neurosys-pl / objects_counting_dmap Goto Github PK

How I can run this part? Maybe I could be wrong...
This is what I am running:
% python infer.py -n UNet -c cell_FCRN_A.pth -i /Users/elena/Desktop/objects_counting_dmap-master/examples/example_cell.png --visualize

The error:
Traceback (most recent call last):
File "infer.py", line 128, in
infer()
File "/opt/anaconda3/lib/python3.8/site-packages/click/core.py", line 829, in call
return self.main(*args, **kwargs)
File "/opt/anaconda3/lib/python3.8/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/opt/anaconda3/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/opt/anaconda3/lib/python3.8/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "infer.py", line 83, in infer
network.load_state_dict(torch.load(checkpoint.name))
File "/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1497, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for DataParallel:
Missing key(s) in state_dict: "module.block1.0.0.weight", "module.block1.0.1.weight", "module.block1.0.1.bias", "module.block1.0.1.running_mean", "module.block1.0.1.running_var", "module.block1.1.0.weight", "module.block1.1.1.weight", "module.block1.1.1.bias", "module.block1.1.1.running_mean", "module.block1.1.1.running_var", "module.block2.0.0.weight", "module.block2.0.1.weight", "module.block2.0.1.bias", "module.block2.0.1.running_mean", "module.block2.0.1.running_var", "module.block2.1.0.weight", "module.block2.1.1.weight", "module.block2.1.1.bias", "module.block2.1.1.running_mean", "module.block2.1.1.running_var", "module.block3.0.0.weight", "module.block3.0.1.weight", "module.block3.0.1.bias", "module.block3.0.1.running_mean", "module.block3.0.1.running_var", "module.block3.1.0.weight", "module.block3.1.1.weight", "module.block3.1.1.bias", "module.block3.1.1.running_mean", "module.block3.1.1.running_var", "module.block4.conv.0.0.0.weight", "module.block4.conv.0.0.1.weight", "module.block4.conv.0.0.1.bias", "module.block4.conv.0.0.1.running_mean", "module.block4.conv.0.0.1.running_var", "module.block4.conv.0.1.0.weight", "module.block4.conv.0.1.1.weight", "module.block4.conv.0.1.1.bias", "module.block4.conv.0.1.1.running_mean", "module.block4.conv.0.1.1.running_var", "module.block5.conv.0.0.0.weight", "module.block5.conv.0.0.1.weight", "module.block5.conv.0.0.1.bias", "module.block5.conv.0.0.1.running_mean", "module.block5.conv.0.0.1.running_var", "module.block5.conv.0.1.0.weight", "module.block5.conv.0.1.1.weight", "module.block5.conv.0.1.1.bias", "module.block5.conv.0.1.1.running_mean", "module.block5.conv.0.1.1.running_var", "module.block6.conv.0.0.0.weight", "module.block6.conv.0.0.1.weight", "module.block6.conv.0.0.1.bias", "module.block6.conv.0.0.1.running_mean", "module.block6.conv.0.0.1.running_var", "module.block6.conv.0.1.0.weight", "module.block6.conv.0.1.1.weight", "module.block6.conv.0.1.1.bias", "module.block6.conv.0.1.1.running_mean", "module.block6.conv.0.1.1.running_var", "module.block7.0.0.weight", "module.block7.0.1.weight", "module.block7.0.1.bias", "module.block7.0.1.running_mean", "module.block7.0.1.running_var", "module.block7.1.0.weight", "module.block7.1.1.weight", "module.block7.1.1.bias", "module.block7.1.1.running_mean", "module.block7.1.1.running_var", "module.density_pred.weight".
Unexpected key(s) in state_dict: "module.model.0.0.0.weight", "module.model.0.0.1.weight", "module.model.0.0.1.bias", "module.model.0.0.1.running_mean", "module.model.0.0.1.running_var", "module.model.0.0.1.num_batches_tracked", "module.model.0.1.0.weight", "module.model.0.1.1.weight", "module.model.0.1.1.bias", "module.model.0.1.1.running_mean", "module.model.0.1.1.running_var", "module.model.0.1.1.num_batches_tracked", "module.model.0.2.0.weight", "module.model.0.2.1.weight", "module.model.0.2.1.bias", "module.model.0.2.1.running_mean", "module.model.0.2.1.running_var", "module.model.0.2.1.num_batches_tracked", "module.model.2.0.0.weight", "module.model.2.0.1.weight", "module.model.2.0.1.bias", "module.model.2.0.1.running_mean", "module.model.2.0.1.running_var", "module.model.2.0.1.num_batches_tracked", "module.model.2.1.0.weight", "module.model.2.1.1.weight", "module.model.2.1.1.bias", "module.model.2.1.1.running_mean", "module.model.2.1.1.running_var", "module.model.2.1.1.num_batches_tracked", "module.model.2.2.0.weight", "module.model.2.2.1.weight", "module.model.2.2.1.bias", "module.model.2.2.1.running_mean", "module.model.2.2.1.running_var