samin9796
commented
2 weeks ago @upper127 Could you solve the issue later? I am facing the same error.
Open upper127 opened 2 months ago
samin9796
commented
2 weeks ago @upper127 Could you solve the issue later? I am facing the same error.
I put the modified cnt dataset on the model and ran it sending some errors. D:\ProgramData\anaconda3\envs\labram\python.exe E:\lab\DL\LaBraM-main\run_class_finetuning.py Not using distributed mode Namespace(batch_size=64, epochs=30, update_freq=1, save_ckpt_freq=5, robust_test=None, model='labram_base_patch200_200', qkv_bias=True, rel_pos_bias=True, abs_pos_emb=True, layer_scale_init_value=0.1, input_size=200, drop=0.0, attn_drop_rate=0.0, drop_path=0.1, disable_eval_during_finetuning=False, model_ema=False, model_ema_decay=0.9999, model_ema_force_cpu=False, opt='adamw', opt_eps=1e-08, opt_betas=None, clip_grad=None, momentum=0.9, weight_decay=0.05, weight_decay_end=None, lr=0.0005, layer_decay=0.9, warmup_lr=1e-06, min_lr=1e-06, warmup_epochs=5, warmup_steps=-1, smoothing=0.1, reprob=0.25, remode='pixel', recount=1, resplit=False, finetune='', model_key='model|module', model_prefix='', model_filter_name='gzp', init_scale=0.001, use_mean_pooling=True, disable_weight_decay_on_rel_pos_bias=False, nb_classes=4, output_dir='E:/lab/DL/LaBraM-main/checkpoints/finetune_MI_base', log_dir='E:/lab/DL/LaBraM-main/log/finetune_MI_base', device='cuda', seed=0, resume='', auto_resume=True, save_ckpt=True, start_epoch=0, eval=False, dist_eval=False, num_workers=10, pin_mem=True, world_size=1, local_rank=-1, dist_on_itp=False, dist_url='env://', enable_deepspeed=False, dataset='MI', distributed=False) 2199 399 680 Sampler_train = <torch.utils.data.distributed.DistributedSampler object at 0x000001F93E230D50> Patch size = 200 Model = NeuralTransformer( (patch_embed): TemporalConv( (conv1): Conv2d(1, 8, kernel_size=(1, 15), stride=(1, 8), padding=(0, 7)) (gelu1): GELU(approximate='none') (norm1): GroupNorm(4, 8, eps=1e-05, affine=True) (conv2): Conv2d(8, 8, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (gelu2): GELU(approximate='none') (norm2): GroupNorm(4, 8, eps=1e-05, affine=True) (conv3): Conv2d(8, 8, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1)) (norm3): GroupNorm(4, 8, eps=1e-05, affine=True) (gelu3): GELU(approximate='none') ) (pos_drop): Dropout(p=0.0, inplace=False) (blocks): ModuleList( (0): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): Identity() (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.00909090880304575) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (2): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.0181818176060915) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (3): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.027272727340459824) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (4): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.036363635212183) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (5): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.045454543083906174) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (6): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.054545458406209946) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (7): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.06363636255264282) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (8): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.0727272778749466) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (9): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.08181818574666977) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (10): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.09090909361839294) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (11): Block( (norm1): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (attn): Attention( (qkv): Linear(in_features=200, out_features=600, bias=False) (q_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (k_norm): LayerNorm((20,), eps=1e-06, elementwise_affine=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=200, out_features=200, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (drop_path): DropPath(p=0.10000000149011612) (norm2): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=200, out_features=800, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=800, out_features=200, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (norm): Identity() (fc_norm): LayerNorm((200,), eps=1e-06, elementwise_affine=True) (head): Linear(in_features=200, out_features=4, bias=True) ) number of params: 5825540 LR = 0.00050000 Batch size = 64 Update frequent = 1 Number of training examples = 2199 Number of training training per epoch = 34 Assigned values = [0.2541865828329001, 0.2824295364810001, 0.31381059609000006, 0.3486784401000001, 0.3874204890000001, 0.4304672100000001, 0.4782969000000001, 0.531441, 0.5904900000000001, 0.6561, 0.7290000000000001, 0.81, 0.9, 1.0] Skip weight decay name marked in model: {'time_embed', 'cls_token', 'pos_embed'} Param groups = { "layer_0_no_decay": { "weight_decay": 0.0, "params": [ "cls_token", "pos_embed", "patch_embed.conv1.bias", "patch_embed.norm1.weight", "patch_embed.norm1.bias", "patch_embed.conv2.bias", "patch_embed.norm2.weight", "patch_embed.norm2.bias", "patch_embed.conv3.bias", "patch_embed.norm3.weight", "patch_embed.norm3.bias" ], "lr_scale": 0.2541865828329001 }, "layer_13_no_decay": { "weight_decay": 0.0, "params": [ "time_embed", "fc_norm.weight", "fc_norm.bias", "head.bias" ], "lr_scale": 1.0 }, "layer_0_decay": { "weight_decay": 0.05, "params": [ "patch_embed.conv1.weight", "patch_embed.conv2.weight", "patch_embed.conv3.weight" ], "lr_scale": 0.2541865828329001 }, "layer_1_no_decay": { "weight_decay": 0.0, "params": [ "blocks.0.gamma_1", "blocks.0.gamma_2", "blocks.0.norm1.weight", "blocks.0.norm1.bias", "blocks.0.attn.q_bias", "blocks.0.attn.v_bias", "blocks.0.attn.q_norm.weight", "blocks.0.attn.q_norm.bias", "blocks.0.attn.k_norm.weight", "blocks.0.attn.k_norm.bias", "blocks.0.attn.proj.bias", "blocks.0.norm2.weight", "blocks.0.norm2.bias", "blocks.0.mlp.fc1.bias", "blocks.0.mlp.fc2.bias" ], "lr_scale": 0.2824295364810001 }, "layer_1_decay": { "weight_decay": 0.05, "params": [ "blocks.0.attn.qkv.weight", "blocks.0.attn.proj.weight", "blocks.0.mlp.fc1.weight", "blocks.0.mlp.fc2.weight" ], "lr_scale": 0.2824295364810001 }, "layer_2_no_decay": { "weight_decay": 0.0, "params": [ "blocks.1.gamma_1", "blocks.1.gamma_2", "blocks.1.norm1.weight", "blocks.1.norm1.bias", "blocks.1.attn.q_bias", "blocks.1.attn.v_bias", "blocks.1.attn.q_norm.weight", "blocks.1.attn.q_norm.bias", "blocks.1.attn.k_norm.weight", "blocks.1.attn.k_norm.bias", "blocks.1.attn.proj.bias", "blocks.1.norm2.weight", "blocks.1.norm2.bias", "blocks.1.mlp.fc1.bias", "blocks.1.mlp.fc2.bias" ], "lr_scale": 0.31381059609000006 }, "layer_2_decay": { "weight_decay": 0.05, "params": [ "blocks.1.attn.qkv.weight", "blocks.1.attn.proj.weight", "blocks.1.mlp.fc1.weight", "blocks.1.mlp.fc2.weight" ], "lr_scale": 0.31381059609000006 }, "layer_3_no_decay": { "weight_decay": 0.0, "params": [ "blocks.2.gamma_1", "blocks.2.gamma_2", "blocks.2.norm1.weight", "blocks.2.norm1.bias", "blocks.2.attn.q_bias", "blocks.2.attn.v_bias", "blocks.2.attn.q_norm.weight", "blocks.2.attn.q_norm.bias", "blocks.2.attn.k_norm.weight", "blocks.2.attn.k_norm.bias", "blocks.2.attn.proj.bias", "blocks.2.norm2.weight", "blocks.2.norm2.bias", "blocks.2.mlp.fc1.bias", "blocks.2.mlp.fc2.bias" ], "lr_scale": 0.3486784401000001 }, "layer_3_decay": { "weight_decay": 