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refactoring loss calculation

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Jan 2021-11-25 08:40:43 +01:00
commit 79d3b473d3
1 changed files with 23 additions and 23 deletions
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models/Model_AMP/Model.py
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@ -519,42 +519,42 @@ class AMPModel(ModelBase):
if self.options['loss_function'] == 'MS-SSIM':
gpu_src_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0)
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked - gpu_pred_src_src_masked ), axis=[1,2,3])
elif self.options['loss_function'] == 'MS-SSIM+L1':
gpu_src_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0)
else:
gpu_src_loss = tf.reduce_mean (5*nn.dssim(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean (5*nn.dssim(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
# Pixel loss
gpu_src_loss += tf.reduce_mean (10*tf.square(gpu_target_src_masked-gpu_pred_src_src_masked), axis=[1,2,3])
if self.options['loss_function'] == 'MS-SSIM':
gpu_dst_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0)
gpu_dst_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_dst_masked - gpu_pred_dst_dst_masked ), axis=[1,2,3])
elif self.options['loss_function'] == 'MS-SSIM+L1':
gpu_dst_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0)
else:
gpu_dst_loss = tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
gpu_dst_loss += tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
# Pixel loss
gpu_dst_loss += tf.reduce_mean (10*tf.square(gpu_target_dst_masked-gpu_pred_dst_dst_masked), axis=[1,2,3])
if bg_factor > 0:
if self.options['loss_function'] == 'MS-SSIM':
if bg_factor > 0:
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])
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':
elif self.options['loss_function'] == 'MS-SSIM+L1':
gpu_src_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0)
gpu_dst_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0)
if bg_factor > 0:
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)
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:
else:
gpu_src_loss = tf.reduce_mean (5*nn.dssim(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean (5*nn.dssim(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
gpu_dst_loss = tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
gpu_dst_loss += tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
# Pixel loss
gpu_dst_loss += tf.reduce_mean (10*tf.square(gpu_target_dst_masked-gpu_pred_dst_dst_masked), axis=[1,2,3])
gpu_src_loss += tf.reduce_mean (10*tf.square(gpu_target_src_masked-gpu_pred_src_src_masked), axis=[1,2,3])
if bg_factor > 0:
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_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])
if bg_factor > 0:
gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_dst - gpu_pred_dst_dst ), axis=[1,2,3])
gpu_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src - gpu_pred_src_src ), axis=[1,2,3])