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# -*- coding: utf-8 -*-
"""
Created on Wed Dec 20 16:13:55 2023
"""
import json
import os
import argparse
import jax
import pickle
import shutil
import sys
import gc
# allow sibling-package imports (dloaders, cli, utils, etc.) when run as a package
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from dloaders.init_dataloader import init_dataloader
def main():
### for now, running models on single GPU
err_ms = 'SELECT GPU TO RUN THIS COMPUTATION ON with CUDA_VISIBLE_DEVICES=DEVICE_NUM'
assert len(jax.devices()) == 1, err_ms
del err_ms
###########################################################################
### INITIALIZE PARSER ###################################################
###########################################################################
parser = argparse.ArgumentParser(prog='Pair_Alignment')
### which program do you want to run?
valid_tasks = ['train',
'continue_train',
'eval']
parser.add_argument('-task',
type=str,
required=True,
choices = valid_tasks,
help=f'What do you want to do? Pick from: {valid_tasks}')
parser.add_argument('-configs',
type = str,
required=True,
help='Load configs from file or folder of files, in json format.')
# only needed when continuing training
parser.add_argument('-new_training_wkdir',
type = str,
help='FOR CONTINUE_TRAIN OPTION; Name for a new training working dir')
parser.add_argument('-prev_model_ckpts_dir',
type = str,
help='FOR CONTINUE_TRAIN OPTION; Path to previous trainstate, argparse object')
parser.add_argument('-tstate_to_load',
type = str,
help='FOR CONTINUE_TRAIN OPTION; The suffix (not including file extension) of the tstate object to load')
# parse the arguments
top_level_args = parser.parse_args()
### helper functions
# open a single config file and extract additional arguments
def read_config_file(config_file):
with open(config_file, 'r') as f:
contents = json.load(f)
t_args = argparse.Namespace()
t_args.__dict__.update(contents)
args = parser.parse_args(namespace=t_args)
return args
###########################################################################
### TRAINING: basic function ############################################
###########################################################################
if top_level_args.task == 'train':
# read argparse
assert top_level_args.configs.endswith('.json'), "input is one JSON file"
print(f'TRAINING WITH: {top_level_args.configs}')
args = read_config_file(top_level_args.configs)
pred_model_type = args.pred_model_type
# import correct wrappers, dataloader initializers
if 'pairhmm_indp_sites' in pred_model_type:
from cli.train_pairhmm_indp_sites import train_pairhmm_indp_sites as train_fn
from dloaders.init_counts_dset import init_counts_dset as init_datasets
from dloaders.CountsDset import jax_collator as collate_fn
elif pred_model_type in ['pairhmm_frag_and_site_classes',
'pairhmm_nested_tkf',
'neural_hmm',
'feedforward']:
from dloaders.init_full_len_dset import init_full_len_dset as init_datasets
from dloaders.FullLenDset import jax_collator as collate_fn
if pred_model_type in ['pairhmm_frag_and_site_classes', 'pairhmm_nested_tkf']:
from cli.train_pairhmm_transit_mixes import train_pairhmm_transit_mixes as train_fn
elif pred_model_type == 'neural_hmm':
from cli.train_neural_hmm import train_neural_hmm as train_fn
elif pred_model_type == 'feedforward':
from cli.train_feedforward import train_feedforward as train_fn
# make dataloder list
dload_dict = init_datasets( args,
'train',
training_argparse = None,
include_dataloader = True )
# train model
train_fn( args, dload_dict )
###########################################################################
### TRAINING: continue one training experiment ##########################
###########################################################################
elif top_level_args.task == 'continue_train':
# read argparse
assert top_level_args.configs.endswith('.json'), "input is one JSON file"
