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Change to add the w_avg latent code to style encoding before layer swapping. This is a bug in the previous code. Also set training=True for encoder since this affect the encoding performance.

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PiperOrigin-RevId: 553234376
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MediaPipe Team 2023-08-02 13:09:01 -07:00 committed by Copybara-Service
parent 6e54d8c204
commit 366a3290cf
1 changed files with 3 additions and 6 deletions
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9
mediapipe/model_maker/python/vision/face_stylizer/face_stylizer.py
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@ -146,7 +146,7 @@ class FaceStylizer(object):
batch_size = self._hparams.batch_size
label_in = tf.zeros(shape=[batch_size, 0])
style_encoding = self._encoder(style_img)
style_encoding = self._encoder(style_img, training=True) + self.w_avg
optimizer = tf.keras.optimizers.Adam(
learning_rate=self._hparams.learning_rate,
@ -176,10 +176,7 @@ class FaceStylizer(object):
)
with tf.GradientTape() as tape:
outputs = self._decoder(
{'inputs': in_latent + self.w_avg},
training=True,
)
outputs = self._decoder({'inputs': in_latent.numpy()}, training=True)
gen_img = outputs['image'][-1]
real_feature = self._discriminator(
@ -194,7 +191,7 @@ class FaceStylizer(object):
tf.keras.losses.MeanAbsoluteError()(real_feature, gen_feature)
* self._model_options.adv_loss_weight
)
tf.compat.v1.logging.info(f'Iteration {i} loss: {style_loss.numpy()}')
print(f'Iteration {i} loss: {style_loss.numpy()}')
tvars = self._decoder.trainable_variables
grads = tape.gradient(style_loss, tvars)