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utils.py
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utils.py
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##########################################################
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import configparser
import sys
import os.path
import random
import subprocess
import numpy as np
import re
import glob
from distutils.util import strtobool
import importlib
import torch
import torch.nn as nn
import torch.optim as optim
import math
def l1_norm(model, l1_lambda):
l1_reg = torch.tensor(0, dtype=torch.float32).cuda()
for key in model:
for param in model[key].parameters():
dim = param.size()
if dim.__len__() > 1 and not model[key].skip_regularization:
l1_reg += torch.norm(param, 1)
return l1_reg * float(l1_lambda)
def l2_norm(model, l2_lambda):
l2_reg = torch.tensor(0, dtype=torch.float32).cuda()
for key in model:
for param in model[key].parameters():
dim = param.size()
if dim.__len__() > 1 and not model[key].skip_regularization:
l2_reg += torch.norm(param, 2)
return l2_reg * float(l2_lambda)
def gl_norm(model, gl_lambda, num_blk):
gl_reg = torch.tensor(0., dtype=torch.float32).cuda()
i = 0
j = 0
for key in model:
for param in model[key].parameters():
dim = param.size()
if dim.__len__() > 1 and not model[key].skip_regularization:
div1 = list(torch.chunk(param,int(num_blk),1))
all_blks = []
for div2 in div1:
temp = list(torch.chunk(div2,int(num_blk),0))
for blk in temp:
all_blks.append(blk)
for l2_param in all_blks:
gl_reg += torch.norm(l2_param, 2)
j += 1
i += 1
return gl_reg * float(gl_lambda)
# def gl_norm(model, gl_lambda, num_blk):
# gl_reg = torch.tensor(0., dtype=torch.float32).cuda()
# i = 0
# j = 0
# for key in model:
# all_params = model[key].state_dict()
# for param_name in all_params:
# if 'weight' in param_name:
# param = all_params[param_name]
# dim = param.size()
# if dim.__len__() > 1 and not model[key].skip_regularization:
# div1 = list(torch.chunk(param,int(num_blk),1))
# all_blks = []
# for div2 in div1:
# temp = list(torch.chunk(div2,int(num_blk),0))
# for blk in temp:
# all_blks.append(blk)
# for l2_param in all_blks:
# gl_reg += torch.norm(l2_param, 2)
# j += 1
# i += 1
# return gl_reg * float(gl_lambda)
def run_command(cmd):
"""from http://blog.kagesenshi.org/2008/02/teeing-python-subprocesspopen-output.html
"""
p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
stdout = []
while True:
line = p.stdout.readline()
stdout.append(line)
print(line.decode("utf-8"))
if line == '' and p.poll() != None:
break
return ''.join(stdout)
def run_shell_display(cmd):
p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
while True:
out = p.stdout.read(1).decode('utf-8')
if out == '' and p.poll() != None:
break
if out != '':
sys.stdout.write(out)
sys.stdout.flush()
return
def run_shell(cmd, log_file):
p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
(output, err) = p.communicate()
p.wait()
with open(log_file, 'a+') as logfile:
logfile.write(output.decode("utf-8") + '\n')
logfile.write(err.decode("utf-8") + '\n')
# print(output.decode("utf-8"))
return output
def read_args_command_line(args, config):
sections = []
fields = []
values = []
for i in range(2, len(args)):
# check if the option is valid for second level
r2 = re.compile('--.*,.*=.*')
# check if the option is valid for 4 level
r4 = re.compile('--.*,.*,.*,.*=".*"')
if r2.match(args[i]) is None and r4.match(args[i]) is None:
sys.stderr.write(
'ERROR: option \"%s\" from command line is not valid! (the format must be \"--section,field=value\")\n' % (
args[i]))
sys.exit(0)
sections.append(re.search('--(.*),', args[i]).group(1))
fields.append(re.search(',(.*)', args[i].split('=')[0]).group(1))
values.append(re.search('=(.*)', args[i]).group(1))
# parsing command line arguments
for i in range(len(sections)):