", "module.model.2.2.1.num_batches_tracked", "module.model.4.0.0.weight", "module.model.4.0.1.weight", "module.model.4.0.1.bias", "module.model.4.0.1.running_mean", "module.model.4.0.1.running_var", "module.model.4.0.1.num_batches_tracked", "module.model.4.1.0.weight", "module.model.4.1.1.weight", "module.model.4.1.1.bias", "module.model.4.1.1.running_mean", "module.model.4.1.1.running_var", "module.model.4.1.1.num_batches_tracked", "module.model.4.2.0.weight", "module.model.4.2.1.weight", "module.model.4.2.1.bias", "module.model.4.2.1.running_mean", "module.model.4.2.1.running_var", "module.model.4.2.1.num_batches_tracked", "module.model.6.0.0.weight", "module.model.6.0.1.weight", "module.model.6.0.1.bias", "module.model.6.0.1.running_mean", "module.model.6.0.1.running_var", "module.model.6.0.1.num_batches_tracked", "module.model.6.1.0.weight", "module.model.6.1.1.weight", "module.model.6.1.1.bias", "module.model.6.1.1.running_mean", "module.model.6.1.1.running_var", "module.model.6.1.1.num_batches_tracked", "module.model.6.2.0.weight", "module.model.6.2.1.weight", "module.model.6.2.1.bias", "module.model.6.2.1.running_mean", "module.model.6.2.1.running_var", "module.model.6.2.1.num_batches_tracked", "module.model.8.0.0.weight", "module.model.8.0.1.weight", "module.model.8.0.1.bias", "module.model.8.0.1.running_mean", "module.model.8.0.1.running_var", "module.model.8.0.1.num_batches_tracked", "module.model.8.1.0.weight", "module.model.8.1.1.weight", "module.model.8.1.1.bias", "module.model.8.1.1.running_mean", "module.model.8.1.1.running_var", "module.model.8.1.1.num_batches_tracked", "module.model.8.2.0.weight", "module.model.8.2.1.weight", "module.model.8.2.1.bias", "module.model.8.2.1.running_mean", "module.model.8.2.1.running_var", "module.model.8.2.1.num_batches_tracked", "module.model.10.0.0.weight", "module.model.10.0.1.weight", "module.model.10.0.1.bias", "module.model.10.0.1.running_mean", "module.model.10.0.1.running_var", "module.model.10.0.1.num_batches_tracked", "module.model.10.1.0.weight", "module.model.10.1.1.weight", "module.model.10.1.1.bias", "module.model.10.1.1.running_mean", "module.model.10.1.1.running_var", "module.model.10.1.1.num_batches_tracked", "module.model.10.2.0.weight", "module.model.10.2.1.weight", "module.model.10.2.1.bias", "module.model.10.2.1.running_mean", "module.model.10.2.1.running_var", "module.model.10.2.1.num_batches_tracked", "module.model.12.0.0.weight", "module.model.12.0.1.weight", "module.model.12.0.1.bias", "module.model.12.0.1.running_mean", "module.model.12.0.1.running_var", "module.model.12.0.1.num_batches_tracked", "module.model.12.1.0.weight", "module.model.12.1.1.weight", "module.model.12.1.1.bias", "module.model.12.1.1.running_mean", "module.model.12.1.1.running_var", "module.model.12.1.1.num_batches_tracked", "module.model.12.2.0.weight", "module.model.12.2.1.weight", "module.model.12.2.1.bias", "module.model.12.2.1.running_mean", "module.model.12.2.1.running_var", "module.model.12.2.1.num_batches_tracked".

Hi!
Ifind you're work interesting, I'd like to test it. I have downloaded mall dataset with your get_data.py and I started to train with train.py. It's been 2 or 3 hours already, and all I can see in console is "Epoch 1". How long (aprox) should an epoch take? Is it possible it takes so long? Or I do something wrong?

Hello, I find your repo very interesting and helpful. Could you please upload your inference code? Thank you

Hello, thank you for your code. I have a question about the Mall, in many places, we should make use of the feat to build roi, but I don't find any operation about the feat. So can you tell me how to deal with the roi in the dataset of Mall ?
Looking forward your reply!

I hope you will provide infer.py for inference from image.
Thank you !

h5.create_dataset('images', (size, in_channels, *img_size))

Can you elaborate this statement, specially '*img_size' whats mean?

Hello,
Can I use my own custom dataset?

Thanks

Can you provide the pretrained checkpoint if there is no copyrights issues?

Hello,Is the generated h5 file (train.h5/valid.h5)a collection of h5 images generated by each image? Thank you

I'm new in python but used to program on Matlab. I would like to reproduce this example. The network seems to work fine but I don't understand why the plots aren't work. Tried everything, but I can't see nothing either on Spyder or PyCharm. How do I get the plot working? Thanks.