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Change loss input order of CGAN and PCGAN #157

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Merged
merged 1 commit into from
Nov 30, 2022
Merged

Change loss input order of CGAN and PCGAN #157

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6 changes: 3 additions & 3 deletions deeptrack/models/gans/cgan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,8 +104,8 @@ def train_step(self, data):
shape = tf.shape(disc_pred_real)
valid, fake = tf.ones(shape), tf.zeros(shape)
d_loss = (
self.discriminator.compiled_loss(disc_pred_real, valid)
+ self.discriminator.compiled_loss(disc_pred_fake, fake)
self.discriminator.compiled_loss(valid, disc_pred_real)
+ self.discriminator.compiled_loss(fake, disc_pred_fake)
) / 2

# Compute gradient and apply gradient
Expand All @@ -124,8 +124,8 @@ def train_step(self, data):
batch_y_copies = [batch_y] * (self.num_losses - 1)

g_loss = self.assemble.compiled_loss(
[assemble_output[0], *generated_image_copies],
[valid, *batch_y_copies],
[assemble_output[0], *generated_image_copies],
)

# Compute gradient and apply gradient
Expand Down
8 changes: 4 additions & 4 deletions deeptrack/models/gans/pcgan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,7 @@ def __init__(
metrics=[],
**kwargs
):
super(PCGAN).__init__()
super().__init__()

# Build and compile the discriminator
self.discriminator = discriminator
Expand Down Expand Up @@ -146,8 +146,8 @@ def train_step(self, data):
shape = tf.shape(disc_pred_1)
valid, fake = tf.ones(shape), tf.zeros(shape)
d_loss = (
self.discriminator.compiled_loss(disc_pred_1, valid)
+ self.discriminator.compiled_loss(disc_pred_2, fake)
self.discriminator.compiled_loss(valid, disc_pred_1)
+ self.discriminator.compiled_loss(fake, disc_pred_2)
) / 2

# Compute gradient and apply gradient
Expand All @@ -168,12 +168,12 @@ def train_step(self, data):
batch_y_copies = [batch_y] * (self.num_losses - 1)

g_loss = self.assemble.compiled_loss(
[valid, perceptual_valid, *batch_y_copies],
[
assemble_output[0],
assemble_output[1],
*generated_image_copies,
],
[valid, perceptual_valid, *batch_y_copies],
)

# Compute gradient and apply gradient
Expand Down