# Remove multi level is level >= 2
sections[i] = sections[i].split(',')[0]
if sections[i] in config.sections():
# Case of args level > than 2 like --sec,fields,0,field="value"
if len(fields[i].split(',')) >= 2:
splitted = fields[i].split(',')
# Get the actual fields
field = splitted[0]
number = int(splitted[1])
f_name = splitted[2]
if field in list(config[sections[i]]):
# Get the current string of the corresponding field
current_config_field = config[sections[i]][field]
# Count the number of occurence of the required field
matching = re.findall(f_name + '.', current_config_field)
if number >= len(matching):
sys.stderr.write(
'ERROR: the field number \"%s\" provided from command line is not valid, we found \"%s\" \"%s\" field(s) in section \"%s\"!\n' % (
number, len(matching), f_name, field))
sys.exit(0)
else:
# Now replace
str_to_be_replaced = re.findall(f_name + '.*', current_config_field)[number]
new_str = str(f_name + '=' + values[i])
replaced = nth_replace_string(current_config_field, str_to_be_replaced, new_str, number + 1)
config[sections[i]][field] = replaced
else:
sys.stderr.write('ERROR: field \"%s\" of section \"%s\" from command line is not valid!")\n' % (
field, sections[i]))
sys.exit(0)
else:
if fields[i] in list(config[sections[i]]):
config[sections[i]][fields[i]] = values[i]
else:
sys.stderr.write('ERROR: field \"%s\" of section \"%s\" from command line is not valid!")\n' % (
fields[i], sections[i]))
sys.exit(0)
else:
sys.stderr.write('ERROR: section \"%s\" from command line is not valid!")\n' % (sections[i]))
sys.exit(0)
return [sections, fields, values]
def compute_avg_performance(info_lst):
losses = []
errors = []
times = []
for tr_info_file in info_lst:
config_res = configparser.ConfigParser()
config_res.read(tr_info_file)
losses.append(float(config_res['results']['loss']))
errors.append(float(config_res['results']['err']))
times.append(float(config_res['results']['elapsed_time_chunk']))
loss = np.mean(losses)
error = np.mean(errors)
time = np.sum(times)
return [loss, error, time]
def check_field(inp, type_inp, field):
valid_field = True
if inp == '' and field != 'cmd':
sys.stderr.write("ERROR: The the field \"%s\" of the config file is empty! \n" % (field))
valid_field = False
sys.exit(0)
if type_inp == 'path':
if not (os.path.isfile(inp)) and not (os.path.isdir(inp)) and inp != 'none':
sys.stderr.write(
"ERROR: The path \"%s\" specified in the field \"%s\" of the config file does not exists! \n" % (
inp, field))
valid_field = False
sys.exit(0)
if '{' and '}' in type_inp:
arg_list = type_inp[1:-1].split(',')
if inp not in arg_list:
sys.stderr.write("ERROR: The field \"%s\" can only contain %s arguments \n" % (field, arg_list))
valid_field = False
sys.exit(0)
if 'int(' in type_inp:
try:
int(inp)
except ValueError:
sys.stderr.write("ERROR: The field \"%s\" can only contain an integer (got \"%s\") \n" % (field, inp))
valid_field = False
sys.exit(0)
# Check if the value if within the expected range
lower_bound = type_inp.split(',')[0][4:]
upper_bound = type_inp.split(',')[1][:-1]
if lower_bound != "-inf":
if int(inp) < int(lower_bound):
sys.stderr.write(
"ERROR: The field \"%s\" can only contain an integer greater than %s (got \"%s\") \n" % (
field, lower_bound, inp))
valid_field = False
sys.exit(0)
if upper_bound != "inf":
if int(inp) > int(upper_bound):
sys.stderr.write(
"ERROR: The field \"%s\" can only contain an integer smaller than %s (got \"%s\") \n" % (
field, upper_bound, inp))
valid_field = False
sys.exit(0)
if 'float(' in type_inp:
try:
float(inp)
except ValueError:
sys.stderr.write("ERROR: The field \"%s\" can only contain a float (got \"%s\") \n" % (field, inp))
valid_field = False
sys.exit(0)
# Check if the value if within the expected range
lower_bound = type_inp.split(',')[0][6:]
