I want to segment pelvic in MRI from the SMIR dataset using the MONAILABEL plugin. I have read relatively a lot about this plugin. However, I can’t perform the segmentation well enough yet.
These are the steps I take to do so:
connecting the server using the anaconda prompt
enabling the plugin in Slicer and loading one image
labeling the pelvis manually using the paint button in the scribbles part and the updating
click “submit label”
and then repeating the process for some other images while the network is being trained.
After the training process, I actually could not see anything when opening the mask files.
I also encounter these two errors:
" AssertionError: Not a valid Label "
" TypeError: object of type ‘NoneType’ has no len() "
Is it the correct way of using MONAI or am I missing something?
Which aspects should I take into account before starting the process?
I would appreciate your help and suggestions.
Thank you
Related
I am training a Neural Network model with pytorch.
Since this model is very complicated, I made use of the pytorch_xla package to use TPU. I finished training the model and now I want to save the weight so I will be able to use them from any envrionment.
I tried to save the data like so
file_name = "model_params"
torch.save(model.state_dict(), file_name)
and when I tried to load them (from environment which does not support TPU)
model.load_state_dict(torch.load(file name))
I got the following error
NotImplementedError: Could not run 'aten::empty_strided' with arguments from the 'XLA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::empty_strided' is only available for these backends: [CPU, Meta, BackendSelect, Named, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, UNKNOWN_TENSOR_TYPE_ID, AutogradMLC, AutogradHPU, AutogradNestedTensor, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].
Is there a way to do what I want?
i want to train a neural network with flexible output size. In the beginning, i used the matlab deep network designer to manually replace the classification and fully connected layer to the desired output size. Now, i want to automatically replace it, using a script.
Which command is working for that?
Simply trying the line:
net.Layers(142,1).InputSize = 10;
gives me the error message
Unable to set the 'InputSize' property of class 'FullyConnectedLayer' because it is read-only.
Trying to replace the complete layer (not only inputsize) is resulting in the same error message.
Is this possible with matlab, and if yes, which commands will do the job?
Thanks in advance!
Okay, i think i solved it. When changing the network manually inside of the deepnetworkdesigner, there is the possibility to click "export code" which exports the desired code for the adapted output size
I want to make a network graph which shows the distribution of our documents in our folder structure.
I have the nodefile, edgefile and gephi graph file in this location:
https://1drv.ms/f/s!AuVfRBdVHkO7hgs5K9r9f7jBBAUH
What I do is:
Run the algorithm ForceAtlas2 with scaling 10-20, dissuade hub marked and prevent overlap marked, all other standard setting.
What I get is a graph with groups radial/spherical distributed. However, what I want is a tree directed network graph.
Anyone know how I can adjust Gephi to make this?
Thanks!
I just found a solution.
I tested the file format as shown on the Yed site "import excel file" page
http://yed.yworks.com/support/manual/import_excel.html
This gave me the Yed import dialog (took a life time to figure out that it's a pop up menu and not selectable through the standard menu)
Anyway, it worked and I've adjusted the test files with the data prepared for the Gehpi. This was pretty easy, I could used the source target ID's etc. Just copy paste.
I load it into Yed and used some directed and radial clustering algorithms on it. Works fine!
Below you can find the excel node/edge file used to import in Yed and the graph file you can open with Yed to see the final radial result.
https://1drv.ms/f/s!AuVfRBdVHkO7hg6DExK_eVkm5_mR
Only thing to figure out is how to combine the weight (which represents the number of documents) with the node size.
Unfortunately, as of version 0.9.0, Gephi no longer supports hierarchical graphs. Maybe try using a previous version?
Other alternatives involve more complex software, such as Graphviz, but you need a .dot file instead of your .csv. I looked all over, but could not find an easy-to-use csv to dot converter.
You could try looking at d3-hierarchy, a node.js program, but then again you need to use the not-so-user-friendly npm. If you look at the link, it looks like it can produce the kind of diagram you're looking for.
I 'm new here, and I am encountering a problem. I have taken an already existing code for 2 new products entering the market and I am trying to modify it to support 3 products. I have added all the extra variables and conditions needed but it shows an error on plotting. Could it be the excel or...? I could use some help on this...If any extra info is needed, I 'd be happy to provide you! Thanks for your time in advance!
the error is:
http://prntscr.com/9gy84l
It appears that you added variables and conditions, but did not add an additional plot pen to the plot. You can add the pen by editing the plot itself.
I am trying to create a graph of the connections between the users in my database using nodebox(ubuntu 12.04, python 2.7, django 1.3), but when I enter the following instructions, I get the error message underneath it:
"""
g.draw(weighted=False, directed=False, highlight=[], traffic=None)
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2721, in run_code
exec code_obj in self.user_global_ns, self.user_ns
File "<ipython-input-1-0a219395b690>", line 1, in <module>
g.draw(weighted=False, directed=False, highlight=[], traffic=None)
File "/home/raymond/Documents/exchange/cet/cet/graph/__init__.py", line 453, in draw
self.update()
File "/path/to/myproject//graph/__init__.py", line 416, in update
self.x = _ctx.WIDTH - max.x*self.d - min_.x*self.d
AttributeError: 'NoneType' object has no attribute 'WIDTH'
"""
How can I get nodebox to draw my graph? should I add a try/catch or if statement to graph/init.py in order to prevent object of type none from being written to? I know displaying more code would help solve the problem faster; I am running into formatting problem, but I will add code to the question as soon as I can.
Nodebox has a graph web page which is concise, informative and seemed what I need. As the file to download is on the same page it seemed ideal, BUT it fails to mention it is Nodebox 1 which is for Mac ...only!
Then a further rummage finds NodeBox OpenGL which has a new methodology and is cross platform, BUT it only lists compatibility with python 2.5-2.6 (so with 2.7 should be worth a try?) BUT installing the graphics util Pyglet failed the first time ....so more investigation required.
Sadly Nodebox 3 seems all different and haven't yet seen a comparable graph command
Looking back on the outcome
After achieving an install, I prototyped network viewer with a mouse selectable attribute viewer, low frame rate and a constant data set. A flavour of the interactiveness is demonstrated in this site
http://www.visualthesaurus.com/app/view?word=link
(try a click and drag on the central word)
But problems I needed to solve were how to
show a live data set consistently ie dealing with orphans,
transitions as branches you may be viewing may disappear from the
data
partitioning the viewing of large amounts of data ie view 2 to 20 nodes from several hundred items
displaying data without overlapping/obscuring or going off the display area
displaying differences when not on the current level
portability
...
In my case a periodically updated, structured html table with colours and zoom levels was the best solution