CalvinBackup/iperov-DeepFaceLab
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transfercolor via lab converter now implemented by tensorflow-cpu, which is x2 faster than skimage.

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We cannot use GPU for lab converter in converter multiprocesses, because almost all VRAM ate by model process, so even 300Mb free VRAM not enough for tensorflow lab converter.
Removed skimage dependency.
Refactorings.
This commit is contained in:
iperov
2018-12-01 12:11:54 +04:00
parent 6f0d38d171
commit 5c43f4245e
7 changed files with 229 additions and 67 deletions
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models/ModelBase.py
+14 -32
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@@ -20,10 +20,6 @@ class ModelBase(object):
#DONT OVERRIDE
def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None,
batch_size=0,
multi_gpu = False,
choose_worst_gpu = False,
force_best_gpu_idx = -1,
force_gpu_idxs = None,
write_preview_history = False,
debug = False, **in_options
):
@@ -70,37 +66,23 @@ class ModelBase(object):
for filename in Path_utils.get_image_paths(self.preview_history_path):
Path(filename).unlink()
self.multi_gpu = multi_gpu
gpu_idx = force_best_gpu_idx if (force_best_gpu_idx >= 0 and gpufmkmgr.isValidDeviceIdx(force_best_gpu_idx)) else gpufmkmgr.getBestDeviceIdx() if not choose_worst_gpu else gpufmkmgr.getWorstDeviceIdx()
gpu_total_vram_gb = gpufmkmgr.getDeviceVRAMTotalGb (gpu_idx)
is_gpu_low_mem = (gpu_total_vram_gb < 4)
self.gpu_total_vram_gb = gpu_total_vram_gb
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'] = gpu_total_vram_gb
self.created_vram_gb = gpu_total_vram_gb
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'] = gpu_total_vram_gb
self.created_vram_gb = gpu_total_vram_gb
self.options['created_vram_gb'] = self.gpu_total_vram_gb
self.created_vram_gb = self.gpu_total_vram_gb
if force_gpu_idxs is not None:
self.gpu_idxs = [ int(x) for x in force_gpu_idxs.split(',') ]
else:
if self.multi_gpu:
self.gpu_idxs = gpufmkmgr.getDeviceIdxsEqualModel( gpu_idx )
if len(self.gpu_idxs) <= 1:
self.multi_gpu = False
else:
self.gpu_idxs = [gpu_idx]
self.tf = gpufmkmgr.import_tf(self.gpu_idxs,allow_growth=False)
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()
@@ -131,12 +113,12 @@ class ModelBase(object):
print ("==")
print ("== Options:")
print ("== |== batch_size : %s " % (self.batch_size) )
print ("== |== multi_gpu : %s " % (self.multi_gpu) )
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:")
for idx in self.gpu_idxs:
for idx in self.gpu_config.gpu_idxs:
print ("== |== [%d : %s]" % (idx, gpufmkmgr.getDeviceName(idx)) )
if self.gpu_total_vram_gb == 2:
@@ -188,18 +170,18 @@ class ModelBase(object):
return ConverterBase(self, **in_options)
def to_multi_gpu_model_if_possible (self, models_list):
if len(self.gpu_idxs) > 1:
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_idxs) )
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_idxs)
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_idxs ) ]
result += [ self.keras.utils.multi_gpu_model( model, self.gpu_config.gpu_idxs ) ]
return result
else: