size mismatch when run "python i3d_tf_to_pt.py --rgb"

Hi,
Thank you for your work, firstly.
I want to transfer the pre-training parameters in Tensorflow to PyTorch. But when I run "python i3d_tf_to_pt.py --rgb", I have the bugs as follows:
Additionally, I want to know, the pre-training parameters (model/model_rgb.pth) in PyTorch you provide if as same as the Tensorflow ? Thank you very much !

2018-11-02 16:23:32.948892: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Traceback (most recent call last):
  File "i3d_tf_to_pt.py", line 177, in <module>
    modality='rgb')
  File "i3d_tf_to_pt.py", line 73, in transfer_weights
    i3nception_pt.load_tf_weights(sess)
  File "/Users/cuixiankun01/project/baidu/kinetics_i3d_pytorch-master/src/i3dpt.py", line 310, in load_tf_weights
    self.load_state_dict(state_dict)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/torch/nn/modules/module.py", line 719, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for I3D:
        size mismatch for conv3d_1a_7x7.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_1a_7x7.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_1a_7x7.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_2b_1x1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_2b_1x1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_2b_1x1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_2c_3x3.batch3d.running_var: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for conv3d_2c_3x3.batch3d.bias: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for conv3d_2c_3x3.batch3d.running_mean: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_3b.branch_0.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3b.branch_0.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3b.branch_0.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3b.branch_1.0.batch3d.running_var: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3b.branch_1.0.batch3d.bias: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3b.branch_1.0.batch3d.running_mean: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3b.branch_1.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3b.branch_1.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3b.branch_1.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3b.branch_2.0.batch3d.running_var: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_3b.branch_2.0.batch3d.bias: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_3b.branch_2.0.batch3d.running_mean: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_3b.branch_2.1.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_2.1.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_2.1.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_3.1.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_3.1.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_3.1.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3c.branch_0.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_0.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_0.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_1.0.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_1.0.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_1.0.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_1.1.batch3d.running_var: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_3c.branch_1.1.batch3d.bias: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_3c.branch_1.1.batch3d.running_mean: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_3c.branch_2.0.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3c.branch_2.0.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3c.branch_2.0.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3c.branch_2.1.batch3d.running_var: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3c.branch_2.1.batch3d.bias: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3c.branch_2.1.batch3d.running_mean: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3c.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3c.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3c.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4b.branch_0.batch3d.running_var: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_4b.branch_0.batch3d.bias: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_4b.branch_0.batch3d.running_mean: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_4b.branch_1.0.batch3d.running_var: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_4b.branch_1.0.batch3d.bias: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_4b.branch_1.0.batch3d.running_mean: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_4b.branch_1.1.batch3d.running_var: copying a param of torch.Size([208]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 208]) in current model.
        size mismatch for mixed_4b.branch_1.1.batch3d.bias: copying a param of torch.Size([208]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 208]) in current model.
        size mismatch for mixed_4b.branch_1.1.batch3d.running_mean: copying a param of torch.Size([208]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 208]) in current model.
        size mismatch for mixed_4b.branch_2.0.batch3d.running_var: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_4b.branch_2.0.batch3d.bias: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_4b.branch_2.0.batch3d.running_mean: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_4b.branch_2.1.batch3d.running_var: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_4b.branch_2.1.batch3d.bias: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_4b.branch_2.1.batch3d.running_mean: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_4b.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4b.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4b.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_0.batch3d.running_var: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4c.branch_0.batch3d.bias: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4c.branch_0.batch3d.running_mean: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4c.branch_1.0.batch3d.running_var: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4c.branch_1.0.batch3d.bias: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4c.branch_1.0.batch3d.running_mean: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4c.branch_1.1.batch3d.running_var: copying a param of torch.Size([224]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 224]) in current model.
        size mismatch for mixed_4c.branch_1.1.batch3d.bias: copying a param of torch.Size([224]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 224]) in current model.
        size mismatch for mixed_4c.branch_1.1.batch3d.running_mean: copying a param of torch.Size([224]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 224]) in current model.
        size mismatch for mixed_4c.branch_2.0.batch3d.running_var: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4c.branch_2.0.batch3d.bias: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4c.branch_2.0.batch3d.running_mean: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4c.branch_2.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_2.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_2.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_0.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_0.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_0.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_1.0.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_1.0.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_1.0.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_1.1.batch3d.running_var: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4d.branch_1.1.batch3d.bias: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4d.branch_1.1.batch3d.running_mean: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4d.branch_2.0.batch3d.running_var: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4d.branch_2.0.batch3d.bias: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4d.branch_2.0.batch3d.running_mean: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4d.branch_2.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_2.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_2.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_0.batch3d.running_var: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4e.branch_0.batch3d.bias: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4e.branch_0.batch3d.running_mean: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4e.branch_1.0.batch3d.running_var: copying a param of torch.Size([144]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 144]) in current model.
        size mismatch for mixed_4e.branch_1.0.batch3d.bias: copying a param of torch.Size([144]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 144]) in current model.
        size mismatch for mixed_4e.branch_1.0.batch3d.running_mean: copying a param of torch.Size([144]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 144]) in current model.
        size mismatch for mixed_4e.branch_1.1.batch3d.running_var: copying a param of torch.Size([288]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 288]) in current model.
        size mismatch for mixed_4e.branch_1.1.batch3d.bias: copying a param of torch.Size([288]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 288]) in current model.
        size mismatch for mixed_4e.branch_1.1.batch3d.running_mean: copying a param of torch.Size([288]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 288]) in current model.
        size mismatch for mixed_4e.branch_2.0.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4e.branch_2.0.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4e.branch_2.0.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4e.branch_2.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_2.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_2.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4f.branch_0.batch3d.running_var: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4f.branch_0.batch3d.bias: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4f.branch_0.batch3d.running_mean: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4f.branch_1.0.batch3d.running_var: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4f.branch_1.0.batch3d.bias: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4f.branch_1.0.batch3d.running_mean: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4f.branch_1.1.batch3d.running_var: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_4f.branch_1.1.batch3d.bias: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_4f.branch_1.1.batch3d.running_mean: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_4f.branch_2.0.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4f.branch_2.0.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4f.branch_2.0.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4f.branch_2.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_2.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_2.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_3.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_3.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_3.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_0.batch3d.running_var: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_5b.branch_0.batch3d.bias: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_5b.branch_0.batch3d.running_mean: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_5b.branch_1.0.batch3d.running_var: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_5b.branch_1.0.batch3d.bias: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_5b.branch_1.0.batch3d.running_mean: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_5b.branch_1.1.batch3d.running_var: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_5b.branch_1.1.batch3d.bias: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_5b.branch_1.1.batch3d.running_mean: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_5b.branch_2.0.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_5b.branch_2.0.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_5b.branch_2.0.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_5b.branch_2.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_2.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_2.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_3.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_3.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_3.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_0.batch3d.running_var: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_0.batch3d.bias: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_0.batch3d.running_mean: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_1.0.batch3d.running_var: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_5c.branch_1.0.batch3d.bias: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_5c.branch_1.0.batch3d.running_mean: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_5c.branch_1.1.batch3d.running_var: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_1.1.batch3d.bias: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_1.1.batch3d.running_mean: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_2.0.batch3d.running_var: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_5c.branch_2.0.batch3d.bias: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_5c.branch_2.0.batch3d.running_mean: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_5c.branch_2.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_2.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_2.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_3.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_3.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_3.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.

