mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
451 lines
No EOL
19 KiB
Python
451 lines
No EOL
19 KiB
Python
import os
|
|
import time
|
|
import inspect
|
|
import pickle
|
|
import colorsys
|
|
from pathlib import Path
|
|
from utils import Path_utils
|
|
from utils import std_utils
|
|
from utils import image_utils
|
|
from utils.console_utils import *
|
|
from utils.cv2_utils import *
|
|
import numpy as np
|
|
import cv2
|
|
from samples import SampleGeneratorBase
|
|
from nnlib import nnlib
|
|
'''
|
|
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, debug = False, force_gpu_idx=-1, **in_options):
|
|
|
|
if force_gpu_idx == -1:
|
|
idxs_names_list = nnlib.device.getAllDevicesIdxsWithNamesList()
|
|
if len(idxs_names_list) > 1:
|
|
print ("You have multi GPUs in a system: ")
|
|
for idx, name in idxs_names_list:
|
|
print ("[%d] : %s" % (idx, name) )
|
|
|
|
force_gpu_idx = input_int("Which GPU idx to choose? ( skip: best GPU ) : ", -1, [ x[0] for x in idxs_names_list] )
|
|
self.force_gpu_idx = force_gpu_idx
|
|
|
|
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.debug = debug
|
|
self.is_training_mode = (training_data_src_path is not None and training_data_dst_path is not None)
|
|
|
|
self.supress_std_once = os.environ.get('TF_SUPPRESS_STD', '0') == '1'
|
|
|
|
self.epoch = 0
|
|
self.options = {}
|
|
self.loss_history = []
|
|
self.sample_for_preview = None
|
|
if self.model_data_path.exists():
|
|
model_data = pickle.loads ( self.model_data_path.read_bytes() )
|
|
self.epoch = model_data['epoch']
|
|
if self.epoch != 0:
|
|
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
|
|
|
|
ask_override = self.is_training_mode and self.epoch != 0 and input_in_time ("Press enter in 2 seconds to override model settings.", 2)
|
|
|
|
if self.epoch == 0:
|
|
print ("\nModel first run. Enter model options as default for each run.")
|
|
|
|
if self.epoch == 0 or ask_override:
|
|
default_write_preview_history = False if self.epoch == 0 else self.options.get('write_preview_history',False)
|
|
self.options['write_preview_history'] = input_bool("Write preview history? (y/n ?:help skip:n/default) : ", default_write_preview_history, help_message="Preview history will be writed to <ModelName>_history folder.")
|
|
else:
|
|
self.options['write_preview_history'] = self.options.get('write_preview_history', False)
|
|
|
|
if self.epoch == 0 or ask_override:
|
|
self.options['target_epoch'] = max(0, input_int("Target epoch (skip:unlimited/default) : ", 0))
|
|
else:
|
|
self.options['target_epoch'] = self.options.get('target_epoch', 0)
|
|
|
|
if self.epoch == 0 or ask_override:
|
|
default_batch_size = 0 if self.epoch == 0 else self.options.get('batch_size',0)
|
|
self.options['batch_size'] = max(0, input_int("Batch_size (?:help skip:0/default) : ", default_batch_size, help_message="Larger batch size is always better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
|
|
else:
|
|
self.options['batch_size'] = self.options.get('batch_size', 0)
|
|
|
|
if self.epoch == 0:
|
|
self.options['sort_by_yaw'] = input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:n) : ", False, help_message="NN will not learn src face directions that don't match dst face directions." )
|
|
else:
|
|
self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
|
|
|
|
if self.epoch == 0:
|
|
self.options['random_flip'] = input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
|
|
else:
|
|
self.options['random_flip'] = self.options.get('random_flip', True)
|
|
|
|
if self.epoch == 0:
|
|
self.options['src_scale_mod'] = np.clip( input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
|
|
else:
|
|
self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
|
|
|
|
self.write_preview_history = self.options['write_preview_history']
|
|
if not self.options['write_preview_history']:
|
|
self.options.pop('write_preview_history')
|
|
|
|
self.target_epoch = self.options['target_epoch']
|
|
if self.options['target_epoch'] == 0:
|
|
self.options.pop('target_epoch')
|
|
|
|
self.batch_size = self.options['batch_size']
|
|
self.sort_by_yaw = self.options['sort_by_yaw']
|
|
self.random_flip = self.options['random_flip']
|
|
|
|
self.src_scale_mod = self.options['src_scale_mod']
|
|
if self.src_scale_mod == 0:
|
|
self.options.pop('src_scale_mod')
|
|
|
|
self.onInitializeOptions(self.epoch == 0, ask_override)
|
|
|
|
nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, force_gpu_idx=self.force_gpu_idx, **in_options) )
|
|
self.device_config = nnlib.active_DeviceConfig
|
|
|
|
self.onInitialize(**in_options)
|
|
|
|
self.options['batch_size'] = self.batch_size
|
|
|
|
if self.debug or self.batch_size == 0:
|
|
self.batch_size = 1
|
|
|
|
if self.is_training_mode:
|
|
if self.write_preview_history:
|
|
if self.force_gpu_idx == -1:
|
|
self.preview_history_path = self.model_path / ( '%s_history' % (self.get_model_name()) )
|
|
else:
|
|
self.preview_history_path = self.model_path / ( '%d_%s_history' % (self.force_gpu_idx, 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()
|
|
|
|
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) or (self.epoch == 0):
|
|
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 ("== Model options:")
|
|
for key in self.options.keys():
|
|
print ("== |== %s : %s" % (key, self.options[key]) )
|
|
|
|
if self.device_config.multi_gpu:
|
|
print ("== |== multi_gpu : True ")
|
|
|
|
print ("== Running on:")
|
|
if self.device_config.cpu_only:
|
|
print ("== |== [CPU]")
|
|
else:
|
|
for idx in self.device_config.gpu_idxs:
|
|
print ("== |== [%d : %s]" % (idx, nnlib.device.getDeviceName(idx)) )
|
|
|
|
if not self.device_config.cpu_only and self.device_config.gpu_vram_gb[0] == 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 onInitializeOptions(self, is_first_run, ask_override):
|
|
pass
|
|
|
|
#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 get_target_epoch(self):
|
|
return self.target_epoch
|
|
|
|
def is_reached_epoch_goal(self):
|
|
return self.target_epoch != 0 and self.epoch >= self.target_epoch
|
|
|
|
#multi gpu in keras actually is fake and doesn't work for training https://github.com/keras-team/keras/issues/11976
|
|
#def to_multi_gpu_model_if_possible (self, models_list):
|
|
# if len(self.device_config.gpu_idxs) > 1:
|
|
# #make batch_size to divide on GPU count without remainder
|
|
# self.batch_size = int( self.batch_size / len(self.device_config.gpu_idxs) )
|
|
# if self.batch_size == 0:
|
|
# self.batch_size = 1
|
|
# self.batch_size *= len(self.device_config.gpu_idxs)
|
|
#
|
|
# result = []
|
|
# for model in models_list:
|
|
# for i in range( len(model.output_names) ):
|
|
# model.output_names = 'output_%d' % (i)
|
|
# result += [ nnlib.keras.utils.multi_gpu_model( model, self.device_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 load_weights_safe(self, model_filename_list):
|
|
for model, filename in model_filename_list:
|
|
if Path(filename).exists():
|
|
model.load_weights(filename)
|
|
|
|
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:
|
|
preview = self.get_static_preview()
|
|
preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.epoch, preview.shape[1], preview.shape[2])
|
|
img = (np.concatenate ( [preview_lh, preview], axis=0 ) * 255).astype(np.uint8)
|
|
cv2_imwrite ( str (self.preview_history_path / ('%.6d.jpg' %( self.epoch) )), img )
|
|
|
|
self.epoch += 1
|
|
|
|
if epoch_time >= 10000:
|
|
#............."Saving...