upper_bound = type_inp.split(',')[1][:-1]
if lower_bound != "-inf":
if float(inp) < float(lower_bound):
sys.stderr.write("ERROR: The field \"%s\" can only contain a float greater than %s (got \"%s\") \n" % (
field, lower_bound, inp))
valid_field = False
sys.exit(0)
if upper_bound != "inf":
if float(inp) > float(upper_bound):
sys.stderr.write("ERROR: The field \"%s\" can only contain a float smaller than %s (got \"%s\") \n" % (
field, upper_bound, inp))
valid_field = False
sys.exit(0)
if type_inp == 'bool':
lst = {'True', 'true', '1', 'False', 'false', '0'}
if not (inp in lst):
sys.stderr.write("ERROR: The field \"%s\" can only contain a boolean (got \"%s\") \n" % (field, inp))
valid_field = False
sys.exit(0)
if 'int_list(' in type_inp:
lst = inp.split(',')
try:
list(map(int, lst))
except ValueError:
sys.stderr.write(
"ERROR: The field \"%s\" can only contain a list of integer (got \"%s\"). Make also sure there aren't white spaces between commas.\n" % (
field, inp))
valid_field = False
sys.exit(0)
# Check if the value if within the expected range
lower_bound = type_inp.split(',')[0][9:]
upper_bound = type_inp.split(',')[1][:-1]
for elem in lst:
if lower_bound != "-inf":
if int(elem) < int(lower_bound):
sys.stderr.write(
"ERROR: The field \"%s\" can only contain an integer greater than %s (got \"%s\") \n" % (
field, lower_bound, elem))
valid_field = False
sys.exit(0)
if upper_bound != "inf":
if int(elem) > int(upper_bound):
sys.stderr.write(
"ERROR: The field \"%s\" can only contain an integer smaller than %s (got \"%s\") \n" % (
field, upper_bound, elem))
valid_field = False
sys.exit(0)
if 'float_list(' in type_inp:
lst = inp.split(',')
try:
list(map(float, lst))
except ValueError:
sys.stderr.write(
"ERROR: The field \"%s\" can only contain a list of floats (got \"%s\"). Make also sure there aren't white spaces between commas. \n" % (
field, inp))
valid_field = False
sys.exit(0)
# Check if the value if within the expected range
lower_bound = type_inp.split(',')[0][11:]
upper_bound = type_inp.split(',')[1][:-1]
for elem in lst:
if lower_bound != "-inf":
if float(elem) < float(lower_bound):
sys.stderr.write(
"ERROR: The field \"%s\" can only contain a float greater than %s (got \"%s\") \n" % (
field, lower_bound, elem))
valid_field = False
sys.exit(0)
if upper_bound != "inf":
if float(elem) > float(upper_bound):
sys.stderr.write(
"ERROR: The field \"%s\" can only contain a float smaller than %s (got \"%s\") \n" % (
field, upper_bound, elem))
valid_field = False
sys.exit(0)
if type_inp == 'bool_list':
lst = {'True', 'true', '1', 'False', 'false', '0'}
inps = inp.split(',')
for elem in inps:
if not (elem in lst):
sys.stderr.write(
"ERROR: The field \"%s\" can only contain a list of boolean (got \"%s\"). Make also sure there aren't white spaces between commas.\n" % (
field, inp))
valid_field = False
sys.exit(0)
return valid_field
def get_all_archs(config):
arch_lst = []
for sec in config.sections():
if 'architecture' in sec:
arch_lst.append(sec)
return arch_lst
def expand_section(config_proto, config):
# expands config_proto with fields in prototype files
name_data = []
name_arch = []
for sec in config.sections():
if 'dataset' in sec:
config_proto.add_section(sec)
config_proto[sec] = config_proto['dataset']
name_data.append(config[sec]['data_name'])
if 'architecture' in sec:
name_arch.append(config[sec]['arch_name'])
config_proto.add_section(sec)
config_proto[sec] = config_proto['architecture']
proto_file = config[sec]['arch_proto']
# Reading proto file (architecture)
config_arch = configparser.ConfigParser()
config_arch.read(proto_file)
# Reading proto options
fields_arch = list(dict(config_arch.items('proto')).keys())
fields_arch_type = list(dict(config_arch.items('proto')).values())
for i in range(len(fields_arch)):
config_proto.set(sec, fields_arch[i], fields_arch_type[i])
