run_exp.py

##########################################################
# pytorch-kaldi v.0.1                                      
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################


from __future__ import print_function

import os
import sys
import glob
import configparser
import numpy as np
from utils import check_cfg,create_lists,create_configs, compute_avg_performance, \
                  read_args_command_line, run_shell,compute_n_chunks, get_all_archs,cfg_item2sec, \
                  dump_epoch_results, create_curves,change_lr_cfg
from shutil import copyfile
import re
from distutils.util import strtobool
import importlib


# Reading global cfg file (first argument-mandatory file) 
cfg_file=sys.argv[1]
if not(os.path.exists(cfg_file)):
     sys.stderr.write('ERROR: The config file %s does not exist!\n'%(cfg_file))
     sys.exit(0)
else:
    config = configparser.ConfigParser()
    config.read(cfg_file)


# Reading and parsing optional arguments from command line (e.g.,--optimization,lr=0.002)
[section_args,field_args,value_args]=read_args_command_line(sys.argv,config)


# Output folder creation
out_folder=config['exp']['out_folder']
if not os.path.exists(out_folder):
    os.makedirs(out_folder+'/exp_files')

# Log file path    
log_file=config['exp']['out_folder']+'/log.log'


    
# Read, parse, and check the config file     
cfg_file_proto=config['cfg_proto']['cfg_proto']
[config,name_data,name_arch]=check_cfg(cfg_file,config,cfg_file_proto)


# Read cfg file options
is_production=strtobool(config['exp']['production'])
cfg_file_proto_chunk=config['cfg_proto']['cfg_proto_chunk']

cmd=config['exp']['cmd']
N_ep=int(config['exp']['N_epochs_tr'])
tr_data_lst=config['data_use']['train_with'].split(',')
valid_data_lst=config['data_use']['valid_with'].split(',')
forward_data_lst=config['data_use']['forward_with'].split(',')
max_seq_length_train=int(config['batches']['max_seq_length_train'])
forward_save_files=list(map(strtobool,config['forward']['save_out_file'].split(',')))


print("- Reading config file......OK!")

     
# Copy the global cfg file into the output folder
cfg_file=out_folder+'/conf.cfg'
with open(cfg_file, 'w') as configfile:   
    config.write(configfile) 
    

# Load the run_nn function from core libriary    
# The run_nn is a function that process a single chunk of data
run_nn_script=config['exp']['run_nn_script'].split('.py')[0]
module = importlib.import_module('core')
run_nn=getattr(module, run_nn_script)

         
         
# Splitting data into chunks (see out_folder/additional_files)
create_lists(config)

# Writing the config files
create_configs(config)      

            

print("- Chunk creation......OK!\n")

# create res_file
res_file_path=out_folder+'/res.res'
res_file = open(res_file_path, "w")
res_file.close()



# Learning rates and architecture-specific optimization parameters
arch_lst=get_all_archs(config)
lr={}
improvement_threshold={}
halving_factor={}
pt_files={}

for arch in arch_lst:
    lr[arch]=float(config[arch]['arch_lr'])
    improvement_threshold[arch]=float(config[arch]['arch_improvement_threshold'])
    halving_factor[arch]=float(config[arch]['arch_halving_factor'])
    pt_files[arch]=config[arch]['arch_pretrain_file']

    
# If production, skip training and forward directly from last saved models
if is_production:
    ep           = N_ep-1
    N_ep         = 0
    model_files  = {}

    for arch in pt_files.keys():
        model_files[arch] = out_folder+'/exp_files/final_'+arch+'.pkl'


op_counter=1 # used to dected the next configuration file from the list_chunks.txt

# Reading the ordered list of config file to process 
cfg_file_list = [line.rstrip('\n') for line in open(out_folder+'/exp_files/list_chunks.txt')]
cfg_file_list.append(cfg_file_list[-1])


# A variable that tells if the current chunk is the first one that is being processed:
processed_first=True

data_name=[]
data_set=[]
data_end_index=[]
fea_dict=[]
lab_dict=[]
arch_dict=[]

