remove s loss entirely

model.py

@@ -4,7 +4,6 @@ import torch.nn as nn
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 
 from losses import calculate_separation_loss, preservation_loss  # noqa: F401
-from utils import PURE_HSV, PURE_RGB
 
 
 class ColorTransformerModel(L.LightningModule):
@@ -46,23 +45,14 @@ class ColorTransformerModel(L.LightningModule):
     def training_step(self, batch, batch_idx):
         inputs, labels = batch  # x are the RGB inputs, labels are the strings
         outputs = self.forward(inputs)
-        # s_loss = calculate_separation_loss(model=self)
-        # preserve distance to pure R, G, B. this acts kind of like labeled data.
-        s_loss = preservation_loss(
-            inputs,
-            outputs,
-            target_inputs=PURE_RGB,
-            target_outputs=PURE_HSV,
-        )
         p_loss = preservation_loss(
             inputs,
             outputs,
         )
-        alpha = self.hparams.alpha
+        # alpha = self.hparams.alpha  # TODO: decide what to do with this...
-        loss = p_loss + alpha * s_loss
+        loss = p_loss
         self.log("hp_metric", loss)
         self.log("p_loss", p_loss)
-        self.log("s_loss", s_loss)
         return loss
 
     def validation_step(self, batch):

newsearch.py

@@ -21,7 +21,7 @@ NUM_JOBS = 100
 # alpha_values = list(np.round(np.linspace(2, 4, 21), 4))
 # learning_rate_values = list(np.round(np.logspace(-5, -3, 21), 5))
 learning_rate_values = [1e-2]
-alpha_values = [0, 1, 2]
+alpha_values = [0]
 widths = [2**k for k in range(4, 15)]
 # learning_rate_values = [5e-4]
 batch_size_values = [256]