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facebookresearch / detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
https://detectron2.readthedocs.io/en/latest/
Apache License 2.0
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AttributeError: Attribute 'stuff_classes' does not exist in the metadata of dataset 'coco_my_val_separated'. Available keys are dict_keys(['name', 'panoptic_root', 'image_root', 'panoptic_json', 'sem_seg_root', 'json_file', 'evaluator_type', 'ignore_label', 'thing_classes', 'thing_dataset_id_to_contiguous_id']). #4917

Open quentinagao opened 1 year ago

quentinagao commented 1 year ago

Instructions To Reproduce the 🐛 Bug:

  1. Full changes I have made:

    def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
args.config_file = 'configfile/panoptic/panoptic_fpn_R_50_3x.yaml'
#cfg.MODEL.DEVICE = 'cpu'
#cfg.INPUT.MASK_FORMAT = 'bitmask'

cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)

cfg.DATASETS.TRAIN = ("coco_my_train_separated",) 
cfg.DATASETS.TEST = ("coco_my_val_separated",)
cfg.DATALOADER.NUM_WORKERS = 0

cfg.INPUT.CROP.ENABLED = True

cfg.MODEL.ROI_HEADS.NUM_CLASSES = 80
cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54
cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255
#cfg.MODEL.RETINANET.NUM_CLASSES = 134
cfg.MODEL.WEIGHTS = 'configfile/model_final_c10459.pkl'
cfg.SOLVER.IMS_PER_BATCH = 1  # batch_size=2
ITERS_IN_ONE_EPOCH = int(1000 / cfg.SOLVER.IMS_PER_BATCH)

cfg.SOLVER.MAX_ITER = 10000
cfg.SOLVER.BASE_LR = 0.0002
cfg.SOLVER.MOMENTUM = 0.9
cfg.SOLVER.WEIGHT_DECAY = 0.0001
cfg.SOLVER.WEIGHT_DECAY_NORM = 0.0
cfg.SOLVER.GAMMA = 0.1
cfg.SOLVER.STEPS = (9000,)
cfg.SOLVER.WARMUP_FACTOR = 1.0 / 1000
cfg.SOLVER.WARMUP_ITERS = 500
cfg.SOLVER.WARMUP_METHOD = "linear"

