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now you can train models on multiple GPU's on same workspace without cloning any folders.

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Model files names will be prefixed with GPU index if GPU choosed explicitly on train/convert start.
if you leave GPU idx choice default, then best GPU idx will be choosed and model file names will not contain index prefix.
It gives you possibility to train same fake with various models or options on multiple GPUs.

H64 and H128: now you can choose 'Lighter autoencoder'. It is same as vram gb <= 4 before this update.

added archived_models.zip contains old experiments

RecycleGAN: archived

devicelib: if your system has no NVML installed (some old cards), then it will work with gpu_idx=0 as 'Generic GeForce GPU' with 2GB vram.

refactorings
This commit is contained in:
iperov 2019-01-14 10:48:23 +04:00
parent e2f4677987
commit 1f2b1481ef
9 changed files with 180 additions and 479 deletions
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24
models/Model_H64/Model.py
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@ -11,13 +11,25 @@ class Model(ModelBase):
encoderH5 = 'encoder.h5'
decoder_srcH5 = 'decoder_src.h5'
decoder_dstH5 = 'decoder_dst.h5'
#override
def onInitializeOptions(self, is_first_run, ask_override):
if is_first_run:
self.options['lighter_ae'] = input_bool ("Use lightweight autoencoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight autoencoder is faster, requires less VRAM, sacrificing overall quality. If your GPU VRAM <= 4, you should to choose this option.")
else:
default_lighter_ae = self.options.get('created_vram_gb', 99) <= 4 #temporally support old models, deprecate in future
if 'created_vram_gb' in self.options.keys():
self.options.pop ('created_vram_gb')
self.options['lighter_ae'] = self.options.get('lighter_ae', default_lighter_ae)
#override
def onInitialize(self, **in_options):
exec(nnlib.import_all(), locals(), globals())
self.set_vram_batch_requirements( {1.5:2,2:2,3:8,4:16,5:24,6:32,7:40,8:48} )
bgr_shape, mask_shape, self.encoder, self.decoder_src, self.decoder_dst = self.Build(self.created_vram_gb)
bgr_shape, mask_shape, self.encoder, self.decoder_src, self.decoder_dst = self.Build(self.options['lighter_ae'])
if not self.is_first_run():
self.encoder.load_weights (self.get_strpath_storage_for_file(self.encoderH5))
self.decoder_src.load_weights (self.get_strpath_storage_for_file(self.decoder_srcH5))
@ -27,12 +39,12 @@ class Model(ModelBase):
input_src_mask = Input(mask_shape)
input_dst_bgr = Input(bgr_shape)
input_dst_mask = Input(mask_shape)
rec_src_bgr, rec_src_mask = self.decoder_src( self.encoder(input_src_bgr) )
rec_dst_bgr, rec_dst_mask = self.decoder_dst( self.encoder(input_dst_bgr) )
self.ae = Model([input_src_bgr,input_src_mask,input_dst_bgr,input_dst_mask], [rec_src_bgr, rec_src_mask, rec_dst_bgr, rec_dst_mask] )
self.ae.compile(optimizer=Adam(lr=5e-5, beta_1=0.5, beta_2=0.999),
loss=[ DSSIMMaskLoss([input_src_mask]), 'mae', DSSIMMaskLoss([input_dst_mask]), 'mae' ] )
@ -122,7 +134,7 @@ class Model(ModelBase):
base_blur_mask_modifier=100,
**in_options)
def Build(self, created_vram_gb):
def Build(self, lighter_ae):
exec(nnlib.code_import_all, locals(), globals())
bgr_shape = (64, 64, 3)
@ -141,7 +153,7 @@ class Model(ModelBase):
def Encoder(input_shape):
input_layer = Input(input_shape)
x = input_layer
if created_vram_gb >= 4:
if not lighter_ae:
x = downscale(128)(x)
x = downscale(256)(x)
x = downscale(512)(x)
@ -162,7 +174,7 @@ class Model(ModelBase):
return Model(input_layer, x)
def Decoder():
if created_vram_gb >= 4:
if not lighter_ae:
input_ = Input(shape=(8, 8, 512))
x = input_