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background power

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Jeremy Hummel 2021-06-19 23:43:21 -07:00
commit 1005ed167f
1 changed files with 33 additions and 0 deletions
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models/Model_AMP/Model.py
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@ -490,6 +490,23 @@ class AMPModel(ModelBase):
gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_em - gpu_pred_dst_dst*gpu_target_dstm_em ), axis=[1,2,3]) gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_em - gpu_pred_dst_dst*gpu_target_dstm_em ), axis=[1,2,3])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] ) gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_dst_loss += 0.1*tf.reduce_mean(tf.square(gpu_pred_dst_dst_anti_masked-gpu_target_dst_anti_masked),axis=[1,2,3] ) gpu_dst_loss += 0.1*tf.reduce_mean(tf.square(gpu_pred_dst_dst_anti_masked-gpu_target_dst_anti_masked),axis=[1,2,3] )
if self.options['background_power'] > 0:
bg_factor = self.options['background_power']
if self.options['loss_function'] == 'MS-SSIM':
gpu_dst_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0)
gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_dst - gpu_pred_dst_dst ), axis=[1,2,3])
elif self.options['loss_function'] == 'MS-SSIM+L1':
gpu_dst_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0)
else:
if resolution < 256:
gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
else:
gpu_dst_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_dst_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_dst - gpu_pred_dst_dst ), axis=[1,2,3])
gpu_dst_losses += [gpu_dst_loss] gpu_dst_losses += [gpu_dst_loss]
if not pretrain: if not pretrain:
@ -510,6 +527,22 @@ class AMPModel(ModelBase):
gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_em - gpu_pred_src_src*gpu_target_srcm_em ), axis=[1,2,3]) gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_em - gpu_pred_src_src*gpu_target_srcm_em ), axis=[1,2,3])
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] ) gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
if self.options['background_power'] > 0:
bg_factor = self.options['background_power']
if self.options['loss_function'] == 'MS-SSIM':
gpu_src_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_src, gpu_pred_src_src, max_val=1.0)
gpu_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src - gpu_pred_src_src ), axis=[1,2,3])
elif self.options['loss_function'] == 'MS-SSIM+L1':
gpu_src_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_src, gpu_pred_src_src, max_val=1.0)
else:
if resolution < 256:
gpu_src_loss += bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
else:
gpu_src_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
gpu_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src - gpu_pred_src_src ), axis=[1,2,3])
else: else:
gpu_src_loss = gpu_dst_loss gpu_src_loss = gpu_dst_loss