Hi,
Thank you for your work, firstly.
I want to transfer the pre-training parameters in Tensorflow to PyTorch. But when I run "python i3d_tf_to_pt.py --rgb", I have the bugs as follows:
Additionally, I want to know, the pre-training parameters (model/model_rgb.pth) in PyTorch you provide if as same as the Tensorflow ? Thank you very much !

2018-11-02 16:23:32.948892: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Traceback (most recent call last):
  File "i3d_tf_to_pt.py", line 177, in <module>
    modality='rgb')
  File "i3d_tf_to_pt.py", line 73, in transfer_weights
    i3nception_pt.load_tf_weights(sess)
  File "/Users/cuixiankun01/project/baidu/kinetics_i3d_pytorch-master/src/i3dpt.py", line 310, in load_tf_weights
    self.load_state_dict(state_dict)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/torch/nn/modules/module.py", line 719, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for I3D:
        size mismatch for conv3d_1a_7x7.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_1a_7x7.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_1a_7x7.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_2b_1x1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_2b_1x1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_2b_1x1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for conv3d_2c_3x3.batch3d.running_var: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for conv3d_2c_3x3.batch3d.bias: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for conv3d_2c_3x3.batch3d.running_mean: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_3b.branch_0.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3b.branch_0.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3b.branch_0.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3b.branch_1.0.batch3d.running_var: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3b.branch_1.0.batch3d.bias: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3b.branch_1.0.batch3d.running_mean: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3b.branch_1.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3b.branch_1.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3b.branch_1.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3b.branch_2.0.batch3d.running_var: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_3b.branch_2.0.batch3d.bias: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_3b.branch_2.0.batch3d.running_mean: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_3b.branch_2.1.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_2.1.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_2.1.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_3.1.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_3.1.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3b.branch_3.1.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3c.branch_0.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_0.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_0.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_1.0.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_1.0.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_1.0.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_3c.branch_1.1.batch3d.running_var: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_3c.branch_1.1.batch3d.bias: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_3c.branch_1.1.batch3d.running_mean: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_3c.branch_2.0.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3c.branch_2.0.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3c.branch_2.0.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_3c.branch_2.1.batch3d.running_var: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3c.branch_2.1.batch3d.bias: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3c.branch_2.1.batch3d.running_mean: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_3c.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3c.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_3c.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4b.branch_0.batch3d.running_var: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_4b.branch_0.batch3d.bias: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_4b.branch_0.batch3d.running_mean: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_4b.branch_1.0.batch3d.running_var: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_4b.branch_1.0.batch3d.bias: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_4b.branch_1.0.batch3d.running_mean: copying a param of torch.Size([96]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 96]) in current model.
        size mismatch for mixed_4b.branch_1.1.batch3d.running_var: copying a param of torch.Size([208]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 208]) in current model.
        size mismatch for mixed_4b.branch_1.1.batch3d.bias: copying a param of torch.Size([208]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 208]) in current model.
        size mismatch for mixed_4b.branch_1.1.batch3d.running_mean: copying a param of torch.Size([208]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 208]) in current model.
        size mismatch for mixed_4b.branch_2.0.batch3d.running_var: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_4b.branch_2.0.batch3d.bias: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_4b.branch_2.0.batch3d.running_mean: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 16]) in current model.
        size mismatch for mixed_4b.branch_2.1.batch3d.running_var: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_4b.branch_2.1.batch3d.bias: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_4b.branch_2.1.batch3d.running_mean: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_4b.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4b.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4b.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_0.batch3d.running_var: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4c.branch_0.batch3d.bias: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4c.branch_0.batch3d.running_mean: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4c.branch_1.0.batch3d.running_var: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4c.branch_1.0.batch3d.bias: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4c.branch_1.0.batch3d.running_mean: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4c.branch_1.1.batch3d.running_var: copying a param of torch.Size([224]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 224]) in current model.
        size mismatch for mixed_4c.branch_1.1.batch3d.bias: copying a param of torch.Size([224]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 224]) in current model.
        size mismatch for mixed_4c.branch_1.1.batch3d.running_mean: copying a param of torch.Size([224]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 224]) in current model.
        size mismatch for mixed_4c.branch_2.0.batch3d.running_var: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4c.branch_2.0.batch3d.bias: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4c.branch_2.0.batch3d.running_mean: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4c.branch_2.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_2.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_2.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4c.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_0.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_0.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_0.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_1.0.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_1.0.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_1.0.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4d.branch_1.1.batch3d.running_var: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4d.branch_1.1.batch3d.bias: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4d.branch_1.1.batch3d.running_mean: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4d.branch_2.0.batch3d.running_var: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4d.branch_2.0.batch3d.bias: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4d.branch_2.0.batch3d.running_mean: copying a param of torch.Size([24]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 24]) in current model.
        size mismatch for mixed_4d.branch_2.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_2.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_2.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4d.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_0.batch3d.running_var: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4e.branch_0.batch3d.bias: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4e.branch_0.batch3d.running_mean: copying a param of torch.Size([112]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 112]) in current model.
        size mismatch for mixed_4e.branch_1.0.batch3d.running_var: copying a param of torch.Size([144]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 144]) in current model.
        size mismatch for mixed_4e.branch_1.0.batch3d.bias: copying a param of torch.Size([144]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 144]) in current model.
        size mismatch for mixed_4e.branch_1.0.batch3d.running_mean: copying a param of torch.Size([144]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 144]) in current model.
        size mismatch for mixed_4e.branch_1.1.batch3d.running_var: copying a param of torch.Size([288]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 288]) in current model.
        size mismatch for mixed_4e.branch_1.1.batch3d.bias: copying a param of torch.Size([288]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 288]) in current model.
        size mismatch for mixed_4e.branch_1.1.batch3d.running_mean: copying a param of torch.Size([288]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 288]) in current model.
        size mismatch for mixed_4e.branch_2.0.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4e.branch_2.0.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4e.branch_2.0.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4e.branch_2.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_2.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_2.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_3.1.batch3d.running_var: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_3.1.batch3d.bias: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4e.branch_3.1.batch3d.running_mean: copying a param of torch.Size([64]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 64]) in current model.
        size mismatch for mixed_4f.branch_0.batch3d.running_var: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4f.branch_0.batch3d.bias: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4f.branch_0.batch3d.running_mean: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_4f.branch_1.0.batch3d.running_var: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4f.branch_1.0.batch3d.bias: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4f.branch_1.0.batch3d.running_mean: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_4f.branch_1.1.batch3d.running_var: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_4f.branch_1.1.batch3d.bias: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_4f.branch_1.1.batch3d.running_mean: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_4f.branch_2.0.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4f.branch_2.0.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4f.branch_2.0.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_4f.branch_2.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_2.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_2.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_3.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_3.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_4f.branch_3.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_0.batch3d.running_var: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_5b.branch_0.batch3d.bias: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_5b.branch_0.batch3d.running_mean: copying a param of torch.Size([256]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 256]) in current model.
        size mismatch for mixed_5b.branch_1.0.batch3d.running_var: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_5b.branch_1.0.batch3d.bias: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_5b.branch_1.0.batch3d.running_mean: copying a param of torch.Size([160]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 160]) in current model.
        size mismatch for mixed_5b.branch_1.1.batch3d.running_var: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_5b.branch_1.1.batch3d.bias: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_5b.branch_1.1.batch3d.running_mean: copying a param of torch.Size([320]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 320]) in current model.
        size mismatch for mixed_5b.branch_2.0.batch3d.running_var: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_5b.branch_2.0.batch3d.bias: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_5b.branch_2.0.batch3d.running_mean: copying a param of torch.Size([32]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 32]) in current model.
        size mismatch for mixed_5b.branch_2.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_2.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_2.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_3.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_3.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5b.branch_3.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_0.batch3d.running_var: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_0.batch3d.bias: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_0.batch3d.running_mean: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_1.0.batch3d.running_var: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_5c.branch_1.0.batch3d.bias: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_5c.branch_1.0.batch3d.running_mean: copying a param of torch.Size([192]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 192]) in current model.
        size mismatch for mixed_5c.branch_1.1.batch3d.running_var: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_1.1.batch3d.bias: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_1.1.batch3d.running_mean: copying a param of torch.Size([384]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 384]) in current model.
        size mismatch for mixed_5c.branch_2.0.batch3d.running_var: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_5c.branch_2.0.batch3d.bias: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_5c.branch_2.0.batch3d.running_mean: copying a param of torch.Size([48]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 48]) in current model.
        size mismatch for mixed_5c.branch_2.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_2.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_2.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_3.1.batch3d.running_var: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_3.1.batch3d.bias: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.
        size mismatch for mixed_5c.branch_3.1.batch3d.running_mean: copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128]) in current model.

