github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-07-08 05:51:40 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

optimized sample generator

Browse source
This commit is contained in:
Colombo 2020-01-05 11:53:31 +04:00
parent b5c234dac3
commit 21b25038ac
6 changed files with 201 additions and 160 deletions
Download patch file Download diff file Expand all files Collapse all files

36
samplelib/SampleGeneratorFace.py
View file

@ -7,8 +7,8 @@ import numpy as np
from facelib import LandmarksProcessor
from samplelib import (SampleGeneratorBase, SampleHost, SampleProcessor,
SampleType)
from utils import iter_utils
from utils import mp_utils
from utils import iter_utils, mp_utils
'''
arg
@ -30,8 +30,13 @@ class SampleGeneratorFace(SampleGeneratorBase):
self.output_sample_types = output_sample_types
self.add_sample_idx = add_sample_idx
samples_host = SampleHost.mp_host (SampleType.FACE, self.samples_path)
self.samples_len = len(samples_host.get_list())
if self.debug:
self.generators_count = 1
else:
self.generators_count = np.clip(multiprocessing.cpu_count(), 2, 6)
samples_clis = SampleHost.host (SampleType.FACE, self.samples_path, number_of_clis=self.generators_count)
self.samples_len = len(samples_clis[0])
if self.samples_len == 0:
raise ValueError('No training data provided.')
@ -39,18 +44,16 @@ class SampleGeneratorFace(SampleGeneratorBase):
index_host = mp_utils.IndexHost(self.samples_len)
if random_ct_samples_path is not None:
ct_samples_host = SampleHost.mp_host (SampleType.FACE, random_ct_samples_path)
ct_index_host = mp_utils.IndexHost( len(ct_samples_host.get_list()) )
ct_samples_clis = SampleHost.host (SampleType.FACE, random_ct_samples_path, number_of_clis=self.generators_count)
ct_index_host = mp_utils.IndexHost( len(ct_samples_clis[0]) )
else:
ct_samples_host = None
ct_samples_clis = None
ct_index_host = None
if self.debug:
self.generators_count = 1
self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, (samples_host.create_cli(), index_host.create_cli(), ct_samples_host.create_cli() if ct_index_host is not None else None, ct_index_host.create_cli() if ct_index_host is not None else None) )]
self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, (samples_clis[0], index_host.create_cli(), ct_samples_clis[0] if ct_index_host is not None else None, ct_index_host.create_cli() if ct_index_host is not None else None) )]
else:
self.generators_count = np.clip(multiprocessing.cpu_count(), 2, 4)
self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (samples_host.create_cli(), index_host.create_cli(), ct_samples_host.create_cli() if ct_index_host is not None else None, ct_index_host.create_cli() if ct_index_host is not None else None), start_now=True ) for i in range(self.generators_count) ]
self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (samples_clis[i], index_host.create_cli(), ct_samples_clis[i] if ct_index_host is not None else None, ct_index_host.create_cli() if ct_index_host is not None else None), start_now=True ) for i in range(self.generators_count) ]
self.generator_counter = -1
@ -72,13 +75,16 @@ class SampleGeneratorFace(SampleGeneratorBase):
while True:
batches = None
indexes = index_host.get(bs)
ct_indexes = ct_index_host.get(bs) if ct_samples is not None else None
indexes = index_host.multi_get(bs)
ct_indexes = ct_index_host.multi_get(bs) if ct_samples is not None else None
batch_samples = samples.multi_get (indexes)
batch_ct_samples = ct_samples.multi_get (ct_indexes) if ct_samples is not None else None
for n_batch in range(bs):
sample_idx = indexes[n_batch]
sample = samples[ sample_idx ]
ct_sample = ct_samples[ ct_indexes[n_batch] ] if ct_samples is not None else None
sample = batch_samples[n_batch]
ct_sample = batch_ct_samples[n_batch] if ct_samples is not None else None
try:
x, = SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug, ct_sample=ct_sample)

