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iperov 2019-04-26 21:43:43 +04:00
parent f89200d236
commit 9e05e58ac4
1 changed files with 1 additions and 3 deletions
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models/Model_SAE/Model.py
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@ -65,14 +65,12 @@ class SAEModel(ModelBase):
self.options['e_ch_dims'] = np.clip ( io.input_int("Encoder dims per channel (21-85 ?:help skip:%d) : " % (default_e_ch_dims) , default_e_ch_dims, help_message="More encoder dims help to recognize more facial features, but require more VRAM. You can fine-tune model size to fit your GPU." ), 21, 85 ) self.options['e_ch_dims'] = np.clip ( io.input_int("Encoder dims per channel (21-85 ?:help skip:%d) : " % (default_e_ch_dims) , default_e_ch_dims, help_message="More encoder dims help to recognize more facial features, but require more VRAM. You can fine-tune model size to fit your GPU." ), 21, 85 )
default_d_ch_dims = self.options['e_ch_dims'] // 2 default_d_ch_dims = self.options['e_ch_dims'] // 2
self.options['d_ch_dims'] = np.clip ( io.input_int("Decoder dims per channel (10-85 ?:help skip:%d) : " % (default_d_ch_dims) , default_d_ch_dims, help_message="More decoder dims help to get better details, but require more VRAM. You can fine-tune model size to fit your GPU." ), 10, 85 ) self.options['d_ch_dims'] = np.clip ( io.input_int("Decoder dims per channel (10-85 ?:help skip:%d) : " % (default_d_ch_dims) , default_d_ch_dims, help_message="More decoder dims help to get better details, but require more VRAM. You can fine-tune model size to fit your GPU." ), 10, 85 )
#self.options['remove_gray_border'] = io.input_bool ("Remove gray border? (y/n, ?:help skip:n) : ", False, help_message="Removes gray border of predicted face, but requires more computing resources.")
self.options['multiscale_decoder'] = io.input_bool ("Use multiscale decoder? (y/n, ?:help skip:n) : ", False, help_message="Multiscale decoder helps to get better details.") self.options['multiscale_decoder'] = io.input_bool ("Use multiscale decoder? (y/n, ?:help skip:n) : ", False, help_message="Multiscale decoder helps to get better details.")
self.options['ca_weights'] = io.input_bool ("Use CA weights? (y/n, ?:help skip: %s ) : " % (yn_str[def_ca_weights]), def_ca_weights, help_message="Initialize network with 'Convolution Aware' weights. This may help to achieve a higher accuracy model, but consumes a time at first run.") self.options['ca_weights'] = io.input_bool ("Use CA weights? (y/n, ?:help skip: %s ) : " % (yn_str[def_ca_weights]), def_ca_weights, help_message="Initialize network with 'Convolution Aware' weights. This may help to achieve a higher accuracy model, but consumes a time at first run.")
else: else:
self.options['ae_dims'] = self.options.get('ae_dims', default_ae_dims) self.options['ae_dims'] = self.options.get('ae_dims', default_ae_dims)
self.options['e_ch_dims'] = self.options.get('e_ch_dims', default_e_ch_dims) self.options['e_ch_dims'] = self.options.get('e_ch_dims', default_e_ch_dims)
self.options['d_ch_dims'] = self.options.get('d_ch_dims', default_d_ch_dims) self.options['d_ch_dims'] = self.options.get('d_ch_dims', default_d_ch_dims)
self.options['remove_gray_border'] = self.options.get('remove_gray_border', False)
self.options['multiscale_decoder'] = self.options.get('multiscale_decoder', False) self.options['multiscale_decoder'] = self.options.get('multiscale_decoder', False)
self.options['ca_weights'] = self.options.get('ca_weights', def_ca_weights) self.options['ca_weights'] = self.options.get('ca_weights', def_ca_weights)
@ -463,7 +461,7 @@ class SAEModel(ModelBase):
base_blur_mask_modifier=base_blur_mask_modifier, base_blur_mask_modifier=base_blur_mask_modifier,
default_erode_mask_modifier=default_erode_mask_modifier, default_erode_mask_modifier=default_erode_mask_modifier,
default_blur_mask_modifier=default_blur_mask_modifier, default_blur_mask_modifier=default_blur_mask_modifier,
clip_hborder_mask_per=0.0625 if (not self.options['remove_gray_border'] and self.options['face_type'] == 'f') else 0) clip_hborder_mask_per=0.0625 if (self.options['face_type'] == 'f') else 0)
@staticmethod @staticmethod
def initialize_nn_functions(): def initialize_nn_functions():