|
|
loss_string = "Training [#{0:06d}][{1:.5s}s]".format ( self.epoch, '{:0.4f}'.format(epoch_time / 1000) )
|
|
else:
|
|
loss_string = "Training [#{0:06d}][{1:04d}ms]".format ( self.epoch, int(epoch_time*1000) )
|
|
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):
|
|
nnlib.finalize_all()
|
|
|
|
def is_first_run(self):
|
|
return self.epoch == 0
|
|
|
|
def is_debug(self):
|
|
return self.debug
|
|
|
|
def set_batch_size(self, batch_size):
|
|
self.batch_size = batch_size
|
|
|
|
def get_batch_size(self):
|
|
return self.batch_size
|
|
|
|
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):
|
|
if self.force_gpu_idx == -1:
|
|
return str( self.model_path / ( self.get_model_name() + '_' + filename) )
|
|
else:
|
|
return str( self.model_path / ( str(self.force_gpu_idx) + '_' + 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.device_config.cpu_only:
|
|
if self.batch_size == 0:
|
|
self.batch_size = 2
|
|
else:
|
|
if self.batch_size == 0:
|
|
for x in keys:
|
|
if self.device_config.gpu_vram_gb[0] <= x:
|
|
self.batch_size = d[x]
|
|
break
|
|
|
|
if self.batch_size == 0:
|
|
self.batch_size = d[ keys[-1] ]
|
|
|
|
@staticmethod
|
|
def get_loss_history_preview(loss_history, epoch, w, c):
|
|
loss_history = np.array (loss_history.copy())
|
|
|
|
lh_height = 100
|
|
lh_img = np.ones ( (lh_height,w,c) ) * 0.1
|
|
loss_count = len(loss_history[0])
|
|
lh_len = len(loss_history)
|
|
|
|
l_per_col = lh_len / w
|
|
plist_max = [ [ max (0.0, loss_history[int(col*l_per_col)][p],
|
|
*[ loss_history[i_ab][p]
|
|
for i_ab in range( int(col*l_per_col), int((col+1)*l_per_col) )
|
|
]
|
|
)
|
|
for p in range(loss_count)
|
|
]
|
|
for col in range(w)
|
|
]
|
|
|
|
plist_min = [ [ min (plist_max[col][p], loss_history[int(col*l_per_col)][p],
|
|
*[ loss_history[i_ab][p]
|
|
for i_ab in range( int(col*l_per_col), int((col+1)*l_per_col) )
|
|
]
|
|
)
|
|
for p in range(loss_count)
|
|
]
|
|
for col in range(w)
|
|
]
|
|
|
|
plist_abs_max = np.mean(loss_history[ len(loss_history) // 5 : ]) * 2
|
|
|
|
for col in range(0, w):
|
|
for p in range(0,loss_count):
|
|
point_color = [1.0]*c
|
|
point_color[0:3] = colorsys.hsv_to_rgb ( p * (1.0/loss_count), 1.0, 1.0 )
|
|
|
|
ph_max = int ( (plist_max[col][p] / plist_abs_max) * (lh_height-1) )
|
|
ph_max = np.clip( ph_max, 0, lh_height-1 )
|
|
|
|
ph_min = int ( (plist_min[col][p] / plist_abs_max) * (lh_height-1) )
|
|
ph_min = np.clip( ph_min, 0, lh_height-1 )
|
|
|
|
for ph in range(ph_min, ph_max+1):
|
|
lh_img[ (lh_height-ph-1), col ] = point_color
|
|
|
|
lh_lines = 5
|
|
lh_line_height = (lh_height-1)/lh_lines
|
|
for i in range(0,lh_lines+1):
|
|
lh_img[ int(i*lh_line_height), : ] = (0.8,)*c
|
|
|
|
last_line_t = int((lh_lines-1)*lh_line_height)
|
|
last_line_b = int(lh_lines*lh_line_height)
|
|
|
|
lh_text = 'Epoch: %d' % (epoch) if epoch != 0 else ''
|
|
|
|
lh_img[last_line_t:last_line_b, 0:w] += image_utils.get_text_image ( (w,last_line_b-last_line_t,c), lh_text, color=[0.8]*c )
|
|
return lh_img |