1
samplelib/SampleGeneratorFacePerson.py
View file

@ -30,6 +30,7 @@ class SampleGeneratorFacePerson(SampleGeneratorBase):
self.output_sample_types = output_sample_types
self.person_id_mode = person_id_mode
raise NotImplementedError("Currently SampleGeneratorFacePerson is not implemented.")
samples_host = SampleHost.mp_host (SampleType.FACE, self.samples_path)
samples = samples_host.get_list()

20
samplelib/SampleGeneratorFaceTemporal.py
View file

@ -20,14 +20,17 @@ class SampleGeneratorFaceTemporal(SampleGeneratorBase):
self.sample_process_options = sample_process_options
self.output_sample_types = output_sample_types
self.samples = SampleHost.load (SampleType.FACE_TEMPORAL_SORTED, self.samples_path)
if self.debug:
self.generators_count = 1
self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, 0 )]
else:
self.generators_count = min ( generators_count, len(self.samples) )
self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, i ) for i in range(self.generators_count) ]
self.generators_count = generators_count
samples_clis = SampleHost.host (SampleType.FACE_TEMPORAL_SORTED, self.samples_path, number_of_clis=self.generators_count)
if self.debug:
self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, (0, samples_clis[0]) )]
else:
self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (i, samples_clis[i]) ) for i in range(self.generators_count) ]
self.generator_counter = -1
@ -39,8 +42,9 @@ class SampleGeneratorFaceTemporal(SampleGeneratorBase):
generator = self.generators[self.generator_counter % len(self.generators) ]
return next(generator)
def batch_func(self, generator_id):
samples = self.samples
def batch_func(self, param):
generator_id, samples = param
samples_len = len(samples)
if samples_len == 0:
raise ValueError('No training data provided.')
@ -56,10 +60,8 @@ class SampleGeneratorFaceTemporal(SampleGeneratorBase):
shuffle_idxs = []
while True:
batches = None
for n_batch in range(self.batch_size):
if len(shuffle_idxs) == 0:
shuffle_idxs = samples_idxs.copy()
np.random.shuffle (shuffle_idxs)

53
samplelib/SampleHost.py
View file

@ -1,7 +1,5 @@
import gc
import multiprocessing
import operator
import pickle
import traceback
from pathlib import Path
@ -16,9 +14,11 @@ from .Sample import Sample, SampleType
class SampleHost:
samples_cache = dict()
host_cache = dict()
samples_cache = dict()
@staticmethod
def get_person_id_max_count(samples_path):
samples = None
@ -35,7 +35,7 @@ class SampleHost:
return len(list(persons_name_idxs.keys()))
@staticmethod
def load(sample_type, samples_path):
def host(sample_type, samples_path, number_of_clis):
samples_cache = SampleHost.samples_cache
if str(samples_path) not in samples_cache.keys():
@ -46,9 +46,11 @@ class SampleHost:
if sample_type == SampleType.IMAGE:
if samples[sample_type] is None:
samples[sample_type] = [ Sample(filename=filename) for filename in io.progress_bar_generator( Path_utils.get_image_paths(samples_path), "Loading") ]
elif sample_type == SampleType.FACE:
if samples[sample_type] is None:
elif sample_type == SampleType.FACE or \
sample_type == SampleType.FACE_TEMPORAL_SORTED:
result = None
if samples[sample_type] is None:
try:
result = samplelib.PackedFaceset.load(samples_path)
except:
@ -60,33 +62,26 @@ class SampleHost:
if result is None:
result = SampleHost.load_face_samples( Path_utils.get_image_paths(samples_path) )
result_dmp = pickle.dumps(result)
del result
gc.collect()
result = pickle.loads(result_dmp)
samples[sample_type] = mp_utils.ListHost()
samples[sample_type] = result
if sample_type == SampleType.FACE_TEMPORAL_SORTED:
result = SampleHost.upgradeToFaceTemporalSortedSamples(result)
elif sample_type == SampleType.FACE_TEMPORAL_SORTED:
if samples[sample_type] is None:
samples[sample_type] = SampleHost.upgradeToFaceTemporalSortedSamples( SampleHost.load(SampleType.FACE, samples_path) )
list_host = samples[sample_type]
clis = [ list_host.create_cli() for _ in range(number_of_clis) ]
if result is not None:
while True:
if len(result) == 0:
break
items = result[0:10000]
del result[0:10000]
clis[0].extend(items)
return clis
return samples[sample_type]
@staticmethod
def mp_host(sample_type, samples_path):
result = SampleHost.load (sample_type, samples_path)
host_cache = SampleHost.host_cache
if str(samples_path) not in host_cache.keys():
host_cache[str(samples_path)] = [None]*SampleType.QTY
hosts = host_cache[str(samples_path)]
if hosts[sample_type] is None:
hosts[sample_type] = mp_utils.ListHost(result)
return hosts[sample_type]
@staticmethod
def load_face_samples ( image_paths):
result = FaceSamplesLoaderSubprocessor(image_paths).run()