# Reading proto file (architecture_optimizer)
opt_type = config[sec]['arch_opt']
if opt_type == 'sgd':
proto_file = 'proto/sgd.proto'
if opt_type == 'rmsprop':
proto_file = 'proto/rmsprop.proto'
if opt_type == 'adam':
proto_file = 'proto/adam.proto'
config_arch = configparser.ConfigParser()
config_arch.read(proto_file)
# Reading proto options
fields_arch = list(dict(config_arch.items('proto')).keys())
fields_arch_type = list(dict(config_arch.items('proto')).values())
for i in range(len(fields_arch)):
config_proto.set(sec, fields_arch[i], fields_arch_type[i])
config_proto.remove_section('dataset')
config_proto.remove_section('architecture')
return [config_proto, name_data, name_arch]
def expand_section_proto(config_proto, config):
# Read config proto file
config_proto_optim_file = config['optimization']['opt_proto']
config_proto_optim = configparser.ConfigParser()
config_proto_optim.read(config_proto_optim_file)
for optim_par in list(config_proto_optim['proto']):
config_proto.set('optimization', optim_par, config_proto_optim['proto'][optim_par])
def check_cfg_fields(config_proto, config, cfg_file):
# Check mandatory sections and fields
sec_parse = True
for sec in config_proto.sections():
if any(sec in s for s in config.sections()):
# Check fields
for field in list(dict(config_proto.items(sec)).keys()):
if not (field in config[sec]):
sys.stderr.write(
"ERROR: The confg file %s does not contain the field \"%s=\" in section \"[%s]\" (mandatory)!\n" % (
cfg_file, field, sec))
sec_parse = False
else:
field_type = config_proto[sec][field]
if not (check_field(config[sec][field], field_type, field)):
sec_parse = False
# If a mandatory section doesn't exist...
else:
sys.stderr.write(
"ERROR: The confg file %s does not contain \"[%s]\" section (mandatory)!\n" % (cfg_file, sec))
sec_parse = False
if sec_parse == False:
sys.stderr.write("ERROR: Revise the confg file %s \n" % (cfg_file))
sys.exit(0)
return sec_parse
def check_consistency_with_proto(cfg_file, cfg_file_proto):
sec_parse = True
# Check if cfg file exists
try:
open(cfg_file, 'r')
except IOError:
sys.stderr.write("ERROR: The confg file %s does not exist!\n" % (cfg_file))
sys.exit(0)
# Check if cfg proto file exists
try:
open(cfg_file_proto, 'r')
except IOError:
sys.stderr.write("ERROR: The confg file %s does not exist!\n" % (cfg_file_proto))
sys.exit(0)
# Parser Initialization
config = configparser.ConfigParser()
# Reading the cfg file
config.read(cfg_file)
# Reading proto cfg file
config_proto = configparser.ConfigParser()
config_proto.read(cfg_file_proto)
# Adding the multiple entries in data and architecture sections
[config_proto, name_data, name_arch] = expand_section(config_proto, config)
# Check mandatory sections and fields
sec_parse = check_cfg_fields(config_proto, config, cfg_file)
if sec_parse == False:
sys.exit(0)
return [config_proto, name_data, name_arch]
def check_cfg(cfg_file, config, cfg_file_proto):
# Check consistency between cfg_file and cfg_file_proto
[config_proto, name_data, name_arch] = check_consistency_with_proto(cfg_file, cfg_file_proto)
# Reload data_name because they might be altered by arguments
name_data = []
for sec in config.sections():
if 'dataset' in sec:
name_data.append(config[sec]['data_name'])
# check consistency between [data_use] vs [data*]
sec_parse = True
data_use_with = []
for data in list(dict(config.items('data_use')).values()):
data_use_with.append(data.split(','))
data_use_with = sum(data_use_with, [])
if not (set(data_use_with).issubset(name_data)):
sys.stderr.write("ERROR: in [data_use] you are using a dataset not specified in [dataset*] %s \n" % (cfg_file))
sec_parse = False
# Set to false the first layer norm layer if the architecture is sequential (to avoid numerical instabilities)
seq_model = False
for sec in config.sections():
