Closed Arrebol2020 closed 3 months ago
When I trained a model on a custom dataset and used export_onnx.py to export the ONNX model, I encountered the following error:r: The model can load and infer successfully. This is my configuration content:
# nanodet-plus-m-1.5x_416 # COCO mAP(0.5:0.95) = 0.341 # AP_50 = 0.506 # AP_75 = 0.357 # AP_small = 0.143 # AP_m = 0.363 # AP_l = 0.539 save_dir: workspace/nanodet-plus-m-1.5x_yolo_taunets_x0.5_occ model: weight_averager: name: ExpMovingAverager decay: 0.9998 arch: name: NanoDetPlus detach_epoch: 10 backbone: name: ShuffleNetV2 model_size: 1.5x out_stages: [2,3,4] activation: LeakyReLU fpn: name: GhostPAN in_channels: [176, 352, 704] out_channels: 128 kernel_size: 5 num_extra_level: 1 use_depthwise: True activation: LeakyReLU head: name: NanoDetPlusHead num_classes: 8 input_channel: 128 feat_channels: 128 stacked_convs: 2 kernel_size: 5 strides: [8, 16, 32, 64] activation: LeakyReLU reg_max: 7 norm_cfg: type: BN loss: loss_qfl: name: QualityFocalLoss use_sigmoid: True beta: 2.0 loss_weight: 1.0 loss_dfl: name: DistributionFocalLoss loss_weight: 0.25 loss_bbox: name: GIoULoss loss_weight: 2.0 # Auxiliary head, only use in training time. aux_head: name: SimpleConvHead num_classes: 8 input_channel: 256 feat_channels: 256 stacked_convs: 4 strides: [8, 16, 32, 64] activation: LeakyReLU reg_max: 7 class_names: &class_names ['obj1', 'obj2', 'obj3', 'obj4', 'obj5', 'obj6', 'obj7', 'obj8'] data: train: name: YoloDataset img_path: /home/fan/prjs/surfemb/data/bop/taunets/det_0.5/images/train ann_path: /home/fan/prjs/surfemb/data/bop/taunets/det_0.5/labels/train class_names: *class_names input_size: [320,240] #[w,h] keep_ratio: False pipeline: perspective: 0.0 scale: [0.6, 1.4] stretch: [[0.8, 1.2], [0.8, 1.2]] rotation: 0 shear: 0 translate: 0.2 flip: 0.5 brightness: 0.2 contrast: [0.6, 1.4] saturation: [0.5, 1.2] random_occ: True max_occ_ratio: 0.5 normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]] val: name: YoloDataset img_path: /home/fan/prjs/surfemb/data/bop/taunets/det_0.5/images/val ann_path: /home/fan/prjs/surfemb/data/bop/taunets/det_0.5/labels/val class_names: *class_names input_size: [320,240] #[w,h] keep_ratio: False pipeline: normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]] device: gpu_ids: [2] workers_per_gpu: 10 batchsize_per_gpu: 96 schedule: # resume: # load_model: optimizer: name: AdamW lr: 0.001 weight_decay: 0.05 warmup: name: linear steps: 500 ratio: 0.0001 total_epochs: 100 lr_schedule: name: CosineAnnealingLR T_max: 100 eta_min: 0.00005 val_intervals: 10 grad_clip: 35 evaluator: name: CocoDetectionEvaluator save_key: mAP log: interval: 50
When I trained a model on a custom dataset and used export_onnx.py to export the ONNX model, I encountered the following error:r:
The model can load and infer successfully. This is my configuration content: