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LmDataset.py
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LmDataset.py
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from __future__ import print_function
import sys
from Dataset import DatasetSeq
from CachedDataset2 import CachedDataset2
import gzip
import xml.etree.ElementTree as etree
from Util import parse_orthography, parse_orthography_into_symbols, load_json, NumbersDict, BackendEngine
from Log import log
import numpy
import time
from random import Random
class LmDataset(CachedDataset2):
def __init__(self,
corpus_file,
orth_symbols_file=None,
orth_symbols_map_file=None,
orth_replace_map_file=None,
word_based=False,
seq_end_symbol="[END]",
unknown_symbol="[UNKNOWN]",
parse_orth_opts=None,
phone_info=None,
add_random_phone_seqs=0,
partition_epoch=1,
auto_replace_unknown_symbol=False,
log_auto_replace_unknown_symbols=10,
log_skipped_seqs=10,
error_on_invalid_seq=True,
add_delayed_seq_data=False,
delayed_seq_data_start_symbol="[START]",
**kwargs):
"""
:param str|()->str corpus_file: Bliss XML or line-based txt. optionally can be gzip.
:param dict|None phone_info: if you want to get phone seqs, dict with lexicon_file etc. see PhoneSeqGenerator
:param str|()->str|None orth_symbols_file: list of orthography symbols, if you want to get orth symbol seqs
:param str|()->str|None orth_symbols_map_file: list of orth symbols, each line: "symbol index"
:param str|()->str|None orth_replace_map_file: JSON file with replacement dict for orth symbols
:param bool word_based: whether to parse single words, or otherwise will be char-based
:param str|None seq_end_symbol: what to add at the end, if given.
will be set as postfix=[seq_end_symbol] or postfix=[] for parse_orth_opts.
:param dict[str]|None parse_orth_opts: kwargs for parse_orthography()
:param int add_random_phone_seqs: will add random seqs with the same len as the real seq as additional data
:param bool|int log_auto_replace_unknown_symbols: write about auto-replacements with unknown symbol.
if this is an int, it will only log the first N replacements, and then keep quiet.
:param bool|int log_skipped_seqs: write about skipped seqs to logging, due to missing lexicon entry or so.
if this is an int, it will only log the first N entries, and then keep quiet.
:param bool error_on_invalid_seq: if there is a seq we would have to skip, error
:param bool add_delayed_seq_data: will add another data-key "delayed" which will have the sequence
delayed_seq_data_start_symbol + original_sequence[:-1]
:param str delayed_seq_data_start_symbol: used for add_delayed_seq_data
:param int partition_epoch: whether to partition the epochs into multiple parts. like epoch_split
"""
super(LmDataset, self).__init__(**kwargs)
if callable(corpus_file):
corpus_file = corpus_file()
if callable(orth_symbols_file):
orth_symbols_file = orth_symbols_file()
if callable(orth_symbols_map_file):
orth_symbols_map_file = orth_symbols_map_file()
if callable(orth_replace_map_file):
orth_replace_map_file = orth_replace_map_file()
print("LmDataset, loading file", corpus_file, file=log.v4)
self.word_based = word_based
self.seq_end_symbol = seq_end_symbol
self.unknown_symbol = unknown_symbol
self.parse_orth_opts = parse_orth_opts or {}
self.parse_orth_opts.setdefault("word_based", self.word_based)
self.parse_orth_opts.setdefault("postfix", [self.seq_end_symbol] if self.seq_end_symbol is not None else [])
if orth_symbols_file:
assert not phone_info
assert not orth_symbols_map_file
orth_symbols = open(orth_symbols_file).read().splitlines()
self.orth_symbols_map = {sym: i for (i, sym) in enumerate(orth_symbols)}
self.orth_symbols = orth_symbols
self.labels["data"] = orth_symbols
self.seq_gen = None
elif orth_symbols_map_file:
assert not phone_info
orth_symbols_imap_list = [(int(b), a)
for (a, b) in [l.split(None, 1)
for l in open(orth_symbols_map_file).read().splitlines()]]
orth_symbols_imap_list.sort()
assert orth_symbols_imap_list[0][0] == 0
assert orth_symbols_imap_list[-1][0] == len(orth_symbols_imap_list) - 1
self.orth_symbols_map = {sym: i for (i, sym) in orth_symbols_imap_list}
self.orth_symbols = [sym for (i, sym) in orth_symbols_imap_list]
self.labels["data"] = self.orth_symbols
self.seq_gen = None
else:
assert not orth_symbols_file
assert isinstance(phone_info, dict)
self.seq_gen = PhoneSeqGenerator(**phone_info)
self.orth_symbols = None
self.labels["data"] = self.seq_gen.get_class_labels()
if orth_replace_map_file:
orth_replace_map = load_json(filename=orth_replace_map_file)
assert isinstance(orth_replace_map, dict)
self.orth_replace_map = {key: parse_orthography_into_symbols(v, word_based=self.word_based)
for (key, v) in orth_replace_map.items()}
if self.orth_replace_map:
if len(self.orth_replace_map) <= 5:
print(" orth_replace_map: %r" % self.orth_replace_map, file=log.v5)
else:
print(" orth_replace_map: %i entries" % len(self.orth_replace_map), file=log.v5)
else:
self.orth_replace_map = {}
num_labels = len(self.labels["data"])
use_uint_types = False
if BackendEngine.is_tensorflow_selected():
use_uint_types = True
if num_labels <= 2 ** 7:
self.dtype = "int8"
elif num_labels <= 2 ** 8 and use_uint_types:
self.dtype = "uint8"
elif num_labels <= 2 ** 31:
self.dtype = "int32"
elif num_labels <= 2 ** 32 and use_uint_types:
self.dtype = "uint32"
elif num_labels <= 2 ** 61:
self.dtype = "int64"
elif num_labels <= 2 ** 62 and use_uint_types:
self.dtype = "uint64"
else:
raise Exception("cannot handle so much labels: %i" % num_labels)
self.num_outputs = {"data": [len(self.labels["data"]), 1]}
self.num_inputs = self.num_outputs["data"][0]
self.seq_order = None
self.auto_replace_unknown_symbol = auto_replace_unknown_symbol
self.log_auto_replace_unknown_symbols = log_auto_replace_unknown_symbols
self.log_skipped_seqs = log_skipped_seqs
self.error_on_invalid_seq = error_on_invalid_seq
self.partition_epoch = partition_epoch
self.add_random_phone_seqs = add_random_phone_seqs
for i in range(add_random_phone_seqs):
self.num_outputs["random%i" % i] = self.num_outputs["data"]
self.add_delayed_seq_data = add_delayed_seq_data
self.delayed_seq_data_start_symbol = delayed_seq_data_start_symbol
if add_delayed_seq_data:
self.num_outputs["delayed"] = self.num_outputs["data"]
if _is_bliss(corpus_file):
iter_f = _iter_bliss
else:
iter_f = _iter_txt
self.orths = []
iter_f(corpus_file, self.orths.append)
# It's only estimated because we might filter some out or so.
self._estimated_num_seqs = len(self.orths) // self.partition_epoch
print(" done, loaded %i sequences" % len(self.orths), file=log.v4)
def get_target_list(self):
return sorted([k for k in self.num_outputs.keys() if k != "data"])
def get_data_dtype(self, key):
return self.dtype
def init_seq_order(self, epoch=None, seq_list=None):
assert seq_list is None
super(LmDataset, self).init_seq_order(epoch=epoch)
epoch = epoch or 1
self.orths_epoch = self.orths[
len(self.orths) * (epoch % self.partition_epoch) // self.partition_epoch:
len(self.orths) * ((epoch % self.partition_epoch) + 1) // self.partition_epoch]
self.seq_order = self.get_seq_order_for_epoch(
epoch=epoch, num_seqs=len(self.orths_epoch), get_seq_len=lambda i: len(self.orths_epoch[i]))
self.next_orth_idx = 0
self.next_seq_idx = 0
self.num_skipped = 0
self.num_unknown = 0
if self.seq_gen:
self.seq_gen.random_seed(epoch)
return True
def _reduce_log_skipped_seqs(self):
if isinstance(self.log_skipped_seqs, bool):
return
assert isinstance(self.log_skipped_seqs, int)
assert self.log_skipped_seqs >= 1
self.log_skipped_seqs -= 1
if not self.log_skipped_seqs:
print("LmDataset: will stop logging about skipped sequences now", file=log.v4)
def _reduce_log_auto_replace_unknown_symbols(self):
if isinstance(self.log_auto_replace_unknown_symbols, bool):
return
assert isinstance(self.log_auto_replace_unknown_symbols, int)
assert self.log_auto_replace_unknown_symbols >= 1
self.log_auto_replace_unknown_symbols -= 1
if not self.log_auto_replace_unknown_symbols:
print("LmDataset: will stop logging about auto-replace with unknown symbol now", file=log.v4)
def _collect_single_seq(self, seq_idx):
"""
:type seq_idx: int
:rtype: DatasetSeq | None
:returns DatasetSeq or None if seq_idx >= num_seqs.
"""
while True:
if self.next_orth_idx >= len(self.orths_epoch):
assert self.next_seq_idx <= seq_idx, "We expect that we iterate through all seqs."
if self.num_skipped > 0:
print("LmDataset: reached end, skipped %i sequences" % self.num_skipped)
return None
assert self.next_seq_idx == seq_idx, "We expect that we iterate through all seqs."
orth = self.orths_epoch[self.seq_order[self.next_orth_idx]]
self.next_orth_idx += 1
if orth == "</s>": continue # special sentence end symbol. empty seq, ignore.
if self.seq_gen:
try:
phones = self.seq_gen.generate_seq(orth)
except KeyError as e:
if self.log_skipped_seqs:
print("LmDataset: skipping sequence %r because of missing lexicon entry: %s" % (orth, e), file=log.v4)
self._reduce_log_skipped_seqs()
if self.error_on_invalid_seq:
raise Exception("LmDataset: invalid seq %r, missing lexicon entry %r" % (orth, e))
self.num_skipped += 1
continue # try another seq
data = self.seq_gen.seq_to_class_idxs(phones, dtype=self.dtype)
elif self.orth_symbols:
orth_syms = parse_orthography(orth, **self.parse_orth_opts)
while True:
orth_syms = sum([self.orth_replace_map.get(s, [s]) for s in orth_syms], [])
i = 0
while i < len(orth_syms) - 1:
if orth_syms[i:i+2] == [" ", " "]:
orth_syms[i:i+2] = [" "] # collapse two spaces
else:
i += 1
if self.auto_replace_unknown_symbol:
try:
map(self.orth_symbols_map.__getitem__, orth_syms)
except KeyError as e:
orth_sym = e.message
if self.log_auto_replace_unknown_symbols:
print("LmDataset: unknown orth symbol %r, adding to orth_replace_map as %r" % (orth_sym, self.unknown_symbol), file=log.v3)
self._reduce_log_auto_replace_unknown_symbols()
self.orth_replace_map[orth_sym] = [self.unknown_symbol] if self.unknown_symbol is not None else []
continue # try this seq again with updated orth_replace_map
break
self.num_unknown += orth_syms.count(self.unknown_symbol)
if self.word_based:
orth_debug_str = repr(orth_syms)
else:
orth_debug_str = repr("".join(orth_syms))
try:
data = numpy.array(map(self.orth_symbols_map.__getitem__, orth_syms), dtype=self.dtype)
except KeyError as e:
if self.log_skipped_seqs:
print("LmDataset: skipping sequence %s because of missing orth symbol: %s" % (orth_debug_str, e), file=log.v4)
self._reduce_log_skipped_seqs()
if self.error_on_invalid_seq:
raise Exception("LmDataset: invalid seq %s, missing orth symbol %s" % (orth_debug_str, e))
self.num_skipped += 1
continue # try another seq
else:
assert False
targets = {}
for i in range(self.add_random_phone_seqs):
assert self.seq_gen # not implemented atm for orths
phones = self.seq_gen.generate_garbage_seq(target_len=data.shape[0])
targets["random%i" % i] = self.seq_gen.seq_to_class_idxs(phones, dtype=self.dtype)
if self.add_delayed_seq_data:
targets["delayed"] = numpy.concatenate(
([self.orth_symbols_map[self.delayed_seq_data_start_symbol]], data[:-1])).astype(self.dtype)
assert targets["delayed"].shape == data.shape
self.next_seq_idx = seq_idx + 1
return DatasetSeq(seq_idx=seq_idx, features=data, targets=targets)
def _is_bliss(filename):
try:
corpus_file = open(filename, 'rb')
if filename.endswith(".gz"):
corpus_file = gzip.GzipFile(fileobj=corpus_file)
context = iter(etree.iterparse(corpus_file, events=('start', 'end')))
_, root = next(context) # get root element
return True
except IOError: # 'Not a gzipped file' or so
pass
except etree.ParseError: # 'syntax error' or so
pass
return False
def _iter_bliss(filename, callback):
corpus_file = open(filename, 'rb')
if filename.endswith(".gz"):
corpus_file = gzip.GzipFile(fileobj=corpus_file)
def getelements(tag):
"""Yield *tag* elements from *filename_or_file* xml incrementally."""