if "architecture" in sec:
if strtobool(config[sec]['arch_seq_model']):
seq_model = True
break
if seq_model:
for item in list(config['architecture1'].items()):
if 'use_laynorm' in item[0] and '_inp' not in item[0]:
ln_list = item[1].split(',')
if ln_list[0] == 'True':
ln_list[0] = 'False'
config['architecture1'][item[0]] = ','.join(ln_list)
# Parse fea and lab fields in datasets*
cnt = 0
fea_names_lst = []
lab_names_lst = []
for data in name_data:
# Check for production case 'none' lab name
[lab_names, _, _] = parse_lab_field(config[cfg_item2sec(config, 'data_name', data)]['lab'])
config['exp']['production'] = str('False')
if lab_names == ["none"] and data == config['data_use']['forward_with']:
config['exp']['production'] = str('True')
continue
elif lab_names == ["none"] and data != config['data_use']['forward_with']:
continue
[fea_names, fea_lsts, fea_opts, cws_left, cws_right] = parse_fea_field(
config[cfg_item2sec(config, 'data_name', data)]['fea'])
[lab_names, lab_folders, lab_opts] = parse_lab_field(config[cfg_item2sec(config, 'data_name', data)]['lab'])
fea_names_lst.append(sorted(fea_names))
lab_names_lst.append(sorted(lab_names))
# Check that fea_name doesn't contain special characters
for name_features in fea_names_lst[cnt]:
if not (re.match("^[a-zA-Z0-9]*$", name_features)):
sys.stderr.write(
"ERROR: features names (fea_name=) must contain only letters or numbers (no special characters as \"_,$,..\") \n")
sec_parse = False
sys.exit(0)
if cnt > 0:
if fea_names_lst[cnt - 1] != fea_names_lst[cnt]:
sys.stderr.write("ERROR: features name (fea_name) must be the same of all the datasets! \n")
sec_parse = False
sys.exit(0)
if lab_names_lst[cnt - 1] != lab_names_lst[cnt]:
sys.stderr.write("ERROR: labels name (lab_name) must be the same of all the datasets! \n")
sec_parse = False
sys.exit(0)
cnt = cnt + 1
# Create the output folder
out_folder = config['exp']['out_folder']
if not os.path.exists(out_folder) or not (os.path.exists(out_folder + '/exp_files')):
os.makedirs(out_folder + '/exp_files')
# Parsing forward field
model = config['model']['model']
possible_outs = list(re.findall('(.*)=', model.replace(' ', '')))
forward_out_lst = config['forward']['forward_out'].split(',')
forward_norm_lst = config['forward']['normalize_with_counts_from'].split(',')
forward_norm_bool_lst = config['forward']['normalize_posteriors'].split(',')
lab_lst = list(re.findall('lab_name=(.*)\n', config['dataset1']['lab'].replace(' ', '')))
lab_folders = list(re.findall('lab_folder=(.*)\n', config['dataset1']['lab'].replace(' ', '')))
N_out_lab = ['none'] * len(lab_lst)
for i in range(len(lab_opts)):
# Compute number of monophones if needed
if "ali-to-phones" in lab_opts[i]:
log_file = config['exp']['out_folder'] + '/log.log'
folder_lab_count = lab_folders[i]
cmd = "hmm-info " + folder_lab_count + "/final.mdl | awk '/phones/{print $4}'"
output = run_shell(cmd, log_file)
if output.decode().rstrip() == '':
sys.stderr.write(
"ERROR: hmm-info command doesn't exist. Make sure your .bashrc contains the Kaldi paths and correctly exports it.\n")
sys.exit(0)
N_out = int(output.decode().rstrip())
N_out_lab[i] = N_out
for i in range(len(forward_out_lst)):
if forward_out_lst[i] not in possible_outs:
sys.stderr.write(
'ERROR: the output \"%s\" in the section \"forward_out\" is not defined in section model)\n' % (
forward_out_lst[i]))
sys.exit(0)
if strtobool(forward_norm_bool_lst[i]):
if forward_norm_lst[i] not in lab_lst:
if not os.path.exists(forward_norm_lst[i]):
sys.stderr.write(
'ERROR: the count_file \"%s\" in the section \"forward_out\" is does not exist)\n' % (
forward_norm_lst[i]))
sys.exit(0)
else:
# Check if the specified file is in the right format
f = open(forward_norm_lst[i], "r")
cnts = f.read()
if not (bool(re.match("(.*)\[(.*)\]", cnts))):
sys.stderr.write(
