mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 05:22:06 -07:00
added ability to save optimizers states which work with K.function,
added custom Adam that can save 'iterations' param
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iperov
parent
e50dc0d748
commit
e4637336ef
3 changed files with 133 additions and 26 deletions
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@ -1,4 +1,5 @@
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import os
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import json
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import time
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import inspect
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import pickle
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@ -119,6 +120,7 @@ class ModelBase(object):
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nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, **self.device_args) )
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self.device_config = nnlib.active_DeviceConfig
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self.keras = nnlib.keras
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self.onInitialize()
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@ -271,25 +273,52 @@ class ModelBase(object):
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}
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self.model_data_path.write_bytes( pickle.dumps(model_data) )
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def load_weights_safe(self, model_filename_list):
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def load_weights_safe(self, model_filename_list, optimizer_filename_list=[]):
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for model, filename in model_filename_list:
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filename = self.get_strpath_storage_for_file(filename)
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if Path(filename).exists():
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if Path(filename).exists():
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model.load_weights(filename)
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def save_weights_safe(self, model_filename_list):
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if len(optimizer_filename_list) != 0:
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opt_filename = self.get_strpath_storage_for_file('opt.h5')
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if Path(opt_filename).exists():
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h5dict = self.keras.utils.io_utils.H5Dict(opt_filename, mode='r')
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try:
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for x in optimizer_filename_list:
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opt, filename = x
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if filename in h5dict:
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opt = opt.__class__.from_config( json.loads(h5dict[filename]) )
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x[0] = opt
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finally:
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h5dict.close()
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return [x[0] for x in optimizer_filename_list]
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def save_weights_safe(self, model_filename_list, optimizer_filename_list=[]):
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for model, filename in model_filename_list:
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filename = self.get_strpath_storage_for_file(filename)
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model.save_weights( filename + '.tmp' )
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for model, filename in model_filename_list:
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rename_list = model_filename_list
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if len(optimizer_filename_list) != 0:
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opt_filename = self.get_strpath_storage_for_file('opt.h5')
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h5dict = self.keras.utils.io_utils.H5Dict(opt_filename + '.tmp', mode='w')
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try:
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for opt, filename in optimizer_filename_list:
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h5dict[filename] = json.dumps(opt.get_config())
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finally:
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h5dict.close()
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rename_list += [('', 'opt.h5')]
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for _, filename in rename_list:
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filename = self.get_strpath_storage_for_file(filename)
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source_filename = Path(filename+'.tmp')
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target_filename = Path(filename)
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if target_filename.exists():
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target_filename.unlink()
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source_filename.rename ( str(target_filename) )
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if source_filename.exists():
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target_filename = Path(filename)
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if target_filename.exists():
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target_filename.unlink()
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source_filename.rename ( str(target_filename) )
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def debug_one_epoch(self):
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images = []
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@ -84,7 +84,6 @@ class SAEModel(ModelBase):
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def onInitialize(self):
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exec(nnlib.import_all(), locals(), globals())
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SAEModel.initialize_nn_functions()
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self.set_vram_batch_requirements({1.5:4})
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resolution = self.options['resolution']
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@ -111,6 +110,7 @@ class SAEModel(ModelBase):
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target_dst_ar = [ Input ( ( bgr_shape[0] // (2**i) ,)*2 + (bgr_shape[-1],) ) for i in range(ms_count-1, -1, -1)]
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target_dstm_ar = [ Input ( ( mask_shape[0] // (2**i) ,)*2 + (mask_shape[-1],) ) for i in range(ms_count-1, -1, -1)]
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weights_to_load = []
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if self.options['archi'] == 'liae':
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self.encoder = modelify(SAEModel.LIAEEncFlow(resolution, self.options['lighter_encoder'], ed_ch_dims=ed_ch_dims) ) (Input(bgr_shape))
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@ -223,8 +223,13 @@ class SAEModel(ModelBase):
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pred_dst_dstm = self.decoder_dstm(warped_dst_code)
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pred_src_dstm = self.decoder_srcm(warped_dst_code)
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self.load_weights_safe(weights_to_load)
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self.src_dst_opt, \
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self.src_dst_mask_opt = self.load_weights_safe(
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weights_to_load,
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[ [Adam(lr=5e-5, beta_1=0.5, beta_2=0.999), 'src_dst_opt'],
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[Adam(lr=5e-5, beta_1=0.5, beta_2=0.999), 'src_dst_mask_opt']
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])
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pred_src_src, pred_dst_dst, pred_src_dst, = [ [x] if type(x) != list else x for x in [pred_src_src, pred_dst_dst, pred_src_dst, ] ]
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if self.options['learn_mask']:
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@ -262,9 +267,6 @@ class SAEModel(ModelBase):
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psd_target_dst_anti_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))]
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if self.is_training_mode:
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def optimizer():
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return Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
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if self.options['archi'] == 'liae':
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src_dst_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
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if self.options['learn_mask']:
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@ -307,7 +309,7 @@ class SAEModel(ModelBase):
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feed += target_dst_ar[::-1]
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feed += target_dstm_ar[::-1]
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self.src_dst_train = K.function (feed,[src_loss,dst_loss], optimizer().get_updates(src_loss+dst_loss, src_dst_loss_train_weights) )
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self.src_dst_train = K.function (feed,[src_loss,dst_loss], self.src_dst_opt.get_updates(src_loss+dst_loss, src_dst_loss_train_weights) )
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if self.options['learn_mask']:
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src_mask_loss = sum([ K.mean(K.square(target_srcm_ar[-1]-pred_src_srcm[-1])) for i in range(len(target_srcm_ar)) ])
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@ -317,7 +319,7 @@ class SAEModel(ModelBase):
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feed += target_srcm_ar[::-1]
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feed += target_dstm_ar[::-1]
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self.src_dst_mask_train = K.function (feed,[src_mask_loss, dst_mask_loss], optimizer().get_updates(src_mask_loss+dst_mask_loss, src_dst_mask_loss_train_weights) )
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self.src_dst_mask_train = K.function (feed,[src_mask_loss, dst_mask_loss], self.src_dst_mask_opt.get_updates(src_mask_loss+dst_mask_loss, src_dst_mask_loss_train_weights) )
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if self.options['learn_mask']:
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self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_src_dst[-1], pred_src_dstm[-1]])
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@ -353,8 +355,12 @@ class SAEModel(ModelBase):
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])
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#override
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def onSave(self):
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opt_ar = [ [self.src_dst_opt, 'src_dst_opt'],
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[self.src_dst_mask_opt, 'src_dst_mask_opt']
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]
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ar = []
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if self.options['archi'] == 'liae':
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ar = [[self.encoder, 'encoder.h5'],
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ar += [[self.encoder, 'encoder.h5'],
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[self.inter_B, 'inter_B.h5'],
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[self.inter_AB, 'inter_AB.h5'],
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[self.decoder, 'decoder.h5']
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@ -362,15 +368,15 @@ class SAEModel(ModelBase):
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if self.options['learn_mask']:
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ar += [ [self.decoderm, 'decoderm.h5'] ]
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elif self.options['archi'] == 'df' or self.options['archi'] == 'vg':
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ar = [[self.encoder, 'encoder.h5'],
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ar += [[self.encoder, 'encoder.h5'],
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[self.decoder_src, 'decoder_src.h5'],
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[self.decoder_dst, 'decoder_dst.h5']
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]
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if self.options['learn_mask']:
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ar += [ [self.decoder_srcm, 'decoder_srcm.h5'],
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[self.decoder_dstm, 'decoder_dstm.h5'] ]
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self.save_weights_safe(ar)
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self.save_weights_safe(ar, opt_ar)
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#override
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@ -71,17 +71,18 @@ ZeroPadding2D = keras.layers.ZeroPadding2D
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RandomNormal = keras.initializers.RandomNormal
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Model = keras.models.Model
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Adam = keras.optimizers.Adam
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#Adam = keras.optimizers.Adam
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Adam = nnlib.Adam
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modelify = nnlib.modelify
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gaussian_blur = nnlib.gaussian_blur
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style_loss = nnlib.style_loss
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dssim = nnlib.dssim
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PixelShuffler = nnlib.PixelShuffler
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SubpixelUpscaler = nnlib.SubpixelUpscaler
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Scale = nnlib.Scale
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#ReflectionPadding2D = nnlib.ReflectionPadding2D
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#AddUniformNoise = nnlib.AddUniformNoise
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"""
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@ -181,7 +182,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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if device_config.use_fp16:
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nnlib.keras.backend.set_floatx('float16')
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if "tensorflow" in device_config.backend:
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nnlib.keras.backend.set_session(nnlib.tf_sess)
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@ -432,7 +433,78 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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base_config = super(Scale, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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nnlib.Scale = Scale
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class Adam(keras.optimizers.Optimizer):
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def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999,
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epsilon=None, decay=0., amsgrad=False, iterations=0, **kwargs):
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super(Adam, self).__init__(**kwargs)
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with K.name_scope(self.__class__.__name__):
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self.iterations = K.variable(iterations, dtype='int64', name='iterations')
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self.lr = K.variable(lr, name='lr')
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self.beta_1 = K.variable(beta_1, name='beta_1')
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self.beta_2 = K.variable(beta_2, name='beta_2')
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self.decay = K.variable(decay, name='decay')
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if epsilon is None:
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epsilon = K.epsilon()
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self.epsilon = epsilon
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self.initial_decay = decay
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self.amsgrad = amsgrad
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@keras.legacy.interfaces.legacy_get_updates_support
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def get_updates(self, loss, params):
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grads = self.get_gradients(loss, params)
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self.updates = [K.update_add(self.iterations, 1)]
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lr = self.lr
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if self.initial_decay > 0:
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lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
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K.dtype(self.decay))))
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t = K.cast(self.iterations, K.floatx()) + 1
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lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
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(1. - K.pow(self.beta_1, t)))
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ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
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vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
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if self.amsgrad:
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vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
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else:
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vhats = [K.zeros(1) for _ in params]
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self.weights = [self.iterations] + ms + vs + vhats
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for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
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m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
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v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
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if self.amsgrad:
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vhat_t = K.maximum(vhat, v_t)
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p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
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self.updates.append(K.update(vhat, vhat_t))
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else:
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p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
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self.updates.append(K.update(m, m_t))
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self.updates.append(K.update(v, v_t))
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new_p = p_t
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# Apply constraints.
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if getattr(p, 'constraint', None) is not None:
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new_p = p.constraint(new_p)
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self.updates.append(K.update(p, new_p))
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return self.updates
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def get_config(self):
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config = {'iterations': int(K.get_value(self.iterations)),
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'lr': float(K.get_value(self.lr)),
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'beta_1': float(K.get_value(self.beta_1)),
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'beta_2': float(K.get_value(self.beta_2)),
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'decay': float(K.get_value(self.decay)),
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'epsilon': self.epsilon,
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'amsgrad': self.amsgrad}
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base_config = super(Adam, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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nnlib.Adam = Adam
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'''
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not implemented in plaidML
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class ReflectionPadding2D(keras.layers.Layer):
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