2 problems happened when I used entry.py for evaluate

When I executed the following code in the command line in ubuntu, two problems occurred，

WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation.
AssertionError: /home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/xdecoder_data/coco/panoptic_val2017/000000000139.png。

First,I don't mske any changes to focalt_unicl_lang.yaml; Second,I found 000000000139.jpg in the path "/xdecoder_data/coco/panoptic_val2017/" from the COCO official website's val2017 image dataset, which contains a total of 5000 jpg images. Is 000000000139.jpg the same as 000000000139.png?How do I obtain 000000000139.png?

Waiting for the author's response.Thx！！！！

The following content is the detailed information of the command and error.

CUDA_VISIBLE_DEVICES=0,1 mpirun -n 2 python entry.py evaluate \

        --conf_files configs/xdecoder/focalt_unicl_lang.yaml \
        --overrides \
        COCO.INPUT.IMAGE_SIZE 1024 \
        MODEL.DECODER.CAPTIONING.ENABLED True \
        MODEL.DECODER.RETRIEVAL.ENABLED True \
        MODEL.DECODER.GROUNDING.ENABLED True \
        COCO.TEST.BATCH_SIZE_TOTAL 2 \
        COCO.TRAIN.BATCH_SIZE_TOTAL 2 \
        COCO.TRAIN.BATCH_SIZE_PER_GPU 1 \
        VLP.TEST.BATCH_SIZE_TOTAL 2 \
        VLP.TRAIN.BATCH_SIZE_TOTAL 2 \
        VLP.TRAIN.BATCH_SIZE_PER_GPU 1 \
        MODEL.DECODER.HIDDEN_DIM 512 \
        MODEL.ENCODER.CONVS_DIM 512 \
        MODEL.ENCODER.MASK_DIM 512 \
        ADE20K.TEST.BATCH_SIZE_TOTAL 2 \
        FP16 True \
        WEIGHT True \
        RESUME_FROM /home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/pretrained_model/xdecoder_focalt_last.pt

No protocol specified WARNING:utils.arguments:Overrided COCO.INPUT.IMAGE_SIZE from 1024 to 1024 WARNING:utils.arguments:Overrided MODEL.DECODER.CAPTIONING.ENABLED from True to True WARNING:utils.arguments:Overrided MODEL.DECODER.RETRIEVAL.ENABLED from True to True WARNING:utils.arguments:Overrided MODEL.DECODER.GROUNDING.ENABLED from True to True WARNING:utils.arguments:Overrided COCO.TEST.BATCH_SIZE_TOTAL from 8 to 2 WARNING:utils.arguments:Overrided COCO.TRAIN.BATCH_SIZE_TOTAL from 2 to 2 WARNING:utils.arguments:Overrided COCO.TRAIN.BATCH_SIZE_PER_GPU from 1 to 1 WARNING:utils.arguments:Overrided VLP.TEST.BATCH_SIZE_TOTAL from 3 to 2 WARNING:utils.arguments:Overrided VLP.TRAIN.BATCH_SIZE_TOTAL from 2 to 2 WARNING:utils.arguments:Overrided VLP.TRAIN.BATCH_SIZE_PER_GPU from 1 to 1 WARNING:utils.arguments:Overrided MODEL.DECODER.HIDDEN_DIM from 512 to 512 WARNING:utils.arguments:Overrided MODEL.ENCODER.CONVS_DIM from 512 to 512 WARNING:utils.arguments:Overrided MODEL.ENCODER.MASK_DIM from 512 to 512 WARNING:utils.arguments:Overrided