'ERROR: the count_file \"%s\" in the section \"forward_out\" is not in the right format)\n' % (
forward_norm_lst[i]))
else:
# Try to automatically retrieve the count file from the config file
# Compute the number of context-dependent phone states
if "ali-to-pdf" in lab_opts[lab_lst.index(forward_norm_lst[i])]:
log_file = config['exp']['out_folder'] + '/log.log'
folder_lab_count = lab_folders[lab_lst.index(forward_norm_lst[i])]
cmd = "hmm-info " + folder_lab_count + "/final.mdl | awk '/pdfs/{print $4}'"
output = run_shell(cmd, log_file)
if output.decode().rstrip() == '':
sys.stderr.write(
"ERROR: hmm-info command doesn't exist. Make sure your .bashrc contains the Kaldi paths and correctly exports it.\n")
sys.exit(0)
N_out = int(output.decode().rstrip())
N_out_lab[lab_lst.index(forward_norm_lst[i])] = N_out
count_file_path = out_folder + '/exp_files/forward_' + forward_out_lst[i] + '_' + forward_norm_lst[
i] + '.count'
cmd = "analyze-counts --print-args=False --verbose=0 --binary=false --counts-dim=" + str(
N_out) + " \"ark:ali-to-pdf " + folder_lab_count + "/final.mdl \\\"ark:gunzip -c " + folder_lab_count + "/ali.*.gz |\\\" ark:- |\" " + count_file_path
run_shell(cmd, log_file)
forward_norm_lst[i] = count_file_path
else:
sys.stderr.write(
'ERROR: Not able to automatically retrieve count file for the label \"%s\". Please add a valid count file path in \"normalize_with_counts_from\" or set normalize_posteriors=False \n' % (
forward_norm_lst[i]))
sys.exit(0)
# Update the config file with the count_file paths
config['forward']['normalize_with_counts_from'] = ",".join(forward_norm_lst)
# When possible replace the pattern "N_out_lab*" with the detected number of output
for sec in config.sections():
for field in list(config[sec]):
for i in range(len(lab_lst)):
pattern = 'N_out_' + lab_lst[i]
if pattern in config[sec][field]:
if N_out_lab[i] != 'none':
config[sec][field] = config[sec][field].replace(pattern, str(N_out_lab[i]))
else:
sys.stderr.write(
'ERROR: Cannot automatically retrieve the number of output in %s. Please, add manually the number of outputs \n' % (
pattern))
sys.exit(0)
# Check the model field
parse_model_field(cfg_file)
# Create block diagram picture of the model
create_block_diagram(cfg_file)
if sec_parse == False:
sys.exit(0)
return [config, name_data, name_arch]
def cfg_item2sec(config, field, value):
for sec in config.sections():
if field in list(dict(config.items(sec)).keys()):
if value in list(dict(config.items(sec)).values()):
return sec
sys.stderr.write("ERROR: %s=%s not found in config file \n" % (field, value))
sys.exit(0)
return -1
def split_chunks(seq, size):
newseq = []
splitsize = 1.0 / size * len(seq)
for i in range(size):
newseq.append(seq[int(round(i * splitsize)):int(round((i + 1) * splitsize))])
return newseq
def create_configs(config):
# This function create the chunk-specific config files
cfg_file_proto_chunk = config['cfg_proto']['cfg_proto_chunk']
N_ep = int(config['exp']['N_epochs_tr'])
N_ep_str_format = '0' + str(int(max(math.ceil(np.log10(N_ep)), 1))) + 'd'
tr_data_lst = config['data_use']['train_with'].split(',')
valid_data_lst = config['data_use']['valid_with'].split(',')
max_seq_length_train = config['batches']['max_seq_length_train']
forward_data_lst = config['data_use']['forward_with'].split(',')
out_folder = config['exp']['out_folder']
cfg_file = out_folder + '/conf.cfg'
chunk_lst = out_folder + '/exp_files/list_chunks.txt'
lst_chunk_file = open(chunk_lst, 'w')
# Read the batch size string
batch_size_tr_str = config['batches']['batch_size_train']
batch_size_tr_arr = expand_str_ep(batch_size_tr_str, 'int', N_ep, '|', '*')
# Read the max_seq_length_train
max_seq_length_tr_arr = expand_str_ep(max_seq_length_train, 'int', N_ep, '|', '*')
cfg_file_proto = config['cfg_proto']['cfg_proto']
