Readme section 5.2
mepster opened this issue · comments
The README outlines installation and a tutorial for use.
Section 5 says:
Visualiazation of DNABERT consists of 2 steps. Calcualate attention scores and Plot.
However section 5.2 seems to be incomplete. It consists only of:
####5.2 Plotting tool
Do you have any further instructions how to use the plotting tool?
Thanks for making DNABERT available!
P.S. The following generates one plot for me. Not sure I know what it is. :-)
pip install matplotlib
pip install seaborn
cd data_process_template
export KMER=6
export MODEL_PATH=../ft/$KMER
python ../visualize.py --model_path $MODEL_PATH --kmer $KMER
You can use BerViz as well for visualization. DNABert is available on the HuggingFace platform, find it here: https://huggingface.co/zhihan1996. Load it using HiggingFace then use BerViz for visualization of weights.
I assume you can load it like this:
from transformers import AutoTokenizer, AutoModel
from bertviz import head_view, model_view
tokenizer = auto_tokenizer.from_pretrained('https://huggingface.co/zhihan1996/DNA_bert_6', do_lower_case=False)
model = auto_model.from_pretrained('https://huggingface.co/zhihan1996/DNA_bert_6', output_attentions=True)
def call_html():
display(IPython.core.display.HTML('''
<script src="/static/components/requirejs/require.js"></script>
<script>
requirejs.config({
paths: {
base: '/static/base',
"d3": "https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.8/d3.min",
jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',
},
});
</script>
'''))
sequence = # your sequence
call_html()
inputs = tokenizer.encode_plus(sequence, return_tensors='pt', add_special_tokens=True)
input_ids = inputs['input_ids']
attention = model(input_ids.to(device))[-1]
input_id_list = input_ids[0].tolist() # Batch index 0
tokens = tokenizer.convert_ids_to_tokens(input_id_list)
model_view(attention, tokens)
I haven't run this code, however, I have written this code and it works with ESM and ProtTrans models, I assume it works with DNABert as well. Though there might be slight differences that you should handle, but this is the general way of doing it.
Thanks @Moeinh77 !
I would greatly appreciate your wise feedback on the following.
If I modify your code to this
%matplotlib widget
from bertviz import head_view, model_view
from transformers import AutoTokenizer, AutoModel
import IPython
# I downloaded the model to a local directory 'DNA_bert_6'
tokenizer = AutoTokenizer.from_pretrained('DNA_bert_6', do_lower_case=False)
model = AutoModel.from_pretrained('DNA_bert_6', output_attention=True)
def call_html():
display(IPython.core.display.HTML('''
<script src="/static/components/requirejs/require.js"></script>
<script>
requirejs.config({
paths: {
base: '/static/base',
"d3": "https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.8/d3.min",
jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',
},
});
</script>
'''))
sequence = 'CACAGCACAGCCCAGCCAAGCCAGGCCAGCCCAGCCCAGCCAAGCCACGCCACTCCACTACACTAGACTAGGCTAGGCTAGGCCAGGCCCGGCCCTGCCCTGCCCTGTCCTGTCCTGTCCTGTCCTGTCCTGTCCTGCCCTGCACTGCAGTGCAGCGCAGCCCAGCCCAGCCCCGCCCCCCCCCCTCCCCTGCCCTGTCCTGTACTGTAGTGTAGGGTAGGGTAGGGGAGGGGTGGGGTCGGGTCTGGTCTGGTCTGGTCTGGACTGGAATGGAACGGAACAGAACAGAACAGCACAGCCCAGCCAAGCCAGGCCAGGCCAGGACAGGAGAGGAGTGGAGTGGAGTGGAGTGGTGTGGTTTGGTTTGGTTTAGTTTAATTTAAGTTAAGATAAGAGAAGAGGAGAGGCGAGGCAAGGCAGGGCAGGGCAGGGCAGGGGAGGGGAGGGGAGGGGAGTGGAGTCGAGTCGAGTCGCGTCGCCTCGCCTCGCCTTGCCTTGCCTTGCCTTGCCTTGCCCTGCCCTGCCCTGCCCTGTCCTGTGCTGTGCTGTGCCGTGCCATGCCACGCCACACCACAC'
#print(sequence, len(sequence))
call_html()
inputs = tokenizer.encode_plus(sequence, return_tensors='pt', add_special_tokens=True)
