Comments (8)
mortonjt
commented on June 12, 2024
Hi, you need a couple of intermediate steps to do end-to-end training. I'd
start with the training script
https://github.com/flatironinstitute/deepblast/blob/master/scripts/deepblast-trai
I"m not sure what you are trying to accomplish with your test. I'd
recommend looking at the unittests
https://github.com/flatironinstitute/deepblast/blob/master/deepblast/tests/test_sw.py
…On Tue, Feb 27, 2024 at 6:30 AM SorrowAir ***@***.***> wrote:
Hi,
I want to do some end-to-end training using your sw_cuda modual. But it
seems that I don't how it works. My code:
from deepblast.sw_cuda import SmithWatermanDecoder as SWDecoderCUDA
ddp = SmithWatermanDecoder(operator='softmax')
match = torch.randn(100, 120, 108).requires_grad_(True)
gap = torch.zeros(100, 120, 108).requires_grad_(True)
aln = ddp.decode(match.to(device), gap.to(device))
In my understanding, aln should return the probability of alignment.
However, the aln tensor always get 0 in the first row and column, which
means aln don't return what I want. So is my code wrong, or I just
misunderstand the output of ddp.decode?
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Zhu-Siqi
commented on June 12, 2024
Thanks for your quick reply!
I have used my model to create the match and gap scoring matrices, and expect to use your differentiable_sw modual to get the alignment prediction. In other words, I have match scoring matrix M # (size: a*b) and gap scoring matrix G # (size: a*b), a and b are the lengths of two sequence. Now, I want to get a align matrix A # (size: a*b) and A[i][j] is the predicted alignment of the ith and jth residue. How can I get such a align matrix from your differentiable_sw modual?
Due to the lack of comments, it is a little difficult to understand the result returned by SmithWatermanDecoder. Could you explain what the aln tensor means and add some comments in your code? Your code is elegantly written but lacks comments, which hinders its readability XD. As I just want to use a modual from your work instead of finetuning your model, the comments are not enough to tell me where I can start my work.
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mortonjt
commented on June 12, 2024
I see the confusion.
The `aln` matrix here is the expected traceback matrix (i.e. the e matrix
in algorithm 1 in the paper :
https://www.nature.com/articles/s41587-023-01917-2#Sec10)
Also see equation 11 here https://arxiv.org/pdf/1802.03676.pdf
…On Tue, Feb 27, 2024 at 9:15 AM SorrowAir ***@***.***> wrote:
Thanks for your quick reply!
I have used my model to create the match and gap scoring matrices, and
expect to use your differentiable_sw modual to get the alignment
prediction. In other words, I have match scoring matrix M # (size: N*M)
and gap scoring matrix G # (size: N*M), N and M are the lengths of two
sequence. Now, I want to get a align matrix A # (size: N*M) and A[i][j]
is the predicted alignment of the ith and jth residue. How can I get such a
align matrix from your differentiable_sw modual?
Due to the lack of comments, it is a little difficult to understand the
result returned by SmithWatermanDecoder. Could you explain what the aln
tensor means and add some comments in your code? Your code is elegantly
written but lacks comments, which hinders its readability XD.
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Zhu-Siqi
commented on June 12, 2024
Does the aln matrix means the expected alignment between the two proteins? Why do I always get 0 in the first row and column of aln matrix and 1 as the last element of 'aln' matrix? The picture below shows the result of my test code.
Because I define the match and gap scoring matrices randomly, the locations of gap should be different. Do I confuse what the align matrix means or give the inappropriate inputs?
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Zhu-Siqi
commented on June 12, 2024
After running your test code (https://github.com/flatironinstitute/deepblast/blob/master/deepblast/tests/test_sw.py), I also get the same result.
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mortonjt
commented on June 12, 2024
Hi yes this is by construction -- we zero out the first row / column to make autograd work ...
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Zhu-Siqi
commented on June 12, 2024
Thanks! I understand the zeros in the first row and column. However, why is the last element always one? If so, it means that the last element is always aligned.
Thanks again for your reply! I also look forward to see an example like https://github.com/spetti/SMURF/blob/main/examples/SSW_examples/sw_in_tensorflow_pytorch.ipynb. Your work is similar with it. However, without a good example file, I couldn't follow your work well :(
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mortonjt
commented on June 12, 2024
Got it. Yes we generate those types of alignment visualizations as depicted in the tensorboard readouts.
https://github.com/flatironinstitute/deepblast/blob/master/deepblast/trainer.py#L248-L251
We are currently in the middle of doing a overhaul of the tutorials, more updates to come by July
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Related Issues (20)
- Consistent usage of 'cuda' or 'gpu'
- Pass the device in load_model to Neural aligner HOT 1
- cannot import name 'LightningAligner' from 'deepblast.trainer' HOT 2
- DeepBLAST issue with model load HOT 3
- mat1 and mat2 must have the same dtype HOT 1
- Encountering an Issue While Building a Queryable Protein Sequence Database HOT 2
- Running time for searching query
- Running time for large query sequences HOT 1
- Smith-waterman test broken HOT 1
- ImportError: cannot import name 'LightningAligner' from 'deepblast.trainer' for deepblast and AttributeError: 'tuple' object has no attribute 'strip' for tm-vec HOT 12
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- RuntimeError: The NVIDIA driver on your system is too old HOT 3
- After entering the command line, the program keeps running without any result. HOT 10
- Questions about evaluation metrics for sequence alignment HOT 4
- Unexpected keys in state_dict when I run this code on cpu HOT 3
- TM-score calculation for DeepBlast HOT 4
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