你解决这个问题了吗

Hi !
If you look at the error the size mismatches are between tensors which have the same number of elements, also they only happen on the batchnorm parameters.
Like "copying a param of torch.Size([128]) from checkpoint, where the shape is torch.Size([1, 1, 1, 1, 128])". My guess is that it might be a pytorch version issue. Which one are you using ?
An alternate solution might be to add the extra dimensions (using something like view(1, 1, 1, 1, -1) on each of the weights obtained from tensorflow probably somewhere here
Right now is pretty busy and I won't be able to look more into depth  into this  (meaning actually running code, experimenting with pytorch versions...) before the next 10 days :)
Best !
Yana

Hi,
Thanks for your advise.
I've found the problem. the shape of parameters from Tensorflow  is [1, 1, 1, 1, n], mismatch the size from PyTorch ([n]).
I've fixed the bug and make a pull request.
Thanks for your help and great work !
谢谢😁

Hi,
Thanks for your advise.
I've found the problem. the shape of parameters from Tensorflow is [1, 1, 1, 1, n], mismatch the size from PyTorch ([n]).
I've fixed the bug and make a pull request.
Thanks for your help and great work !
谢谢😁

$ python i3d_tf_to_pt.py --rgb
2018-11-07 15:09:42.636465: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
max diff : 0.0115141868591, mean diff : 2.91702690447e-05
mean val: tf 0.00250000040978 pt 0.00250000040978
max vals: tf 0.943557560444 pt 0.943576276302
max relative diff: tf 0.21941703558 pt 0.281093806028
===============
max diff : 0.00800371170044, mean diff : 4.55450135632e-05
mean val: tf 0.00249999971129 pt 0.00249999994412
max vals: tf 0.602078437805 pt 0.610082149506
max relative diff: tf 0.148675397038 pt 0.174640089273
===============
max diff : 0.0120238959789, mean diff : 4.93937077408e-05
mean val: tf 0.00249999994412 pt 0.00249999994412
max vals: tf 0.355097353458 pt 0.367121249437
max relative diff: tf 0.120408758521 pt 0.136891722679

Thank you for the pull request !
Before accepting it, I would like to look at what causes the problem (I still think it might be pytorch or tensorflow-version specific, so best before I merge would be to understand why it happened to you and not to me ! :) )
Best !
Yana

Thank you for the pull request !
Before accepting it, I would like to look at what causes the problem (I still think it might be pytorch or tensorflow-version specific, so best before I merge would be to understand why it happened to you and not to me ! :) )
Best !
Yana
Yes, you reminded me ! I update my Pytorch from 0.3 to 0.4.1 recently, and yesterday I found the similar problem in another program.