input_ids = inputs['input_ids']
device = 'cpu'
attention = model(input_ids.to(device))[-1]
print(attention)
input_id_list = input_ids[0].tolist() # Batch index 0
tokens = tokenizer.convert_ids_to_tokens(input_id_list)
model_view(attention, tokens)
I get the error
============================================================
<class 'transformers.tokenization_bert.BertTokenizerFast'>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_131706/782624532.py in <module>
7 # Also note I commented out "output_attention=True"
8 tokenizer = AutoTokenizer.from_pretrained('DNA_bert_6', do_lower_case=False)
----> 9 model = AutoModel.from_pretrained('DNA_bert_6', output_attention=True)
10
11 def call_html():
~/Repos/DNABERT/src/transformers/modeling_auto.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
382 for config_class, model_class in MODEL_MAPPING.items():
383 if isinstance(config, config_class):
--> 384 return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
385 raise ValueError(
386 "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
~/Repos/DNABERT/src/transformers/modeling_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
645
646 # Instantiate model.
--> 647 model = cls(config, *model_args, **model_kwargs)
648
649 if state_dict is None and not from_tf:
TypeError: __init__() got an unexpected keyword argument 'output_attention'
But if I comment out 'output_attention' like this:
model = AutoModel.from_pretrained('DNA_bert_6')#, output_attention=True)
then I get the error:
============================================================
<class 'transformers.tokenization_bert.BertTokenizerFast'>
tensor([[ 0.6328, -0.1791, 0.0956, -0.5731, -0.1763, 0.0412, 0.0398, -0.1986,
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0.2291, 0.3454, 0.0991, -0.5713, -0.1992, -0.0625, -0.3561, 0.3915,
-0.3596, 0.2565, 0.0120, -0.2206, 0.4874, 0.5524, -0.2768, 0.3071,
0.4948, 0.1323, -0.4300, 0.1927, 0.2829, -0.0604, -0.2676, -0.0276,
0.3177, 0.0729, -0.6886, -0.0284, 0.3048, 0.0920, 0.2971, -0.0899]],
grad_fn=<TanhBackward0>)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/tmp/ipykernel_131706/694873838.py in <module>
35 input_id_list = input_ids[0].tolist() # Batch index 0
36 tokens = tokenizer.convert_ids_to_tokens(input_id_list)
---> 37 model_view(attention, tokens)
~/anaconda3/envs/dnabert37/lib/python3.7/site-packages/bertviz/model_view.py in model_view(attention, tokens, sentence_b_start, prettify_tokens, display_mode, encoder_attention, decoder_attention, cross_attention, encoder_tokens, decoder_tokens, include_layers, include_heads, html_action)
60 raise ValueError("If you specify 'attention' you may not specify any encoder-decoder arguments. This"
61 " argument is only for self-attention models.")
---> 62 n_heads = num_heads(attention)
63 if include_layers is None:
64 include_layers = list(range(num_layers(attention)))
~/anaconda3/envs/dnabert37/lib/python3.7/site-packages/bertviz/util.py in num_heads(attention)
24
25 def num_heads(attention):
---> 26 return attention[0][0].size(0)
27
28
IndexError: Dimension specified as 0 but tensor has no dimensions
(Note, it is not raising that ValueError on line 60 of model_view.py. The error is on line 62.)
And at that point I am stuck. Any ideas? Thanks a lot!
Hi @mepster I don't specifically know why this error is raised but I suggest trying BertModel and BertTokenizer instead of the AutoModel and AutoTokenizer. When you comment out the output_attention then there is no attention for visualizing. So avoid commenting it out and try the BertModel hopefully it will fix it up.