diff arch, optimizer

check.py

@@ -86,7 +86,11 @@ if __name__ == "__main__":
         name = f"out/v{v}"
         # ckpt = f"/teamspace/jobs/{name}/work/colors/lightning_logs/version_2/checkpoints/epoch=999-step=8000.ckpt"
         ckpt_path = f"/teamspace/studios/this_studio/colors/lightning_logs/version_{v}/checkpoints/*.ckpt"
-        ckpt = glob.glob(ckpt_path)[-1]
-        print(f"Generating image for checkpoint: {ckpt}")
-        create_circle(ckpt, fname=name)
+        ckpt = glob.glob(ckpt_path)
+        if len(ckpt) > 0:
+            ckpt = ckpt[-1]
+            print(f"Generating image for checkpoint: {ckpt}")
+            create_circle(ckpt, fname=name)
+        else:
+            print(f"No checkpoint found for version {v}")
         # make_image(ckpt, fname=name + "b", color=False)

makefile

@@ -4,7 +4,7 @@ lint:
 	flake8 --ignore E501,W503 .
 
 test:
-	python main.py --alpha 2 --lr 2e-4 --max_epochs 200
+	python main.py --alpha 4 --lr 2e-4 --max_epochs 200
 
 search:
 	python search.py

model.py

@@ -5,70 +5,106 @@ from torch.optim.lr_scheduler import ReduceLROnPlateau
 
 from losses import calculate_separation_loss, preservation_loss
 
+# class ColorTransformerModel(pl.LightningModule):
+# def __init__(self, params):
+#     super().__init__()
+#     self.save_hyperparameters(params)
+
+#     # Model layers
+#     self.layers = nn.Sequential(
+#         nn.Linear(5, 128, bias=False),
+#         nn.Linear(128, 3, bias=False),
+#         nn.ReLU(),
+#         nn.Linear(3, 64, bias=False),
+#         nn.Linear(64, 128, bias=False),
+#         nn.Linear(128, 256, bias=False),
+#         nn.Linear(256, 128, bias=False),
+#         nn.ReLU(),
+#         nn.Linear(128, 1, bias=False),
+#     )
+
+# def forward(self, x):
+#     x = self.layers(x)
+#     x = (torch.sin(x) + 1) / 2
+#     return x
+
+# class ColorTransformerModel(pl.LightningModule):
+#     def __init__(self, params):
+#         super().__init__()
+#         self.save_hyperparameters(params)
+
+#         # Embedding layer to expand the input dimensions
+#         self.embedding = nn.Linear(3, 128, bias=False)
+
+#         # Transformer encoder-decoder
+#         encoder = nn.TransformerEncoderLayer(
+#             d_model=128, nhead=4, dim_feedforward=512, dropout=0.3
+#         )
+#         self.transformer_encoder = nn.TransformerEncoder(
+#             encoder, num_layers=3
+#         )
+#         # lower dimensionality decoder
+#         decoder = nn.TransformerDecoderLayer(
+#             d_model=128, nhead=4, dim_feedforward=512, dropout=0.3
+#         )
+#         self.transformer_decoder = nn.TransformerDecoder(
+#             decoder, num_layers=3
+#         )
+
+#         # Final linear layer to map back to 1D space
+#         self.final_layer = nn.Linear(128, 1, bias=False)
+
+#     def forward(self, x):
+#         # Embedding the input
+#         x = self.embedding(x)
+
+#         # Adjusting the shape for the transformer
+#         x = x.unsqueeze(1)  # Adding a fake sequence dimension
+
+#         # Passing through the transformer
+#         x = self.transformer_encoder(x)
+
+#         # Passing through the decoder
+#         x = self.transformer_decoder(x, memory=x)
+
+#         # Reshape back to original shape
+#         x = x.squeeze(1)
+
+#         # Final linear layer
+#         x = self.final_layer(x)
+
+#         # Apply sigmoid activation to ensure output is in (0, 1)
+#         # x = torch.sigmoid(x)
+#         x = (torch.sin(x) + 1) / 2
+#         return x
+
 
 class ColorTransformerModel(pl.LightningModule):
     def __init__(self, params):
         super().__init__()
         self.save_hyperparameters(params)
 