Thank you for the pull request !
Before accepting it, I would like to look at what causes the problem (I still think it might be pytorch or tensorflow-version specific, so best before I merge would be to understand why it happened to you and not to me ! :) )
Best !
Yana

Yes, you reminded me ! I updated my Pytorch from 0.3 to 0.4.1 recently, and yesterday I found the similar problem in another program.
Thank you for your help even you are very busy now, I hope you will have a great reward !😊
Best ！
Kun


Java Install Instructions for Mac Refer to Java10 (should be 8)

https://github.com/standardhealth/shr-cli/wiki/CIMPL-Installation#fhir-ig-generation-tool-1

Well, shoot.  Apparently I fixed it in the Windows instructions but missed it in the Mac instructions.  Thanks for reporting it.

Fixed in the doc.


"Create new" erroneously caches the last channel you created

Apologies for the bad title, I couldn't figure out how to phrase this.
Steps to reproduce:

Hit +

Create a new channel. Select any privacy preference.

Hit "save"

3.5 <-- bonus bug - on iOS the keyboard will get stuck open here.

Go to the home screen

Press +

You will now see the "New Channel" screen with the title of the channel you just created already prepopulated!

Good catch, thanks for the detailed notes 👍


[Bug]: Seeing Duplicate configuration on trying to save third environment for any DS

Is there an existing issue for this?

[X] I have searched the existing issues

Description
When I try to save a third environment on mongo  or postgres ds, I get a duplicate configuration error.
Steps To Reproduce

Go to an application in a workspace, having many custom environments
Configure Mongo for Staging, test and save, one custom env test and save, second custom env try to test and save
An error pops up on Save

https://github.com/appsmithorg/appsmith/assets/101863839/fd4cb3a0-002c-46d8-93a2-b9b9c009f595

Public Sample App
No response
Environment
Deploy Preview
Issue video log
No response
Version
Cloud

I have fixed the method, should not be seen in newly created datasources

Tested - not able to see the issue any more


CreateTable in HIVE connector

Syntax:
CREATE TABLE [IF NOT EXISTS] table_name (col_name data_type [NOT NULL], ...) [PARTITIONED BY (col_name data_type, ...)
This feature is disabled by default for the Hive connector.
Enable by setting 'hive.allow-create-table' to 'true'.

rebased in a new PR:
https://github.com/facebook/presto/pull/2401


feat: Helm chart v2

Description
This PR presents all changes we would like to introduce in Flowfuse Helm v2.
Related Issue(s)

Checklist

[x] I have read the contribution guidelines
[ ] Suitable unit/system level tests have been added and they pass
[ ] Documentation has been updated

[ ] Upgrade instructions
[ ] Configuration details
[ ] Concepts

[ ] Changes flowforge.yml?

[ ] Issue/PR raised on FlowFuse/helm to update ConfigMap Template
[ ] Issue/PR raised on FlowFuse/CloudProject to update values for Staging/Production

Labels

[ ] Backport needed? -> add the backport label
[ ] Includes a DB migration? -> add the area:migration label

Looks good, will let the tests finish and then approve


Support for derivatives?

Aquan toolbox is good for linear instruments(stocks/indices). But this is not sufficient to backtest options/futures strategies. Are there any plans to provide support for derivatives?

Hi, you can try using the newer version of the toolbox: https://links.auquan.com/auquan-toolbox, if the issue persists you can create an issue in the newer repo


Retest For HackerOne #2125690

Retesting H1 ticket, please ignore

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

Looks like it's fixed


[stable/fluent-bit] Add collection of node logs via systemd input

@kfox1111 @edsiper
What this PR does / why we need it:
Adds a boolean value that enabled kubelet log collection.
Checklist
[Place an '[x]' (no spaces) in all applicable fields. Please remove unrelated fields.]

[x] DCO signed
[x] Chart Version bumped
[x] Variables are documented in the README.md

So in my case, I'm using a managed kubernetes service (GKE) and want to worry as little as possible about the nodes, however the liveness and readiness probes failing are important events that relate to the workloads running atop which are outputted by kubelet.
On second thought, it would be nice for people managing their own clusters to collect all system logs... this PR might be too specific for my case, do you have any suggestions?

maybe make the Systemd_Filter  _SYSTEMD_UNIT=kubelet.service configurable? then you could gather all logs, or some subset of logs like just kubelet.service?

Hi @kfox1111 , changes have been made! Please review.


The whole module is not working now

Last time forgot to restart the server
TypeError: Cannot read property '_context' of undefined
getting this error on the latest version the older version is still working

could it be some dependency problem or something else because I know it is working on codesandbox

at Object.useContext (react.development.js:1446)
at MultiCascader.js:69

Found the exact problem, it's the version of Antd
I'm using 4.6.1
which doesn't have that context thing

Is there anything I can do to make it work on an older version of Antd ?

try to fork the code and modify the dependencies

I instead tried updating antd first but the dropdown was still moving with the main scroll bar

Fixed, please try<EMAIL_ADDRESS>

Last time forgot to restart the server
TypeError: Cannot read property '_context' of undefined
getting this error on the latest version the older version is still working

Have been fixed in<EMAIL_ADDRESS>

I instead tried updating antd first but the dropdown was still moving with the main scroll bar

If you provide a minimal online demo using code sandbox, I'd willing to help you out.