2
utils/iter_utils.py
View file

@ -22,7 +22,7 @@ class ThisThreadGenerator(object):
return next(self.generator_func)
class SubprocessGenerator(object):
def __init__(self, generator_func, user_param=None, prefetch=2, start_now=False):
def __init__(self, generator_func, user_param=None, prefetch=3, start_now=False):
super().__init__()
self.prefetch = prefetch
self.generator_func = generator_func

145
utils/mp_utils.py
View file

@ -1,22 +1,25 @@
import multiprocessing
import threading
import time
import traceback
import numpy as np
class Index2DHost():
"""
Provides random shuffled 2D indexes for multiprocesses
"""
def __init__(self, indexes2D):
def __init__(self, indexes2D, max_number_of_clis=128):
self.sq = multiprocessing.Queue()
self.cqs = []
self.clis = []
self.thread = threading.Thread(target=self.host_thread, args=(indexes2D,) )
self.thread.daemon = True
self.thread.start()
self.cqs = [ multiprocessing.Queue() for _ in range(max_number_of_clis) ]
self.n_clis = 0
self.max_number_of_clis = max_number_of_clis
def host_thread(self, indexes2D):
self.p = multiprocessing.Process(target=self.host_proc, args=(indexes2D, self.sq, self.cqs) )
self.p.daemon = True
self.p.start()
def host_proc(self, indexes2D, sq, cqs):
indexes_counts_len = len(indexes2D)
idxs = [*range(indexes_counts_len)]
@ -27,8 +30,6 @@ class Index2DHost():
idxs_2D[i] = indexes2D[i]
shuffle_idxs_2D[i] = []
sq = self.sq
while True:
while not sq.empty():
obj = sq.get()
@ -43,7 +44,7 @@ class Index2DHost():
shuffle_idxs = idxs.copy()
np.random.shuffle(shuffle_idxs)
result.append(shuffle_idxs.pop())
self.cqs[cq_id].put (result)
cqs[cq_id].put (result)
elif cmd == 1: #get_2D
targ_idxs,count = obj[2], obj[3]
result = []
@ -57,7 +58,7 @@ class Index2DHost():
np.random.shuffle(ar)
sub_idxs.append(ar.pop())
result.append (sub_idxs)
self.cqs[cq_id].put (result)
cqs[cq_id].put (result)
time.sleep(0.005)
@ -99,18 +100,19 @@ class IndexHost():
"""
Provides random shuffled indexes for multiprocesses
"""
def __init__(self, indexes_count):
def __init__(self, indexes_count, max_number_of_clis=128):
self.sq = multiprocessing.Queue()
self.cqs = []
self.clis = []
self.thread = threading.Thread(target=self.host_thread, args=(indexes_count,) )
self.thread.daemon = True
self.thread.start()
self.cqs = [ multiprocessing.Queue() for _ in range(max_number_of_clis) ]
self.n_clis = 0
self.max_number_of_clis = max_number_of_clis
def host_thread(self, indexes_count):
self.p = multiprocessing.Process(target=self.host_proc, args=(indexes_count, self.sq, self.cqs) )
self.p.daemon = True
self.p.start()
def host_proc(self, indexes_count, sq, cqs):
idxs = [*range(indexes_count)]
shuffle_idxs = []
sq = self.sq
while True:
while not sq.empty():
@ -123,15 +125,18 @@ class IndexHost():
shuffle_idxs = idxs.copy()
np.random.shuffle(shuffle_idxs)
result.append(shuffle_idxs.pop())
self.cqs[cq_id].put (result)
cqs[cq_id].put (result)
time.sleep(0.005)
def create_cli(self):
cq = multiprocessing.Queue()