cfg.TEST.EVAL_PERIOD = ITERS_IN_ONE_EPOCH
cfg.TEST.EVAL_PERIOD = 100

cfg.freeze()
default_setup(cfg, args)
return cfg

  2. What exact command you run: python train_net.py

  3. Full logs or other relevant observations:

    
[04/18 09:09:05 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [RandomCrop(crop_type='relative_range', crop_size=[0.9, 0.9]), ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[04/18 09:09:05 d2.data.build]: Using training sampler TrainingSampler
[04/18 09:09:05 d2.data.common]: Serializing 118287 elements to byte tensors and concatenating them all ...
[04/18 09:09:08 d2.data.common]: Serialized dataset takes 461.71 MiB
2023-04-18 09:09:11.993950: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll
[04/18 09:09:14 fvcore.common.checkpoint]: [Checkpointer] Loading from configfile/model_final_c10459.pkl ...
[04/18 09:09:14 fvcore.common.checkpoint]: Reading a file from 'Detectron2 Model Zoo'
[04/18 09:09:15 d2.engine.train_loop]: Starting training from iteration 0
[04/18 09:09:29 d2.utils.events]:  eta: 1:42:08  iter: 19  total_loss: 0.8808  loss_sem_seg: 0.163  loss_rpn_cls: 0.01234  loss_rpn_loc: 0.02302  loss_cls: 0.1425  loss_box_reg: 0.2256  loss_mask: 0.1804  time: 0.6293  data_time: 0.0296  lr: 7.7924e-06  max_mem: 2437M
D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\fvcore\transforms\transform.py:724: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
for poly in cropped:
D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\fvcore\transforms\transform.py:724: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
for poly in cropped:
D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\fvcore\transforms\transform.py:724: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
for poly in cropped:
[04/18 09:09:41 d2.utils.events]:  eta: 1:43:32  iter: 39  total_loss: 0.673  loss_sem_seg: 0.09647  loss_rpn_cls: 0.01844  loss_rpn_loc: 0.04783  loss_cls: 0.1308  loss_box_reg: 0.1333  loss_mask: 0.188  time: 0.6390  data_time: 0.0271  lr: 1.5784e-05  max_mem: 2437M
D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\fvcore\transforms\transform.py:724: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
for poly in cropped:
[04/18 09:09:54 d2.utils.events]:  eta: 1:44:25  iter: 59  total_loss: 0.8675  loss_sem_seg: 0.1861  loss_rpn_cls: 0.01036  loss_rpn_loc: 0.01953  loss_cls: 0.2103  loss_box_reg: 0.2019  loss_mask: 0.2035  time: 0.6389  data_time: 0.0248  lr: 2.3776e-05  max_mem: 2437M
D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\fvcore\transforms\transform.py:724: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
for poly in cropped:
D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\fvcore\transforms\transform.py:724: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
for poly in cropped:
[04/18 09:10:07 d2.utils.events]:  eta: 1:43:45  iter: 79  total_loss: 0.8721  loss_sem_seg: 0.1211  loss_rpn_cls: 0.01267  loss_rpn_loc: 0.03563  loss_cls: 0.1317  loss_box_reg: 0.1683  loss_mask: 0.216  time: 0.6371  data_time: 0.0266  lr: 3.1768e-05  max_mem: 2437M
D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\fvcore\transforms\transform.py:724: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
for poly in cropped:
[04/18 09:10:20 d2.data.datasets.coco]: Loaded 5000 images in COCO format from E:\数据集\MS COCO\annotations\instances_val2017.json
[04/18 09:10:20 d2.data.datasets.coco]: Loaded 5000 images with semantic segmentation from E:\数据集\MS COCO\val2017
[04/18 09:10:20 d2.data.build]: Distribution of instances among all 80 categories:
|   category    | #instances   |   category   | #instances   |   category    | #instances   |
|:-------------:|:-------------|:------------:|:-------------|:-------------:|:-------------|
|    person     | 10777        |   bicycle    | 314          |      car      | 1918         |
|  motorcycle   | 367          |   airplane   | 143          |      bus      | 283          |
|     train     | 190          |    truck     | 414          |     boat      | 424          |
| traffic light | 634          | fire hydrant | 101          |   stop sign   | 75           |
| parking meter | 60           |    bench     | 411          |     bird      | 427          |
|      cat      | 202          |     dog      | 218          |     horse     | 272          |
|     sheep     | 354          |     cow      | 372          |   elephant    | 252          |
|     bear      | 71           |    zebra     | 266          |    giraffe    | 232          |
|   backpack    | 371          |   umbrella   | 407          |    handbag    | 540          |
|      tie      | 252          |   suitcase   | 299          |    frisbee    | 115          |
|     skis      | 241          |  snowboard   | 69           |  sports ball  | 260          |
|     kite      | 327          | baseball bat | 145          | baseball gl.. | 148          |
|  skateboard   | 179          |  surfboard   | 267          | tennis racket | 225          |
|    bottle     | 1013         |  wine glass  | 341          |      cup      | 895          |
|     fork      | 215          |    knife     | 325          |     spoon     | 253          |
|     bowl      | 623          |    banana    | 370          |     apple     | 236          |
|   sandwich    | 177          |    orange    | 285          |   broccoli    | 312          |
|    carrot     | 365          |   hot dog    | 125          |     pizza     | 284          |
|     donut     | 328          |     cake     | 310          |     chair     | 1771         |
|     couch     | 261          | potted plant | 342          |      bed      | 163          |
