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34393ef | 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 | from sklearn.preprocessing import OneHotEncoder
import logging
import numpy as np
import pandas as pd
import os
import json
import re
import torch
import collections
# from models import reader
from models.ScheduleOptimizer import ScheduledOptim
print(os.path.dirname(__file__))
# ====================| some path |=======================
global script_dir
global data_dir
global log_dir
global pth_dir
# global cell_lines
global egfp_seq
with open(os.path.join(os.path.dirname(__file__),"machine_configure.json"),'r') as f:
config = json.load(f)
script_dir = config['script_dir']
data_dir = config['data_dir']
log_dir = config['log_dir']
pth_dir = config['pth_dir']
# =====================| one hot encode |=======================
class Seq_one_hot(object):
def __init__(self,seq_type='nn',seq_len=100):
"""
initiate the sequence one hot encoder
"""
self.seq_len=seq_len
self.seq_type =seq_type
self.enable_encoder()
def enable_encoder(self):
if self.seq_type == 'nn':
self.encoder = OneHotEncoder(sparse=False)
self.encoder.drop_idx_ = None
self.encoder.categories_ = [np.array(['A', 'C', 'G', 'T'], dtype='<U1')]*self.seq_len
def discretize_seq(self,data):
"""
discretize sequence into character
argument:
...data: can be dataframe with UTR columns , or can be single string
"""
if type(data) is pd.DataFrame:
return np.stack(data.UTR.apply(lambda x: list(x)))
elif type(data) is str:
return np.array(list(data))
def transform(self,data,flattern=True):
"""
One hot encode
argument:
data : is a 2D array
flattern : True
"""
X = self.encoder.transform(data) # 400 for each seq
X_M = np.stack([seq.reshape(self.seq_len,4) for seq in X]) # i.e 100*4
return X if flattern else X_M
def d_transform(self,data,flattern=True):
"""
discretize data and put into transform
"""
X = self.discretize_seq(data)
return self.transform(X,flattern)
# =====================| logger |=======================
def setup_logs(vae_log_path,level=None):
"""
:param save_dir: the directory to set up logs
:param type: 'model' for saving logs in 'logs/cpc'; 'imp' for saving logs in 'logs/imp'
:param run_name:
:return:logger
"""
# initialize logger
logger = logging.getLogger("VAE")
logger.setLevel(logging.INFO)
if level=='warning':
logger.setLevel(logging.WARNING)
# create the logging file handler
log_file = os.path.join(vae_log_path)
fh = logging.FileHandler(log_file)
# create the logging console handler
ch = logging.StreamHandler()
# format
formatter = logging.Formatter("%(asctime)s - %(message)s")
fh.setFormatter(formatter)
# add handlers to logger object
logger.addHandler(fh)
logger.addHandler(ch)
return logger
def clean_value_dict(dict):
"""
deal with verbose dict where the values maybe torch object, extact the item and return clean dict
"""
clean_dict={}
for k,v in dict.items():
try:
v = v.item()
except:
v = v
clean_dict[k] = v
return clean_dict
def fix_parameter(model,modual_to_fix,fix_or_unfix=False):
"""
for a given model, fix part of the parameter to fine-tuning / transfering
args:
model : `nn.Modual`,initiated model instance
modual_to_fix : str, define which part of the model will not update by gradient
e.g. "soft_share" then
"""
fix_part = eval("model."+modual_to_fix) # e.g. model.shoft_share
for param in fix_part.parameters():
param.requires_grad = fix_or_unfix
return model
def unfix_parameter(model,modual_to_fix,fix_or_unfix=False):
return fix_parameter(model,modual_to_fix,fix_or_unfix=True)
def snapshot(vae_pth_path, state):
logger = logging.getLogger("VAE")
# torch.save can save any object
# dict type object in our cases
torch.save(state, vae_pth_path)
logger.info("Snapshot saved to {}\n".format(vae_pth_path))
def load_model(popen,model,logger=None):
info = lambda x: print(x) if logger==None else logger.info(x)
popen.vae_pth_path = '/mnt/sina/run/ml/gan/dev/git/UTRGAN/src/mrl_optimization/script/checkpoint/RL_hard_share_MTL/3M/small_repective_filed_strides1113-model_best_cv1.pth'
checkpoint = torch.load(popen.vae_pth_path, map_location=torch.device('cpu'))
if isinstance(checkpoint['state_dict'], collections.OrderedDict):
# optimizer.load_state_dict(checkpoint['optimizer'])
model.load_state_dict(checkpoint['state_dict'])
else:
model = checkpoint['state_dict']
info(' \t \t ==============<<< encoder load from >>>============== \t \t ')
info(" \t"+popen.vae_pth_path)
return model
def get_config_cuda(config_file):
with open(config_file,'r') as f:
lines = f.read_lines()
for line in lines:
if "cuda_id =" in line:
device = line.split("=")[1].strip()
break
device = int(device) if device.isdigit() else device
return device
def resume(popen,optimizer,logger):
"""
for a experiment, check whether it;s a new run, and create dir
"""
#run_name = model_stype + time.strftime("__%Y_%m_%d_%H:%M"))
if popen.Resumable:
checkpoint = torch.load(popen.vae_pth_path, map_location=torch.device('cpu')) # xx-model-best.pth
previous_epoch = checkpoint['epoch']
previous_loss = checkpoint['validation_loss']
previous_acc = checkpoint['validation_acc']
# very important
if (type(optimizer) == ScheduledOptim):
optimizer.n_current_steps = popen.n_current_steps
optimizer.delta = popen.delta
logger.info(" \t \t ========================================================= \t \t ")
logger.info(' \t \t ==============<<< Resume from checkpoint>>>============== \t \t \n')
logger.info(" \t"+popen.vae_pth_path+'\n')
logger.info(" \t \t ========================================================= \t \t \n")
return previous_epoch,previous_loss,previous_acc
egfp_seq = "atgggcgaattaagtaagggcgaggagctgttcaccggggtggtgcccatcctggtcgagctggacggcgacgtaaacggccacaagttcagcgtgtccggcgagggcgagggcgatgccacctacggcaagctgaccctgaagttcatctgcaccaccggcaagctgcccgtgccctggcccaccctcgtgaccaccctgacctacggcgtgcagtgcttcagccgctaccccgaccacatgaagcagcacgacttcttcaagtccgccatgcccgaaggctacgtccaggagcgcaccatcttct"
eGFP_seq = egfp_seq.upper() |