ADE20K.TEST.BATCH_SIZE_TOTAL from 8 to 2 WARNING:utils.arguments:Overrided COCO.INPUT.IMAGE_SIZE from 1024 to 1024 WARNING:utils.arguments:Overrided MODEL.DECODER.CAPTIONING.ENABLED from True to True WARNING:utils.arguments:Overrided MODEL.DECODER.RETRIEVAL.ENABLED from True to True WARNING:utils.arguments:Overrided MODEL.DECODER.GROUNDING.ENABLED from True to True WARNING:utils.arguments:Overrided COCO.TEST.BATCH_SIZE_TOTAL from 8 to 2 WARNING:utils.arguments:Overrided COCO.TRAIN.BATCH_SIZE_TOTAL from 2 to 2 WARNING:utils.arguments:Overrided COCO.TRAIN.BATCH_SIZE_PER_GPU from 1 to 1 WARNING:utils.arguments:Overrided VLP.TEST.BATCH_SIZE_TOTAL from 3 to 2 WARNING:utils.arguments:Overrided VLP.TRAIN.BATCH_SIZE_TOTAL from 2 to 2 WARNING:utils.arguments:Overrided VLP.TRAIN.BATCH_SIZE_PER_GPU from 1 to 1 WARNING:utils.arguments:Overrided MODEL.DECODER.HIDDEN_DIM from 512 to 512 WARNING:utils.arguments:Overrided MODEL.ENCODER.CONVS_DIM from 512 to 512 WARNING:utils.arguments:Overrided MODEL.ENCODER.MASK_DIM from 512 to 512 WARNING:utils.arguments:Overrided ADE20K.TEST.BATCH_SIZE_TOTAL from 8 to 2 INFO:trainer.distributed_trainer:Setting SAVE_DIR as ../../data/output/test INFO:trainer.distributed_trainer:Using CUDA WARNING:trainer.utils.mpi_adapter:---------------- WARNING:trainer.utils.mpi_adapter:MPI Adapter data WARNING:trainer.utils.mpi_adapter:---------------- WARNING:trainer.utils.mpi_adapter:environment info: single-node AML or other MPI environment WARNING:trainer.utils.mpi_adapter:init method url: tcp://127.0.0.1:36873 WARNING:trainer.utils.mpi_adapter:world size: 2 WARNING:trainer.utils.mpi_adapter:local size: 2 WARNING:trainer.utils.mpi_adapter:rank: 0 WARNING:trainer.utils.mpi_adapter:local rank: 0 WARNING:trainer.utils.mpi_adapter:master address: 127.0.0.1 WARNING:trainer.utils.mpi_adapter:master port: 36873 WARNING:trainer.utils.mpi_adapter:---------------- WARNING:trainer.utils.mpi_adapter:trying to initialize process group ... INFO:trainer.distributed_trainer:Setting SAVE_DIR as ../../data/output/test INFO:trainer.distributed_trainer:Using CUDA WARNING:trainer.utils.mpi_adapter:---------------- WARNING:trainer.utils.mpi_adapter:MPI Adapter data WARNING:trainer.utils.mpi_adapter:---------------- WARNING:trainer.utils.mpi_adapter:environment info: single-node AML or other MPI environment WARNING:trainer.utils.mpi_adapter:init method url: tcp://127.0.0.1:36873 WARNING:trainer.utils.mpi_adapter:world size: 2 WARNING:trainer.utils.mpi_adapter:local size: 2 WARNING:trainer.utils.mpi_adapter:rank: 1 WARNING:trainer.utils.mpi_adapter:local rank: 1 WARNING:trainer.utils.mpi_adapter:master address: 127.0.0.1 WARNING:trainer.utils.mpi_adapter:master