| dining table  | 695          |    toilet    | 179          |      tv       | 288          |
|    laptop     | 231          |    mouse     | 106          |    remote     | 283          |
|   keyboard    | 153          |  cell phone  | 262          |   microwave   | 55           |
|     oven      | 143          |   toaster    | 9            |     sink      | 225          |
| refrigerator  | 126          |     book     | 1129         |     clock     | 267          |
|     vase      | 274          |   scissors   | 36           |  teddy bear   | 190          |
|  hair drier   | 11           |  toothbrush  | 57           |               |              |
|     total     | 36335        |              |              |               |              |
[04/18 09:10:20 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[04/18 09:10:20 d2.data.common]: Serializing 5000 elements to byte tensors and concatenating them all ...
[04/18 09:10:20 d2.data.common]: Serialized dataset takes 19.53 MiB
[04/18 09:10:21 d2.data.datasets.coco]: Loaded 5000 images in COCO format from E:\数据集\MS COCO\annotations\instances_val2017.json
[04/18 09:10:21 d2.data.datasets.coco]: Loaded 5000 images with semantic segmentation from E:\数据集\MS COCO\val2017
ERROR [04/18 09:10:22 d2.engine.train_loop]: Exception during training:
Traceback (most recent call last):
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\train_loop.py", line 150, in train
self.after_step()
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\train_loop.py", line 180, in after_step
h.after_step()
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\hooks.py", line 555, in after_step
self._do_eval()
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\hooks.py", line 528, in _do_eval
results = self._func()
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\defaults.py", line 453, in test_and_save_results
self._last_eval_results = self.test(self.cfg, self.model)
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\defaults.py", line 609, in test
evaluator = cls.build_evaluator(cfg, dataset_name)
File "D:/Users/gaotsingyuan/PycharmProjects/pythonProject3/test3-panoptic.py", line 123, in build_evaluator
return build_evaluator(cfg, dataset_name, output_folder)
File "D:/Users/gaotsingyuan/PycharmProjects/pythonProject3/test3-panoptic.py", line 89, in build_evaluator
output_dir=output_folder,
File "C:\Users\gaotsingyuan\detectron2\detectron2\evaluation\sem_seg_evaluation.py", line 89, in __init__
self._class_names = meta.stuff_classes
File "C:\Users\gaotsingyuan\detectron2\detectron2\data\catalog.py", line 128, in __getattr__
"keys are {}.".format(key, self.name, str(self.__dict__.keys()))
AttributeError: Attribute 'stuff_classes' does not exist in the metadata of dataset 'coco_my_val_separated'. Available keys are dict_keys(['name', 'panoptic_root', 'image_root', 'panoptic_json', 'sem_seg_root', 'json_file', 'evaluator_type', 'ignore_label', 'thing_classes', 'thing_dataset_id_to_contiguous_id']).
[04/18 09:10:22 d2.engine.hooks]: Overall training speed: 97 iterations in 0:01:01 (0.6389 s / it)
[04/18 09:10:22 d2.engine.hooks]: Total training time: 0:01:04 (0:00:02 on hooks)
[04/18 09:10:22 d2.utils.events]:  eta: 1:43:08  iter: 99  total_loss: 0.6138  loss_sem_seg: 0.08962  loss_rpn_cls: 0.006415  loss_rpn_loc: 0.02325  loss_cls: 0.1036  loss_box_reg: 0.1353  loss_mask: 0.1793  time: 0.6324  data_time: 0.0248  lr: 3.976e-05  max_mem: 2437M
Traceback (most recent call last):
File "D:/Users/gaotsingyuan/PycharmProjects/pythonProject3/test3-panoptic.py", line 456, in <module>
args=(args,),
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\launch.py", line 82, in launch
main_func(*args)
File "D:/Users/gaotsingyuan/PycharmProjects/pythonProject3/test3-panoptic.py", line 440, in main
return trainer.train()
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\defaults.py", line 484, in train
super().train(self.start_iter, self.max_iter)
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\train_loop.py", line 150, in train
self.after_step()
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\train_loop.py", line 180, in after_step
h.after_step()
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\hooks.py", line 555, in after_step
self._do_eval()
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\hooks.py", line 528, in _do_eval
results = self._func()
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\defaults.py", line 453, in test_and_save_results
self._last_eval_results = self.test(self.cfg, self.model)
File "C:\Users\gaotsingyuan\detectron2\detectron2\engine\defaults.py", line 609, in test
evaluator = cls.build_evaluator(cfg, dataset_name)
File "D:/Users/gaotsingyuan/PycharmProjects/pythonProject3/test3-panoptic.py", line 123, in build_evaluator
return build_evaluator(cfg, dataset_name, output_folder)
File "D:/Users/gaotsingyuan/PycharmProjects/pythonProject3/test3-panoptic.py", line 89, in build_evaluator
output_dir=output_folder,
File "C:\Users\gaotsingyuan\detectron2\detectron2\evaluation\sem_seg_evaluation.py", line 89, in __init__
self._class_names = meta.stuff_classes
File "C:\Users\gaotsingyuan\detectron2\detectron2\data\catalog.py", line 128, in __getattr__
"keys are {}.".format(key, self.name, str(self.__dict__.keys()))
AttributeError: Attribute 'stuff_classes' does not exist in the metadata of dataset 'coco_my_val_separated'. Available keys are dict_keys(['name', 'panoptic_root', 'image_root', 'panoptic_json', 'sem_seg_root', 'json_file', 'evaluator_type', 'ignore_label', 'thing_classes', 'thing_dataset_id_to_contiguous_id']).

