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ab6c03c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | import torch
import matplotlib.pyplot as plt
import seaborn as sns
import argparse
import os
import numpy as np
from transformers import BertTokenizer, BertModel, DNATokenizer
from process_pretrain_data import get_kmer_sentence
def format_attention(attention):
squeezed = []
for layer_attention in attention:
# 1 x num_heads x seq_len x seq_len
if len(layer_attention.shape) != 4:
raise ValueError("The attention tensor does not have the correct number of dimensions. Make sure you set "
"output_attentions=True when initializing your model.")
squeezed.append(layer_attention.squeeze(0))
# num_layers x num_heads x seq_len x seq_len
return torch.stack(squeezed)
def get_attention_dna(model, tokenizer, sentence_a, start, end):
inputs = tokenizer.encode_plus(sentence_a, sentence_b=None, return_tensors='pt', add_special_tokens=True)
input_ids = inputs['input_ids']
attention = model(input_ids)[-1]
input_id_list = input_ids[0].tolist() # Batch index 0
tokens = tokenizer.convert_ids_to_tokens(input_id_list)
attn = format_attention(attention)
attn_score = []
for i in range(1, len(tokens)-1):
attn_score.append(float(attn[start:end+1,:,0,i].sum()))
return attn_score
def get_real_score(attention_scores, kmer, metric):
counts = np.zeros([len(attention_scores)+kmer-1])
real_scores = np.zeros([len(attention_scores)+kmer-1])
if metric == "mean":
for i, score in enumerate(attention_scores):
for j in range(kmer):
counts[i+j] += 1.0
real_scores[i+j] += score
real_scores = real_scores/counts
else:
pass
return real_scores
SEQUENCE = "TGCCTGGCTTTTTGTAATTTTTGAAGAGACGGGGTTTTGCCATGATG"
def Visualize(args):
if args.kmer == 0:
KMER_LIST = [3,4,5,6]
for kmer in KMER_LIST:
tokenizer_name = 'dna' + str(kmer)
model_path = os.path.join(args.model_path, str(kmer))
model = BertModel.from_pretrained(model_path, output_attentions=True)
tokenizer = DNATokenizer.from_pretrained(tokenizer_name, do_lower_case=False)
raw_sentence = args.sequence if args.sequence else SEQUENCE
sentence_a = get_kmer_sentence(raw_sentence, kmer)
tokens = sentence_a.split()
attention = get_attention_dna(model, tokenizer, sentence_a, start=args.start_layer, end=args.end_layer)
attention_scores = np.array(attention).reshape(np.array(attention).shape[0],1)
# attention_scores[0] = 0
real_scores = get_real_score(attention_scores, kmer, args.metric)
real_scores = real_scores / np.linalg.norm(real_scores)
if kmer != KMER_LIST[0]:
scores += real_scores.reshape(1, real_scores.shape[0])
else:
scores = real_scores.reshape(1, real_scores.shape[0])
else:
# load model and calculate attention
tokenizer_name = 'dna' + str(args.kmer)
model_path = args.model_path
model = BertModel.from_pretrained(model_path, output_attentions=True)
tokenizer = DNATokenizer.from_pretrained(tokenizer_name, do_lower_case=False)
raw_sentence = args.sequence if args.sequence else SEQUENCE
sentence_a = get_kmer_sentence(raw_sentence, args.kmer)
tokens = sentence_a.split()
attention = get_attention_dna(model, tokenizer, sentence_a, start=args.start_layer, end=args.end_layer)
attention_scores = np.array(attention).reshape(np.array(attention).shape[0],1)
# attention_scores[0] = 0
real_scores = get_real_score(attention_scores, args.kmer, args.metric)
scores = real_scores.reshape(1, real_scores.shape[0])
ave = np.sum(scores)/scores.shape[1]
print(ave)
print(scores)
# plot
sns.set()
ax = sns.heatmap(scores, cmap='YlGnBu', vmin=0)
plt.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--kmer",
default=0,
type=int,
help="K-mer",
)
parser.add_argument(
"--model_path",
default="/home/zhihan/dna/dna-transformers/examples/ft/690/p53-small/TAp73beta/3/",
type=str,
help="The path of the finetuned model",
)
parser.add_argument(
"--start_layer",
default=11,
type=int,
help="Which layer to start",
)
parser.add_argument(
"--end_layer",
default=11,
type=int,
help="which layer to end",
)
parser.add_argument(
"--metric",
default="mean",
type=str,
help="the metric used for integrate predicted kmer result to real result",
)
parser.add_argument(
"--sequence",
default=None,
type=str,
help="the sequence for visualize",
)
args = parser.parse_args()
Visualize(args)
if __name__ == "__main__":
main() |