[config, name_data, name_arch] = check_cfg(cfg_file, config, cfg_file_proto)
arch_lst = get_all_archs(config)
lr = {}
improvement_threshold = {}
halving_factor = {}
pt_files = {}
drop_rates = {}
for arch in arch_lst:
lr_arr = expand_str_ep(config[arch]['arch_lr'], 'float', N_ep, '|', '*')
lr[arch] = lr_arr
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']
# Loop over all the sections and look for a "_drop" field (to perform dropout scheduling
for (field_key, field_val) in config.items(arch):
if "_drop" in field_key:
drop_lay = field_val.split(',')
N_lay = len(drop_lay)
drop_rates[arch] = []
for lay_id in range(N_lay):
drop_rates[arch].append(expand_str_ep(drop_lay[lay_id], 'float', N_ep, '|', '*'))
# Check dropout factors
for dropout_factor in drop_rates[arch][0]:
if float(dropout_factor) < 0.0 or float(dropout_factor) > 1.0:
sys.stderr.write(
'The dropout rate should be between 0 and 1. Got %s in %s.\n' % (dropout_factor, field_key))
sys.exit(0)
if strtobool(config['batches']['increase_seq_length_train']):
max_seq_length_train_curr = int(config['batches']['start_seq_len_train'])
for ep in range(N_ep):
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, N_ep_str_format, 'train')
N_ck_str_format = '0' + str(int(max(math.ceil(np.log10(N_ck_tr)), 1))) + 'd'
# ***Epoch training***
for ck in range(N_ck_tr):
# path of the list of features for this chunk
lst_file = out_folder + '/exp_files/train_' + tr_data + '_ep' + format(ep,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '_*.lst'
# paths of the output files (info,model,chunk_specific cfg file)
info_file = out_folder + '/exp_files/train_' + tr_data + '_ep' + format(ep,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '.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,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '.cfg'
lst_chunk_file.write(config_chunk_file + '\n')
if strtobool(config['batches']['increase_seq_length_train']) == False:
max_seq_length_train_curr = int(max_seq_length_tr_arr[ep])
# Write chunk-specific cfg file
write_cfg_chunk(cfg_file, config_chunk_file, cfg_file_proto_chunk, pt_files, lst_file, info_file,
'train', tr_data, lr, max_seq_length_train_curr, name_data, ep, ck,
batch_size_tr_arr[ep], drop_rates)
# 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,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '_' + pt_arch + '.pkl'
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, N_ep_str_format, 'valid')
N_ck_str_format = '0' + str(int(max(math.ceil(np.log10(N_ck_valid)), 1))) + 'd'
for ck in range(N_ck_valid):
# path of the list of features for this chunk
lst_file = out_folder + '/exp_files/valid_' + valid_data + '_ep' + format(ep,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '_*.lst'
# paths of the output files
info_file = out_folder + '/exp_files/valid_' + valid_data + '_ep' + format(ep,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '.info'
config_chunk_file = out_folder + '/exp_files/valid_' + valid_data + '_ep' + format(ep,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '.cfg'
lst_chunk_file.write(config_chunk_file + '\n')
# Write chunk-specific cfg file
write_cfg_chunk(cfg_file, config_chunk_file, cfg_file_proto_chunk, model_files, lst_file, info_file,
'valid', valid_data, lr, max_seq_length_train_curr, name_data, ep, ck,
batch_size_tr_arr[ep], drop_rates)
# if needed, update sentence_length
if strtobool(config['batches']['increase_seq_length_train']):
max_seq_length_train_curr = max_seq_length_train_curr * int(
config['batches']['multply_factor_seq_len_train'])
if max_seq_length_train_curr > int(max_seq_length_tr_arr[ep]):
max_seq_length_train_curr = int(max_seq_length_tr_arr[ep])
for forward_data in forward_data_lst:
# Compute the number of chunks
N_ck_forward = compute_n_chunks(out_folder, forward_data, ep, N_ep_str_format, 'forward')