port: 36873 WARNING:trainer.utils.mpi_adapter:---------------- WARNING:trainer.utils.mpi_adapter:trying to initialize process group ... WARNING:trainer.utils.mpi_adapter:process group initialized WARNING:trainer.utils.mpi_adapter:process group initialized INFO:trainer.distributed_trainer:Save config file to ../../data/output/test/conf_copy.yaml INFO:trainer.distributed_trainer:Base learning rate: 0.0001 INFO:trainer.distributed_trainer:Number of GPUs: 2 INFO:trainer.distributed_trainer:Gradient accumulation steps: 1 INFO:trainer.utils.hook:Adding global except hook for the distributed job to shutdown MPI if unhandled exception is raised on some of the ranks. INFO:trainer.distributed_trainer:Base learning rate: 0.0001 INFO:trainer.distributed_trainer:Number of GPUs: 2 INFO:trainer.distributed_trainer:Gradient accumulation steps: 1 INFO:trainer.utils.hook:Adding global except hook for the distributed job to shutdown MPI if unhandled exception is raised on some of the ranks. INFO:trainer.default_trainer:Imported base_dir at base_path ./ INFO:trainer.default_trainer:Imported base_dir at base_path ./ WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. WARNING:datasets.registration.register_vlp_datasets:WARNING: Cannot find VLPreDataset. Make sure datasets are accessible if you want to use them for training or evaluation. INFO:trainer.default_trainer:Pipeline for training: XDecoderPipeline INFO:trainer.default_trainer:----------------------------------------------- INFO:trainer.default_trainer:Evaluating model ... cfg, here is modeling.language: {'PIPELINE': 'XDecoderPipeline', 'TRAINER': 'xdecoder', 'SAVE_DIR': '../../data/output/test', 'base_path': './', 'RESUME': False, 'WEIGHT': True, 'RESET_DATA_LOADER': False, 'RESUME_FROM': '/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/pretrained_model/xdecoder_focalt_last.pt', 'EVAL_AT_START': False, 'WANDB': False, 'LOG_EVERY': 100, 'FIND_UNUSED_PARAMETERS': False, 'FP16': True, 'PORT': '36873', 'LOADER': {'JOINT': True, 'KEY_DATASET': 'coco'}, 'VERBOSE': True, 'MODEL': {'NAME': 'xdecoder_model', 'HEAD': 'xdecoder_head', 'MASK_ON': False, 'KEYPOINT_ON': False, 'LOAD_PROPOSALS': False, 'DIM_PROJ': 512, 'BACKBONE_DIM': 768, 'TEXT': {'ARCH': 'vlpencoder', 'NAME': 'transformer', 'TOKENIZER': 'clip', 'CONTEXT_LENGTH': 77, 'WIDTH': 512, 'HEADS': 8, 'LAYERS': 12, 'AUTOGRESSIVE': True}, 'BACKBONE': {'NAME': 'focal_dw', 'PRETRAINED': '', 'LOAD_PRETRAINED': False, 'FOCAL': {'PRETRAIN_IMG_SIZE': 224, 'PATCH_SIZE': 4, 'EMBED_DIM': 96, 'DEPTHS': [2, 2, 6, 2], 'FOCAL_LEVELS': [3, 3, 3, 3], 'FOCAL_WINDOWS': [3, 3, 3, 3], 'DROP_PATH_RATE': 0.3, 'MLP_RATIO': 4.0, 'DROP_RATE': 0.0, 'PATCH_NORM': True, 