I have downgrade the antd version to 4.6.1 and it works just fine. https://codesandbox.io/s/dreamy-jennings-2y1ff?file=/src/App.tsx

oh thank you
Checking the latest version

okay, everything is working great now!!!
Thank you so much for your hard work


Add package_from_path method to ParsePackwerk

Allows a user to get a Package object from a filepath. The results are
memoized to provide fast subsequent lookups on the same filepath.

@geshwho Looks like type check is failing. Could ya push up a commit to fix that then we should be good to go?


Add license

Would it be possible for you to add a license for this project? Possible LGPL to match Racket's and SDL's license.
Thank you.

Sure, ill do it when i get home.
On Mon, Feb 15, 2016 at 1:22 PM, Leif Andersen<EMAIL_ADDRESS>wrote:

Would it be possible for you to add a license for this project? Possible
LGPL to match Racket's and SDL's license.
Thank you.
—
Reply to this email directly or view it on GitHub
https://github.com/cosmez/racket-sdl/issues/1.

Any update on the license?

Oh cool, thank you for adding that. I am going to add this to the package server. If you want to maintain the link rather than me, ping me and I'll take it down so you can have it up there.
Thank you so much. :)


Muti language support

Hi!
I am from China and I studying in New Zealand.
I am interest in to add Chinese for buzz. However as a begaining learner could you give any suggestion that how to do that.
Thx!

For multi language support you can contribute the *.po file for your language. At this point some issues have to be resolved for this to fully work, but this is on my todo, so when the issues are fixed multi language support will be available.
For some useful information see https://github.com/chidiwilliams/buzz/blob/main/CONTRIBUTING.md or existing localizations in https://github.com/chidiwilliams/buzz/tree/main/buzz/locale


Rabbitmq support

Added RabbitMQ output with the relevant configuration.
pika added as an option as this is the package described in RabbitMQ's tutorial

Thank for this PR. Could you check why all test results are failed ? something about the "pika" module.
 Traceback (most recent call last):\n  File "<string>", line 1, in <module>\n  File "/home/runner/work/dnstap-receiver/dnstap-receiver/dnstap_receiver/__init__.py", line 1, in <module>\n    from dnstap_receiver.receiver import start_receiver\n  File "/home/runner/work/dnstap-receiver/dnstap-receiver/dnstap_receiver/receiver.py", line 32, in <module>\n    from dnstap_receiver.outputs import output_rabbitmq\n  File "/home/runner/work/dnstap-receiver/dnstap-receiver/dnstap_receiver/outputs/output_rabbitmq.py", line 6, in <module>\n    from pika.adapters.blocking_connection import BlockingChannel\nModuleNotFoundError: No module named \'pika\'\n'

My bad.
I fixed the issue and verified it by building a wheel and importing in Python docker in order to be sure the environment is missing pika :)
build_wheel.sh:
#!/bin/bash
TAG=`git describe --tags --abbrev=0`
COMMIT=`git log -n1 --format=format:"%h"`
SETUP_PY=`sed "s/{{ version }}/$TAG-$COMMIT+PV/g" setup.j2`

python3 -c "$SETUP_PY" bdist_wheel

docker_test.sh
#!/bin/bash
docker run \
    -ti \
    -v "$PWD"/dist:/dist \
    python \
    python3 -m pip install /dist/dnstap_receiver-v4.4.1_01bb653_PV-py3-none-any.whl && python3 -c 'import dnstap_receiver' && echo "SUCCESS"


Sharing UI

This issue is for determining a sharing UI to implement (see #470 for original super-issue). The options are as follows:
Option 1 (combined with shareable link UI)

Option 2 (like Option 1, but showing title and version number as well)

Option 3 (separated from shareable link UI)

Please vote for which option you like best:

"hooray" for Option 1
"rocket" for Option 2
"eyes" for Option 3

and feel free to discuss as usual in the comment thread. Once we determine which option we want to go with, we can continue with an implementation.

I myself like Option 2 best, because the shareable links always feel like the final, backup method of sharing in case none of the readymade sharing options is what you want. Including the title and version number is a subtle cue to make sure you are sharing the dataset and revision you mean to.

From a meeting: Satra said he likes option 2 (rocket) with the bottom part sliced off, and then in addition, option 3 (eyes), which is where the sliced off part goes.
I'm for this also.
🚀 🪒 👀

Could also add DataLad/GitHub: https://github.com/dandisets/000029 (well, pointing to a  tag whenever we have them)

Here's a Frankenstein's monster screenshot showing what we'd like (as described by Mike above):

The placement of the shareable links menu is not correct; I placed it to the left of where it should go so it wouldn't crash into the sharing menu, for illustrative purposes. In the app, both menus will not generally be openable at the same time.
@dchiquit, is this sufficient to execute on this feature?

I'm surprised this is still open... I'm pretty sure we have solved this by now.


add dependency sodium-universal

Fixes #9

Can you change the binary to use sodium-native instead?
Il giorno sab 23 giu 2018 alle 10:04 Tom<EMAIL_ADDRESS>ha
scritto:

Fixes #9 https://github.com/mcollina/fastify-secure-session/issues/9
You can view, comment on, or merge this pull request online at:
https://github.com/mcollina/fastify-secure-session/pull/11
Commit Summary

add dependency sodium-universal

File Changes

M package.json
https://github.com/mcollina/fastify-secure-session/pull/11/files#diff-0
(3)

Patch Links:

https://github.com/mcollina/fastify-secure-session/pull/11.patch
https://github.com/mcollina/fastify-secure-session/pull/11.diff

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.

Using sodium-native instead does indeed fix it

Fixed!


This allows specifying context as arguments

Master rebased #400.
Fixes #389.
Allows specifying or overriding variables by adding key=value arguments after the template.
E.g.:
[marca@marca-mac2 test-cookiecutter-supervisor]$ cookiecutter --no-input https://github.com/msabramo/cookiecutter-supervisor.git program_name=foobar
Cloning into 'cookiecutter-supervisor'...
remote: Counting objects: 16, done.
remote: Compressing objects: 100% (13/13), done.
remote: Total 16 (delta 5), reused 8 (delta 2), pack-reused 0
Unpacking objects: 100% (16/16), done.
Checking connectivity... done.