N_ck_str_format = '0' + str(int(max(math.ceil(np.log10(N_ck_forward)), 1))) + 'd'
for ck in range(N_ck_forward):
# path of the list of features for this chunk
lst_file = out_folder + '/exp_files/forward_' + forward_data + '_ep' + format(ep,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '_*.lst'
# output file
info_file = out_folder + '/exp_files/forward_' + forward_data + '_ep' + format(ep,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '.info'
config_chunk_file = out_folder + '/exp_files/forward_' + forward_data + '_ep' + format(ep,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '.cfg'
lst_chunk_file.write(config_chunk_file + '\n')
# Write chunk-specific cfg file
write_cfg_chunk(cfg_file, config_chunk_file, cfg_file_proto_chunk, model_files, lst_file, info_file,
'forward', forward_data, lr, max_seq_length_train_curr, name_data, ep, ck,
batch_size_tr_arr[ep], drop_rates)
lst_chunk_file.close()
def create_lists(config):
# splitting data into chunks (see out_folder/additional_files)
out_folder = config['exp']['out_folder']
seed = int(config['exp']['seed'])
N_ep = int(config['exp']['N_epochs_tr'])
N_ep_str_format = '0' + str(int(max(math.ceil(np.log10(N_ep)), 1))) + 'd'
# Setting the random seed
random.seed(seed)
# training chunk lists creation
tr_data_name = config['data_use']['train_with'].split(',')
# Reading validation feature lists
for dataset in tr_data_name:
sec_data = cfg_item2sec(config, 'data_name', dataset)
[fea_names, list_fea, fea_opts, cws_left, cws_right] = parse_fea_field(
config[cfg_item2sec(config, 'data_name', dataset)]['fea'])
N_chunks = int(config[sec_data]['N_chunks'])
N_ck_str_format = '0' + str(int(max(math.ceil(np.log10(N_chunks)), 1))) + 'd'
full_list = []
for i in range(len(fea_names)):
full_list.append([line.rstrip('\n') + ',' for line in open(list_fea[i])])
full_list[i] = sorted(full_list[i])
# concatenating all the featues in a single file (useful for shuffling consistently)
full_list_fea_conc = full_list[0]
for i in range(1, len(full_list)):
full_list_fea_conc = list(map(str.__add__, full_list_fea_conc, full_list[i]))
for ep in range(N_ep):
# randomize the list
random.shuffle(full_list_fea_conc)
tr_chunks_fea = list(split_chunks(full_list_fea_conc, N_chunks))
tr_chunks_fea.reverse()
for ck in range(N_chunks):
for i in range(len(fea_names)):
tr_chunks_fea_split = [];
for snt in tr_chunks_fea[ck]:
# print(snt.split(',')[i])
tr_chunks_fea_split.append(snt.split(',')[i])
output_lst_file = out_folder + '/exp_files/train_' + dataset + '_ep' + format(ep,
N_ep_str_format) + '_ck' + format(
ck, N_ck_str_format) + '_' + fea_names[i] + '.lst'
f = open(output_lst_file, 'w')
tr_chunks_fea_wr = map(lambda x: x + '\n', tr_chunks_fea_split)
f.writelines(tr_chunks_fea_wr)
f.close()
# Validation chunk lists creation
valid_data_name = config['data_use']['valid_with'].split(',')
# Reading validation feature lists
for dataset in valid_data_name:
sec_data = cfg_item2sec(config, 'data_name', dataset)
[fea_names, list_fea, fea_opts, cws_left, cws_right] = parse_fea_field(
config[cfg_item2sec(config, 'data_name', dataset)]['fea'])
N_chunks = int(config[sec_data]['N_chunks'])
N_ck_str_format = '0' + str(int(max(math.ceil(np.log10(N_chunks)), 1))) + 'd'
full_list = []
for i in range(len(fea_names)):
full_list.append([line.rstrip('\n') + ',' for line in open(list_fea[i])])
full_list[i] = sorted(full_list[i])
# concatenating all the featues in a single file (useful for shuffling consistently)
full_list_fea_conc = full_list[0]
for i in range(1, len(full_list)):
full_list_fea_conc = list(map(str.__add__, full_list_fea_conc, full_list[i]))
# randomize the list
random.shuffle(full_list_fea_conc)
valid_chunks_fea = list(split_chunks(full_list_fea_conc, N_chunks))
for ep in range(N_ep):
for ck in range(N_chunks):
for i in range(len(fea_names)):