 
# --------TRAINING LOOP--------#
for ep in range(N_ep):
    
    tr_loss_tot=0
    tr_error_tot=0
    tr_time_tot=0
    
    print('------------------------------ Epoch %s / %s ------------------------------'%(format(ep, "03d"),format(N_ep-1, "03d")))

    for tr_data in tr_data_lst:
        
        # Compute the total number of chunks for each training epoch
        N_ck_tr=compute_n_chunks(out_folder,tr_data,ep,'train')
    
     
        # ***Epoch training***
        for ck in range(N_ck_tr):
            
            
            # paths of the output files (info,model,chunk_specific cfg file)
            info_file=out_folder+'/exp_files/train_'+tr_data+'_ep'+format(ep, "03d")+'_ck'+format(ck, "02d")+'.info'
            
            if ep+ck==0:
                model_files_past={}
            else:
                model_files_past=model_files
                
            model_files={}
            for arch in pt_files.keys():
                model_files[arch]=info_file.replace('.info','_'+arch+'.pkl')
            
            config_chunk_file=out_folder+'/exp_files/train_'+tr_data+'_ep'+format(ep, "03d")+'_ck'+format(ck, "02d")+'.cfg'
            
            # update learning rate
            change_lr_cfg(config_chunk_file,lr)
                        
            
            # if this chunk has not already been processed, do training...
            if not(os.path.exists(info_file)):
                
                    print('Training %s chunk = %i / %i' %(tr_data,ck+1, N_ck_tr))
                                 
                    # getting the next chunk 
                    next_config_file=cfg_file_list[op_counter]

                        
                    # run chunk processing                    
                    [data_name,data_set,data_end_index,fea_dict,lab_dict,arch_dict]=run_nn(data_name,data_set,data_end_index,fea_dict,lab_dict,arch_dict,config_chunk_file,processed_first,next_config_file)
                                        
                    
                    # update the first_processed variable
                    processed_first=False
                                                            
                    if not(os.path.exists(info_file)):
                        sys.stderr.write("ERROR: training epoch %i, chunk %i not done! File %s does not exist.\nSee %s \n" % (ep,ck,info_file,log_file))
                        sys.exit(0)
                                  
            # update the operation counter
            op_counter+=1          
            
            
            # update pt_file (used to initialized the DNN for the next chunk)  
            for pt_arch in pt_files.keys():
                pt_files[pt_arch]=out_folder+'/exp_files/train_'+tr_data+'_ep'+format(ep, "03d")+'_ck'+format(ck, "02d")+'_'+pt_arch+'.pkl'
                
            # remove previous pkl files
            if len(model_files_past.keys())>0:
                for pt_arch in pt_files.keys():
                    if os.path.exists(model_files_past[pt_arch]):
                        os.remove(model_files_past[pt_arch]) 
    
    
        # Training Loss and Error    
        tr_info_lst=sorted(glob.glob(out_folder+'/exp_files/train_'+tr_data+'_ep'+format(ep, "03d")+'*.info'))
        [tr_loss,tr_error,tr_time]=compute_avg_performance(tr_info_lst)
        
        tr_loss_tot=tr_loss_tot+tr_loss
        tr_error_tot=tr_error_tot+tr_error
        tr_time_tot=tr_time_tot+tr_time
        
        
        # ***Epoch validation***
        if ep>0:
            # store previous-epoch results (useful for learnig rate anealling)
            valid_peformance_dict_prev=valid_peformance_dict
        
        valid_peformance_dict={}  
        tot_time=tr_time  
    
    
    for valid_data in valid_data_lst:
        
        # Compute the number of chunks for each validation dataset
        N_ck_valid=compute_n_chunks(out_folder,valid_data,ep,'valid')

    
        for ck in range(N_ck_valid):
            
            
            # paths of the output files
            info_file=out_folder+'/exp_files/valid_'+valid_data+'_ep'+format(ep, "03d")+'_ck'+format(ck, "02d")+'.info'            
            config_chunk_file=out_folder+'/exp_files/valid_'+valid_data+'_ep'+format(ep, "03d")+'_ck'+format(ck, "02d")+'.cfg'
    
