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feat: use one-sided smoothing (eg: positive/real labels 1.0 -> 0.9), and only apply to discriminator

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jh 2021-03-22 15:22:28 -07:00
commit c36eca3610
1 changed files with 13 additions and 14 deletions
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27
models/Model_SAEHD/Model.py
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@ -168,7 +168,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="4-64", help_message="The dimensions of the GAN network. The higher dimensions, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is 16." ), 4, 64 )
self.options['gan_dims'] = gan_dims
self.options['gan_smoothing'] = np.clip ( io.input_number("GAN label smoothing", default_gan_smoothing, add_info="0 - 0.5", help_message="Uses soft labels with values slightly off from 0/1 for GAN, has a regularizing effect"), 0, 0.5)
self.options['gan_noise'] = np.clip ( io.input_number("GAN noisy labels", default_gan_noise, add_info="0 - 0.5", help_message="Marks some images with the wrong label, helps prevent collapse"), 0, 0.5)
@ -551,29 +551,28 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
x = tf.random.categorical(probs, num_labels)
x = tf.cast(x, tf.float32)
x = tf.math.scalar_mul(1-smoothing, x)
x = x + (smoothing/num_labels)
# x = x + (smoothing/num_labels)
x = tf.reshape(x, (self.batch_size,) + tensor.shape[1:])
return x
smoothing = self.options['gan_smoothing']
noise = self.options['gan_noise']
gpu_pred_src_src_d_ones = get_smooth_noisy_labels(1, gpu_pred_src_src_d, smoothing=smoothing, noise=noise)
gpu_pred_src_src_d_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d, smoothing=smoothing, noise=noise)
gpu_pred_src_src_d_ones = tf.ones_like(gpu_pred_src_src_d)
gpu_pred_src_src_d2_ones = tf.ones_like(gpu_pred_src_src_d2)
gpu_pred_src_src_d2_ones = get_smooth_noisy_labels(1, gpu_pred_src_src_d2, smoothing=smoothing, noise=noise)
gpu_pred_src_src_d2_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d2, smoothing=smoothing, noise=noise)
gpu_pred_src_src_d_smooth_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d, smoothing=smoothing, noise=noise)
gpu_pred_src_src_d2_smooth_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d2, smoothing=smoothing, noise=noise)
gpu_target_src_d, \
gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
gpu_target_src_d, gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
gpu_target_src_d_ones = get_smooth_noisy_labels(1, gpu_target_src_d, smoothing=smoothing, noise=noise)
gpu_target_src_d2_ones = get_smooth_noisy_labels(1, gpu_target_src_d2, smoothing=smoothing, noise=noise)
gpu_target_src_d_smooth_ones = get_smooth_noisy_labels(1, gpu_target_src_d, smoothing=smoothing, noise=noise)
gpu_target_src_d2_smooth_ones = get_smooth_noisy_labels(1, gpu_target_src_d2, smoothing=smoothing, noise=noise)
gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \
(DLoss(gpu_target_src_d2_ones , gpu_target_src_d2) + \
DLoss(gpu_pred_src_src_d2_zeros , gpu_pred_src_src_d2) ) * 0.5
gpu_D_src_dst_loss = DLoss(gpu_target_src_d_smooth_ones, gpu_target_src_d) \
+ DLoss(gpu_pred_src_src_d_smooth_zeros, gpu_pred_src_src_d) \
+ DLoss(gpu_target_src_d2_smooth_ones, gpu_target_src_d2) \
+ DLoss(gpu_pred_src_src_d2_smooth_zeros, gpu_pred_src_src_d2)
gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights() ) ]#+self.D_src_x2.get_weights()