mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
318 lines
No EOL
12 KiB
Python
318 lines
No EOL
12 KiB
Python
import os
|
|
import time
|
|
import inspect
|
|
import operator
|
|
import pickle
|
|
from pathlib import Path
|
|
from utils import Path_utils
|
|
from utils import std_utils
|
|
from utils import image_utils
|
|
import numpy as np
|
|
import cv2
|
|
import gpufmkmgr
|
|
from samples import SampleGeneratorBase
|
|
|
|
'''
|
|
You can implement your own model. Check examples.
|
|
'''
|
|
class ModelBase(object):
|
|
|
|
#DONT OVERRIDE
|
|
def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None,
|
|
batch_size=0,
|
|
write_preview_history = False,
|
|
debug = False, **in_options
|
|
):
|
|
print ("Loading model...")
|
|
self.model_path = model_path
|
|
self.model_data_path = Path( self.get_strpath_storage_for_file('data.dat') )
|
|
|
|
self.training_data_src_path = training_data_src_path
|
|
self.training_data_dst_path = training_data_dst_path
|
|
|
|
self.src_images_paths = None
|
|
self.dst_images_paths = None
|
|
self.src_yaw_images_paths = None
|
|
self.dst_yaw_images_paths = None
|
|
self.src_data_generator = None
|
|
self.dst_data_generator = None
|
|
self.is_training_mode = (training_data_src_path is not None and training_data_dst_path is not None)
|
|
self.batch_size = batch_size
|
|
self.write_preview_history = write_preview_history
|
|
self.debug = debug
|
|
self.supress_std_once = ('TF_SUPPRESS_STD' in os.environ.keys() and os.environ['TF_SUPPRESS_STD'] == '1')
|
|
|
|
if self.model_data_path.exists():
|
|
model_data = pickle.loads ( self.model_data_path.read_bytes() )
|
|
self.epoch = model_data['epoch']
|
|
self.options = model_data['options']
|
|
self.loss_history = model_data['loss_history'] if 'loss_history' in model_data.keys() else []
|
|
self.sample_for_preview = model_data['sample_for_preview'] if 'sample_for_preview' in model_data.keys() else None
|
|
else:
|
|
self.epoch = 0
|
|
self.options = {}
|
|
self.loss_history = []
|
|
self.sample_for_preview = None
|
|
|
|
if self.write_preview_history:
|
|
self.preview_history_path = self.model_path / ( '%s_history' % (self.get_model_name()) )
|
|
|
|
if not self.preview_history_path.exists():
|
|
self.preview_history_path.mkdir(exist_ok=True)
|
|
else:
|
|
if self.epoch == 0:
|
|
for filename in Path_utils.get_image_paths(self.preview_history_path):
|
|
Path(filename).unlink()
|
|
|
|
|
|
self.gpu_config = gpufmkmgr.GPUConfig(allow_growth=False, **in_options)
|
|
self.gpu_total_vram_gb = self.gpu_config.gpu_total_vram_gb
|
|
|
|
if self.epoch == 0:
|
|
#first run
|
|
self.options['created_vram_gb'] = self.gpu_total_vram_gb
|
|
self.created_vram_gb = self.gpu_total_vram_gb
|
|
else:
|
|
#not first run
|
|
if 'created_vram_gb' in self.options.keys():
|
|
self.created_vram_gb = self.options['created_vram_gb']
|
|
else:
|
|
self.options['created_vram_gb'] = self.gpu_total_vram_gb
|
|
self.created_vram_gb = self.gpu_total_vram_gb
|
|
|
|
self.tf = gpufmkmgr.import_tf( self.gpu_config )
|
|
self.tf_sess = gpufmkmgr.get_tf_session()
|
|
self.keras = gpufmkmgr.import_keras()
|
|
self.keras_contrib = gpufmkmgr.import_keras_contrib()
|
|
|
|
self.onInitialize(**in_options)
|
|
|
|
if self.debug or self.batch_size == 0:
|
|
self.batch_size = 1
|
|
|
|
if self.is_training_mode:
|
|
if self.generator_list is None:
|
|
raise Exception( 'You didnt set_training_data_generators()')
|
|
else:
|
|
for i, generator in enumerate(self.generator_list):
|
|
if not isinstance(generator, SampleGeneratorBase):
|
|
raise Exception('training data generator is not subclass of SampleGeneratorBase')
|
|
|
|
if self.sample_for_preview is None:
|
|
self.sample_for_preview = self.generate_next_sample()
|
|
|
|
print ("===== Model summary =====")
|
|
print ("== Model name: " + self.get_model_name())
|
|
print ("==")
|
|
print ("== Current epoch: " + str(self.epoch) )
|
|
print ("==")
|
|
print ("== Options:")
|
|
print ("== |== batch_size : %s " % (self.batch_size) )
|
|
print ("== |== multi_gpu : %s " % (self.gpu_config.multi_gpu) )
|
|
for key in self.options.keys():
|
|
print ("== |== %s : %s" % (key, self.options[key]) )
|
|
|
|
print ("== Running on:")
|
|
if self.gpu_config.cpu_only:
|
|
print ("== |== [CPU]")
|
|
else:
|
|
for idx in self.gpu_config.gpu_idxs:
|
|
print ("== |== [%d : %s]" % (idx, gpufmkmgr.getDeviceName(idx)) )
|
|
|
|
if not self.gpu_config.cpu_only and self.gpu_total_vram_gb == 2:
|
|
print ("==")
|
|
print ("== WARNING: You are using 2GB GPU. Result quality may be significantly decreased.")
|
|
print ("== If training does not start, close all programs and try again.")
|
|
print ("== Also you can disable Windows Aero Desktop to get extra free VRAM.")
|
|
print ("==")
|
|
|
|
print ("=========================")
|
|
|
|
#overridable
|
|
def onInitialize(self, **in_options):
|
|
'''
|
|
initialize your keras models
|
|
|
|
store and retrieve your model options in self.options['']
|
|
|
|
check example
|
|
'''
|
|
pass
|
|
|
|
#overridable
|
|
def onSave(self):
|
|
#save your keras models here
|
|
pass
|
|
|
|
#overridable
|
|
def onTrainOneEpoch(self, sample):
|
|
#train your keras models here
|
|
|
|
#return array of losses
|
|
return ( ('loss_src', 0), ('loss_dst', 0) )
|
|
|
|
#overridable
|
|
def onGetPreview(self, sample):
|
|
#you can return multiple previews
|
|
#return [ ('preview_name',preview_rgb), ... ]
|
|
return []
|
|
|
|
#overridable if you want model name differs from folder name
|
|
def get_model_name(self):
|
|
return Path(inspect.getmodule(self).__file__).parent.name.rsplit("_", 1)[1]
|
|
|
|
#overridable
|
|
def get_converter(self, **in_options):
|
|
#return existing or your own converter which derived from base
|
|
from .ConverterBase import ConverterBase
|
|
return ConverterBase(self, **in_options)
|
|
|
|
def to_multi_gpu_model_if_possible (self, models_list):
|
|
if len(self.gpu_config.gpu_idxs) > 1:
|
|
#make batch_size to divide on GPU count without remainder
|
|
self.batch_size = int( self.batch_size / len(self.gpu_config.gpu_idxs) )
|
|
if self.batch_size == 0:
|
|
self.batch_size = 1
|
|
self.batch_size *= len(self.gpu_config.gpu_idxs)
|
|
|
|
result = []
|
|
for model in models_list:
|
|
for i in range( len(model.output_names) ):
|
|
model.output_names = 'output_%d' % (i)
|
|
result += [ self.keras.utils.multi_gpu_model( model, self.gpu_config.gpu_idxs ) ]
|
|
|
|
return result
|
|
else:
|
|
return models_list
|
|
|
|
def get_previews(self):
|
|
return self.onGetPreview ( self.last_sample )
|
|
|
|
def get_static_preview(self):
|
|
return self.onGetPreview (self.sample_for_preview)[0][1] #first preview, and bgr
|
|
|
|
def save(self):
|
|
print ("Saving...")