'USE_CONV_EMBED': True, 'SCALING_MODULATOR': True, 'USE_CHECKPOINT': False, 'USE_POSTLN': True, 'USE_POSTLN_IN_MODULATION': False, 'USE_LAYERSCALE': True, 'OUT_FEATURES': ['res2', 'res3', 'res4', 'res5'], 'OUT_INDICES': [0, 1, 2, 3]}}, 'ENCODER': {'NAME': 'transformer_encoder_fpn', 'IGNORE_VALUE': 255, 'NUM_CLASSES': 133, 'LOSS_WEIGHT': 1.0, 'CONVS_DIM': 512, 'MASK_DIM': 512, 'NORM': 'GN', 'IN_FEATURES': ['res2', 'res3', 'res4', 'res5'], 'DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES': ['res3', 'res4', 'res5'], 'COMMON_STRIDE': 4, 'TRANSFORMER_ENC_LAYERS': 6}, 'DECODER': {'NAME': 'xdecoder', 'TRANSFORMER_IN_FEATURE': 'multi_scale_pixel_decoder', 'MASK': True, 'GROUNDING': {'ENABLED': True, 'MAX_LEN': 5, 'TEXT_WEIGHT': 2.0, 'CLASS_WEIGHT': 0.5}, 'DETECTION': False, 'CAPTION': {'ENABLED': True, 'PHRASE_PROB': 0.0, 'SIM_THRES': 0.95}, 'CAPTIONING': {'ENABLED': True, 'STEP': 50}, 'RETRIEVAL': {'ENABLED': True, 'DIM_IMG': 768, 'ENSEMBLE': True}, 'DEEP_SUPERVISION': True, 'NO_OBJECT_WEIGHT': 0.1, 'CAPTION_WEIGHT': 1.0, 'CAPTIONING_WEIGHT': 2.0, 'RETRIEVAL_WEIGHT': 2.0, 'BACKBONER_WEIGHT': 8.0, 'GCLASS_WEIGHT': 0.4, 'GMASK_WEIGHT': 1.0, 'GDICE_WEIGHT': 1.0, 'OCLASS_WEIGHT': 0.4, 'OMASK_WEIGHT': 1.0, 'ODICE_WEIGHT': 1.0, 'CLASS_WEIGHT': 2.0, 'MASK_WEIGHT': 5.0, 'DICE_WEIGHT': 5.0, 'BBOX_WEIGHT': 5.0, 'GIOU_WEIGHT': 2.0, 'HIDDEN_DIM': 512, 'NUM_OBJECT_QUERIES': 101, 'NHEADS': 8, 'DROPOUT': 0.0, 'DIM_FEEDFORWARD': 2048, 'PRE_NORM': False, 'ENFORCE_INPUT_PROJ': False, 'SIZE_DIVISIBILITY': 32, 'TRAIN_NUM_POINTS': 12544, 'OVERSAMPLE_RATIO': 3.0, 'IMPORTANCE_SAMPLE_RATIO': 0.75, 'DEC_LAYERS': 10, 'TOP_GROUNDING_LAYERS': 3, 'TOP_CAPTION_LAYERS': 3, 'TOP_CAPTIONING_LAYERS': 3, 'TOP_RETRIEVAL_LAYERS': 3, 'TEST': {'SEMANTIC_ON': True, 'INSTANCE_ON': True, 'PANOPTIC_ON': True, 'OVERLAP_THRESHOLD': 0.8, 'OBJECT_MASK_THRESHOLD': 0.8, 'SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE': False}}}, 'COCO': {'INPUT': {'MIN_SIZE_TRAIN': 800, 'MAX_SIZE_TRAIN': 1333, 'MIN_SIZE_TRAIN_SAMPLING': 'choice', 'MIN_SIZE_TEST': 800, 'MAX_SIZE_TEST': 1333, 'IMAGE_SIZE': 1024, 'MIN_SCALE': 0.1, 'MAX_SCALE': 2.0, 'DATASET_MAPPER_NAME': 'coco_panoptic_lsj', 'IGNORE_VALUE': 255, 'COLOR_AUG_SSD': False, 'SIZE_DIVISIBILITY': 32, 'RANDOM_FLIP': 'horizontal', 'MASK_FORMAT': 'polygon', 'FORMAT': 'RGB', 'CROP': {'ENABLED': True}}, 'DATASET': {'DATASET': 'coco'}, 'TEST': {'DETECTIONS_PER_IMAGE': 100, 'NAME': 'coco_eval', 'IOU_TYPE': ['bbox', 'segm'], 'USE_MULTISCALE': False, 'BATCH_SIZE_TOTAL': 2, 'MODEL_FILE': '', 'AUG': {'ENABLED': False}}, 'TRAIN': {'ASPECT_RATIO_GROUPING': True, 'BATCH_SIZE_TOTAL': 2, 'BATCH_SIZE_PER_GPU': 1, 'SHUFFLE': True}, 'DATALOADER': {'FILTER_EMPTY_ANNOTATIONS': False, 