print(f'CONTINUE TRAINING WITH: {top_level_args.configs}, IN NEW DIR {top_level_args.new_training_wkdir}')
args_from_training_config = read_config_file(top_level_args.configs)
pred_model_type = args_from_training_config.pred_model_type
# import correct wrappers, dataloader initializers
if 'pairhmm_indp_sites' in pred_model_type:
from cli.cont_training_pairhmm_indp_sites import cont_training_pairhmm_indp_sites as cont_train_fn
from dloaders.init_counts_dset import init_counts_dset as init_datasets
from dloaders.CountsDset import jax_collator as collate_fn
elif pred_model_type in ['pairhmm_frag_and_site_classes',
'pairhmm_nested_tkf',
'neural_hmm',
'feedforward']:
from dloaders.init_full_len_dset import init_full_len_dset as init_datasets
from dloaders.FullLenDset import jax_collator as collate_fn
if pred_model_type in ['pairhmm_frag_and_site_classes', 'pairhmm_nested_tkf']:
from cli.cont_training_pairhmm_transit_mixes import cont_training_pairhmm_transit_mixes as cont_train_fn
elif pred_model_type == 'neural_hmm':
from cli.cont_training_neural_hmm import cont_training_neural_hmm as cont_train_fn
elif pred_model_type == 'feedforward':
from cli.cont_training_feedforward import cont_training_feedforward as cont_train_fn
# make dataloader objects
dload_dict = init_datasets( args_from_training_config,
'train',
training_argparse = None,
include_dataloader = True )
# train model
cont_train_fn( args=args_from_training_config,
dataloader_dict=dload_dict,
new_training_wkdir=top_level_args.new_training_wkdir,
prev_model_ckpts_dir=top_level_args.prev_model_ckpts_dir,
tstate_to_load=top_level_args.tstate_to_load
)
###########################################################################
### EVAL: basic function ################################################
###########################################################################
elif top_level_args.task == 'eval':
# read argparse
assert top_level_args.configs.endswith('.json'), "input is one JSON file"
print(f'EVALUATING WITH: {top_level_args.configs}')
args = read_config_file(top_level_args.configs)
# find and read training argparse
model_ckpts_dir = f'{os.getcwd()}/{args.training_wkdir}/model_ckpts'
training_argparse_filename = model_ckpts_dir + '/' + 'TRAINING_ARGPARSE.pkl'
with open(training_argparse_filename,'rb') as g:
training_argparse = pickle.load(g)
### determine pred_model_type
# automatically detect
pred_model_type = training_argparse.pred_model_type
override_with_pred_model_type = None
# uncomment to override; useful for debugging
# pred_model_type = override_with_pred_model_type
# override_with_pred_model_type = override_with_pred_model_type
### import correct wrappers, dataloader initializers
if 'pairhmm_indp_sites' in pred_model_type:
from cli.eval_pairhmm_indp_sites import eval_pairhmm_indp_sites as eval_fn
from dloaders.init_counts_dset import init_counts_dset as init_datasets
from dloaders.CountsDset import jax_collator as collate_fn
elif pred_model_type in ['pairhmm_frag_and_site_classes',
'pairhmm_nested_tkf',
'neural_hmm',
'feedforward']:
from dloaders.init_full_len_dset import init_full_len_dset as init_datasets
from dloaders.FullLenDset import jax_collator as collate_fn
if pred_model_type in ['pairhmm_frag_and_site_classes', 'pairhmm_nested_tkf']:
from cli.eval_pairhmm_transit_mixes import eval_pairhmm_transit_mixes as eval_fn
elif pred_model_type == 'neural_hmm':
from cli.eval_neural_hmm import eval_neural_hmm as eval_fn
elif pred_model_type == 'feedforward':
from cli.eval_feedforward import eval_feedforward as eval_fn
# load data; saved under trianing_wkdir name
dload_dict = init_datasets( args,
'eval',
training_argparse,
include_dataloader = True )
# evaluate model
eval_fn( args = args,
training_argparse = training_argparse,
dataloader_dict = dload_dict,
override_with_pred_model_type = override_with_pred_model_type)
if __name__ == '__main__':
main() |