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https://github.com/iperov/DeepFaceLab.git
synced 2024-03-22 13:10:55 +08:00
fixes
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parent
eda6433936
commit
d731930537
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@ -21,7 +21,7 @@ def scantree(path):
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else:
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yield entry
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def get_image_paths(dir_path, image_extensions=image_extensions, subdirs=False):
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def get_image_paths(dir_path, image_extensions=image_extensions, subdirs=False, return_Path_class=False):
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dir_path = Path (dir_path)
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result = []
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@ -34,7 +34,7 @@ def get_image_paths(dir_path, image_extensions=image_extensions, subdirs=False):
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for x in list(gen):
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if any([x.name.lower().endswith(ext) for ext in image_extensions]):
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result.append(x.path)
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result.append( x.path if not return_Path_class else Path(x.path) )
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return sorted(result)
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def get_image_unique_filestem_paths(dir_path, verbose_print_func=None):
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@ -25,7 +25,7 @@ class FANSegModel(ModelBase):
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if self.is_first_run() or ask_override:
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self.ask_autobackup_hour()
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self.ask_target_iter()
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self.ask_batch_size(4)
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self.ask_batch_size(24)
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#if self.is_first_run():
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#resolution = io.input_int("Resolution", default_resolution, add_info="64-512")
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@ -56,7 +56,7 @@ class FANSegModel(ModelBase):
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mask_shape = nn.get4Dshape(resolution,resolution,1)
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# Initializing model classes
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self.model = TernausNet('FANSeg',
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self.model = TernausNet(f'{self.model_name}_FANSeg',
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resolution,
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FaceType.toString(self.face_type),
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load_weights=not self.is_first_run(),
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@ -102,8 +102,7 @@ class FANSegModel(ModelBase):
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loss = tf.reduce_mean(gpu_losses)
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loss_gv_op = self.model.opt.get_update_op (nn.tf_average_gv_list (gpu_loss_gvs))
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# Initializing training and view functions
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def train(input_np, target_np):
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@ -126,7 +125,7 @@ class FANSegModel(ModelBase):
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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(),
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sample_process_options=SampleProcessor.Options(random_flip=True),
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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},
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'ct_mode':'lct', '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},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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generators_count=src_generators_count )
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