            # Do validation if the chunk was not already processed
            if not(os.path.exists(info_file)):
                print('Validating %s chunk = %i / %i' %(valid_data,ck+1,N_ck_valid))
                    
                # Doing eval
                
                # getting the next chunk 
                next_config_file=cfg_file_list[op_counter]
                                         
                # run chunk processing                    
                [data_name,data_set,data_end_index,fea_dict,lab_dict,arch_dict]=run_nn(data_name,data_set,data_end_index,fea_dict,lab_dict,arch_dict,config_chunk_file,processed_first,next_config_file)
                                                   
                # update the first_processed variable
                processed_first=False
                
                if not(os.path.exists(info_file)):
                    sys.stderr.write("ERROR: validation on epoch %i, chunk %i of dataset %s not done! File %s does not exist.\nSee %s \n" % (ep,ck,valid_data,info_file,log_file))
                    sys.exit(0)
    
            # update the operation counter
            op_counter+=1
        
        # Compute validation performance  
        valid_info_lst=sorted(glob.glob(out_folder+'/exp_files/valid_'+valid_data+'_ep'+format(ep, "03d")+'*.info'))
        [valid_loss,valid_error,valid_time]=compute_avg_performance(valid_info_lst)
        valid_peformance_dict[valid_data]=[valid_loss,valid_error,valid_time]
        tot_time=tot_time+valid_time
       
        
    # Print results in both res_file and stdout
    dump_epoch_results(res_file_path, ep, tr_data_lst, tr_loss_tot, tr_error_tot, tot_time, valid_data_lst, valid_peformance_dict, lr, N_ep)

        
    # Check for learning rate annealing
    if ep>0:
        # computing average validation error (on all the dataset specified)
        err_valid_mean=np.mean(np.asarray(list(valid_peformance_dict.values()))[:,1])
        err_valid_mean_prev=np.mean(np.asarray(list(valid_peformance_dict_prev.values()))[:,1])
        
        for lr_arch in lr.keys():
            if ((err_valid_mean_prev-err_valid_mean)/err_valid_mean)<improvement_threshold[lr_arch]:
                lr[lr_arch]=lr[lr_arch]*halving_factor[lr_arch]

# Training has ended, copy the last .pkl to final_arch.pkl for production
for pt_arch in pt_files.keys():
    if os.path.exists(model_files[pt_arch]) and not os.path.exists(out_folder+'/exp_files/final_'+pt_arch+'.pkl'):
        copyfile(model_files[pt_arch], out_folder+'/exp_files/final_'+pt_arch+'.pkl')
  
                
# --------FORWARD--------#
for forward_data in forward_data_lst:
           
         # Compute the number of chunks
         N_ck_forward=compute_n_chunks(out_folder,forward_data,ep,'forward')
         
         for ck in range(N_ck_forward):
            
            if not is_production:
                print('Testing %s chunk = %i / %i' %(forward_data,ck+1, N_ck_forward))
            else: 
                print('Forwarding %s chunk = %i / %i' %(forward_data,ck+1, N_ck_forward))
            
            # output file
            info_file=out_folder+'/exp_files/forward_'+forward_data+'_ep'+format(ep, "03d")+'_ck'+format(ck, "02d")+'.info'
            config_chunk_file=out_folder+'/exp_files/forward_'+forward_data+'_ep'+format(ep, "03d")+'_ck'+format(ck, "02d")+'.cfg'

            
            # Do forward if the chunk was not already processed
            if not(os.path.exists(info_file)):
                                
                # Doing forward
                
                # getting the next chunk 
                next_config_file=cfg_file_list[op_counter]
                                         
                # run chunk processing                    
                [data_name,data_set,data_end_index,fea_dict,lab_dict,arch_dict]=run_nn(data_name,data_set,data_end_index,fea_dict,lab_dict,arch_dict,config_chunk_file,processed_first,next_config_file)
                
