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update FANSeg

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This commit is contained in:
Colombo 2020-03-08 10:34:48 +04:00
parent 6f4ea69d4d
commit 18d93376fc
4 changed files with 61 additions and 17 deletions
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8
models/ModelBase.py
View file

@ -412,7 +412,13 @@ class ModelBase(object):
return imagelib.equalize_and_stack_square (images) return imagelib.equalize_and_stack_square (images)
def generate_next_samples(self): def generate_next_samples(self):
self.last_sample = sample = [ generator.generate_next() for generator in self.generator_list] sample = []
for generator in self.generator_list:
if generator.is_initialized():
sample.append ( generator.generate_next() )
else:
sample.append ( [] )
self.last_sample = sample
return sample return sample
def train_one_iter(self): def train_one_iter(self):

51
models/Model_FANSeg/Model.py
View file

@ -24,7 +24,6 @@ class FANSegModel(ModelBase):
ask_override = self.ask_override() ask_override = self.ask_override()
if self.is_first_run() or ask_override: if self.is_first_run() or ask_override:
self.ask_autobackup_hour() self.ask_autobackup_hour()
self.ask_write_preview_history()
self.ask_target_iter() self.ask_target_iter()
self.ask_batch_size(4) self.ask_batch_size(4)
@ -117,21 +116,30 @@ class FANSegModel(ModelBase):
# initializing sample generators # initializing sample generators
training_data_src_path = self.training_data_src_path training_data_src_path = self.training_data_src_path
#training_data_dst_path = self.training_data_dst_path training_data_dst_path = self.training_data_dst_path
cpu_count = min(multiprocessing.cpu_count(), 8) cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2 src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2 dst_generators_count = cpu_count // 2
src_generators_count = int(src_generators_count * 1.5) src_generators_count = int(src_generators_count * 1.5)
self.set_training_data_generators ([ src_generator = SampleGeneratorFace(training_data_src_path, random_ct_samples_path=training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(), sample_process_options=SampleProcessor.Options(random_flip=True),
sample_process_options=SampleProcessor.Options(random_flip=True), output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'ct_mode':'idt', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'ct_mode':'idt', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution}, {'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.NONE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.NONE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution}, ],
], generators_count=src_generators_count )
generators_count=src_generators_count ),
]) dst_generator = SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=True),
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
],
generators_count=dst_generators_count,
raise_on_no_data=False )
if not dst_generator.is_initialized():
io.log_info(f"\nTo view the model on unseen faces, place any aligned faces in {training_data_dst_path}.\n")
self.set_training_data_generators ([src_generator, dst_generator])
#override #override
def get_model_filename_list(self): def get_model_filename_list(self):
@ -143,7 +151,7 @@ class FANSegModel(ModelBase):
#override #override
def onTrainOneIter(self): def onTrainOneIter(self):
( (source_np, target_np), ) = self.generate_next_samples() source_np, target_np = self.generate_next_samples()[0]
loss = self.train (source_np, target_np) loss = self.train (source_np, target_np)
return ( ('loss', loss ), ) return ( ('loss', loss ), )
@ -152,7 +160,8 @@ class FANSegModel(ModelBase):
def onGetPreview(self, samples): def onGetPreview(self, samples):
n_samples = min(4, self.get_batch_size(), 800 // self.resolution ) n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
( (source_np, target_np), ) = samples src_samples, dst_samples = samples
source_np, target_np = src_samples
S, T, SM, = [ np.clip(x, 0.0, 1.0) for x in ([source_np,target_np] + self.view (source_np) ) ] S, T, SM, = [ np.clip(x, 0.0, 1.0) for x in ([source_np,target_np] + self.view (source_np) ) ]
T, SM, = [ np.repeat (x, (3,), -1) for x in [T, SM] ] T, SM, = [ np.repeat (x, (3,), -1) for x in [T, SM] ]
@ -164,8 +173,22 @@ class FANSegModel(ModelBase):
ar = S[i], T[i], SM[i], S[i]*SM[i] ar = S[i], T[i], SM[i], S[i]*SM[i]
#todo green bg #todo green bg
st.append ( np.concatenate ( ar, axis=1) ) st.append ( np.concatenate ( ar, axis=1) )
result += [ ('FANSeg', np.concatenate (st, axis=0 )), ] result += [ ('FANSeg training faces', np.concatenate (st, axis=0 )), ]
if len(dst_samples) != 0:
dst_np, = dst_samples
D, DM, = [ np.clip(x, 0.0, 1.0) for x in ([dst_np] + self.view (dst_np) ) ]
DM, = [ np.repeat (x, (3,), -1) for x in [DM] ]
st = []
for i in range(n_samples):
ar = D[i], DM[i], D[i]*DM[i]
#todo green bg
st.append ( np.concatenate ( ar, axis=1) )
result += [ ('FANSeg unseen faces', np.concatenate (st, axis=0 )), ]
return result return result
Model = FANSegModel Model = FANSegModel

4
samplelib/SampleGeneratorBase.py
View file

@ -33,3 +33,7 @@ class SampleGeneratorBase(object):
def __next__(self): def __next__(self):
#implement your own iterator #implement your own iterator
return None return None
#overridable
def is_initialized(self):
return True

15
samplelib/SampleGeneratorFace.py
View file

@ -27,6 +27,7 @@ class SampleGeneratorFace(SampleGeneratorBase):
output_sample_types=[], output_sample_types=[],
add_sample_idx=False, add_sample_idx=False,
generators_count=4, generators_count=4,
raise_on_no_data=True,
**kwargs): **kwargs):
super().__init__(samples_path, debug, batch_size) super().__init__(samples_path, debug, batch_size)
@ -42,8 +43,12 @@ class SampleGeneratorFace(SampleGeneratorBase):
samples = SampleLoader.load (SampleType.FACE, self.samples_path) samples = SampleLoader.load (SampleType.FACE, self.samples_path)
self.samples_len = len(samples) self.samples_len = len(samples)
self.initialized = False
if self.samples_len == 0: if self.samples_len == 0:
raise ValueError('No training data provided.') if raise_on_no_data:
raise ValueError('No training data provided.')
else:
return
index_host = mplib.IndexHost(self.samples_len) index_host = mplib.IndexHost(self.samples_len)
@ -66,7 +71,13 @@ class SampleGeneratorFace(SampleGeneratorBase):
SubprocessGenerator.start_in_parallel( self.generators ) SubprocessGenerator.start_in_parallel( self.generators )
self.generator_counter = -1 self.generator_counter = -1
self.initialized = True
#overridable
def is_initialized(self):
return self.initialized
def __iter__(self): def __iter__(self):
return self return self