Thanks for your time, I encounter some problems using Google Drive to download PKUMMD dataset. It can help a lot if both parts of the datasets can be shared on BaiduYun if it is convenient for you, I appreciate it a lot for your kindness.

By the way, is the preprocessing and experiment settings same for part1 and part2? Looking forward to your reply.

[04.26.22|13:33:35] Can not find weights [encoder_q.fc.bias]. [04.26.22|13:33:35] Can not find weights [encoder_q.fc.weight]. Traceback (most recent call last): File "main.py", line 50, in p = Processor(sys.argv[2:]) File "/home/lailai/lailai_file/action/AimCLR/processor/processor.py", line 60, in init self.load_data() File "/home/lailai/lailai_file/action/AimCLR/processor/processor.py", line 92, in load_data dataset=train_feeder(**self.arg.train_feeder_args), File "/home/lailai/lailai_file/action/AimCLR/feeder/ntu_feeder.py", line 17, in init self.load_data(mmap) File "/home/lailai/lailai_file/action/AimCLR/feeder/ntu_feeder.py", line 22, in load_data self.sample_name, self.label = pickle.load(f) _pickle.UnpicklingError: STACK_GLOBAL requires str

Your paper has classified as "Notably, the KNN classifier does not require learning extra weights compared with the linear classifier". What is the "extra weights"？ Can you explain it clearly?

when i run: $ python main.py linear_evaluation --config config/ntu60/linear_eval/linear_eval_aimclr_xview_joint.yaml out: Traceback (most recent call last): File "main.py", line 50, in p = Processor(sys.argv[2:]) File "/home/xxx/project1/AimCLR/processor/processor.py", line 43, in init self.init_environment() File "/home/xxx/project1/AimCLR/processor/processor.py", line 76, in init_environment super().init_environment() File "/home/xxx/project1/AimCLR/processor/io.py", line 55, in init_environment self.io.save_arg(self.arg) File "/home/xxx/anaconda3/envs/TEST2/lib/python3.7/site-packages/torchlight-1.0-py3.7.egg/torchlight/io.py", line 116, in save_arg File "/home/xxx/anaconda3/envs/TEST2/lib/python3.7/os.py", line 211, in makedirs makedirs(head, exist_ok=exist_ok) File "/home/xxx/anaconda3/envs/TEST2/lib/python3.7/os.py", line 211, in makedirs makedirs(head, exist_ok=exist_ok) File "/home/xxx/anaconda3/envs/TEST2/lib/python3.7/os.py", line 211, in makedirs makedirs(head, exist_ok=exist_ok) [Previous line repeated 1 more time] File "/home/xxx/anaconda3/envs/TEST2/lib/python3.7/os.py", line 221, in makedirs mkdir(name, mode) PermissionError: [Errno 13] Permission denied: '/data/gty'

my .yaml: work_dir: /data/gty/AAAI_github/ntu60_cv/aimclr_joint/linear_eval

weights: /data/gty/released_model/ntu60_xview_joint.pt

weights: /data/gty/AAAI_github/ntu60_cv/aimclr_joint/pretext/epoch300_model.pt

ignore_weights: [encoder_q.fc, encoder_k, queue]

feeder

train_feeder: feeder.ntu_feeder.Feeder_single train_feeder_args: data_path: /home/wuyushan/project1/data/NTU60_frame50/xview/train_position.npy label_path: /home/wuyushan/project1/data/NTU60_frame50/xview/train_label.pkl shear_amplitude: -1 temperal_padding_ratio: -1 mmap: True test_feeder: feeder.ntu_feeder.Feeder_single test_feeder_args: data_path: /home/xxx/project1/data/NTU60_frame50/xview/val_position.npy label_path: /home/xxx/project1/data/NTU60_frame50/xview/val_label.pkl shear_amplitude: -1 temperal_padding_ratio: -1 mmap: True

model

model: net.aimclr.AimCLR model_args: base_encoder: net.st_gcn.Model pretrain: False

feature_dim: 128

queue_size: 32768

momentum: 0.999

Temperature: 0.07

mlp: True

in_channels: 3 hidden_channels: 16 hidden_dim: 256 num_class: 60 dropout: 0.5 graph_args: layout: 'ntu-rgb+d' strategy: 'spatial' edge_importance_weighting: True

optim

nesterov: False weight_decay: 0.0 base_lr: 3. optimizer: SGD step: [80]

training

device: [1] # 3 batch_size: 64 # 128 test_batch_size: 64 # 128 num_epoch: 100 stream: 'joint'

log

save_interval: -1 eval_interval: 5

i don't know why,can you help me,please!

Hi, Thank you for sharing your work! You used KNN evaluation method in your paper, but I did not find the related implementation details in the article, nor did I find the corresponding code. Would please provide some more details of the KNN evaluation? And as to the finetune setting, did you adjust the weight decay when doing finetune? Looking forward to your reply

I am very interested in your work and thanks for your sharing. I'd like to know how to finetune and semi-supervised evaluate AimCLR? Have the two evaluation been included in your code? If not , could you please share them for me? My email is :[email protected]

Hi, thank you for sharing your work with the community! I was intersted in training a model using 150 frames long sequences, but after preprocessing the NTU dataset I ended up with just .npy files, while .pkl are needed for the label. Could you please share the code to convert the .npy files into .pkl? Thank you, Paolo

Hello, for the papers,“For all the reported results of three streams, we use the weights of [0.6, 0.6, 0.4] for weighted fusion like other multi-stream GCN methods.” But I still don’t understand, can you teach me how to calculate the comprehensive results of the three streams? For example, how did the 83.8 in the last line of the fourth column in Table II calculated?