[marca@marca-mac2 test-cookiecutter-supervisor]$ cat foobar/supervisor.conf
[program:foobar]
numprocs                 = 1
process_name             = %(program_name)s%(process_num)02d
user                     = foobaruser
command                  = gunicorn --bind <IP_ADDRESS>:$PORT --paste development.ini
startsecs                = 30
priority                 = 12
redirect_stderr          = true
autorestart              = true
autostart                = true
stopwaitsecs             = 10
stopsignal               = QUIT
directory                = /opt/webapp/foobar
stdout_logfile           = /var/log/sm/foobar-app-out.log
stdout_logfile_maxbytes  = 10MB
stdout_logfile_backups   = 0
stdout_capture_maxbytes  = 1MB
stderr_logfile           = /var/log/sm/foobar-app-err.log
stderr_logfile_maxbytes  = 10MB
stderr_logfile_backups   = 0
stderr_capture_maxbytes  = 1MB
environment              = NEW_RELIC_CONFIG_FILE="/opt/webapp/foobar/etc/newrelic.ini"

Current coverage is 99.65%

Merging #666 into master will not affect coverage as of e903b6d

@@            master    #666   diff @@
======================================
  Files           13      13
  Stmts          577     586     +9
  Branches         0       0
  Methods          0       0
======================================
+ Hit            575     584     +9
  Partial          0       0
  Missed           2       2

Review entire Coverage Diff as of e903b6d

Uncovered Suggestions

+0.35% via ...ookiecutter/utils.py#27...28

Powered by Codecov. Updated on successful CI builds.

:bell:

:bell:

👍

Cool, thanks @michaeljoseph for rebasing for me!


how to handle the response from the looping card's OnLoad event? The response get overwritten by the last API call.

Discussed in https://github.com/EnsembleUI/ensemble/discussions/173

Originally posted by gaurikalp January 18, 2023
As we call the Slot API on the onLoad event of the doctor card, the slot card is overwritten by the previous API call,
as recorded in the clip.
https://user-images.githubusercontent.com/115991454/213126524-edda71ca-e0e6-4bad-be07-a47ae0f54de7.mp4

@vusters what's the prescribed way to handle this?

Yes, By calling the Single API with nested JSON, this problem was fixed.

closing as the issue was resolved by making a different call


Placeholder ticket - ORC-12-2 and ORC-12-3 to not be blank in Imagemover's schema"

@BerniXiongA6 @jorg3lopez refine this ticket
Problem statement
DE reached out to us and shared that some test results were failing their system due to ORC-12-2 and ORC-12-3 being blanked. These tests results came from Imagemover.
After working on this issue, it was found that the schema from Imagemover was purposely blanking these fields. The reason was that these fields were being used, at some instances, for the patient's first-name and last-name.
When the name of a patient comes together with other information, such as their address, this becomes PHI/PII. In order to prevent a PHI/PII leak, imagemover asked us to blank both fields while they worked on a solution on their end.
What you need to know
The good news are that we followed up with Imagemover and they said that they had fixed this issue on their end by disabling the input fields for all OTC data. This prevents the use of ORC-12-2 and ORC-12-3 for all OTC tests.
Acceptance criteria
ORC-12-2 and ORC-12-3 should be populated on the outgoing file. This doesn't mean that the fields will be populated, they can be blanked on the inbound file.
To do

Make changes to schema from imagemover, that will allow these fields to be populated.

@jorg3lopez you rock, thank YOU for cleaning up the ticket AC!


closing wayland-native application crashes Gnome Shell (3.30.2)

If I open any wayland application (tested gnome-terminal, firefox on wayland) and move it to my secondary monitor, closing that window causes gnome-shell to crash. Doesn't happen when using xterm, firefox (for X), or qutebrowser in xwayland.
Edit: Additional Datapoints (will add as I find)

putting the wayland application in float mode before closing avoids the crash.
closing is only action I've found that causes the crash. moving the tile around the layout or to another monitor is fine.

Unfortunately, I can not confirm this (using gnome-terminal or firefox). Can you provide detailed instructions (e.g. shortcuts you used)

I could reproduce the error.
steps

Open gnome-terminal on the secondary screen.
Tile gnome-terminal (Floating mode -> Ctrl + e -> the next tiled mode)
Close gnome-terminal.

mayble relevant settings

Workspace  on primary displays only enabled.
Workspaces span display not enabled

crash log (var_log_messages from Gnomes problem reporting program)
var_log_messages.log


Add support for AWS GWLB

https://registry.terraform.io/providers/AviatrixSystems/aviatrix/latest/docs/resources/aviatrix_transit_gateway#enable_gateway_load_balancer

Added in 2.1.5.


drag & drop should delete first and insert next?

my test was first insert and delete.
version 1.4.0

no insert event trigger

sos pls

I`ve the same problem.
Bug due to additional element in the top of the list. Library somehow thinks that it is a draggable item. So the index is offset by 1.


EphemeralDiskUpdate

Updated info on ephemeral OS disks

@runcai
Can you review the proposed changes?
When the changes are ready for publication, add a #sign-off comment to signal that the PR is ready for the review team to merge.
#label:"aq-pr-triaged"
@MicrosoftDocs/public-repo-pr-review-team

#sign-off


exceptionInfoRequest never fires

Does this issue occur when all extensions are disabled?: Yes. Only one extension running, custom debug adapter.

VS Code Version: 1.67.2
OS Version: Ubuntu 22.04

Steps to Reproduce:
For the debug adapter response.body.supportsExceptionInfoRequest = true;
When an exception happens, send sendEvent(new StoppedEvent("exception", threadId))
Never reaches exceptionInfoRequest for the inline debug adapter implementation.