Process finished with exit code 1


4. please simplify the steps as much as possible so they do not require additional resources to
   run, such as a private dataset.
I used **official** COCO2017 datsets, and registered by using `register_coco_panoptic_separated`. The **image_root, panoptic_root, panoptic_json and instances_json** are referred to the files directly downloaded from [(https://cocodataset.org/#download)](url). The **sem_seg_root** refers to the file which is coverted from panoptic files, following [https://github.com/facebookresearch/detectron2/blob/main/datasets/prepare_panoptic_fpn.py](url). The **metadata** I put an empty dict.
The other training methods and settings are the same as what I used to train on Mask-RCNN, which can train and evaluate sucessfully.

It can do training, but when it starts to do panoptic evaluations, I get an error: AttributeError: Attribute 'stuff_classes' does not exist in the metadata of dataset 'coco_my_val_separated'. Available keys are dict_keys(['name', 'panoptic_root', 'image_root', 'panoptic_json', 'sem_seg_root', 'json_file', 'evaluator_type', 'ignore_label', 'thing_classes', 'thing_dataset_id_to_contiguous_id']).
When I set `MetadataCatalog.get('coco_my_val_separated').set(stuff_classes= [])`, I get another error: AttributeError: Attribute 'stuff_dataset_id_to_contiguous_id' does not exist in the metadata of dataset 'coco_my_val_separated'. Available keys are dict_keys(['name', 'stuff_classes', 'panoptic_root', 'image_root', 'panoptic_json', 'sem_seg_root', 'json_file', 'evaluator_type', 'ignore_label', 'thing_classes', 'thing_dataset_id_to_contiguous_id']).

It seems that 'stuff_classes' and 'stuff_dataset_id_to_contiguous_id' do not exist in the matadata.
How can I set them? Should I put all stuff categories in the metadata manually? 

## Expected behavior:
I want to train panoptic fpn on official COCO2017 datasets and expect it can works.

## Environment:

sys.platform win32 Python 3.7.11 (default, Jul 27 2021, 09:42:29) [MSC v.1916 64 bit (AMD64)] numpy 1.21.5 detectron2 0.6 @C:\Users\gaotsingyuan\detectron2\detectron2 detectron2._C not built correctly: DLL load failed: 找不到指定的程序。 DETECTRON2_ENV_MODULE PyTorch 1.8.0 @D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\torch PyTorch debug build False GPU available Yes GPU 0 NVIDIA GeForce GTX 1650 Ti (arch=7.5) Driver version 512.78 CUDA_HOME C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2 Pillow 9.2.0 torchvision 0.9.0 @D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\torchvision torchvision arch flags D:\Users\gaotsingyuan\anaconda3\envs\python37\lib\site-packages\torchvision_C.pyd; cannot find cuobjdump fvcore 0.1.5.post20220512 iopath 0.1.9 cv2 4.6.0

PyTorch built with:

zacklew commented 3 months ago

Did you ever get a fix for this issue? I'm trying to run the COCOPanopticEvaluator and I'm getting the same issue with 'stuff_dataset_id_to_contiguous_id' does not exist in the metadata of dataset '