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SAE: revert back to sigmoid. Trainings must be restarted.

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iperov 2019-03-16 09:01:11 +04:00
parent ea5fe28d08
commit 5076feb90f
2 changed files with 21 additions and 17 deletions
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3
models/ModelBase.py
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@ -410,6 +410,9 @@ class ModelBase(object):
def get_training_data_generators (self): def get_training_data_generators (self):
return self.generator_list return self.generator_list
def get_model_root_path(self):
return self.model_path
def get_strpath_storage_for_file(self, filename): def get_strpath_storage_for_file(self, filename):
if self.device_args['force_gpu_idx'] == -1: if self.device_args['force_gpu_idx'] == -1:
return str( self.model_path / ( self.get_model_name() + '_' + filename) ) return str( self.model_path / ( self.get_model_name() + '_' + filename) )

31
models/Model_SAE/Model.py
View file

@ -356,11 +356,11 @@ class SAEModel(ModelBase):
self.set_training_data_generators ([ self.set_training_data_generators ([
SampleGeneratorFace(self.training_data_src_path, sort_by_yaw_target_samples_path=self.training_data_dst_path if self.sort_by_yaw else None, SampleGeneratorFace(self.training_data_src_path, sort_by_yaw_target_samples_path=self.training_data_dst_path if self.sort_by_yaw else None,
debug=self.is_debug(), batch_size=self.batch_size, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, normalize_tanh = True, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ), sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
output_sample_types=output_sample_types ), output_sample_types=output_sample_types ),
SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, normalize_tanh = True), sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
output_sample_types=output_sample_types ) output_sample_types=output_sample_types )
]) ])
@ -418,10 +418,10 @@ class SAEModel(ModelBase):
test_B_m = sample[1][2][0:4] test_B_m = sample[1][2][0:4]
if self.options['learn_mask']: if self.options['learn_mask']:
S, D, SS, DD, SD, SDM = [ np.clip(x / 2 + 0.5, 0.0, 1.0) for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ] S, D, SS, DD, SD, SDM = [ np.clip(x, 0.0, 1.0) for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ]
SDM, = [ np.repeat (x, (3,), -1) for x in [SDM] ] SDM, = [ np.repeat (x, (3,), -1) for x in [SDM] ]
else: else:
S, D, SS, DD, SD, = [ np.clip(x / 2 + 0.5, 0.0, 1.0) for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ] S, D, SS, DD, SD, = [ np.clip(x, 0.0, 1.0) for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ]
st = [] st = []
for i in range(0, len(test_A)): for i in range(0, len(test_A)):
@ -433,15 +433,13 @@ class SAEModel(ModelBase):
return [ ('SAE', np.concatenate (st, axis=0 )), ] return [ ('SAE', np.concatenate (st, axis=0 )), ]
def predictor_func (self, face): def predictor_func (self, face):
face_tanh = np.clip(face * 2.0 - 1.0, -1.0, 1.0)
face_bgr = face_tanh[...,0:3] prd = [ x[0] for x in self.AE_convert ( [ face[np.newaxis,:,:,0:3] ] ) ]
prd = [ (x[0] + 1.0) / 2.0 for x in self.AE_convert ( [ np.expand_dims(face_bgr,0) ] ) ]
if not self.options['learn_mask']: if not self.options['learn_mask']:
prd += [ np.expand_dims(face[...,3],-1) ] prd += [ face[...,3:4] ]
return np.concatenate ( [prd[0], prd[1]], -1 ) return np.concatenate ( prd, -1 )
#override #override
def get_converter(self): def get_converter(self):
@ -470,6 +468,9 @@ class SAEModel(ModelBase):
def initialize_nn_functions(): def initialize_nn_functions():
exec (nnlib.import_all(), locals(), globals()) exec (nnlib.import_all(), locals(), globals())
def conv_initializer():
return RandomNormal(0, 0.02)
class ResidualBlock(object): class ResidualBlock(object):
def __init__(self, filters, kernel_size=3, padding='same', use_reflection_padding=False): def __init__(self, filters, kernel_size=3, padding='same', use_reflection_padding=False):
self.filters = filters self.filters = filters
@ -483,13 +484,13 @@ class SAEModel(ModelBase):
#if self.use_reflection_padding: #if self.use_reflection_padding:
# #var_x = ReflectionPadding2D(stride=1, kernel_size=kernel_size)(var_x) # #var_x = ReflectionPadding2D(stride=1, kernel_size=kernel_size)(var_x)
var_x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding, kernel_initializer=RandomNormal(0, 0.02) )(var_x) var_x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding, kernel_initializer=conv_initializer() )(var_x)
var_x = LeakyReLU(alpha=0.2)(var_x) var_x = LeakyReLU(alpha=0.2)(var_x)
#if self.use_reflection_padding: #if self.use_reflection_padding:
# #var_x = ReflectionPadding2D(stride=1, kernel_size=kernel_size)(var_x) # #var_x = ReflectionPadding2D(stride=1, kernel_size=kernel_size)(var_x)
var_x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding, kernel_initializer=RandomNormal(0, 0.02) )(var_x) var_x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding, kernel_initializer=conv_initializer() )(var_x)
var_x = Scale(gamma_init=keras.initializers.Constant(value=0.1))(var_x) var_x = Scale(gamma_init=keras.initializers.Constant(value=0.1))(var_x)
var_x = Add()([var_x, inp]) var_x = Add()([var_x, inp])
var_x = LeakyReLU(alpha=0.2)(var_x) var_x = LeakyReLU(alpha=0.2)(var_x)
@ -498,25 +499,25 @@ class SAEModel(ModelBase):
def downscale (dim): def downscale (dim):
def func(x): def func(x):
return LeakyReLU(0.1)(Conv2D(dim, kernel_size=5, strides=2, padding='same', kernel_initializer=RandomNormal(0, 0.02))(x)) return LeakyReLU(0.1)(Conv2D(dim, kernel_size=5, strides=2, padding='same', kernel_initializer=conv_initializer())(x))
return func return func
SAEModel.downscale = downscale SAEModel.downscale = downscale
def downscale_sep (dim): def downscale_sep (dim):
def func(x): def func(x):
return LeakyReLU(0.1)(SeparableConv2D(dim, kernel_size=5, strides=2, padding='same', depthwise_initializer=RandomNormal(0, 0.02), pointwise_initializer=RandomNormal(0, 0.02) )(x)) return LeakyReLU(0.1)(SeparableConv2D(dim, kernel_size=5, strides=2, padding='same', depthwise_initializer=conv_initializer(), pointwise_initializer=RandomNormal(0, 0.02) )(x))
return func return func
SAEModel.downscale_sep = downscale_sep SAEModel.downscale_sep = downscale_sep
def upscale (dim): def upscale (dim):
def func(x): def func(x):
return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding='same', kernel_initializer=RandomNormal(0, 0.02) )(x))) return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding='same', kernel_initializer=conv_initializer() )(x)))
return func return func
SAEModel.upscale = upscale SAEModel.upscale = upscale
def to_bgr (output_nc): def to_bgr (output_nc):
def func(x): def func(x):
return Conv2D(output_nc, kernel_size=5, padding='same', activation='tanh', kernel_initializer=RandomNormal(0, 0.02))(x) return Conv2D(output_nc, kernel_size=5, padding='same', activation='sigmoid', kernel_initializer=conv_initializer() )(x)
return func return func
SAEModel.to_bgr = to_bgr SAEModel.to_bgr = to_bgr