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9627ce0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | import torch
from torch.utils.data import Dataset
import pandas as pd
from src.data.protein import Protein
from transformers import AutoTokenizer
import torch.nn.functional as F
from src.utils.utils import pmap_multi
from src.data.esm.sdk.api import ESMProtein
from sklearn.preprocessing import MultiLabelBinarizer
def read_data(aa_seq, name, label, task_type, num_classes, csv_name=""):
try:
if unique_id is None:
unique_id = str(hash(aa_seq))
if task_type == "multi_labels_classification":
mlb = MultiLabelBinarizer(classes=range(int(num_classes)))
label = str(label)
label = torch.tensor(mlb.fit_transform([[int(ele) for ele in label.split(",")]]).flatten().tolist())
elif task_type == "contact":
label = torch.load(label, weights_only=True) # diable warning
elif task_type == "residual_classification":
label = torch.tensor(list(map(int, label.strip('[]').replace('\n', ' ').split())))
else:
label = torch.tensor(label)
name = str(hash(pdb_path))
if pdb_path is not None:
# 解析 pdb 文件，unique_id 作为结构的 id
if "|" not in pdb_path:
structure = ESMProtein.from_pdb(pdb_path)
# TODO
return {
'name':name,
# 'seq': structure.sequence, # aaseq
'seq': aa_seq if "flip" in csv_name.lower() else structure.sequence, # aaseq
'X': structure.coordinates,
'label': label,
'unique_id': unique_id,
'pdb_path': pdb_path,
'smiles': smiles
}
# X, C, S = structure.to_XCS(all_atom=True)
# X, C, S = X[0], C[0], S[0]
else: # PPI
structures, sequences = [], []
for _pdb_path in pdb_path.split("|"):
structure = ESMProtein.from_pdb(_pdb_path)
structures.append(structure.coordinates)
sequences.append(structure.sequence)
return {
'name':name,
'seq': "|".join(sequences),
'X': structures, # coords is organized as a list here
'label': label,
'unique_id': unique_id,
'pdb_path': pdb_path,
'smiles': smiles
}
else:
return {
'name':name,
'seq': aa_seq,
'X': None,
'label': label,
'unique_id': unique_id,
'pdb_path': pdb_path,
'smiles': smiles
}
except:
return None
def read_data_new(aa_seq, name, label, task_type, num_classes, csv_name=""):
try:
if task_type == "multi_labels_classification":
mlb = MultiLabelBinarizer(classes=range(int(num_classes)))
label = str(label)
label = torch.tensor(mlb.fit_transform([[int(ele) for ele in label.split(",")]]).flatten().tolist())
elif task_type == "contact":
label = torch.load(label, weights_only=True) # diable warning
elif task_type == "residual_classification":
label = torch.tensor(list(map(int, label.strip('[]').replace('\n', ' ').split())))
else:
label = torch.tensor(label)
return {
'name': name,
'seq': aa_seq,
'label': label,
}
except:
return None
class ProteinDataset(Dataset):
def __init__(self, csv_file, pretrain_model_name='esm2_650m', max_length=1022, pretrain_model_interface=None, task_name='pretrain', task_type='classification', num_classes=None):
"""
Args:
csv_file (str): CSV 文件路径，文件中包含蛋白质序列和结构等信息。
"""
self.max_length = max_length
self.pretrain_model_name = pretrain_model_name
self.task_name = task_name
self.task_type = task_type
self.num_classes = num_classes
# 读取 CSV 数据
if task_name=="deep_loc_binary":
csv_data = pd.read_csv(csv_file)
path_list = []
for i in range(len(csv_data)):
path_list.append((csv_data.iloc[i].get('aa_seq'), csv_data.iloc[i].get('name'), csv_data.iloc[i]['label'], task_type, num_classes, csv_file)) #列表里面必须是元组，不然debug模式下并行加载数据会报错
# path_list = path_list[:10] # this is for fast debug, please comment it in production
self.data = pmap_multi(read_data, path_list, n_jobs=-1)
self.data = [d for d in self.data if d is not None]
self.max_length = min(self.max_length, max([len(d['seq']) for d in self.data])+2)
self.pretrain_model_interface = pretrain_model_interface
if pretrain_model_interface is not None:
self.data = pretrain_model_interface.inference_datasets(self.data, task_name=self.task_name)
print(f"ProteinDataset: {len(self.data)} samples loaded.")
def __len__(self):
return len(self.data)
def pad_data(self, data, dim=0, pad_value=0, max_length=1022):
if data.shape[dim] < max_length:
data = dynamic_pad(data, [0, max_length-data.shape[dim]], dim=dim, pad_value=pad_value)
else:
start = 0
data = data[start:start+max_length]
return data
def __getitem__(self, idx):
if self.pretrain_model_interface is not None:
max_length_batch = self.max_length
name = self.data[idx]['name']
embedding = self.pad_data(self.data[idx]['embedding'], dim=0, pad_value=0, max_length=max_length_batch)
attention_mask = self.pad_data(self.data[idx]['attention_mask'], dim=0, pad_value=0, max_length=max_length_batch)
label = self.data[idx]['label']
if self.task_type == 'binary_classification':
label = label[None].float()
if self.task_type == 'contact':
label = (label == 0).int()
label = F.pad(label, [0, max_length_batch-label.shape[0], 0, max_length_batch-label.shape[0]])
if self.task_type == 'residual_classification':
label = F.pad(label, [0, max_length_batch-label.shape[0]])
result = {
'name': name,
'embedding': embedding,
'attention_mask': attention_mask,
'label': label,
}
if self.data[idx].get('smiles') is not None:
smiles = self.data[idx]['smiles']
result['smiles'] = smiles
return result
else:
max_length_batch = self.max_length
label = self.data[idx]['label']
if self.task_type == 'binary_classification':
label = label[None].float()
if self.task_type == 'contact':
label = (label == 0).int()
label = F.pad(label, [0, max_length_batch-label.shape[0], 0, max_length_batch-label.shape[0]])
if self.task_type == 'residual_classification':
label = F.pad(label, [0, max_length_batch-label.shape[0]])
data = {
'name': self.data[idx]["name"],
'seq': self.data[idx]["seq"],
'X': self.data[idx]["X"],
'label': label,
'unique_id': self.data[idx]["unique_id"],
'pdb_path': self.data[idx]["pdb_path"],
'smiles': self.data[idx]["smiles"],
}
return data
def dynamic_pad(tensor, pad_size, dim=0, pad_value=0):
# 获取原始形状
shape = list(tensor.shape)
num_dims = len(shape)
# 生成 padding 参数
pad = [0] * (2 * num_dims)
prev_pad_size, post_pad_size = pad_size
pad_index = 2 * (num_dims - dim - 1)
pad[pad_index] = prev_pad_size # 前面 padding
pad[pad_index + 1] = post_pad_size # 后面 padding
# 应用 padding
padded_tensor = F.pad(tensor, pad, mode="constant", value=pad_value)
return padded_tensor
# 示例用法
if __name__ == "__main__":
dataset = ProteinDataset("/nfs_beijing/kubeflow-user/zhangyang_2024/workspace/protein_benchmark/datasets/fold_prediction/fold_prediction_with_glmfold_structure_test.csv")
sample = dataset[0]
print(sample['coords'].shape)
print(sample['chain'])
print(sample['sequence'])
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