|
|
|
|
if self.supress_std_once:
|
|
supressor = std_utils.suppress_stdout_stderr()
|
|
supressor.__enter__()
|
|
|
|
self.onSave()
|
|
|
|
if self.supress_std_once:
|
|
supressor.__exit__()
|
|
|
|
model_data = {
|
|
'epoch': self.epoch,
|
|
'options': self.options,
|
|
'loss_history': self.loss_history,
|
|
'sample_for_preview' : self.sample_for_preview
|
|
}
|
|
self.model_data_path.write_bytes( pickle.dumps(model_data) )
|
|
|
|
def save_weights_safe(self, model_filename_list):
|
|
for model, filename in model_filename_list:
|
|
model.save_weights( filename + '.tmp' )
|
|
|
|
for model, filename in model_filename_list:
|
|
source_filename = Path(filename+'.tmp')
|
|
target_filename = Path(filename)
|
|
if target_filename.exists():
|
|
target_filename.unlink()
|
|
|
|
source_filename.rename ( str(target_filename) )
|
|
|
|
def debug_one_epoch(self):
|
|
images = []
|
|
for generator in self.generator_list:
|
|
for i,batch in enumerate(next(generator)):
|
|
images.append( batch[0] )
|
|
|
|
return image_utils.equalize_and_stack_square (images)
|
|
|
|
def generate_next_sample(self):
|
|
return [next(generator) for generator in self.generator_list]
|
|
|
|
def train_one_epoch(self):
|
|
if self.supress_std_once:
|
|
supressor = std_utils.suppress_stdout_stderr()
|
|
supressor.__enter__()
|
|
|
|
self.last_sample = self.generate_next_sample()
|
|
|
|
epoch_time = time.time()
|
|
|
|
losses = self.onTrainOneEpoch(self.last_sample)
|
|
|
|
epoch_time = time.time() - epoch_time
|
|
|
|
self.loss_history.append ( [float(loss[1]) for loss in losses] )
|
|
|
|
if self.supress_std_once:
|
|
supressor.__exit__()
|
|
self.supress_std_once = False
|
|
|
|
if self.write_preview_history:
|
|
if self.epoch % 10 == 0:
|
|
img = (self.get_static_preview() * 255).astype(np.uint8)
|
|
cv2.imwrite ( str (self.preview_history_path / ('%.6d.jpg' %( self.epoch) )), img )
|
|
|
|
self.epoch += 1
|
|
|
|
#............."Saving...
|
|
if epoch_time >= 10000:
|
|
loss_string = "Training [#{0:06d}][{1:03d}s]".format ( self.epoch, epoch_time / 1000 )
|
|
else:
|
|
loss_string = "Training [#{0:06d}][{1:04d}ms]".format ( self.epoch, int(epoch_time*1000) % 10000 )
|
|
for (loss_name, loss_value) in losses:
|
|
loss_string += " %s:%.3f" % (loss_name, loss_value)
|
|
|
|
return loss_string
|
|
|
|
def pass_one_epoch(self):
|
|
self.last_sample = self.generate_next_sample()
|
|
|
|
def finalize(self):
|
|
gpufmkmgr.finalize_keras()
|
|
|
|
def is_first_run(self):
|
|
return self.epoch == 0
|
|
|
|
def is_debug(self):
|
|
return self.debug
|
|
|
|
def get_epoch(self):
|
|
return self.epoch
|
|
|
|
def get_loss_history(self):
|
|
return self.loss_history
|
|
|
|
def set_training_data_generators (self, generator_list):
|
|
self.generator_list = generator_list
|
|
|
|
def get_training_data_generators (self):
|
|
return self.generator_list
|
|
|
|
def get_strpath_storage_for_file(self, filename):
|
|
return str( self.model_path / (self.get_model_name() + '_' + filename) )
|
|
|
|
def set_vram_batch_requirements (self, d):
|
|
#example d = {2:2,3:4,4:8,5:16,6:32,7:32,8:32,9:48}
|
|
keys = [x for x in d.keys()]
|
|
|
|
if self.gpu_config.cpu_only:
|
|
if self.batch_size == 0:
|
|
self.batch_size = 2
|
|
else:
|
|
if self.gpu_total_vram_gb < keys[0]:
|
|
raise Exception ('Sorry, this model works only on %dGB+ GPU' % ( keys[0] ) )
|
|
|
|
if self.batch_size == 0:
|
|
for x in keys:
|
|
if self.gpu_total_vram_gb <= x:
|
|
self.batch_size = d[x]
|
|
break
|
|
|
|
if self.batch_size == 0:
|
|
self.batch_size = d[ keys[-1] ] |