I've trained the model and I get this error when training, 07/03 15:13:27 - mmengine - ERROR - D:anaconda3envsyoloworld-envlibsite-packagesmmdetevaluationmetricscoco_metric.py - compute_metrics - 461 - The testing results of the whole dataset is empty. When using the test.py to test, the path keeps reporting errors, I double-checked that I only set the path of my own dataset, and did not specify the error path, when using the test.py, I used the config file that I used when using the train.py, I don't know if this is wrong. FileNotFoundError: [Errno 2] No such file or directory: 'data/coco/annotations/instances_val2017.json'

This is the command I commanded when I ran the test.py python tools/test.py D:\Code_project\yolo_world\YOLO-World-master\YOLO-World-master\configs\pretrain\yolo_world_v2_s_1.py 'D:\Code_project\yolo_world\YOLO-World-master\YOLO-World-master\tools\work_dirs\yolo_world_v2_s_1\epoch_100.pth' Please help me see how to fix it Here's my config file base = ( '../../third_party/mmyolo/configs/yolov8/' 'yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py') custom_imports = dict( imports=['yolo_world'], allow_failed_imports=False)

hyper-parameters

num_classes = 5 num_training_classes = 5 max_epochs = 100 # Maximum training epochs close_mosaic_epochs = 10 save_epoch_intervals = 5 text_channels = 512 neck_embed_channels = [128, 256, base.last_stage_out_channels // 2] neck_num_heads = [4, 8, base.last_stage_out_channels // 2 // 32] base_lr = 2e-4 weight_decay = 0.05 train_batch_size_per_gpu = 16 load_from = 'D:\Code_project\yolo_world\YOLO-World-master\YOLO-World-master\weights\yolo_world_v2_s_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_ep80-492dc329.pth'

text_model_name = '../pretrained_models/clip-vit-base-patch32-projection'

text_model_name = 'openai/clip-vit-base-patch32'

text_model_name ='D:\Code_project\yolo_world\YOLO-World-master\YOLO-World-master\openai\clip-vit-base-patch32' persistent_workers = False mixup_prob = 0.15 copypaste_prob = 0.3

model settings

model = dict( type='YOLOWorldDetector', mm_neck=True, num_train_classes=num_training_classes, num_test_classes=num_classes, data_preprocessor=dict(type='YOLOWDetDataPreprocessor'), backbone=dict( delete=True, type='MultiModalYOLOBackbone', image_model={{base.model.backbone}}, text_model=dict( type='HuggingCLIPLanguageBackbone', model_name=text_model_name, frozen_modules=['all'])), neck=dict(type='YOLOWorldPAFPN', guide_channels=text_channels, embed_channels=neck_embed_channels, num_heads=neck_num_heads, block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv')), bbox_head=dict(type='YOLOWorldHead', head_module=dict(type='YOLOWorldHeadModule', use_bn_head=True, embed_dims=text_channels, num_classes=num_training_classes)), train_cfg=dict(assigner=dict(num_classes=num_training_classes)))

dataset settings

text_transform = [ dict(type='RandomLoadText', num_neg_samples=(num_classes, num_classes), max_num_samples=num_training_classes, padding_to_max=True, padding_value=''), dict(type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ] mosaic_affine_transform = [ dict( type='MultiModalMosaic', img_scale=base.img_scale, pad_val=114.0, pre_transform=base.pre_transform), dict(type='YOLOv5CopyPaste', prob=copypaste_prob), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100., scaling_ratio_range=(1 - base.affine_scale, 1 + base.affine_scale),

img_scale is (width, height)

    border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
    border_val=(114, 114, 114),
    min_area_ratio=_base_.min_area_ratio,
    use_mask_refine=_base_.use_mask2refine)

] train_pipeline = [ base.pre_transform, mosaic_affine_transform, dict( type='YOLOv5MultiModalMixUp', prob=mixup_prob, pre_transform=[base.pre_transform, mosaic_affine_transform]), base.last_transform[:-1], text_transform ] train_pipeline_stage2 = [ base.train_pipeline_stage2[:-1], text_transform ] coco_train_dataset = dict( delete=True, type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='tomato.v1i.coco', ann_file=r'D:\Code_project\yolo_world\YOLO-World-master\YOLO-World-master\tomato.v1i.coco\train_annotations.coco.json', data_prefix=dict(img='D:/Code_project/yolo_world/YOLO-World-master/YOLO-World-master/tomato.v1i.coco/train/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path=r'D:\Code_project\yolo_world\YOLO-World-master\YOLO-World-master\tomato.v1i.coco\tomato_class_texts.json', pipeline=train_pipeline)

train_dataloader = dict( persistent_workers=persistent_workers, batch_size=train_batch_size_per_gpu, collate_fn=dict(type='yolow_collate'), dataset=coco_train_dataset) test_pipeline = [ *base.test_pipeline[:-1], dict(type='LoadText'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'texts')) ] coco_val_dataset = dict( delete=True, type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='tomato.v1i.coco', ann_file=r'D:\Code_project\yolo_world\YOLO-World-master\YOLO-World-master\tomato.v1i.coco\valid_annotations.coco.json', data_prefix=dict(img='D:/Code_project/yolo_world/YOLO-World-master/YOLO-World-master/tomato.v1i.coco/valid/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path=r'D:\Code_project\yolo_world\YOLO-World-master\YOLO-World-master\tomato.v1i.coco\tomato_class_texts.json', pipeline=test_pipeline) val_dataloader = dict(dataset=coco_val_dataset) test_dataloader = val_dataloader

training settings

default_hooks = dict( param_scheduler=dict( scheduler_type='linear', lr_factor=0.01, max_epochs=max_epochs), checkpoint=dict( max_keep_ckpts=-1, save_best=None, interval=save_epoch_intervals)) custom_hooks = [ dict( type='EMAHook', ema_type='ExpMomentumEMA', momentum=0.0001, update_buffers=True, strict_load=False, priority=49), dict( type='mmdet.PipelineSwitchHook', switch_epoch=max_epochs - close_mosaic_epochs, switch_pipeline=train_pipeline_stage2) ] train_cfg = dict( max_epochs=max_epochs, val_interval=5, dynamic_intervals=[((max_epochs - close_mosaic_epochs), base.val_interval_stage2)]) optim_wrapper = dict( optimizer=dict( delete=True, type='AdamW', lr=base_lr, weight_decay=weight_decay, batch_size_per_gpu=train_batch_size_per_gpu), paramwise_cfg=dict( custom_keys={'backbone.text_model': dict(lr_mult=0.01), 'logit_scale': dict(weight_decay=0.0)}), constructor='YOLOWv5OptimizerConstructor')

evaluation settings

val_evaluator = dict( delete=True, type='mmdet.CocoMetric', proposal_nums=(100, 1, 10), ann_file=r'D:\Code_project\yolo_world\YOLO-World-master\YOLO-World-master\tomato.v1i.coco\valid_annotations.coco.json', metric='bbox')

AILab-CVC / YOLO-World

Pls where can I find the config configuration file for test.py #418