Can not load dataset from config. Use default CLASSES instead.

[azuryl]

where can get MSKRCNN config file
if I use mmdet config file then
mmdet2trt init_trt_model.......
[01/27/2022-14:52:30] [TRT] [W] TensorRT was linked against cuBLAS/cuBLAS LT 10.2.3 but loaded cuBLAS/cuBLAS LT 10.2.2
[01/27/2022-14:52:30] [TRT] [W] TensorRT was linked against cuBLAS/cuBLAS LT 10.2.3 but loaded cuBLAS/cuBLAS LT 10.2.2
inference.py get_classes_from_config.......
Can not load dataset from config. Use default CLASSES instead.

[grimoire]

This error can be ignored by setting the class name in

mmdetection-to-tensorrt/mmdet2trt/apis/inference.py

Line 62 in 5352f15

def get_classes_from_config(model_cfg):

.

[azuryl]

This error can be ignored by setting the class name in

mmdetection-to-tensorrt/mmdet2trt/apis/inference.py

Line 62 in 5352f15

def get_classes_from_config(model_cfg):

.
@grimoire Dear
yes I modified this section according to you suggestion

def get_classes_from_config(model_cfg):
model_cfg_str = model_cfg
if isinstance(model_cfg, str):
model_cfg = mmcv.Config.fromfile(model_cfg)
print("#####mmdet2trt/apis/inference.py get_classes_from_config.......",model_cfg)
from mmdet.datasets import DATASETS, build_dataset

try:
   dataset = build_dataset(model_cfg)
    
   return dataset.CLASSES

except Exception:
    logger.warning(
        'inference.py Can not load dataset from config. Use default CLASSES instead.')
    classes =['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
    return classes

[azuryl]

This error can be ignored by setting the class name in

mmdetection-to-tensorrt/mmdet2trt/apis/inference.py

Line 62 in 5352f15

def get_classes_from_config(model_cfg):

.

I convert MASK RCNN R-50-FPN |pytorch
from https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn

but there are another issue in

mmdetection-to-tensorrt/mmdet2trt/apis/inference.py

Line 144 in 5352f15

def forward(self, img, img_metas, *args, **kwargs):

the output is Zero

#################model: TRTModule()
TRT infer..................
forward!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
dets:
tensor([], device='cuda:0', size=(0, 5))
labels:
tensor([], device='cuda:0')
^^^^^^^dets_results:
[array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32), array([], shape=(0, 5), dtype=float32)]
###############result: