xbsu
commented
4 months ago The error occurred when not using distributed mode. Since all experiments were conducted in distributed mode, we didn't thoroughly test it in non-distributed mode. This bug will be fixed later.
I am currently using Windows and have installed all the necessary files for the model. I have converted the Linux commands to Windows commands and executed them. The medium model is training correctly, but I encountered an error where the large model fails to load. I do not know what the issue is. Can you help me identify the problem if I attach the Windows commands and training logs?
commands - bat file
@echo off setlocal
set model_name=lwdetr_large_coco
set coco_path=%1
python -u main.py ^ --lr 1e-4 ^ --lr_encoder 1.5e-4 ^ --batch_size 2 ^ --weight_decay 1e-4 ^ --epochs 60 ^ --lr_drop 60 ^ --lr_vit_layer_decay 0.7 ^ --lr_component_decay 0.5 ^ --encoder vit_small ^ --drop_path 0.1 ^ --vit_encoder_num_layers 10 ^ --window_block_indexes 0 1 3 6 7 9 ^ --out_feature_indexes 2 4 5 9 ^ --dec_layers 3 ^ --group_detr 13 ^ --two_stage ^ --projector_scale P3 P5 ^ --hidden_dim 384 ^ --sa_nheads 12 ^ --ca_nheads 24 ^ --dec_n_points 4 ^ --bbox_reparam ^ --lite_refpoint_refine ^ --ia_bce_loss ^ --cls_loss_coef 1 ^ --num_select 300 ^ --dataset_file coco ^ --coco_path %coco_path% ^ --square_resize_div_64 ^ --use_ema ^ --pretrained_encoder pretrain_weights/caev2_small_300e_objects365.pth ^ --pretrain_weights pretrain_weights/LWDETR_large_30e_objects365.pth ^ --pretrain_keys_modify_to_load transformer.enc_out_class_embed.0.weight transformer.enc_out_class_embed.1.weight transformer.enc_out_class_embed.2.weight transformer.enc_out_class_embed.3.weight transformer.enc_out_class_embed.4.weight transformer.enc_out_class_embed.5.weight transformer.enc_out_class_embed.6.weight transformer.enc_out_class_embed.7.weight transformer.enc_out_class_embed.8.weight transformer.enc_out_class_embed.9.weight transformer.enc_out_class_embed.10.weight transformer.enc_out_class_embed.11.weight transformer.enc_out_class_embed.12.weight transformer.enc_out_class_embed.0.bias transformer.enc_out_class_embed.1.bias transformer.enc_out_class_embed.2.bias transformer.enc_out_class_embed.3.bias transformer.enc_out_class_embed.4.bias transformer.enc_out_class_embed.5.bias transformer.enc_out_class_embed.6.bias transformer.enc_out_class_embed.7.bias transformer.enc_out_class_embed.8.bias transformer.enc_out_class_embed.9.bias transformer.enc_out_class_embed.10.bias transformer.enc_out_class_embed.11.bias transformer.enc_out_class_embed.12.bias class_embed.weight class_embed.bias ^ --output_dir output/%model_name%
endlocal
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(lwDETR) C:\Users\USER\Desktop\LW-DETR>C:\Users\USER\Desktop\LW-DETR\scripts\lwdetr_large_coco_train.bat C:\Users\USER\Desktop\LW-DETR\dataset\test Not using distributed mode git: sha: f10c0f39e84ff98e600988a25f6ba92c39f732e4, status: has uncommited changes, branch: main
Namespace(lr=0.0001, lr_encoder=0.00015, batch_size=2, weight_decay=0.0001, epochs=60, lr_drop=60, clip_max_norm=0.1, lr_vit_layer_decay=0.7, lr_component_decay=0.5, dropout=0, drop_path=0.1, drop_mode='standard', drop_schedule='constant', cutoff_epoch=0, pretrained_encoder='pretrain_weights/caev2_small_300e_objects365.pth', pretrain_weights='pretrain_weights/LWDETR_large_30e_objects365.pth', pretrain_exclude_keys=None, pretrain_keys_modify_to_load=['transformer.enc_out_class_embed.0.weight', 'transformer.enc_out_class_embed.1.weight', 'transformer.enc_out_class_embed.2.weight', 'transformer.enc_out_class_embed.3.weight', 'transformer.enc_out_class_embed.4.weight', 'transformer.enc_out_class_embed.5.weight', 'transformer.enc_out_class_embed.6.weight', 'transformer.enc_out_class_embed.7.weight', 'transformer.enc_out_class_embed.8.weight', 'transformer.enc_out_class_embed.9.weight', 'transformer.enc_out_class_embed.10.weight', 'transformer.enc_out_class_embed.11.weight', 'transformer.enc_out_class_embed.12.weight', 'transformer.enc_out_class_embed.0.bias', 'transformer.enc_out_class_embed.1.bias', 'transformer.enc_out_class_embed.2.bias', 'transformer.enc_out_class_embed.3.bias', 'transformer.enc_out_class_embed.4.bias', 'transformer.enc_out_class_embed.5.bias', 'transformer.enc_out_class_embed.6.bias', 'transformer.enc_out_class_embed.7.bias', 'transformer.enc_out_class_embed.8.bias', 'transformer.enc_out_class_embed.9.bias', 'transformer.enc_out_class_embed.10.bias', 'transformer.enc_out_class_embed.11.bias', 'transformer.enc_out_class_embed.12.bias', 'class_embed.weight', 'class_embed.bias'], encoder='vit_small', vit_encoder_num_layers=10, window_block_indexes=[0, 1, 3, 6, 7, 9], position_embedding='sine', out_feature_indexes=[2, 4, 5, 9], dec_layers=3, dim_feedforward=2048, hidden_dim=384, sa_nheads=12, ca_nheads=24, num_queries=300, group_detr=13, two_stage=True, projector_scale=['P3', 'P5'], lite_refpoint_refine=True, num_select=300, dec_n_points=4, decoder_norm='LN', bbox_reparam=True, 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='coco', coco_path='C:\Users\USER\Desktop\LW-DETR\dataset\test', square_resize_div_64=True, output_dir='output/lwdetr_large_coco', checkpoint_interval=10, seed=42, resume='', start_epoch=0, eval=False, use_ema=True, ema_decay=0.9997, num_workers=2, device='cuda', world_size=1, dist_url='env://', sync_bn=True, fp16_eval=False, subcommand=None, distributed=False) _IncompatibleKeys(missing_keys=[], unexpected_keys=['mask_token', 'cls_token', 'norm.weight', 'norm.bias', 'pretext_neck.regressor_blocks.0.gamma_1_cross', 'pretext_neck.regressor_blocks.0.gamma_2_cross', 'pretext_neck.regressor_blocks.0.norm1_q.weight', 'pretext_neck.regressor_blocks.0.norm1_q.bias', 'pretext_neck.regressor_blocks.0.norm1_k.weight', 'pretext_neck.regressor_blocks.0.norm1_k.bias', 'pretext_neck.regressor_blocks.0.norm1_v.weight', 'pretext_neck.regressor_blocks.0.norm1_v.bias', 'pretext_neck.regressor_blocks.0.norm2_cross.weight', 'pretext_neck.regressor_blocks.0.norm2_cross.bias', 'pretext_neck.regressor_blocks.0.cross_attn.q_bias', 'pretext_neck.regressor_blocks.0.cross_attn.v_bias', 'pretext_neck.regressor_blocks.0.cross_attn.q.weight', 'pretext_neck.regressor_blocks.0.cross_attn.k.weight', 'pretext_neck.regressor_blocks.0.cross_attn.v.weight', 'pretext_neck.regressor_blocks.0.cross_attn.proj.weight', 'pretext_neck.regressor_blocks.0.cross_attn.proj.bias', 'pretext_neck.regressor_blocks.0.mlp_cross.fc1.weight', 'pretext_neck.regressor_blocks.0.mlp_cross.fc1.bias', 'pretext_neck.regressor_blocks.0.mlp_cross.fc2.weight', 'pretext_neck.regressor_blocks.0.mlp_cross.fc2.bias', 'pretext_neck.decoder_blocks.0.gamma_1', 'pretext_neck.decoder_blocks.0.gamma_2', 'pretext_neck.decoder_blocks.0.norm1.weight', 'pretext_neck.decoder_blocks.0.norm1.bias', 'pretext_neck.decoder_blocks.0.attn.q_bias', 'pretext_neck.decoder_blocks.0.attn.v_bias', 'pretext_neck.decoder_blocks.0.attn.qkv.weight', 'pretext_neck.decoder_blocks.0.attn.proj.weight', 'pretext_neck.decoder_blocks.0.attn.proj.bias', 'pretext_neck.decoder_blocks.0.norm2.weight', 'pretext_neck.decoder_blocks.0.norm2.bias', 'pretext_neck.decoder_blocks.0.mlp.fc1.weight', 'pretext_neck.decoder_blocks.0.mlp.fc1.bias', 'pretext_neck.decoder_blocks.0.mlp.fc2.weight', 'pretext_neck.decoder_blocks.0.mlp.fc2.bias', 'pretext_neck.norm.weight', 'pretext_neck.norm.bias', 'pretext_neck.norm2.weight', 'pretext_neck.norm2.bias', 'pretext_neck.head.weight', 'blocks.10.gamma_1', 'blocks.10.gamma_2', 'blocks.10.norm1.weight', 'blocks.10.norm1.bias', 'blocks.10.attn.q_bias', 'blocks.10.attn.v_bias', 'blocks.10.attn.qkv.weight', 'blocks.10.attn.proj.weight', 'blocks.10.attn.proj.bias', 'blocks.10.norm2.weight', 'blocks.10.norm2.bias', 'blocks.10.mlp.fc1.weight', 'blocks.10.mlp.fc1.bias', 'blocks.10.mlp.fc2.weight', 'blocks.10.mlp.fc2.bias', 'blocks.11.gamma_1', 'blocks.11.gamma_2', 'blocks.11.norm1.weight', 'blocks.11.norm1.bias', 'blocks.11.attn.q_bias', 'blocks.11.attn.v_bias', 'blocks.11.attn.qkv.weight', 'blocks.11.attn.proj.weight', 'blocks.11.attn.proj.bias', 'blocks.11.norm2.weight', 'blocks.11.norm2.bias', 'blocks.11.mlp.fc1.weight', 'blocks.11.mlp.fc1.bias', 'blocks.11.mlp.fc2.weight', 'blocks.11.mlp.fc2.bias']) number of params: 46823650 name: backbone.0.encoder.pos_embed, lr_decay: 0.019773267429999988 name: backbone.0.encoder.pos_embed, weight_decay rate: 0.0 name: backbone.0.encoder.patch_embed.proj.weight, lr_decay: 0.019773267429999988 name: backbone.0.encoder.patch_embed.proj.weight, weight_decay rate: 1.0 name: backbone.0.encoder.patch_embed.proj.bias, lr_decay: 0.019773267429999988 name: backbone.0.encoder.patch_embed.proj.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.gamma_1, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.gamma_1, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.gamma_2, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.gamma_2, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.norm1.weight, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.norm1.weight, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.norm1.bias, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.norm1.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.attn.q_bias, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.attn.q_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.attn.v_bias, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.attn.v_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.attn.qkv.weight, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.attn.qkv.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.0.attn.proj.weight, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.attn.proj.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.0.attn.proj.bias, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.attn.proj.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.norm2.weight, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.norm2.weight, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.norm2.bias, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.norm2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.mlp.fc1.weight, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.mlp.fc1.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.0.mlp.fc1.bias, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.mlp.fc1.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.0.mlp.fc2.weight, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.mlp.fc2.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.0.mlp.fc2.bias, lr_decay: 0.02824752489999998 name: backbone.0.encoder.blocks.0.mlp.fc2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.gamma_1, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.gamma_1, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.gamma_2, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.gamma_2, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.norm1.weight, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.norm1.weight, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.norm1.bias, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.norm1.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.attn.q_bias, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.attn.q_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.attn.v_bias, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.attn.v_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.attn.qkv.weight, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.attn.qkv.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.1.attn.proj.weight, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.attn.proj.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.1.attn.proj.bias, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.attn.proj.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.norm2.weight, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.norm2.weight, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.norm2.bias, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.norm2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.mlp.fc1.weight, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.mlp.fc1.