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https://github.com/iperov/DeepFaceLab.git
synced 2025-08-19 21:13:20 -07:00
added filename previews
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parent
f746802869
commit
ebb085e31e
5 changed files with 36 additions and 7 deletions
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@ -415,7 +415,7 @@ class ModelBase(object):
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return ( ('loss_src', 0), ('loss_dst', 0) )
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return ( ('loss_src', 0), ('loss_dst', 0) )
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#overridable
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#overridable
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def onGetPreview(self, sample, for_history=False):
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def onGetPreview(self, sample, for_history=False, filenames=None):
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#you can return multiple previews
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#you can return multiple previews
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#return [ ('preview_name',preview_rgb), ... ]
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#return [ ('preview_name',preview_rgb), ... ]
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return []
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return []
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@ -447,7 +447,7 @@ class ModelBase(object):
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return self.target_iter != 0 and self.iter >= self.target_iter
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return self.target_iter != 0 and self.iter >= self.target_iter
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def get_previews(self):
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def get_previews(self):
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return self.onGetPreview ( self.last_sample )
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return self.onGetPreview ( self.last_sample, filenames=self.last_sample_filenames )
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def get_static_previews(self):
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def get_static_previews(self):
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return self.onGetPreview (self.sample_for_preview)
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return self.onGetPreview (self.sample_for_preview)
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@ -585,12 +585,19 @@ class ModelBase(object):
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def generate_next_samples(self):
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def generate_next_samples(self):
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sample = []
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sample = []
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sample_filenames = []
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for generator in self.generator_list:
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for generator in self.generator_list:
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if generator.is_initialized():
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if generator.is_initialized():
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sample.append ( generator.generate_next() )
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batch = generator.generate_next()
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if type(batch) is tuple:
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sample.append ( batch[0] )
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sample_filenames.append( batch[1] )
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else:
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sample.append ( batch )
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else:
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else:
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sample.append ( [] )
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sample.append ( [] )
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self.last_sample = sample
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self.last_sample = sample
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self.last_sample_filenames = sample_filenames
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return sample
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return sample
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#overridable
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#overridable
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@ -13,6 +13,8 @@ from core.cv2ex import *
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from pathlib import Path
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from pathlib import Path
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from utils.label_face import label_face_filename
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class AMPModel(ModelBase):
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class AMPModel(ModelBase):
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#override
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#override
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@ -888,7 +890,7 @@ class AMPModel(ModelBase):
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return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
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return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
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#override
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#override
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def onGetPreview(self, samples, for_history=False):
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def onGetPreview(self, samples, for_history=False, filenames=None):
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( (warped_src, target_src, target_srcm, target_srcm_em),
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( (warped_src, target_src, target_srcm, target_srcm_em),
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
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@ -920,6 +922,10 @@ class AMPModel(ModelBase):
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i = np.random.randint(n_samples) if not for_history else 0
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i = np.random.randint(n_samples) if not for_history else 0
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if filenames is not None and len(filenames) > 0:
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S[i] = label_face_filename(S[i], filenames[0][i])
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D[i] = label_face_filename(D[i], filenames[1][i])
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st = [ np.concatenate ((S[i], D[i], DD[i]*DDM_000[i]), axis=1) ]
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st = [ np.concatenate ((S[i], D[i], DD[i]*DDM_000[i]), axis=1) ]
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st += [ np.concatenate ((SS[i], DD[i], SD_100[i] ), axis=1) ]
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st += [ np.concatenate ((SS[i], DD[i], SD_100[i] ), axis=1) ]
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@ -12,6 +12,8 @@ from samplelib import *
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from pathlib import Path
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from pathlib import Path
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from utils.label_face import label_face_filename
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class QModel(ModelBase):
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class QModel(ModelBase):
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#override
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#override
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def on_initialize_options(self):
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def on_initialize_options(self):
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@ -287,7 +289,7 @@ class QModel(ModelBase):
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return ( ('src_loss', src_loss), ('dst_loss', dst_loss), )
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return ( ('src_loss', src_loss), ('dst_loss', dst_loss), )
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#override
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#override
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def onGetPreview(self, samples, for_history=False):
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def onGetPreview(self, samples, for_history=False, filenames=None):
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( (warped_src, target_src, target_srcm),
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( (warped_src, target_src, target_srcm),
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(warped_dst, target_dst, target_dstm) ) = samples
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(warped_dst, target_dst, target_dstm) ) = samples
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@ -297,6 +299,12 @@ class QModel(ModelBase):
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target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]
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target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]
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n_samples = min(4, self.get_batch_size() )
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n_samples = min(4, self.get_batch_size() )
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if filenames is not None and len(filenames) > 0:
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for i in range(n_samples):
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S[i] = label_face_filename(S[i], filenames[0][i])
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D[i] = label_face_filename(D[i], filenames[1][i])
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result = []
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result = []
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st = []
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st = []
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for i in range(n_samples):
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for i in range(n_samples):
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@ -12,6 +12,8 @@ from samplelib import *
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from pathlib import Path
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from pathlib import Path
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from utils.label_face import label_face_filename
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class SAEHDModel(ModelBase):
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class SAEHDModel(ModelBase):
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#override
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#override
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@ -953,7 +955,7 @@ class SAEHDModel(ModelBase):
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return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
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return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
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#override
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#override
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def onGetPreview(self, samples, for_history=False):
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def onGetPreview(self, samples, for_history=False, filenames=None):
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( (warped_src, target_src, target_srcm, target_srcm_em),
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( (warped_src, target_src, target_srcm, target_srcm_em),
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
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@ -965,6 +967,11 @@ class SAEHDModel(ModelBase):
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n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
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n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
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if filenames is not None and len(filenames) > 0:
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for i in range(n_samples):
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S[i] = label_face_filename(S[i], filenames[0][i])
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D[i] = label_face_filename(D[i], filenames[1][i])
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if self.resolution <= 256:
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if self.resolution <= 256:
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result = []
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result = []
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@ -115,6 +115,7 @@ class SampleGeneratorFace(SampleGeneratorBase):
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samples, index_host, ct_samples, ct_index_host = param
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samples, index_host, ct_samples, ct_index_host = param
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bs = self.batch_size
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bs = self.batch_size
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filenames = []
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while True:
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while True:
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batches = None
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batches = None
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@ -141,4 +142,4 @@ class SampleGeneratorFace(SampleGeneratorBase):
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for i in range(len(x)):
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for i in range(len(x)):
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batches[i].append ( x[i] )
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batches[i].append ( x[i] )
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yield [ np.array(batch) for batch in batches]
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yield ([ np.array(batch) for batch in batches], filenames)
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