context = iter(etree.iterparse(corpus_file, events=('start', 'end')))
_, root = next(context) # get root element
tree = [root]
for event, elem in context:
if event == "start":
tree += [elem]
elif event == "end":
assert tree[-1] is elem
tree = tree[:-1]
if event == 'end' and elem.tag == tag:
yield tree, elem
root.clear() # free memory
for tree, elem in getelements("segment"):
elem_orth = elem.find("orth")
orth_raw = elem_orth.text # should be unicode
orth_split = orth_raw.split()
orth = " ".join(orth_split)
callback(orth)
def _iter_txt(filename, callback):
f = open(filename, 'rb')
if filename.endswith(".gz"):
f = gzip.GzipFile(fileobj=f)
for l in f:
try:
l = l.decode("utf8")
except UnicodeDecodeError:
l = l.decode("latin_1") # or iso8859_15?
l = l.strip()
if not l: continue
callback(l)
class AllophoneState:
# In Sprint, see AllophoneStateAlphabet::index().
id = None # u16 in Sprint. here just str
context_history = () # list[u16] of phone id. here just list[str]
context_future = () # list[u16] of phone id. here just list[str]
boundary = 0 # s16. flags. 1 -> initial (@i), 2 -> final (@f)
state = None # s16, e.g. 0,1,2
_attrs = ["id", "context_history", "context_future", "boundary", "state"]
def __init__(self, id=None, state=None):
self.id = id
self.state = state
def format(self):
s = "%s{%s+%s}" % (
self.id,
"-".join(self.context_history) or "#",
"-".join(self.context_future) or "#")
if self.boundary & 1:
s += "@i"
if self.boundary & 2:
s += "@f"
if self.state is not None:
s += ".%i" % self.state
return s
def __repr__(self):
return self.format()
def mark_initial(self):
self.boundary = self.boundary | 1
def mark_final(self):
self.boundary = self.boundary | 2
def __hash__(self):
return hash(tuple([getattr(self, a) for a in self._attrs]))
def __eq__(self, other):
for a in self._attrs:
if getattr(self, a) != getattr(other, a):
return False
return True
def __ne__(self, other):
return not self == other
class Lexicon:
def __init__(self, filename):
print("Loading lexicon", filename, file=log.v4)
lex_file = open(filename, 'rb')
if filename.endswith(".gz"):
lex_file = gzip.GzipFile(fileobj=lex_file)
self.phonemes = {}
self.lemmas = {}
context = iter(etree.iterparse(lex_file, events=('start', 'end')))
_, root = next(context) # get root element
tree = [root]
for event, elem in context:
if event == "start":
tree += [elem]
elif event == "end":
assert tree[-1] is elem
tree = tree[:-1]
if elem.tag == "phoneme":
symbol = elem.find("symbol").text.strip() # should be unicode
if elem.find("variation") is not None:
variation = elem.find("variation").text.strip()
else:
variation = "context" # default
assert symbol not in self.phonemes
assert variation in ["context", "none"]
self.phonemes[symbol] = {"index": len(self.phonemes), "symbol": symbol, "variation": variation}
root.clear() # free memory
elif elem.tag == "phoneme-inventory":
print("Finished phoneme inventory, %i phonemes" % len(self.phonemes), file=log.v4)
root.clear() # free memory
elif elem.tag == "lemma":
for orth_elem in elem.findall("orth"):
orth = (orth_elem.text or "").strip()
phons = [{"phon": e.text.strip(), "score": float(e.attrib.get("score", 0))} for e in elem.findall("phon")]
assert orth not in self.lemmas
self.lemmas[orth] = {"orth": orth, "phons": phons}
root.clear() # free memory
print("Finished whole lexicon, %i lemmas" % len(self.lemmas), file=log.v4)
class StateTying:
def __init__(self, state_tying_file):
self.allo_map = {} # allophone-state-str -> class-idx
self.class_map = {} # class-idx -> set(allophone-state-str)
ls = open(state_tying_file).read().splitlines()
for l in ls:
allo_str, class_idx_str = l.split()
class_idx = int(class_idx_str)
assert allo_str not in self.allo_map
self.allo_map[allo_str] = class_idx
self.class_map.setdefault(class_idx, set()).add(allo_str)
min_class_idx = min(self.class_map.keys())
max_class_idx = max(self.class_map.keys())
assert min_class_idx == 0
