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fix depth_to_space for tf2.4.0. Removing compute_output_shape in leras, because it uses CPU device, which does not support all ops.

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iperov 2020-12-11 11:28:33 +04:00
parent bbf3a71a96
commit b9c9e7cffd
5 changed files with 44 additions and 44 deletions
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13
models/Model_SAEHD/Model.py
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@ -233,10 +233,10 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
with tf.device (models_opt_device):
if 'df' in archi_type:
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
encoder_out_ch = self.encoder.compute_output_channels ( (nn.floatx, bgr_shape))
encoder_out_ch = self.encoder.get_out_ch()*self.encoder.get_out_res(resolution)**2
self.inter = model_archi.Inter (in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims, name='inter')
inter_out_ch = self.inter.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
inter_out_ch = self.inter.get_out_ch()
self.decoder_src = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder_src')
self.decoder_dst = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder_dst')
@ -248,19 +248,18 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
if self.is_training:
if self.options['true_face_power'] != 0:
self.code_discriminator = nn.CodeDiscriminator(ae_dims, code_res=model_archi.Inter.get_code_res()*2, name='dis' )
self.code_discriminator = nn.CodeDiscriminator(ae_dims, code_res=model_archi.Inter.get_out_res(), name='dis' )
self.model_filename_list += [ [self.code_discriminator, 'code_discriminator.npy'] ]
elif 'liae' in archi_type:
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
encoder_out_ch = self.encoder.compute_output_channels ( (nn.floatx, bgr_shape))
encoder_out_ch = self.encoder.get_out_ch()*self.encoder.get_out_res(resolution)**2
self.inter_AB = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_AB')
self.inter_B = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_B')
inter_AB_out_ch = self.inter_AB.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
inter_B_out_ch = self.inter_B.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
inters_out_ch = inter_AB_out_ch+inter_B_out_ch
inter_out_ch = self.inter_AB.get_out_ch()
inters_out_ch = inter_out_ch*2
self.decoder = model_archi.Decoder(in_ch=inters_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder')
self.model_filename_list += [ [self.encoder, 'encoder.npy'],