-        # Model layers
-        self.layers = nn.Sequential(
-            nn.Linear(5, 128, bias=False),
-            nn.Linear(128, 3, bias=False),
-            nn.ReLU(),
-            nn.Linear(3, 64, bias=False),
-            nn.Linear(64, 128, bias=False),
-            nn.Linear(128, 256, bias=False),
-            nn.Linear(256, 128, bias=False),
-            nn.ReLU(),
-            nn.Linear(128, 1, bias=False),
+        # Neural network layers
+        self.network = nn.Sequential(
+            nn.Linear(5, 64),  # Input layer
+            nn.Tanh(),
+            nn.Linear(64, 128),
+            nn.Tanh(),
+            nn.Linear(128, 128),
+            nn.Tanh(),
+            nn.Linear(128, 64),
+            nn.Tanh(),
+            nn.Linear(64, 1),  # Output layer
         )
 
     def forward(self, x):
-        x = self.layers(x)
-        x = (torch.sin(x) + 1) / 2
+        # Pass the input through the network
+        x = self.network(x)
+        # Circular mapping
+        # x = (torch.sin(x) + 1) / 2
+        x = torch.sigmoid(x)
         return x
 
-    # class ColorTransformerModel(pl.LightningModule):
-    #     def __init__(self, alpha, learning_rate):
-    #         super().__init__()
-    #         self.save_hyperparameters()
-
-    #         # Embedding layer to expand the input dimensions
-    #         self.embedding = nn.Linear(3, 128)
-
-    #         # Transformer block
-    #         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
-    #         )
-
-    #         # 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)
-
-    #         # Adjusting the shape for the transformer
-    #         x = x.unsqueeze(1)  # Adding a fake sequence dimension
-
-    #         # Passing through the transformer
-    #         x = self.transformer_encoder(x)
-
-    #         # Reshape back to original shape
-    #         x = x.squeeze(1)
-
-    #         # 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):
         inputs, labels = batch  # x are the RGB inputs, labels are the strings
         outputs = self.forward(inputs)
@@ -84,11 +120,12 @@ class ColorTransformerModel(pl.LightningModule):
         return loss
 
     def configure_optimizers(self):
-        optimizer = torch.optim.AdamW(
-            self.parameters(), lr=self.hparams.learning_rate,
+        optimizer = torch.optim.SGD(
+            self.parameters(),
+            lr=self.hparams.learning_rate,
         )
         lr_scheduler = ReduceLROnPlateau(
-            optimizer, mode="min", factor=0.1, patience=10, verbose=True
+            optimizer, mode="min", factor=0.05, patience=10, cooldown=20, verbose=True
         )
         return {
             "optimizer": optimizer,

utils.py

@@ -1,21 +1,22 @@
 import torch
 
 
-def preprocess_data(data):
+def preprocess_data(data, skip=False):
     # Assuming 'data' is a tensor of shape [n_samples, 3]
-
-    # Compute argmin and argmax for each row
-    argmin_values = torch.argmin(data, dim=1, keepdim=True).float()
-    argmax_values = torch.argmax(data, dim=1, keepdim=True).float()
-
-    # Normalize or scale argmin and argmax if necessary
-    # For example, here I am just dividing by the number of features
-    argmin_values /= data.shape[1] - 1
-    argmax_values /= data.shape[1] - 1
-
-    # Concatenate the argmin and argmax values to the original data
-    new_data = torch.cat((data, argmin_values, argmax_values), dim=1)
-
+    if not skip:
+        # Compute argmin and argmax for each row
+        argmin_values = torch.argmin(data, dim=1, keepdim=True).float()
+        argmax_values = torch.argmax(data, dim=1, keepdim=True).float()
+
+        # Normalize or scale argmin and argmax if necessary
+        # For example, here I am just dividing by the number of features
+        argmin_values /= data.shape[1] - 1
+        argmax_values /= data.shape[1] - 1
+
+        # Concatenate the argmin and argmax values to the original data
+        new_data = torch.cat((data, argmin_values, argmax_values), dim=1)
+    else:
+        new_data = data
     return new_data