assert max_class_idx == len(self.class_map) - 1, "some classes are not represented"
self.num_classes = len(self.class_map)
class PhoneSeqGenerator:
def __init__(self, lexicon_file,
allo_num_states=3, allo_context_len=1,
state_tying_file=None,
add_silence_beginning=0.1, add_silence_between_words=0.1, add_silence_end=0.1,
repetition=0.9, silence_repetition=0.95):
"""
:param str lexicon_file: lexicon XML file
:param int allo_num_states: how much HMM states per allophone (all but silence)
:param int allo_context_len: how much context to store left and right. 1 -> triphone
:param str | None state_tying_file: for state-tying, if you want that
:param float add_silence_beginning: prob of adding silence at beginning
:param float add_silence_between_words: prob of adding silence between words
:param float add_silence_end: prob of adding silence at end
:param float repetition: prob of repeating an allophone
:param float silence_repetition: prob of repeating the silence allophone
"""
self.lexicon = Lexicon(lexicon_file)
self.phonemes = sorted(self.lexicon.phonemes.keys(), key=lambda s: self.lexicon.phonemes[s]["index"])
self.rnd = Random(0)
self.allo_num_states = allo_num_states
self.allo_context_len = allo_context_len
self.add_silence_beginning = add_silence_beginning
self.add_silence_between_words = add_silence_between_words
self.add_silence_end = add_silence_end
self.repetition = repetition
self.silence_repetition = silence_repetition
self.si_lemma = self.lexicon.lemmas["[SILENCE]"]
self.si_phone = self.si_lemma["phons"][0]["phon"]
if state_tying_file:
self.state_tying = StateTying(state_tying_file)
else:
self.state_tying = None
def random_seed(self, seed):
self.rnd.seed(seed)
def get_class_labels(self):
if self.state_tying:
# State tying labels. Represented by some allophone state str.
return ["|".join(sorted(self.state_tying.class_map[i])) for i in range(self.state_tying.num_classes)]
else:
# The phonemes are the labels.
return self.phonemes
def seq_to_class_idxs(self, phones, dtype=None):
"""
:param list[AllophoneState] phones: list of allophone states
:param str dtype: eg "int32"
:rtype: numpy.ndarray
:returns 1D numpy array with the indices
"""
if dtype is None: dtype = "int32"
if self.state_tying:
# State tying indices.
return numpy.array([self.state_tying.allo_map[a.format()] for a in phones], dtype=dtype)
else:
# Phoneme indices. This must be consistent with get_class_labels.
# It should not happen that we don't have some phoneme. The lexicon should not be inconsistent.
return numpy.array([self.lexicon.phonemes[p.id]["index"] for p in phones], dtype=dtype)
def _iter_orth(self, orth):
if self.rnd.random() < self.add_silence_beginning:
yield self.si_lemma
symbols = list(orth.split())
i = 0
while i < len(symbols):
symbol = symbols[i]
try:
lemma = self.lexicon.lemmas[symbol]
except KeyError:
if "/" in symbol:
symbols[i:i+1] = symbol.split("/")
continue
if "-" in symbol:
symbols[i:i+1] = symbol.split("-")
continue
raise
i += 1
yield lemma
if i < len(symbols):
if self.rnd.random() < self.add_silence_between_words:
yield self.si_lemma
if self.rnd.random() < self.add_silence_end:
yield self.si_lemma
def orth_to_phones(self, orth):
phones = []
for lemma in self._iter_orth(orth):
phon = self.rnd.choice(lemma["phons"])
phones += [phon["phon"]]
return " ".join(phones)
def _phones_to_allos(self, phones):
for p in phones:
a = AllophoneState()
a.id = p
yield a
def _random_allo_silence(self, phone=None):
if phone is None: phone = self.si_phone
while True:
a = AllophoneState()
a.id = phone
a.mark_initial()
a.mark_final()
a.state = 0 # silence only has one state
yield a
if self.rnd.random() >= self.silence_repetition:
break
def _allos_add_states(self, allos):
for _a in allos:
if _a.id == self.si_phone:
for a in self._random_allo_silence(_a.id):
yield a
else: # non-silence
for state in range(self.allo_num_states):
while True:
a = AllophoneState()
a.id = _a.id
a.context_history = _a.context_history
a.context_future = _a.context_future
a.boundary = _a.boundary
a.state = state
yield a
if self.rnd.random() >= self.repetition:
break
def _allos_set_context(self, allos):
if self.allo_context_len == 0: return
ctx = []
for a in allos:
if self.lexicon.phonemes[a.id]["variation"] == "context":
a.context_history = tuple(ctx)
ctx += [a.id]
ctx = ctx[-self.allo_context_len:]
else:
ctx = []
ctx = []
for a in reversed(allos):
if self.lexicon.phonemes[a.id]["variation"] == "context":
a.context_future = tuple(reversed(ctx))
ctx += [a.id]
ctx = ctx[-self.allo_context_len:]
else:
ctx = []
def generate_seq(self, orth):
"""
:param str orth: orthography as a str. orth.split() should give words in the lexicon
:rtype: list[AllophoneState]
:returns allophone state list. those will have repetitions etc
"""
allos = []
for lemma in self._iter_orth(orth):
phon = self.rnd.choice(lemma["phons"])
l_allos = list(self._phones_to_allos(phon["phon"].split()))
l_allos[0].mark_initial()
l_allos[-1].mark_final()
allos += l_allos
self._allos_set_context(allos)
allos = list(self._allos_add_states(allos))
return allos
def _random_phone_seq(self, prob_add=0.8):
while True:
yield self.rnd.choice(self.phonemes)
if self.rnd.random() >= prob_add:
break
def _random_allo_seq(self, prob_word_add=0.8):
allos = []
while True:
phones = self._random_phone_seq()
w_allos = list(self._phones_to_allos(phones))
w_allos[0].mark_initial()
w_allos[-1].mark_final()
allos += w_allos
if self.rnd.random() >= prob_word_add:
break
self._allos_set_context(allos)
return list(self._allos_add_states(allos))
def generate_garbage_seq(self, target_len):
"""
:param int target_len: len of the returned seq
:rtype: list[AllophoneState]
:returns allophone state list. those will have repetitions etc.
It will randomly generate a sequence of phonemes and transform that
into a list of allophones in a similar way than generate_seq().
"""
allos = []
while True:
allos += self._random_allo_seq()
# Add some silence so that left/right context is correct for further allophones.
allos += list(self._random_allo_silence())
if len(allos) >= target_len:
allos = allos[:target_len]
break
return allos
class _TFKerasDataset(CachedDataset2):
"""
Wraps around any dataset from tf.contrib.keras.datasets.
See: https://www.tensorflow.org/versions/master/api_docs/python/tf/contrib/keras/datasets
TODO: Should maybe be moved to a separate file. (Only here because of tf.contrib.keras.datasets.reuters).
"""
# TODO...
class _NltkCorpusReaderDataset(CachedDataset2):
"""
Wraps around any dataset from nltk.corpus.
TODO: Should maybe be moved to a separate file, e.g. CorpusReaderDataset.py or so?
"""
# TODO ...
def _main(argv):
import better_exchook
better_exchook.install()
log.initialize(verbosity=[5])
print("LmDataset demo startup")
kwargs = eval(argv[0])
print("Creating LmDataset with kwargs=%r ..." % kwargs)
dataset = LmDataset(**kwargs)
print("init_seq_order ...")
dataset.init_seq_order(epoch=1)
seq_idx = 0
last_log_time = time.time()
print("start iterating through seqs ...")
while dataset.is_less_than_num_seqs(seq_idx):
if seq_idx == 0:
print("load_seqs with seq_idx=%i ...." % seq_idx)
dataset.load_seqs(seq_idx, seq_idx + 1)
if time.time() - last_log_time > 2.0:
last_log_time = time.time()
print("Loading %s progress, %i/%i (%.0f%%) seqs loaded (%.0f%% skipped), (%.0f%% unknown) total syms %i ..." % (
dataset.__class__.__name__, dataset.next_orth_idx, dataset.estimated_num_seqs,
100.0 * dataset.next_orth_idx / dataset.estimated_num_seqs,
100.0 * dataset.num_skipped / (dataset.next_orth_idx or 1),
100.0 * dataset.num_unknown / dataset._num_timesteps_accumulated["data"],
dataset._num_timesteps_accumulated["data"]))
seq_idx += 1
print("finished iterating, num seqs: %i" % seq_idx)
print("dataset len:", dataset.len_info())
if __name__ == "__main__":
_main(sys.argv[1:])