It seems to have been fixed by this issue https://github.com/microsoft/vscode/issues/113921 - however, as stated, doing the above does not trigger exceptionInfoRequest to be called.

So I've solved it.
If the source can't be found for the focused stack frame, exceptionInfoRequest will not fire at all.
And it's not documented anywhere either, that the "focused" stack frame from a stackFrameRequest is the "first one with source != null so these are two things you should seriously consider documenting.
In C++ the actual "exception" for an uncaught exception is just a signal of SIGABRT (or std::terminate) - these will not be in the workspace folder of any workspace (most likely) thus, as a debug adapter, returning the source file names for these, will make VSCode say "it can't open foo".
I suggest adding documentation for these things.

Sorry for the slow response. Since the exception info is shown in the vscode editor, then if the source can't be loaded in an editor, where do you expect it to be shown?
exceptionInfoRequest will be called when the UI needs it- I don't think we can document any guarantees about exactly when it will be called, and the DA shouldn't make assumptions.

This issue has been closed automatically because it needs more information and has not had recent activity. See also our issue reporting guidelines.
Happy Coding!


Menu title display error

Laravel Version: 5..4
Voyager Version: 1.0
PHP Version:7.1
Database Driver & Version:

Description:
After I upgrade from version 0.11 to version 1.0, all the menu or button name become abnormal , such as "voyager.dimmer.user_link_text" , "1 voyager.dimmer.user" .  I removed the voyager and reinstall again, problem is still there.

Please don't duplicate issues.  We can just reopen your other one.


build failed Undefined symbols for architecture arm64

On m1 mac. I have checked all closed issues and faq but not solved.
Undefined symbols for architecture arm64:   "_read_keys_from_content", referenced from:       +[TYActivatorUtils getSecretKey] in ThingSmartPairingCoreKit(TYActivatorUtils.o)       -[ThingSmartCore ThingCoreOrangesSmallTomato] in ThingSmartUtil(ThingSmartCore.o)   "_read_keys_from_file", referenced from:       -[ThingSmartCore ThingCoreStrawberriesWalnuts:ThingCoreStrawberriesPeach:] in ThingSmartUtil(ThingSmartCore.o)   "_thingctypt_CString", referenced from:       -[ThingSmartCore ThingCoreOrangesSmallTomato] in ThingSmartUtil(ThingSmartCore.o)   "_thingctypt_OCString", referenced from:       +[ThingBLELocalEncryption saveLocalEncryptionUserDefalutDouble:key:] in ThingSmartBLEKit(ThingBLELocalEncryption.o)       +[ThingBLELocalEncryption localEncryptionUserDefalutToData:] in ThingSmartBLEKit(ThingBLELocalEncryption.o)       +[ThingBLELocalEncryption saveLocalEncryptionData:filePath:] in ThingSmartBLEKit(ThingBLELocalEncryption.o)       +[ThingBLELocalEncryption getLocalEncryptionUserDefalutDouble:] in ThingSmartBLEKit(ThingBLELocalEncryption.o)       +[ThingBLELocalEncryption saveLocalEncryptionUserDefalutData:key:] in ThingSmartBLEKit(ThingBLELocalEncryption.o)       +[ThingBLELocalEncryption getLocalEncryptionUserDefalutData:] in ThingSmartBLEKit(ThingBLELocalEncryption.o)       +[ThingBLELocalEncryption saveLocalEncryptionDocumentData:filePath:] in ThingSmartBLEKit(ThingBLELocalEncryption.o)       ... ld: symbol(s) not found for architecture arm64 clang: error: linker command failed with exit code 1 (use -v to see invocation)

On m1 mac. I have checked all closed issues and faq but not solved.
Undefined symbols for architecture arm64: "_read_keys_from_content", referenced from: +[TYActivatorUtils getSecretKey] in ThingSmartPairingCoreKit(TYActivatorUtils.o) -[ThingSmartCore ThingCoreOrangesSmallTomato] in ThingSmartUtil(ThingSmartCore.o) "_read_keys_from_file", referenced from: -[ThingSmartCore ThingCoreStrawberriesWalnuts:ThingCoreStrawberriesPeach:] in ThingSmartUtil(ThingSmartCore.o) "_thingctypt_CString", referenced from: -[ThingSmartCore ThingCoreOrangesSmallTomato] in ThingSmartUtil(ThingSmartCore.o) "_thingctypt_OCString", referenced from: +[ThingBLELocalEncryption saveLocalEncryptionUserDefalutDouble:key:] in ThingSmartBLEKit(ThingBLELocalEncryption.o) +[ThingBLELocalEncryption localEncryptionUserDefalutToData:] in ThingSmartBLEKit(ThingBLELocalEncryption.o) +[ThingBLELocalEncryption saveLocalEncryptionData:filePath:] in ThingSmartBLEKit(ThingBLELocalEncryption.o) +[ThingBLELocalEncryption getLocalEncryptionUserDefalutDouble:] in ThingSmartBLEKit(ThingBLELocalEncryption.o) +[ThingBLELocalEncryption saveLocalEncryptionUserDefalutData:key:] in ThingSmartBLEKit(ThingBLELocalEncryption.o) +[ThingBLELocalEncryption getLocalEncryptionUserDefalutData:] in ThingSmartBLEKit(ThingBLELocalEncryption.o) +[ThingBLELocalEncryption saveLocalEncryptionDocumentData:filePath:] in ThingSmartBLEKit(ThingBLELocalEncryption.o) ... ld: symbol(s) not found for architecture arm64 clang: error: linker command failed with exit code 1 (use -v to see invocation)

you need go to https://iot.tuya.com/, and creat an SDK App, and download "ThingSmartCryption", than add into your Podfile.
you can see it in this docment
https://developer.tuya.com/cn/docs/app-development/integrate-sdk?id=Ka5d52ewngdoi#title-2-构建和下载 SDK

Thanks


References not working with EXT:container

          I have installed EXT:container.

In a two column container element i have a header element. When i use "Paste after as reference", then the new reference is created as an "unused element" and not in the container after the header element.

