optimizations

losses.py

@@ -13,21 +13,45 @@ def weighted_loss(inputs, outputs, alpha):
     return torch.mean(loss)
 
 
-def enhanced_loss(inputs, outputs, alpha, distinct_threshold):
+# def enhanced_loss(inputs, outputs, alpha, distinct_threshold):
+#     # Calculate RGB Norm
+#     rgb_norm = torch.norm(inputs[:, None, :] - inputs[None, :, :], dim=-1)
+
+#     # Calculate 1D Space Norm
+#     transformed_norm = torch.norm(outputs[:, None] - outputs[None, :], dim=-1)
+
+#     # Identify Distinct Colors (based on a threshold in RGB space)
+#     distinct_colors = rgb_norm > distinct_threshold
+
+#     # Penalty for Distinct Colors being too close in the transformed space
+#     # Here we do not take the mean yet, to avoid double averaging
+#     distinct_penalty = (1.0 / (transformed_norm + 1e-6)) * distinct_colors
+
+#     # Combined Loss
+#     # The mean is taken here, once, after all components are combined
+#     loss = alpha * rgb_norm + (1 - alpha) * transformed_norm + distinct_penalty
+#     return torch.mean(loss)
+
+
+def enhanced_loss(inputs, outputs, alpha):
     # Calculate RGB Norm
     rgb_norm = torch.norm(inputs[:, None, :] - inputs[None, :, :], dim=-1)
 
     # Calculate 1D Space Norm
     transformed_norm = torch.norm(outputs[:, None] - outputs[None, :], dim=-1)
 
-    # Identify Distinct Colors (based on a threshold in RGB space)
-    distinct_colors = rgb_norm > distinct_threshold
+    # Distance Preservation Component
+    # Encourages the model to keep relative distances from the RGB space in the transformed space
+    distance_preservation_loss = torch.mean(torch.abs(rgb_norm - transformed_norm))
 
-    # Penalty for Distinct Colors being too close in the transformed space
-    # Here we do not take the mean yet, to avoid double averaging
-    distinct_penalty = (1.0 / (transformed_norm + 1e-6)) * distinct_colors
+    # Sort outputs for smoothness calculation
+    sorted_outputs, _ = torch.sort(outputs, dim=0)
+
+    # Calculate smoothness in the sorted outputs
+    first_derivative = torch.diff(sorted_outputs, n=1, dim=0)
+    second_derivative = torch.diff(first_derivative, n=1, dim=0)
+    smoothness_loss = torch.mean(torch.abs(second_derivative))
 
     # Combined Loss
-    # The mean is taken here, once, after all components are combined
-    loss = alpha * rgb_norm + (1 - alpha) * transformed_norm + distinct_penalty
-    return torch.mean(loss)
+    loss = alpha * distance_preservation_loss + (1 - alpha) * smoothness_loss
+    return loss

main.py

@@ -5,6 +5,7 @@ import pytorch_lightning as pl
 from dataloader import create_named_dataloader as init_data
 from model import ColorTransformerModel
 
+
 def parse_args():
     # Define argument parser
     parser = argparse.ArgumentParser(description="Color Transformer Training Script")
@@ -19,9 +20,7 @@ def parse_args():
     parser.add_argument(
         "-a", "--alpha", type=float, default=0.5, help="Alpha value for loss function"
     )
-    parser.add_argument(
-        "--lr", type=float, default=1e-5, help="Learning rate"
-    )
+    parser.add_argument("--lr", type=float, default=1e-5, help="Learning rate")
     parser.add_argument(
         "-e", "--max_epochs", type=int, default=1000, help="Number of epochs to train"
     )
@@ -29,22 +28,19 @@ def parse_args():
         "-L", "--log_every_n_steps", type=int, default=5, help="Logging frequency"
     )
     parser.add_argument(
-        "-w", "--num_workers", type=int, default=3, help="Number of workers for data loading"
-    )
-    parser.add_argument(
-        "-D",
-        "--distinct_threshold",
-        type=float,
-        default=0.5,
-        help="Threshold for color distinctness penalty",
+        "-w",
+        "--num_workers",
+        type=int,
+        default=3,
+        help="Number of workers for data loading",
     )
 
     # Parse arguments
     args = parser.parse_args()
     return args
 
-if __name__ == "__main__":
 
+if __name__ == "__main__":
     args = parse_args()
 
     # Initialize data loader with parsed arguments
@@ -57,7 +53,6 @@ if __name__ == "__main__":
     # Initialize model with parsed arguments
     model = ColorTransformerModel(
         alpha=args.alpha,
-        distinct_threshold=args.distinct_threshold,
         learning_rate=args.lr,
     )
 

makefile

@@ -4,4 +4,4 @@ lint:
 	flake8 --ignore E501 .
 
