rf-detr训练自己的数据集Loading pretrain weights
num_classes mismatch: model has 90 classes, but your dataset has 40 classes
reinitializing your detection head with 40 classes.
TensorBoard logging initialized. To monitor logs, use 'tensorboard --logdir output_waste_plus' and open http://localhost:6006/ in browser.
Not using distributed mode
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
git:
sha: N/A, status: clean, branch: N/A
Namespace(num_classes=40, grad_accum_steps=4, amp=True, lr=0.0001, lr_encoder=0.00015, batch_size=2, weight_decay=0.0001, epochs=2, lr_drop=100, clip_max_norm=0.1, lr_vit_layer_decay=0.8, lr_component_decay=0.7, do_benchmark=False, dropout=0, drop_path=0.0, drop_mode='standard', drop_schedule='constant', cutoff_epoch=0, pretrained_encoder=None, pretrain_weights='rf-detr-base.pth', pretrain_exclude_keys=None, pretrain_keys_modify_to_load=None, pretrained_distiller=None, encoder='dinov2_windowed_small', vit_encoder_num_layers=12, window_block_indexes=None, position_embedding='sine', out_feature_indexes=[2, 5, 8, 11], freeze_encoder=False, layer_norm=True, rms_norm=False, backbone_lora=False, force_no_pretrain=False, dec_layers=3, dim_feedforward=2048, hidden_dim=256, sa_nheads=8, ca_nheads=16, num_queries=300, group_detr=13, two_stage=True, projector_scale=['P4'], lite_refpoint_refine=True, num_select=300, dec_n_points=2, decoder_norm='LN', bbox_reparam=True, freeze_batch_norm=False, set_cost_class=2, set_cost_bbox=5, set_cost_giou=2, cls_loss_coef=1.0, bbox_loss_coef=5, giou_loss_coef=2, focal_alpha=0.25, aux_loss=True, sum_group_losses=False, use_varifocal_loss=False, use_position_supervised_loss=False, ia_bce_loss=True, dataset_file='roboflow', coco_path=None, dataset_dir='/data/mq/rf-detr-develop/coco_split', square_resize_div_64=True, output_dir='output_waste_plus', dont_save_weights=False, checkpoint_interval=10, seed=42, resume='', start_epoch=0, eval=False, use_ema=True, ema_decay=0.993, ema_tau=100, num_workers=2, device='cuda', world_size=1, dist_url='env://', sync_bn=True, fp16_eval=False, encoder_only=False, backbone_only=False, resolution=560, use_cls_token=False, multi_scale=True, expanded_scales=True, warmup_epochs=0, lr_scheduler='step', lr_min_factor=0.0, early_stopping=False, early_stopping_patience=10, early_stopping_min_delta=0.001, early_stopping_use_ema=False, gradient_checkpointing=False, tensorboard=True, wandb=False, project=None, run=None, class_names=['Disposable food box', 'Contaminated plastic', 'Cigarette butt', 'Used mask', 'Broken flower pots and dishes', 'Construction waste', 'Withered plants', 'Silicone products', 'Leftovers', 'Large bones', 'Fruits', 'Tea leaves', 'Vegetable leaves and roots', 'Eggshells', 'Nut shells', 'Moldy flour products', 'Seashells', 'Wooden sticks', 'Waste paper', 'Foam boxes', 'Plastic bags', 'Plastic bottles', 'Plugs and wires', 'Old clothes', 'Cans', 'Steel pipes', 'Plush toys', 'Glass cups', 'Broken wooden boards', 'Cardboard boxes', 'Paper bags', 'Glass bottles', 'Metal food cans', 'Dry batteries', 'Ointments', 'Expired medicine', 'Power banks', 'Used light tubes', 'Pesticides', 'Used cotton swabs'], run_test=True, distributed=False)
number of params: 31991032
[392, 448, 504, 560, 616, 672, 728, 784]
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
[392, 448, 504, 560, 616, 672, 728, 784]
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
[392, 448, 504, 560, 616, 672, 728, 784]
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
Get benchmark
Start training
Grad accum steps: 4
Total batch size: 8
LENGTH OF DATA LOADER: 175
UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3595.)
Epoch: [0] [ 0/175] eta: 0:47:52 lr: 0.000100 class_error: 100.00 loss: 10.2891 (10.2891) loss_ce: 1.3603 (1.3603) loss_bbox: 0.8382 (0.8382) loss_giou: 0.2727 (0.2727) loss_ce_0: 1.3282 (1.3282) loss_bbox_0: 0.9468 (0.9468) loss_giou_0: 0.3088 (0.3088) loss_ce_1: 1.3688 (1.3688) loss_bbox_1: 0.8349 (0.8349) loss_giou_1: 0.2684 (0.2684) loss_ce_enc: 1.3087 (1.3087) loss_bbox_enc: 1.0503 (1.0503) loss_giou_enc: 0.4031 (0.4031) loss_ce_unscaled: 1.3603 (1.3603) class_error_unscaled: 100.0000 (100.0000) loss_bbox_unscaled: 0.1676 (0.1676) loss_giou_unscaled: 0.1364 (0.1364) cardinality_error_unscaled: 3899.0000 (3899.0000) loss_ce_0_unscaled: 1.3282 (1.3282) loss_bbox_0_unscaled: 0.1894 (0.1894) loss_giou_0_unscaled: 0.1544 (0.1544) cardinality_error_0_unscaled: 3898.5000 (3898.5000) loss_ce_1_unscaled: 1.3688 (1.3688) loss_bbox_1_unscaled: 0.1670 (0.1670) loss_giou_1_unscaled: 0.1342 (0.1342) cardinality_error_1_unscaled: 3899.0000 (3899.0000) loss_ce_enc_unscaled: 1.3087 (1.3087) loss_bbox_enc_unscaled: 0.2101 (0.2101) loss_giou_enc_unscaled: 0.2015 (0.2015) cardinality_error_enc_unscaled: 3899.0000 (3899.0000) time: 16.4160 data: 1.3399 max mem: 2677
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Traceback (most recent call last):
File "/data/mq/rf-detr-develop/train_waste.py", line 6, in <module>
model.train(dataset_dir='/data/mq/rf-detr-develop/coco_split', epochs=2, batch_size=2, grad_accum_steps=4, lr=1e-4, output_dir='output_waste_plus')
File "/data/mq/rf-detr-develop/rfdetr/detr.py", line 58, in train
self.train_from_config(config, **kwargs)
File "/data/mq/rf-detr-develop/rfdetr/detr.py", line 159, in train_from_config
self.model.train(
File "/data/mq/rf-detr-develop/rfdetr/main.py", line 332, in train
train_stats = train_one_epoch(
File "/data/mq/rf-detr-develop/rfdetr/engine.py", line 119, in train_one_epoch
loss_dict = criterion(outputs, new_targets)
File "/data1/Anaconda/envs/py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/data1/Anaconda/envs/py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/data/mq/rf-detr-develop/rfdetr/models/lwdetr.py", line 438, in forward
indices = self.matcher(outputs_without_aux, targets, group_detr=group_detr)
File "/data1/Anaconda/envs/py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/data1/Anaconda/envs/py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/data1/Anaconda/envs/py3.9/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/data/mq/rf-detr-develop/rfdetr/models/matcher.py", line 97, in forward
C = C.view(bs, num_queries, -1).cpu()
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-assertions.出现这个问题
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