0.05, "params": [ "blocks.2.attn.qkv.weight", "blocks.2.attn.proj.weight", "blocks.2.mlp.fc1.weight", "blocks.2.mlp.fc2.weight" ], "lr_scale": 0.3486784401000001 }, "layer_4_no_decay": { "weight_decay": 0.0, "params": [ "blocks.3.gamma_1", "blocks.3.gamma_2", "blocks.3.norm1.weight", "blocks.3.norm1.bias", "blocks.3.attn.q_bias", "blocks.3.attn.v_bias", "blocks.3.attn.q_norm.weight", "blocks.3.attn.q_norm.bias", "blocks.3.attn.k_norm.weight", "blocks.3.attn.k_norm.bias", "blocks.3.attn.proj.bias", "blocks.3.norm2.weight", "blocks.3.norm2.bias", "blocks.3.mlp.fc1.bias", "blocks.3.mlp.fc2.bias" ], "lr_scale": 0.3874204890000001 }, "layer_4_decay": { "weight_decay": 0.05, "params": [ "blocks.3.attn.qkv.weight", "blocks.3.attn.proj.weight", "blocks.3.mlp.fc1.weight", "blocks.3.mlp.fc2.weight" ], "lr_scale": 0.3874204890000001 }, "layer_5_no_decay": { "weight_decay": 0.0, "params": [ "blocks.4.gamma_1", "blocks.4.gamma_2", "blocks.4.norm1.weight", "blocks.4.norm1.bias", "blocks.4.attn.q_bias", "blocks.4.attn.v_bias", "blocks.4.attn.q_norm.weight", "blocks.4.attn.q_norm.bias", "blocks.4.attn.k_norm.weight", "blocks.4.attn.k_norm.bias", "blocks.4.attn.proj.bias", "blocks.4.norm2.weight", "blocks.4.norm2.bias", "blocks.4.mlp.fc1.bias", "blocks.4.mlp.fc2.bias" ], "lr_scale": 0.4304672100000001 }, "layer_5_decay": { "weight_decay": 0.05, "params": [ "blocks.4.attn.qkv.weight", "blocks.4.attn.proj.weight", "blocks.4.mlp.fc1.weight", "blocks.4.mlp.fc2.weight" ], "lr_scale": 0.4304672100000001 }, "layer_6_no_decay": { "weight_decay": 0.0, "params": [ "blocks.5.gamma_1", "blocks.5.gamma_2", "blocks.5.norm1.weight", "blocks.5.norm1.bias", "blocks.5.attn.q_bias", "blocks.5.attn.v_bias", "blocks.5.attn.q_norm.weight", "blocks.5.attn.q_norm.bias", "blocks.5.attn.k_norm.weight", "blocks.5.attn.k_norm.bias", "blocks.5.attn.proj.bias", "blocks.5.norm2.weight", "blocks.5.norm2.bias", "blocks.5.mlp.fc1.bias", "blocks.5.mlp.fc2.bias" ], "lr_scale": 0.4782969000000001 }, "layer_6_decay": { "weight_decay": 0.05, "params": [ "blocks.5.attn.qkv.weight", "blocks.5.attn.proj.weight", "blocks.5.mlp.fc1.weight", "blocks.5.mlp.fc2.weight" ], "lr_scale": 0.4782969000000001 }, "layer_7_no_decay": { "weight_decay": 0.0, "params": [ "blocks.6.gamma_1", "blocks.6.gamma_2", "blocks.6.norm1.weight", "blocks.6.norm1.bias", "blocks.6.attn.q_bias", "blocks.6.attn.v_bias", "blocks.6.attn.q_norm.weight", "blocks.6.attn.q_norm.bias", "blocks.6.attn.k_norm.weight", "blocks.6.attn.k_norm.bias", "blocks.6.attn.proj.bias", "blocks.6.norm2.weight", "blocks.6.norm2.bias", "blocks.6.mlp.fc1.bias", "blocks.6.mlp.fc2.bias" ], "lr_scale": 0.531441 }, "layer_7_decay": { "weight_decay": 0.05, "params": [ "blocks.6.attn.qkv.weight", "blocks.6.attn.proj.weight", "blocks.6.mlp.fc1.weight", "blocks.6.mlp.fc2.weight" ], "lr_scale": 0.531441 }, "layer_8_no_decay": { "weight_decay": 0.0, "params": [ "blocks.7.gamma_1", "blocks.7.gamma_2", "blocks.7.norm1.weight", "blocks.7.norm1.bias", "blocks.7.attn.q_bias", "blocks.7.attn.v_bias", "blocks.7.attn.q_norm.weight", "blocks.7.attn.q_norm.bias", "blocks.7.attn.k_norm.weight", "blocks.7.attn.k_norm.bias", "blocks.7.attn.proj.bias", "blocks.7.norm2.weight", "blocks.7.norm2.bias", "blocks.7.mlp.fc1.bias", "blocks.7.mlp.fc2.bias" ], "lr_scale": 0.5904900000000001 }, "layer_8_decay": { "weight_decay": 0.05, "params": [ "blocks.7.attn.qkv.weight", "blocks.7.attn.proj.weight", "blocks.7.mlp.fc1.weight", "blocks.7.mlp.fc2.weight" ], "lr_scale": 0.5904900000000001 }, "layer_9_no_decay": { "weight_decay": 0.0, "params": [ "blocks.8.gamma_1", "blocks.8.gamma_2", "blocks.8.norm1.weight", "blocks.8.norm1.bias", "blocks.8.attn.q_bias", "blocks.8.attn.v_bias", "blocks.8.attn.q_norm.weight", "blocks.8.attn.q_norm.bias", "blocks.8.attn.k_norm.weight", "blocks.8.attn.k_norm.bias", "blocks.8.attn.proj.bias", "blocks.8.norm2.weight", "blocks.8.norm2.bias", "blocks.8.mlp.fc1.bias", "blocks.8.mlp.fc2.bias" ], "lr_scale": 