                
                # update the first_processed variable
                processed_first=False
            
                if not(os.path.exists(info_file)):
                    sys.stderr.write("ERROR: forward chunk %i of dataset %s not done! File %s does not exist.\nSee %s \n" % (ck,forward_data,info_file,log_file))
                    sys.exit(0)
            
            
            # update the operation counter
            op_counter+=1
                    
               
            
# --------DECODING--------#
dec_lst=glob.glob( out_folder+'/exp_files/*_to_decode.ark')

forward_data_lst=config['data_use']['forward_with'].split(',')
forward_outs=config['forward']['forward_out'].split(',')
forward_dec_outs=list(map(strtobool,config['forward']['require_decoding'].split(',')))


for data in forward_data_lst:
    for k in range(len(forward_outs)):
        if forward_dec_outs[k]:
            
            print('Decoding %s output %s' %(data,forward_outs[k]))
            
            info_file=out_folder+'/exp_files/decoding_'+data+'_'+forward_outs[k]+'.info'
            
            
            # create decode config file
            config_dec_file=out_folder+'/decoding_'+data+'_'+forward_outs[k]+'.conf'
            config_dec = configparser.ConfigParser()
            config_dec.add_section('decoding')
            
            for dec_key in config['decoding'].keys():
                config_dec.set('decoding',dec_key,config['decoding'][dec_key])
 
            # add graph_dir, datadir, alidir
            lab_field=config[cfg_item2sec(config,'data_name',data)]['lab']
            
            
            # Production case, we don't have labels
            if not is_production:
                pattern='lab_folder=(.*)\nlab_opts=(.*)\nlab_count_file=(.*)\nlab_data_folder=(.*)\nlab_graph=(.*)'
                alidir=re.findall(pattern,lab_field)[0][0]
                config_dec.set('decoding','alidir',os.path.abspath(alidir))

                datadir=re.findall(pattern,lab_field)[0][3]
                config_dec.set('decoding','data',os.path.abspath(datadir))
                
                graphdir=re.findall(pattern,lab_field)[0][4]
                config_dec.set('decoding','graphdir',os.path.abspath(graphdir))
            else:
                pattern='lab_data_folder=(.*)\nlab_graph=(.*)'
                datadir=re.findall(pattern,lab_field)[0][0]
                config_dec.set('decoding','data',os.path.abspath(datadir))
                
                graphdir=re.findall(pattern,lab_field)[0][1]
                config_dec.set('decoding','graphdir',os.path.abspath(graphdir))

                # The ali dir is supposed to be in exp/model/ which is one level ahead of graphdir
                alidir = graphdir.split('/')[0:len(graphdir.split('/'))-1]
                alidir = "/".join(alidir)
                config_dec.set('decoding','alidir',os.path.abspath(alidir))

            
            with open(config_dec_file, 'w') as configfile:
                config_dec.write(configfile)
             
            out_folder=os.path.abspath(out_folder)
            files_dec=out_folder+'/exp_files/forward_'+data+'_ep*_ck*_'+forward_outs[k]+'_to_decode.ark'
            out_dec_folder=out_folder+'/decode_'+data+'_'+forward_outs[k]
                
            if not(os.path.exists(info_file)):
                
                # Run the decoder
                cmd_decode=cmd+config['decoding']['decoding_script_folder'] +'/'+ config['decoding']['decoding_script']+ ' '+os.path.abspath(config_dec_file)+' '+ out_dec_folder + ' \"'+ files_dec + '\"' 
                run_shell(cmd_decode,log_file)
                
                # remove ark files if needed
                if not forward_save_files[k]:
                    list_rem=glob.glob(files_dec)
                    for rem_ark in list_rem:
                        os.remove(rem_ark)
                    
                    
            # Print WER results and write info file
            cmd_res='./check_res_dec.sh '+out_dec_folder
            wers=run_shell(cmd_res,log_file).decode('utf-8')
            res_file = open(res_file_path, "a")
            res_file.write('%s\n'%wers)
            print(wers)

# Saving Loss and Err as .txt and plotting curves
if not is_production:
    create_curves(out_folder, N_ep, valid_data_lst)