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Chiaraplizz
Can not find these weights
when I run without bones on NTU-RGB-D, this problem has occurred as the follow picture. Looking forward to your early reply.
when I run without bones on NTU-RGB-D, this problem has occurred as the follow picture. Looking forward to your early reply.
Hi! Can you send me your train.yml? It should be some error in that.
Chiara
I also meet this problem when the last epoch:
Validation: Epoch [119/120], Samples [14206.0/16487], Loss: 0.305077463388443, Validation Accuracy: 86.16485716018681 [ Fri Apr 9 03:30:50 2021 ] Load weights from ./prova20/epoch119_model.pt. Can not find these weights: module.gcn0.bn.weight module.backbone.0.tcn1.bn.num_batches_tracked module.gcn0.conv_list.1.bias module.backbone.1.tcn1.conv.bias module.tcn0.bn.running_mean module.backbone.6.down1.conv.weight module.backbone.5.gcn1.bn.weight module.gcn0.bn.bias module.backbone.0.gcn1.bn.running_mean module.backbone.0.gcn1.conv_list.0.weight module.backbone.3.gcn1.bn.running_mean ......
This is my train.yaml, can you find anything wrong?
feeder
feeder: st_gcn.feeder.Feeder feeder_augmented: st_gcn.feeder.FeederAugmented train_feeder_args: data_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/train_data.npy label_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/train_label.pkl #data_path: ./Output_skeletons_without_missing_skeletons/xsub/train_data_joint_bones.npy #label_path: ./Output_skeletons_without_missing_skeletons/xsub/train_label_filtered.pkl
random_choose: False random_shift: False random_move: False window_size: -1 normalization: False mirroring: False
test_feeder_args: data_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/val_data.npy label_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/val_label.pkl #data_path: ./Output_skeletons_without_missing_skeletons/xsub/val_data_joint_bones.npy #label_path: ./Output_skeletons_without_missing_skeletons/xsub/val_label_filtered.pkl
model
model: st_gcn.net.ST_GCN training: True
model_args: num_class: 26 channel: 3 window_size: 300 num_point: 25 num_person: 2 mask_learning: True use_data_bn: True attention: True only_attention: True tcn_attention: False data_normalization: True skip_conn: True weight_matrix: 2 only_temporal_attention: True bn_flag: True attention_3: False kernel_temporal: 9 more_channels: False double_channel: True drop_connect: True concat_original: True all_layers: False adjacency: False agcn: False dv: 0.25 dk: 0.25 Nh: 8 n: 4 dim_block1: 10 dim_block2: 30 dim_block3: 75 relative: False graph: st_gcn.graph.NTU_RGB_D visualization: False graph_args: labeling_mode: 'spatial' #optical_flow: True
optim
#0: old one, 1: new one scheduler: 1 weight_decay: 0.0001 base_lr: 0.1 step: [60,90]
training
device: [0,1] batch_size: 2 test_batch_size: 8 num_epoch: 120 nesterov: True
This is my train.yaml, can you find anything wrong?
feeder
feeder: st_gcn.feeder.Feeder feeder_augmented: st_gcn.feeder.FeederAugmented train_feeder_args: data_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/train_data.npy label_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/train_label.pkl #data_path: ./Output_skeletons_without_missing_skeletons/xsub/train_data_joint_bones.npy #label_path: ./Output_skeletons_without_missing_skeletons/xsub/train_label_filtered.pkl
random_choose: False random_shift: False random_move: False window_size: -1 normalization: False mirroring: False
test_feeder_args: data_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/val_data.npy label_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/val_label.pkl #data_path: ./Output_skeletons_without_missing_skeletons/xsub/val_data_joint_bones.npy #label_path: ./Output_skeletons_without_missing_skeletons/xsub/val_label_filtered.pkl
model
model: st_gcn.net.ST_GCN training: True
model_args: num_class: 26 channel: 3 window_size: 300 num_point: 25 num_person: 2 mask_learning: True use_data_bn: True attention: True only_attention: True tcn_attention: False data_normalization: True skip_conn: True weight_matrix: 2 only_temporal_attention: True bn_flag: True attention_3: False kernel_temporal: 9 more_channels: False double_channel: True drop_connect: True concat_original: True all_layers: False adjacency: False agcn: False dv: 0.25 dk: 0.25 Nh: 8 n: 4 dim_block1: 10 dim_block2: 30 dim_block3: 75 relative: False graph: st_gcn.graph.NTU_RGB_D visualization: False graph_args: labeling_mode: 'spatial' #optical_flow: True
optim
#0: old one, 1: new one scheduler: 1 weight_decay: 0.0001 base_lr: 0.1 step: [60,90]
training
device: [0,1] batch_size: 2 test_batch_size: 8 num_epoch: 120 nesterov: True
have you save the problem？
This is my train.yaml, can you find anything wrong?