0.6561 }, "layer_9_decay": { "weight_decay": 0.05, "params": [ "blocks.8.attn.qkv.weight", "blocks.8.attn.proj.weight", "blocks.8.mlp.fc1.weight", "blocks.8.mlp.fc2.weight" ], "lr_scale": 0.6561 }, "layer_10_no_decay": { "weight_decay": 0.0, "params": [ "blocks.9.gamma_1", "blocks.9.gamma_2", "blocks.9.norm1.weight", "blocks.9.norm1.bias", "blocks.9.attn.q_bias", "blocks.9.attn.v_bias", "blocks.9.attn.q_norm.weight", "blocks.9.attn.q_norm.bias", "blocks.9.attn.k_norm.weight", "blocks.9.attn.k_norm.bias", "blocks.9.attn.proj.bias", "blocks.9.norm2.weight", "blocks.9.norm2.bias", "blocks.9.mlp.fc1.bias", "blocks.9.mlp.fc2.bias" ], "lr_scale": 0.7290000000000001 }, "layer_10_decay": { "weight_decay": 0.05, "params": [ "blocks.9.attn.qkv.weight", "blocks.9.attn.proj.weight", "blocks.9.mlp.fc1.weight", "blocks.9.mlp.fc2.weight" ], "lr_scale": 0.7290000000000001 }, "layer_11_no_decay": { "weight_decay": 0.0, "params": [ "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.q_norm.weight", "blocks.10.attn.q_norm.bias", "blocks.10.attn.k_norm.weight", "blocks.10.attn.k_norm.bias", "blocks.10.attn.proj.bias", "blocks.10.norm2.weight", "blocks.10.norm2.bias", "blocks.10.mlp.fc1.bias", "blocks.10.mlp.fc2.bias" ], "lr_scale": 0.81 }, "layer_11_decay": { "weight_decay": 0.05, "params": [ "blocks.10.attn.qkv.weight", "blocks.10.attn.proj.weight", "blocks.10.mlp.fc1.weight", "blocks.10.mlp.fc2.weight" ], "lr_scale": 0.81 }, "layer_12_no_decay": { "weight_decay": 0.0, "params": [ "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.q_norm.weight", "blocks.11.attn.q_norm.bias", "blocks.11.attn.k_norm.weight", "blocks.11.attn.k_norm.bias", "blocks.11.attn.proj.bias", "blocks.11.norm2.weight", "blocks.11.norm2.bias", "blocks.11.mlp.fc1.bias", "blocks.11.mlp.fc2.bias" ], "lr_scale": 0.9 }, "layer_12_decay": { "weight_decay": 0.05, "params": [ "blocks.11.attn.qkv.weight", "blocks.11.attn.proj.weight", "blocks.11.mlp.fc1.weight", "blocks.11.mlp.fc2.weight" ], "lr_scale": 0.9 }, "layer_13_decay": { "weight_decay": 0.05, "params": [ "head.weight" ], "lr_scale": 1.0 } } Optimizer config: {'lr': 0.0005, 'weight_decay': 0.0, 'eps': 1e-08} Use step level LR scheduler! Set warmup steps = 170 Set warmup steps = 0 Max WD = 0.0500000, Min WD = 0.0500000 criterion = LabelSmoothingCrossEntropy() Auto resume checkpoint: Start training for 30 epochs Traceback (most recent call last): File "E:\lab\DL\LaBraM-main\run_class_finetuning.py", line 582, in
main(opts, ds_init)
File "E:\lab\DL\LaBraM-main\run_class_finetuning.py", line 496, in main
train_stats = train_one_epoch(
^^^^^^^^^^^^^^^^
File "E:\lab\DL\LaBraM-main\engine_for_finetuning.py", line 77, in train_one_epoch
loss, output = train_class_batch(
^^^^^^^^^^^^^^^^^^
File "E:\lab\DL\LaBraM-main\engine_for_finetuning.py", line 19, in train_class_batch
outputs = model(samples, ch_names)
^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\anaconda3\envs\labram\Lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, *kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ProgramData\anaconda3\envs\labram\Lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\lab\DL\LaBraM-main\modeling_finetune.py", line 395, in forward
x = self.forward_features(x, input_chans=input_chans, return_patch_tokens=return_patch_tokens, return_all_tokens=return_all_tokens, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\lab\DL\LaBraM-main\modeling_finetune.py", line 362, in forward_features
x = x + pos_embed
^~~~~ RuntimeError: The size of tensor a (341) must match the size of tensor b (286) at non-singleton dimension 1进程已结束，退出代码为 1