#!/usr/bin/env python3 # -*- 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()