feeder
feeder: st_gcn.feeder.Feeder feeder_augmented: st_gcn.feeder.FeederAugmented train_feeder_args: data_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/train_data.npy label_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/train_label.pkl #data_path: ./Output_skeletons_without_missing_skeletons/xsub/train_data_joint_bones.npy #label_path: ./Output_skeletons_without_missing_skeletons/xsub/train_label_filtered.pkl random_choose: False random_shift: False random_move: False window_size: -1 normalization: False mirroring: False test_feeder_args: data_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/val_data.npy label_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/val_label.pkl #data_path: ./Output_skeletons_without_missing_skeletons/xsub/val_data_joint_bones.npy #label_path: ./Output_skeletons_without_missing_skeletons/xsub/val_label_filtered.pkl
model
model: st_gcn.net.ST_GCN training: True model_args: num_class: 26 channel: 3 window_size: 300 num_point: 25 num_person: 2 mask_learning: True use_data_bn: True attention: True only_attention: True tcn_attention: False data_normalization: True skip_conn: True weight_matrix: 2 only_temporal_attention: True bn_flag: True attention_3: False kernel_temporal: 9 more_channels: False double_channel: True drop_connect: True concat_original: True all_layers: False adjacency: False agcn: False dv: 0.25 dk: 0.25 Nh: 8 n: 4 dim_block1: 10 dim_block2: 30 dim_block3: 75 relative: False graph: st_gcn.graph.NTU_RGB_D visualization: False graph_args: labeling_mode: 'spatial' #optical_flow: True
optim
#0: old one, 1: new one scheduler: 1 weight_decay: 0.0001 base_lr: 0.1 step: [60,90]
training
device: [0,1] batch_size: 2 test_batch_size: 8 num_epoch: 120 nesterov: True
have you save the problem？
This is my train.yaml, can you find anything wrong?
feeder
feeder: st_gcn.feeder.Feeder feeder_augmented: st_gcn.feeder.FeederAugmented train_feeder_args: data_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/train_data.npy label_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/train_label.pkl #data_path: ./Output_skeletons_without_missing_skeletons/xsub/train_data_joint_bones.npy #label_path: ./Output_skeletons_without_missing_skeletons/xsub/train_label_filtered.pkl random_choose: False random_shift: False random_move: False window_size: -1 normalization: False mirroring: False test_feeder_args: data_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/val_data.npy label_path: /home/lenovo/hxx/actionDetection/data/NTU-RGB-D-twoperson/xview/val_label.pkl #data_path: ./Output_skeletons_without_missing_skeletons/xsub/val_data_joint_bones.npy #label_path: ./Output_skeletons_without_missing_skeletons/xsub/val_label_filtered.pkl
model
model: st_gcn.net.ST_GCN training: True model_args: num_class: 26 channel: 3 window_size: 300 num_point: 25 num_person: 2 mask_learning: True use_data_bn: True attention: True only_attention: True tcn_attention: False data_normalization: True skip_conn: True weight_matrix: 2 only_temporal_attention: True bn_flag: True attention_3: False kernel_temporal: 9 more_channels: False double_channel: True drop_connect: True concat_original: True all_layers: False adjacency: False agcn: False dv: 0.25 dk: 0.25 Nh: 8 n: 4 dim_block1: 10 dim_block2: 30 dim_block3: 75 relative: False graph: st_gcn.graph.NTU_RGB_D visualization: False graph_args: labeling_mode: 'spatial' #optical_flow: True
optim
#0: old one, 1: new one scheduler: 1 weight_decay: 0.0001 base_lr: 0.1 step: [60,90]
training
device: [0,1] batch_size: 2 test_batch_size: 8 num_epoch: 120 nesterov: True
have you save the problem？
Hi 😊 Which model are you loading? I noticed that you set double_channels: True. Please notice that models using joint information only have been trained with double_channels: False.
Chiara
Hi Chiaraplizz, I ran into the same issue, even after changing the double_channels to True, the error persists. Looking forward to your early reply. Thank you.
Hi Chiaraplizz, I ran into the same issue, even after changing the double_channels to True, the error persists. Looking forward to your early reply. Thank you.
Hi,have you solved the problem?
