remove modulo from 3-space

datamodule.py

@@ -38,10 +38,11 @@ class ColorDataModule(L.LightningDataModule):
     def get_xkcd_colors(cls):
         rgb_tensor, xkcd_color_names = extract_colors()
         rgb_tensor = preprocess_data(rgb_tensor, skip=True)
-        return [
-            (rgb_tensor[i], xkcd_color_names[i].replace("xkcd:", ""))
-            for i in range(len(rgb_tensor))
-        ]
+        # return [
+        #     (rgb_tensor[i], xkcd_color_names[i].replace("xkcd:", ""))
+        #     for i in range(len(rgb_tensor))
+        # ]
+        return [(c, cls.get_hue(c)) for c in rgb_tensor]
 
     def setup(self, stage: str):
         # Assign train/val datasets for use in dataloaders

losses.py

@@ -17,6 +17,42 @@ from utils import PURE_RGB
 #     return smoothness_loss
 
 
+def simple_preservation_loss(inputs, outputs, target_inputs=None, target_outputs=None):
+    # Distance Preservation Component (or scaled euclidean if given targets)
+    # Encourages the model to keep relative distances from the RGB space in the transformed space
+    if target_inputs is None:
+        target_inputs = inputs
+    else:
+        assert target_outputs is not None
+    if target_outputs is None:
+        target_outputs = outputs
+
+    # Calculate RGB Norm
+    max_rgb_distance = torch.sqrt(torch.tensor(2 + 1))  # scale to [0, 1]
+    # max_rgb_distance = 1
+    rgb_norm = (
+        torch.triu(torch.norm(inputs[:, None, :] - target_inputs[None, :, :], dim=-1))
+        / max_rgb_distance
+    )
+    # connect (0, 0, 0) and (1, 1, 1): max_rgb_distance in the RGB space
+    # rgb_norm = rgb_norm % 1  # i think this is why yellow and blue end up adjacent.
+    # yes it connects black and white, but also complimentary colors to primary
+    # print(rgb_norm)
+
+    # Calculate 1D Space Norm (modulo 1 to account for circularity)
+    transformed_norm_a = torch.triu(
+        torch.norm((outputs[:, None] - target_outputs[None, :]) % 1, dim=-1)
+    )
+    transformed_norm_b = torch.triu(
+        torch.norm((1 + outputs[:, None] - target_outputs[None, :]) % 1, dim=-1)
+    )
+    transformed_norm = torch.minimum(transformed_norm_a, transformed_norm_b)
+
+    diff = torch.pow(rgb_norm - transformed_norm, 2)
+
+    return torch.mean(diff)
+
+
 def preservation_loss(inputs, outputs, target_inputs=None, target_outputs=None):
     # Distance Preservation Component (or scaled euclidean if given targets)
     # Encourages the model to keep relative distances from the RGB space in the transformed space

model.py

@@ -3,7 +3,12 @@ import torch
 import torch.nn as nn
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 
-from losses import calculate_separation_loss, preservation_loss  # noqa: F401
+from losses import (  # noqa: F401
+    calculate_separation_loss,
+    preservation_loss,
+    simple_preservation_loss,
+)
+from utils import PURE_RGB
 
 
 class ColorTransformerModel(L.LightningModule):
@@ -18,6 +23,7 @@ class ColorTransformerModel(L.LightningModule):
     ):
         super().__init__()
         self.save_hyperparameters()
+
         if self.hparams.transform.lower() == "tanh":
             t = nn.Tanh
         elif self.hparams.transform.lower() == "relu":
@@ -46,9 +52,12 @@ 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)
-        p_loss = preservation_loss(
+        rgb_tensor = PURE_RGB.to(self.device)
+        p_loss = simple_preservation_loss(
             inputs,
             outputs,
+            target_inputs=rgb_tensor,
+            target_outputs=self.forward(rgb_tensor),
         )
         alpha = self.hparams.alpha
         # loss = p_loss

newsearch.py

@@ -32,8 +32,8 @@ alpha_values = [0]
 # depths = [1, 2, 4, 8, 16]
 widths, depths = [512], [4]
 
-batch_size_values = [1024]
-max_epochs_values = [250]
+batch_size_values = [256]
+max_epochs_values = [100]
 seeds = list(range(21, 1992))
 optimizers = [
     # "Adagrad",
@@ -73,7 +73,7 @@ for idx, params in enumerate(search_params):
 python newmain.py fit \
 --seed_everything {s} \
 --data.batch_size {bs} \
---data.train_size 10000 \
+--data.train_size 0 \
 --data.val_size 10000 \
 --model.alpha {a} \
 --model.width {w} \