'NUM_WORKERS': 2, 'LOAD_PROPOSALS': False, 'SAMPLER_TRAIN': 'TrainingSampler', 'ASPECT_RATIO_GROUPING': True}}, 'VLP': {'INPUT': {'IMAGE_SIZE': 224, 'DATASET_MAPPER_NAME': 'vlpretrain', 'IGNORE_VALUE': 255, 'COLOR_AUG_SSD': False, 'SIZE_DIVISIBILITY': 32, 'MASK_FORMAT': 'polygon', 'FORMAT': 'RGB', 'CROP': {'ENABLED': True}}, 'TRAIN': {'BATCH_SIZE_TOTAL': 2, 'BATCH_SIZE_PER_GPU': 1}, 'TEST': {'BATCH_SIZE_TOTAL': 2}, 'DATALOADER': {'FILTER_EMPTY_ANNOTATIONS': False, 'NUM_WORKERS': 16, 'LOAD_PROPOSALS': False, 'SAMPLER_TRAIN': 'TrainingSampler', 'ASPECT_RATIO_GROUPING': True}}, 'INPUT': {'PIXEL_MEAN': [123.675, 116.28, 103.53], 'PIXEL_STD': [58.395, 57.12, 57.375]}, 'DATASETS': {'TRAIN': ['coco_2017_train_panoptic_filtall_with_sem_seg_caption_grounding', 'vlp_train'], 'TEST': ['coco_2017_val_panoptic_with_sem_seg', 'vlp_captioning_val', 'refcocog_val_umd', 'vlp_val', 'ade20k_panoptic_val'], 'SIZE_DIVISIBILITY': 32, 'PROPOSAL_FILES_TRAIN': []}, 'DATALOADER': {'FILTER_EMPTY_ANNOTATIONS': False, 'NUM_WORKERS': 16, 'LOAD_PROPOSALS': False, 'SAMPLER_TRAIN': 'TrainingSampler', 'ASPECT_RATIO_GROUPING': True}, 'SOLVER': {'BASE_LR': 0.0001, 'STEPS': [0.88889, 0.96296], 'MAX_ITER': 1, 'GAMMA': 0.1, 'WARMUP_FACTOR': 1.0, 'WARMUP_ITERS': 10, 'WARMUP_METHOD': 'linear', 'WEIGHT_DECAY': 0.05, 'OPTIMIZER': 'ADAMW', 'LR_SCHEDULER_NAME': 'WarmupMultiStepLR', 'LR_MULTIPLIER': {'backbone': 0.1, 'lang_encoder': 0.1}, 'WEIGHT_DECAY_NORM': 0.0, 'WEIGHT_DECAY_EMBED': 0.0, 'CLIP_GRADIENTS': {'ENABLED': True, 'CLIP_TYPE': 'full_model', 'CLIP_VALUE': 5.0, 'NORM_TYPE': 2.0}, 'AMP': {'ENABLED': True}, 'MAX_NUM_EPOCHS': 50}, 'ADE20K': {'INPUT': {'MIN_SIZE_TRAIN': 640, 'MIN_SIZE_TRAIN_SAMPLING': 'choice', 'MIN_SIZE_TEST': 640, 'MAX_SIZE_TRAIN': 2560, 'MAX_SIZE_TEST': 2560, 'MASK_FORMAT': 'polygon', 'CROP': {'ENABLED': True, 'TYPE': 'absolute', 'SIZE': '(640, 640)', 'SINGLE_CATEGORY_MAX_AREA': 1.0}, 'COLOR_AUG_SSD': True, 'SIZE_DIVISIBILITY': 640, 'DATASET_MAPPER_NAME': 'mask_former_panoptic', 'FORMAT': 'RGB'}, 'DATASET': {'DATASET': 'ade'}, 'TEST': {'BATCH_SIZE_TOTAL': 2}}, 'REF': {'INPUT': {'PIXEL_MEAN': [123.675, 116.28, 103.53], 'PIXEL_STD': [58.395, 57.12, 57.375], 'MIN_SIZE_TEST': 512, 'MAX_SIZE_TEST': 1024, 'FORMAT': 'RGB'}, 'DATALOADER': {'FILTER_EMPTY_ANNOTATIONS': False, 'NUM_WORKERS': 0, 'LOAD_PROPOSALS': False, 'SAMPLER_TRAIN': 'TrainingSampler', 'ASPECT_RATIO_GROUPING': False}, 'TEST': {'BATCH_SIZE_TOTAL': 1}}, 'SUN': {'INPUT': {'PIXEL_MEAN': [123.675, 116.28, 103.53], 'PIXEL_STD': [58.395, 57.12, 57.375], 'MIN_SIZE_TEST': 512, 'MAX_SIZE_TEST': 1024}, 'DATALOADER': {'FILTER_EMPTY_ANNOTATIONS': False, 'NUM_WORKERS': 0, 'LOAD_PROPOSALS': False, 'SAMPLER_TRAIN': 