 test:
-	python main.py --alpha 0.7 --lr 1e-3 -D 0.5 --max_epochs 1000
+	python main.py --alpha 0.7 --lr 1e-3 --max_epochs 1000

model.py

@@ -1,10 +1,10 @@
 import pytorch_lightning as pl
 import torch
 import torch.nn as nn
+from torch.optim.lr_scheduler import ReduceLROnPlateau
 
 from losses import enhanced_loss, weighted_loss
 
-
 # class ColorTransformerModel(pl.LightningModule):
 #     def __init__(self, alpha, distinct_threshold, learning_rate):
 #         super().__init__()
@@ -22,8 +22,9 @@ from losses import enhanced_loss, weighted_loss
 #     def forward(self, x):
 #         return self.layers(x)
 
+
 class ColorTransformerModel(pl.LightningModule):
-    def __init__(self, alpha, distinct_threshold, learning_rate):
+    def __init__(self, alpha, learning_rate):
         super().__init__()
         self.save_hyperparameters()
 
@@ -34,12 +35,13 @@ class ColorTransformerModel(pl.LightningModule):
         transformer_layer = nn.TransformerEncoderLayer(
             d_model=128, nhead=4, dim_feedforward=512, dropout=0.1
         )
-        self.transformer_encoder = nn.TransformerEncoder(transformer_layer, num_layers=3)
+        self.transformer_encoder = nn.TransformerEncoder(
+            transformer_layer, num_layers=3
+        )
 
         # Final linear layer to map back to 1D space
         self.final_layer = nn.Linear(128, 1)
 
- 
     def forward(self, x):
         # Embedding the input
         x = self.embedding(x)
@@ -56,6 +58,9 @@ class ColorTransformerModel(pl.LightningModule):
         # Final linear layer
         x = self.final_layer(x)
 
+        # Apply sigmoid activation to ensure output is in (0, 1)
+        x = torch.sigmoid(x)
+
         return x
 
     def training_step(self, batch, batch_idx):
@@ -66,11 +71,17 @@ class ColorTransformerModel(pl.LightningModule):
             inputs,
             outputs,
             alpha=self.hparams.alpha,
-            distinct_threshold=self.hparams.distinct_threshold,
         )
         self.log("train_loss", loss)
         return loss
 
     def configure_optimizers(self):
-        optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-        return optimizer
+        optimizer = torch.optim.AdamW(self.parameters(), lr=self.hparams.learning_rate, weight_decay=1e-2)
+        lr_scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, verbose=True)
+        return {
+            'optimizer': optimizer,
+            'lr_scheduler': {
+                'scheduler': lr_scheduler,
+                'monitor': 'train_loss',  # Specify the metric to monitor
+            }
+        }

search.py

@@ -1,6 +1,7 @@
-from lightning_sdk import Studio, Machine
 from random import sample
 
+from lightning_sdk import Machine, Studio
+
 NUM_JOBS = 4
 
 # reference to the current studio
@@ -8,31 +9,32 @@ NUM_JOBS = 4
 studio = Studio()
 
 # use the jobs plugin
-studio.install_plugin('jobs')
-job_plugin = studio.installed_plugins['jobs']
+studio.install_plugin("jobs")
+job_plugin = studio.installed_plugins["jobs"]
 
 # do a sweep over learning rates
 
 # Define the ranges or sets of values for each hyperparameter
 alpha_values = [0.1, 0.3, 0.5, 0.7, 0.9]
-distinct_threshold_values = [0.5, 0.6, 0.7, 0.8]
-learning_rate_values = [1e-6, 1-e5, 1e-4, 1e-3, 1e-2]
+learning_rate_values = [1e-6, 1e-5, 1e-4, 1e-3, 1e-2]
 batch_size_values = [32, 64, 128]
 max_epochs_values = [10000]
 
 # Generate all possible combinations of hyperparameters
-all_params = [(alpha, dt, lr, bs, me) for alpha in alpha_values 
-                                         for dt in distinct_threshold_values 
-                                         for lr in learning_rate_values
-                                         for bs in batch_size_values
-                                         for me in max_epochs_values]
+all_params = [
+    (alpha, lr, bs, me)
+    for alpha in alpha_values
+    for lr in learning_rate_values
+    for bs in batch_size_values
+    for me in max_epochs_values
+]
 
 
 # perform random search with a limit
-random_search_params = sample(search_params, NUM_JOBS)
+search_params = sample(all_params, NUM_JOBS)
 
 # start all jobs on an A10G GPU with names containing an index
-for idx, (a, thresh, lr, bs, max_epochs) in enumerate(search_params):
-    cmd = f'python main.py --alpha {a} -D {thresh} --lr {lr} --bs {bs} --max_epochs {max_epochs}'
-    job_name = f'color-exp-{idx}'
+for idx, (a, lr, bs, me) in enumerate(search_params):
+    cmd = f"python main.py --alpha {a} --lr {lr} --bs {bs} --max_epochs {me}"
+    job_name = f"color-exp-{idx}"
     job_plugin.run(cmd, machine=Machine.T4, name=job_name)