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.1.mlp.fc1.bias, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.mlp.fc1.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.1.mlp.fc2.weight, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.mlp.fc2.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.1.mlp.fc2.bias, lr_decay: 0.04035360699999998 name: backbone.0.encoder.blocks.1.mlp.fc2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.2.gamma_1, lr_decay: 0.05764800999999997 name: backbone.0.encoder.blocks.2.gamma_1, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.2.gamma_2, lr_decay: 0.05764800999999997 name: backbone.0.encoder.blocks.2.gamma_2, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.2.norm1.weight, lr_decay: 0.05764800999999997 name: backbone.0.encoder.blocks.2.norm1.weight, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.2.norm1.bias, lr_decay: 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name: backbone.0.encoder.blocks.2.norm2.bias, lr_decay: 0.05764800999999997 name: backbone.0.encoder.blocks.2.norm2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.2.mlp.fc1.weight, lr_decay: 0.05764800999999997 name: backbone.0.encoder.blocks.2.mlp.fc1.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.2.mlp.fc1.bias, lr_decay: 0.05764800999999997 name: backbone.0.encoder.blocks.2.mlp.fc1.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.2.mlp.fc2.weight, lr_decay: 0.05764800999999997 name: backbone.0.encoder.blocks.2.mlp.fc2.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.2.mlp.fc2.bias, lr_decay: 0.05764800999999997 name: backbone.0.encoder.blocks.2.mlp.fc2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.3.gamma_1, lr_decay: 0.08235429999999996 name: backbone.0.encoder.blocks.3.gamma_1, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.3.gamma_2, lr_decay: 0.08235429999999996 name: 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backbone.0.encoder.blocks.3.mlp.fc2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.4.gamma_1, lr_decay: 0.11764899999999996 name: backbone.0.encoder.blocks.4.gamma_1, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.4.gamma_2, lr_decay: 0.11764899999999996 name: backbone.0.encoder.blocks.4.gamma_2, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.4.norm1.weight, lr_decay: 0.11764899999999996 name: backbone.0.encoder.blocks.4.norm1.weight, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.4.norm1.bias, lr_decay: 0.11764899999999996 name: backbone.0.encoder.blocks.4.norm1.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.4.attn.q_bias, lr_decay: 0.11764899999999996 name: backbone.0.encoder.blocks.4.attn.q_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.4.attn.v_bias, lr_decay: 0.11764899999999996 name: backbone.0.encoder.blocks.4.attn.v_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.4.attn.qkv.weight, lr_decay: 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weight_decay rate: 0.0 name: backbone.0.encoder.blocks.5.attn.v_bias, lr_decay: 0.16806999999999994 name: backbone.0.encoder.blocks.5.attn.v_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.5.attn.qkv.weight, lr_decay: 0.16806999999999994 name: backbone.0.encoder.blocks.5.attn.qkv.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.5.attn.proj.weight, lr_decay: 0.16806999999999994 name: backbone.0.encoder.blocks.5.attn.proj.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.5.attn.proj.bias, lr_decay: 0.16806999999999994 name: backbone.0.encoder.blocks.5.attn.proj.