'TrainingSampler', 'ASPECT_RATIO_GROUPING': False}, 'TEST': {'BATCH_SIZE_TOTAL': 8}}, 'SCAN': {'INPUT': {'PIXEL_MEAN': [123.675, 116.28, 103.53], 'PIXEL_STD': [58.395, 57.12, 57.375], 'MIN_SIZE_TEST': 512, 'MAX_SIZE_TEST': 1024}, 'DATALOADER': {'FILTER_EMPTY_ANNOTATIONS': False, 'NUM_WORKERS': 0, 'LOAD_PROPOSALS': False, 'SAMPLER_TRAIN': 'TrainingSampler', 'ASPECT_RATIO_GROUPING': False}, 'TEST': {'BATCH_SIZE_TOTAL': 8}}, 'BDD': {'INPUT': {'PIXEL_MEAN': [123.675, 116.28, 103.53], 'PIXEL_STD': [58.395, 57.12, 57.375], 'MIN_SIZE_TEST': 800, 'MAX_SIZE_TEST': 1333}, 'DATALOADER': {'FILTER_EMPTY_ANNOTATIONS': False, 'NUM_WORKERS': 0, 'LOAD_PROPOSALS': False, 'SAMPLER_TRAIN': 'TrainingSampler', 'ASPECT_RATIO_GROUPING': False}, 'TEST': {'BATCH_SIZE_TOTAL': 8}}, 'CITY': {'INPUT': {'MIN_SIZE_TRAIN': 1024, 'MIN_SIZE_TRAIN_SAMPLING': 'choice', 'MIN_SIZE_TEST': 1024, 'MAX_SIZE_TRAIN': 4096, 'MAX_SIZE_TEST': 2048, 'CROP': {'ENABLED': True, 'TYPE': 'absolute', 'SIZE': '(512, 1024)', 'SINGLE_CATEGORY_MAX_AREA': 1.0}, 'COLOR_AUG_SSD': True, 'SIZE_DIVISIBILITY': -1, 'FORMAT': 'RGB', 'DATASET_MAPPER_NAME': 'mask_former_panoptic', 'MASK_FORMAT': 'polygon'}, 'TEST': {'EVAL_PERIOD': 5000, 'BATCH_SIZE_TOTAL': 8, 'AUG': {'ENABLED': False, 'MIN_SIZES': [512, 768, 1024, 1280, 1536, 1792], 'MAX_SIZE': 4096, 'FLIP': True}}, 'DATALOADER': {'FILTER_EMPTY_ANNOTATIONS': True, 'NUM_WORKERS': 4}}, 'command': 'evaluate', 'conf_files': ['configs/xdecoder/focalt_unicl_lang.yaml'], 'overrides': ['COCO.INPUT.IMAGE_SIZE', '1024', 'MODEL.DECODER.CAPTIONING.ENABLED', 'True', 'MODEL.DECODER.RETRIEVAL.ENABLED', 'True', 'MODEL.DECODER.GROUNDING.ENABLED', 'True', 'COCO.TEST.BATCH_SIZE_TOTAL', '2', 'COCO.TRAIN.BATCH_SIZE_TOTAL', '2', 'COCO.TRAIN.BATCH_SIZE_PER_GPU', '1', 'VLP.TEST.BATCH_SIZE_TOTAL', '2', 'VLP.TRAIN.BATCH_SIZE_TOTAL', '2', 'VLP.TRAIN.BATCH_SIZE_PER_GPU', '1', 'MODEL.DECODER.HIDDEN_DIM', '512', 'MODEL.ENCODER.CONVS_DIM', '512', 'MODEL.ENCODER.MASK_DIM', '512', 'ADE20K.TEST.BATCH_SIZE_TOTAL', '2', 'FP16', 'True', 'WEIGHT', 'True', 'RESUME_FROM', '/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/pretrained_model/xdecoder_focalt_last.pt'], 'world_size': 2, 'local_size': 2, 'rank': 1, 'local_rank': 1, 'CUDA': True, 'GRADIENT_ACCUMULATE_STEP': 1, 'EVAL_PER_UPDATE_NUM': 0, 'LR_SCHEDULER_PARAMS': {}, 'device': device(type='cuda', index=1), 'BASENAME': 'focalt_unicl_lang.yaml'} INFO:trainer.default_trainer:Evaluation start ... Traceback (most recent call last): File "entry.py", line 75, in main() File "entry.py", line 70, in main trainer.eval() File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/trainer/default_trainer.py", line 79, in eval results = self._eval_on_set(self.save_folder) File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/trainer/default_trainer.py", line 87, in _eval_on_set results = self.pipeline.evaluate_model(self, save_folder) File "./pipeline/XDecoderPipeline.py", line 122, in evaluate_model eval_batch_gen = self.get_dataloaders(trainer, dataset_label, is_evaluation=True) File "./pipeline/XDecoderPipeline.py", line 60, in get_dataloaders dataloaders = build_eval_dataloader(self._opt) File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/datasets/build.py", line 421, in build_eval_dataloader dataloaders += [build_detection_test_loader(cfg, dataset_name, mapper=mapper)] File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/modeling/utils/config.py", line 84, in wrapped explicit_args = _get_args_from_config(from_config, *args, **kwargs) File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/modeling/utils/config.py", line 137, in _get_args_from_config ret = from_config_func(*args, kwargs) File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/datasets/build.py", line 154, in _test_loader_from_config dataset = get_detection_dataset_dicts( File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/datasets/build.py", line 124, in get_detection_dataset_dicts dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/datasets/build.py", line 124, in dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] File "/home/xupeng/anaconda3/envs/ape/lib/python3.8/site-packages/detectron2/data/catalog.py", line 58, in get return f() File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/datasets/registration/register_coco_panoptic_annos_semseg.py", line 145, in lambda: load_coco_panoptic_json(panoptic_json, image_root, panoptic_root, sem_seg_root, metadata), File "/home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/datasets/registration/register_coco_panoptic_annos_semseg.py", line 124, in load_coco_panoptic_json assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] AssertionError: /home/xupeng/AIprojects/Segment-Everything-Everywhere-All-At-Once-1.0/xdecoder_data/coco/panoptic_val2017/000000000139.png WARNING:trainer.utils.hook:****** WARNING:trainer.utils.hook:DefaultTrainer: WARNING:trainer.utils.hook: Uncaught exception on rank 1. WARNING:trainer.utils.hook: Calling MPI_Abort() to shut down MPI... WARNING:trainer.utils.hook:

MPI_ABORT was invoked on rank 1 in communicator MPI_COMM_WORLD with errorcode 1.

NOTE: invoking MPI_ABORT causes Open MPI to kill all MPI processes. You may or may not see output from other processes, depending on exactly when Open MPI kills them.

UX-Decoder / Segment-Everything-Everywhere-All-At-Once

2 problems happened when I used entry.py for evaluate #117