self.cqs.append ( cq )
cq_id = len(self.cqs)-1
return IndexHost.Cli(self.sq, cq, cq_id)
if self.n_clis == self.max_number_of_clis:
raise Exception("")
cq_id = self.n_clis
self.n_clis += 1
return IndexHost.Cli(self.sq, self.cqs[cq_id], cq_id)
# disable pickling
def __getstate__(self):
@ -145,7 +150,7 @@ class IndexHost():
self.cq = cq
self.cq_id = cq_id
def get(self, count):
def multi_get(self, count):
self.sq.put ( (self.cq_id,count) )
while True:
@ -154,37 +159,50 @@ class IndexHost():
time.sleep(0.001)
class ListHost():
def __init__(self, list_):
def __init__(self, list_=None, max_number_of_clis=128):
self.sq = multiprocessing.Queue()
self.cqs = []
self.clis = []
self.list_ = list_
self.thread = threading.Thread(target=self.host_thread)
self.thread.daemon = True
self.thread.start()
self.cqs = [ multiprocessing.Queue() for _ in range(max_number_of_clis) ]
self.n_clis = 0
self.max_number_of_clis = max_number_of_clis
self.p = multiprocessing.Process(target=self.host_proc, args=(self.sq, self.cqs) )
self.p.daemon = True
self.p.start()
def host_proc(self, sq, cqs):
m_list = list()
def host_thread(self):
sq = self.sq
while True:
while not sq.empty():
obj = sq.get()
cq_id, cmd = obj[0], obj[1]
if cmd == 0:
item = self.list_[ obj[2] ]
self.cqs[cq_id].put ( item )
cqs[cq_id].put ( len(m_list) )
elif cmd == 1:
self.cqs[cq_id].put ( len(self.list_) )
idx = obj[2]
item = m_list[idx ]
cqs[cq_id].put ( item )
elif cmd == 2:
result = []
for item in obj[2]:
result.append ( m_list[item] )
cqs[cq_id].put ( result )
elif cmd == 3:
m_list.insert(obj[2], obj[3])
elif cmd == 4:
m_list.append(obj[2])
elif cmd == 5:
m_list.extend(obj[2])
time.sleep(0.005)
def create_cli(self):
cq = multiprocessing.Queue()
self.cqs.append ( cq )
cq_id = len(self.cqs)-1
return ListHost.Cli(self.sq, cq, cq_id)
if self.n_clis == self.max_number_of_clis:
raise Exception("")
def get_list(self):
return self.list_
cq_id = self.n_clis
self.n_clis += 1
return ListHost.Cli(self.sq, self.cqs[cq_id], cq_id)
# disable pickling
def __getstate__(self):
@ -198,22 +216,41 @@ class ListHost():
self.cq = cq
self.cq_id = cq_id
def __getitem__(self, key):
self.sq.put ( (self.cq_id,0,key) )
while True:
if not self.cq.empty():
return self.cq.get()
time.sleep(0.001)
def __len__(self):
self.sq.put ( (self.cq_id,1) )
self.sq.put ( (self.cq_id,0) )
while True:
if not self.cq.empty():
return self.cq.get()
time.sleep(0.001)
def __getitem__(self, key):
self.sq.put ( (self.cq_id,1,key) )
while True:
if not self.cq.empty():
return self.cq.get()
time.sleep(0.001)
def multi_get(self, keys):
self.sq.put ( (self.cq_id,2,keys) )
while True:
if not self.cq.empty():
return self.cq.get()
time.sleep(0.001)
def insert(self, index, item):
self.sq.put ( (self.cq_id,3,index,item) )
def append(self, item):
self.sq.put ( (self.cq_id,4,item) )
def extend(self, items):
self.sq.put ( (self.cq_id,5,items) )
class DictHost():
def __init__(self, d, num_users):
self.sqs = [ multiprocessing.Queue() for _ in range(num_users) ]