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.5.norm2.weight, lr_decay: 0.16806999999999994 name: backbone.0.encoder.blocks.5.norm2.weight, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.5.norm2.bias, lr_decay: 0.16806999999999994 name: backbone.0.encoder.blocks.5.norm2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.5.mlp.fc1.weight, lr_decay: 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backbone.0.encoder.blocks.7.mlp.fc2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.8.gamma_1, lr_decay: 0.48999999999999994 name: backbone.0.encoder.blocks.8.gamma_1, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.8.gamma_2, lr_decay: 0.48999999999999994 name: backbone.0.encoder.blocks.8.gamma_2, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.8.norm1.weight, lr_decay: 0.48999999999999994 name: backbone.0.encoder.blocks.8.norm1.weight, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.8.norm1.bias, lr_decay: 0.48999999999999994 name: backbone.0.encoder.blocks.8.norm1.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.8.attn.q_bias, lr_decay: 0.48999999999999994 name: backbone.0.encoder.blocks.8.attn.q_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.8.attn.v_bias, lr_decay: 0.48999999999999994 name: backbone.0.encoder.blocks.8.attn.v_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.8.attn.qkv.weight, lr_decay: 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0.7 name: backbone.0.encoder.blocks.9.attn.v_bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.9.attn.qkv.weight, lr_decay: 0.7 name: backbone.0.encoder.blocks.9.attn.qkv.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.9.attn.proj.weight, lr_decay: 0.7 name: backbone.0.encoder.blocks.9.attn.proj.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.9.attn.proj.bias, lr_decay: 0.7 name: backbone.0.encoder.blocks.9.attn.proj.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.9.norm2.weight, lr_decay: 0.7 name: backbone.0.encoder.blocks.9.norm2.weight, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.9.norm2.bias, lr_decay: 0.7 name: backbone.0.encoder.blocks.9.norm2.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.9.mlp.fc1.weight, lr_decay: 0.7 name: backbone.0.encoder.blocks.9.mlp.fc1.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.9.mlp.fc1.bias, lr_decay: 0.7 name: backbone.0.encoder.blocks.9.mlp.fc1.bias, weight_decay rate: 0.0 name: backbone.0.encoder.blocks.9.mlp.fc2.weight, lr_decay: 0.7 name: backbone.0.encoder.blocks.9.mlp.fc2.weight, weight_decay rate: 1.0 name: backbone.0.encoder.blocks.9.mlp.fc2.bias, lr_decay: 0.7 name: backbone.0.encoder.blocks.9.mlp.fc2.bias, weight_decay rate: 0.0 loading annotations into memory... Done (t=0.02s) creating index... index created! loading annotations into memory... Done (t=0.01s) creating index... index created! Get benchmark Get model size, FLOPs, and FPS 0%| | 0/20 [00:00<?, ?it/s]C:\Users\USER\Desktop\LW-DETR\models\backbone\vit.py:41: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! size = int(math.sqrt(xy_num)) C:\Users\USER\Desktop\LW-DETR\models\backbone\vit.py:42: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! assert size * size == xy_num C:\Users\USER\Desktop\LW-DETR\models\backbone\vit.py:44: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if size != h or size != w: C:\Users\USER\Desktop\LW-DETR\models\backbone\vit.py:354: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! assert (H % 4 == 0) and (W % 4 == 0) C:\Users\USER\Desktop\LW-DETR\models\transformer.py:221: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. spatial_shapes = torch.as_tensor(spatialshapes, dtype=torch.long, device=memory.device) C:\Users\USER\Desktop\LW-DETR\models\transformer.py:85: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results). for lvl, (H, W_) in enumerate(spatial_shapes): C:\Users\USER\anaconda3\envs\lwDETR\lib\site-packages\torch\functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:3484.) return _VF.meshgrid(tensors, *kwargs) # type: ignore[attr-defined] C:\Users\USER\Desktop\LW-DETR\models\transformer.py:54: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if pos_tensor.size(-1) == 2: C:\Users\USER\Desktop\LW-DETR\models\transformer.py:56: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! elif pos_tensor.size(-1) == 4: C:\Users\USER\Desktop\LW-DETR\models\attention.py:303: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! assert embed_dim == embed_dim_to_check, \ C:\Users\USER\Desktop\LW-DETR\models\attention.py:314: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! assert head_dim num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}" C:\Users\USER\Desktop\LW-DETR\models\attention.py:320: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}" C:\Users\USER\Desktop\LW-DETR\models\attention.py:596: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! q = q / math.sqrt(E) C:\Users\USER\Desktop\LW-DETR\models\attention.py:440: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! assert list(attn_output.size()) == [bsz num_heads, tgt_len, v_head_dim] C:\Users\USER\Desktop\LW-DETR\models\ops\modules\ms_deform_attn.py:111: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! assert (input_spatial_shapes[:, 0] input_spatial_shapes[:, 1]).sum() == Len_in C:\Users\USER\Desktop\LW-DETR\models\ops\modules\ms_deform_attn.py:121: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if reference_points.shape[-1] == 2: C:\Users\USER\Desktop\LW-DETR\models\ops\modules\ms_deform_attn.py:125: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! elif reference_points.shape[-1] == 4: C:\Users\USER\Desktop\LW-DETR\models\lwdetr.py:203: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results). for a, b in zip(outputs_class[:-1], outputs_coord[:-1])] C:\Users\USER\anaconda3\envs\lwDETR\lib\collections__init__.py:860: RuntimeWarning: overflow encountered in scalar add self[elem] += count C:\Users\USER\Desktop\LW-DETR\util\benchmark.py:174: RuntimeWarning: overflow encountered in scalar multiply flop = batch_size out_size Cout_dim Cin_dim kernel_size WARNING:root:Skipped operation aten::pad 2 time(s) WARNING:root:Skipped operation aten::upsamplebicubic2d 1 time(s) WARNING:root:Skipped operation aten::unbind 15 time(s) WARNING:root:Skipped operation aten::gelu 10 time(s) WARNING:root:Skipped operation aten::silu 16 time(s) WARNING:root:Skipped operation aten::mean 4 time(s) WARNING:root:Skipped operation aten::sqrt 2 time(s) WARNING:root:Skipped operation aten::bitwise_not 10 time(s) WARNING:root:Skipped operation aten::prod 1 time(s) WARNING:root:Skipped operation aten::ScalarImplicit 4 time(s) WARNING:root:Skipped operation aten::lt 1 time(s) WARNING:root:Skipped operation aten::exp 3 time(s) WARNING:root:Skipped operation prim::PythonOp 3 time(s) WARNING:root:Skipped operation aten::split 1 time(s) 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:17<00:00, 1.13it/s] { "nparam": 46823650, "detailed_flops": { "aten::sub": 0.005222407, "aten::add": 0.017623603, "aten::floordivide": 1.98e-07, "aten::mul": 0.02214301, "norm": 0.081408, "matmul": 8.6016, "softmax": 0.689064, "dropout": 0.0336, "batchnorm": 0.0546816, "aten::pow": 0.002611392, "aten::div": 0.003546599, "aten::relu": 0.0006144, "conv": 0.9437184, "elementwise": 6.8e-06, "aten::sum": 4e-07, "aten::cumsum": 2e-09, "aten::relu": 0.007872, "aten::sin": 0.0001152, "aten::cos": 0.0001152, "bmm": 0.20736, "linear": 2.0708352 }, "flops": { "mean": 12.742138410999996, "std": 1.7763568394002505e-15, "min": 12.742138410999997, "max": 12.742138410999997 }, "time": { "mean": 0.023655427296956386, "std": 0.00031874718595618136, "min": 0.02307140827178955, "max": 0.02422287464141846 }, "fps": 42.27359698248463 } Min DP = 0.1000000, Max DP = 0.1000000 Start training Traceback (most recent call last): File "C:\Users\USER\Desktop\LW-DETR\main.py", line 426, in
main(args)
File "C:\Users\USER\Desktop\LW-DETR\main.py", line 318, in main
train_stats = train_one_epoch(
File "C:\Users\USER\Desktop\LW-DETR\engine.py", line 43, in train_one_epoch
model.module.update_drop_path(schedules['dp'][it], vit_encoder_num_layers)
File "C:\Users\USER\anaconda3\envs\lwDETR\lib\site-packages\torch\nn\modules\module.py", line 1614, in getattr
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'LWDETR' object has no attribute 'module'