Payload paste reference after

Payload copy after

Originally posted by @christophlehmann in https://github.com/Kephson/paste_reference/issues/21#issuecomment-1914688146

Problem is that the information of tx_container_parent is missing. Adding the correct UID into DB and the reference is right.
Will create a PR.

This now works for me in the TYPO3_12 branch.

Make sure to add shortcut to your allowed CType container configuration, because otherwise you can't insert the reference.
'allowed' => ['CType' => 'header,text,image,shortcut'],


Table Height overflowing when 10 rows are showing

Is there a way to get the height of the table to match up with the height of all the rows? It seems like when displaying 10 rows, the last one gets cut off and there's a slight bit of scrolling
https://codesandbox.io/s/k22kzo8zzv

If you use responsive scroll, you can use the following to set a custom height:
const myTheme = createMuiTheme({
  overrides: {
    MUIDataTable: {
      responsiveScroll: {
        maxHeight: '580px'
      }
    }
});

<MuiThemeProvider theme={myTheme}>
      <MUIDataTable ... />
</MuiThemeProvider>

Bear in mind that if you have a horizontal scroll bar, it will take up some space at the bottom of the table, so you may want to test that condition to determine what your optimal table height should be.

@gabrielliwerant Thanks for the response. I can confirm that this works here
Will this be the accepted solution or is this just currently a workaround?

Also, @gabrielliwerant i know this is out of place, but do you know off-hand how to override hovering over the row to show the cursor hand and /or overriding the active state to highlight darker when being pressed? (I can open a separate issue if need be)

@iKrushYou I'm unsure of the exact intent, so I don't know whether or not my answer is the accepted solution. But as to your other question, here you go!
MuiTableRow: {
      root: {
        cursor: 'pointer'
      },
      hover: {
        '&$root': {
          '&:hover': {
            background: 'gray'
          }
        }
      }
    }

thanks @gabrielliwerant
that worked. Of course, it had to be on the MuiTableRow and not on the MUIDataTableRow as I was trying...
My intention was to highlight on hover and highlight darker when pressing. Luckily, i was able to change the style to &:hover:active and it worked as exptected

As of V2.10.0 it is now possible to use the prop responsive="scrollFullHeight" to make the table full height and a scroll behavior at the same time. That should fix the issue.
See https://github.com/gregnb/mui-datatables/pull/867


series_tasks config column and more emit_series search strings

Generate search strings for series with alternate names.
Use additional zero-padded episode numbers for series that are identified by sequence.

Generate search strings for series with alternate names.
Use additional zero-padded episode numbers for series that are identified by sequence.

I think we solved both of these with alternate pulls now.


feat: Use regular expressions to parse the log statement

This PR ties us very close to the current log-format of the Datadog lib, and makes it brittle if it changes. The gain, as far as I see it, is very minimal; mostly moving some parts of the msg into other fields in the log.
What issue are you trying to solve here?

I'm wanted to address the noise in the msg field that in the current implementation causes quite some noise, but I'm fine with dropping this.

FWIW: Datadog has already implemented a leveled logger in their v2-dev branch: https://github.com/DataDog/dd-trace-go/pull/2480

Then we wait for that and upgrade instead.


Font-size is too large on login screen when there is an error on submit

Bug Report
What is the current behavior?
When a user submit incorrect details on the login screen, the error font-size is too large
What is the expected behavior?
Should be of reasonable font-size
What steps did you take to get this behavior?
Login with incorrect information
Additional Info
Operating System
Browser
Screenshots

I'll take this)

Sorry @Ckpuna4 I opened this Issue for myself but you can definitely submit a PR for it! I'm going through the website right now for design issues and there should be some easy ones. Just be on the look out for em.

@lebilly yes, I saw :)


Admin panel to list all vocabularies

We implemented a working Admin panel in https://github.com/ulbmuenster/invenio-vocabularies/tree/all_vocabularies_endpoint

The API is yet to be defined and also we are not sure why one would need the System badge?

missing:

System Badge
optional Delete Button

@slint is it true that we have three types of vocabularies?

system "custom" vocabularies (names, affiliations, awards, funders) (system badge)
system "generic" vocabularies (licenses, languages...) (no badge)
user defined new vocabularies (in the screenshot code:developmentStatus) - are deleteable


chore: use named threads for store sync threadpool

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#429
master

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Merge activity

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Compiling issue

Hi,
I was trying to use good_lp to solve the model in the readme example. However, I had a compiling issues here:
linking with `cc` failed: exit status: 1

ld: warning: ignoring duplicate libraries: '-lm'
ld: library 'CbcSolver' not found
clang: error: linker command failed with exit code 1 (use -v to see invocation)

Could you provide some suggestions on how to resolve this issue?
Thanks,
Hang

Same issue, using m1

If you're on Debian you need to install some libraries
sudo apt install coinor-libcbc-dev -y


Support Basic Authentication

Added two new configuration options:
BASIC_AUTH_USERNAME (default: escape)
BASIC_AUTH_PASSWORD
Also available in configuration files as basic_auth_username and basic_auth_password.
Setting the password will enable basic authorization for all requests.


[Feature Request]: Pipeline Executor online documentation should stress much more the capability to run a named sub pipeline from your meta table and pass in all variables

What would you like to happen?
Just a suggestion, but in the Pipeline Executor online documentation one feature that should be stressed much more is the awesome capability that you can run a named sub pipeline from your meta table and pass in all variables...
Issue Priority
Priority: 3
Issue Component
Component: Documentation

the pipeline executor doc page already mentions this:
If you enable the "Inherit all variables from the pipeline" option, all the variables defined in the parent pipeline are passed to the pipeline.


Fix: Fixed crash that can occur when calculating folder sizes

Resolved / Related Issues

Closes #11006

Details of Changes

Use ConcurrentDictionary to make contains-->add critical path atomic

Validation
How did you test these changes?

[X] Built and ran the app
[X] Tested the changes for accessibility

@jiejasonliu can you add a code comment explaining this change for future reference?

@jiejasonliu can you add a code comment explaining this change for future reference?

done!