]
evaluator.add(res)
if evaluator.cache_exists:
break
evaluator.save()
parser.add_argument(
'--benchmark',
action='store_true',
help="Benchmark the speed of the model + postprocessing")
parser.add_argument(
'--config',
help="A list of KEY=VALUE to overwrite those defined in config.py",
nargs='+')
parser.add_argument('--compact', help='Save a model to .pb')
parser.add_argument('--serving', help='Save a model to serving file')
args = parser.parse_args()
if args.config:
cfg.update_args(args.config)
register_coco(cfg.DATA.BASEDIR) # add COCO datasets to the registry
MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model()
if not tf.test.is_gpu_available():
from tensorflow.python.framework import test_util
assert get_tf_version_tuple() >= (1, 7) and test_util.IsMklEnabled(), \
"Inference requires either GPU support or MKL support!"
assert args.load
finalize_configs(is_training=False)
if args.predict or args.visualize:
cfg.TEST.RESULT_SCORE_THRESH = cfg.TEST.RESULT_SCORE_THRESH_VIS
if args.visualize:
do_visualize(MODEL, args.load)
else:
predcfg = PredictConfig(
