total overhaul of model

dataloader.py

@@ -4,7 +4,7 @@ from torch.utils.data import DataLoader, TensorDataset
 from utils import extract_colors, preprocess_data
 
 
-def create_dataloader(N: int = 1e8, skip: bool = True, **kwargs):
+def create_random_dataloader(N: int = 1e8, skip: bool = True, **kwargs):
     rgb_tensor = torch.rand((int(N), 3), dtype=torch.float32)
     rgb_tensor = preprocess_data(rgb_tensor, skip=skip)
     # Creating a dataset and data loader
@@ -36,7 +36,9 @@ def create_named_dataloader(N: int = 0, skip: bool = True, **kwargs):
 
 if __name__ == "__main__":
     batch_size = 4
-    train_dataloader = create_dataloader(N=1e6, batch_size=batch_size, shuffle=True)
+    train_dataloader = create_random_dataloader(
+        N=1e6, batch_size=batch_size, shuffle=True
+    )
     print(len(train_dataloader.dataset))
     train_dataloader_with_names = create_named_dataloader(
         batch_size=batch_size, shuffle=True

datamodule.py

@@ -0,0 +1,77 @@
+import lightning as L
+import torch
+from matplotlib.colors import rgb_to_hsv
+from torch.utils.data import DataLoader
+
+from utils import extract_colors, preprocess_data
+
+
+class ColorDataModule(L.LightningDataModule):
+    def __init__(self, val_size: int = 10_000, train_size=0, batch_size: int = 32):
+        super().__init__()
+        self.val_size = val_size
+        self.batch_size = batch_size
+        self.train_size = train_size
+
+    def prepare_data(self):
+        # no state. called from main process.
+        pass
+
+    def setup(self, stage: str):
+        rgb_tensor, xkcd_color_names = extract_colors()
+        rgb_tensor = preprocess_data(rgb_tensor, skip=True)
+        self.xkcd_colors = [
+            (rgb_tensor[i], xkcd_color_names[i].replace("xkcd:", ""))
+            for i in range(len(rgb_tensor))
+        ]
+        if self.train_size > 0:
+            train_rgb = torch.rand((int(self.val_size), 3), dtype=torch.float32)
+            train_rgb = preprocess_data(train_rgb, skip=True)
+            self.train_colors = [
+                (
+                    train_rgb[i],
+                    torch.tensor(rgb_to_hsv(train_rgb[i])[:, 0], dtype=torch.float32),
+                )
+                for i in range(len(train_rgb))
+            ]
+
+        val_rgb = torch.rand((int(self.val_size), 3), dtype=torch.float32)
+        val_rgb = preprocess_data(val_rgb, skip=True)
+        self.random_colors = [
+            (
+                val_rgb[i],
+                torch.tensor(rgb_to_hsv(val_rgb[i])[:, 0], dtype=torch.float32),
+            )
+            for i in range(len(val_rgb))
+        ]
+
+        # Assign train/val datasets for use in dataloaders
+        if stage == "fit":
+            self.color_val = self.random_colors
+            if self.train_size > 0:
+                self.color_train = self.train_colors
+            else:
+                self.color_train = self.xkcd_colors
+
+        # Assign test dataset for use in dataloader(s)
+        if stage == "test":
+            self.color_test = self.random_colors
+
+        if stage == "predict":  # for visualizing
+            self.color_predict = self.xkcd_colors
+
+    def train_dataloader(self):
+        return DataLoader(self.color_train, batch_size=self.batch_size)
+
+    def val_dataloader(self):
+        return DataLoader(self.color_val, batch_size=self.batch_size)
+
+    def test_dataloader(self):
+        return DataLoader(self.color_test, batch_size=self.batch_size)
+
+    def predict_dataloader(self):
+        return DataLoader(self.color_predict, batch_size=self.batch_size)
+
+    def teardown(self, stage: str):
+        # Used to clean-up when the run is finished
+        pass

main.py

@@ -79,7 +79,7 @@ if __name__ == "__main__":
     # named_data_loader also has grayscale extras. TODO: remove unnamed
     train_dataloader = create_dataloader(
         # N=1e5,
-        skip=False,
+        skip=True,
         batch_size=args.bs,
         shuffle=True,
         num_workers=args.num_workers,
@@ -90,6 +90,9 @@ if __name__ == "__main__":
         learning_rate=args.lr,
         batch_size=args.bs,
         width=args.width,
+        bias=False,
+        transform="relu",
+        depth=1,
     )
 
     # Initialize model with parsed arguments

model.py

@@ -1,4 +1,4 @@
-import pytorch_lightning as pl
+import lightning as L
 import torch
 import torch.nn as nn
 from torch.optim.lr_scheduler import ReduceLROnPlateau
@@ -6,118 +6,36 @@ 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(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):
+class ColorTransformerModel(L.LightningModule):
+    def __init__(
+        self,
+        transform: str = "relu",
+        width: int = 128,
+        depth: int = 1,
+        bias: bool = False,
+    ):
         super().__init__()
-        self.save_hyperparameters(params)
-        # self.a = nn.Sequential(
-        #     nn.Linear(3, 3, bias=False),
-        #     nn.ReLU(),
-        #     nn.Linear(3, 3, bias=False),
-        #     nn.ReLU(),
-        #     nn.Linear(3, 1, bias=False),
-        #     nn.ReLU(),
-        # )
-        # self.b = nn.Sequential(
-        #     nn.Linear(3, 3, bias=False),
-        #     nn.ReLU(),
-        #     nn.Linear(3, 3, bias=False),
-        #     nn.ReLU(),
-        #     nn.Linear(3, 1, bias=False),
-        #     nn.ReLU(),
-        # )
-        # Neural network layers
+        self.save_hyperparameters()
+        if self.hparams.transform.lower() == "tanh":
+            t = nn.Tanh
+        elif self.hparams.transform.lower() == "relu":
+            t = nn.ReLU
+
+        w = self.hparams.width
+        d = self.hparams.depth
+        bias = self.hparams.bias
+        midlayers = [nn.Linear(w, w, bias=bias), t()] * d
         self.network = nn.Sequential(
-            nn.Linear(5, 64),
-            nn.Tanh(),
-            nn.Linear(64, self.hparams.width),
-            nn.Tanh(),
-            nn.Linear(self.hparams.width, 3),
-            nn.Tanh(),
-            nn.Linear(3, 1),
+            nn.Linear(3, w, bias=bias),
+            t(),
+            *midlayers,
+            nn.Linear(w, 3, bias=bias),
+            t(),
+            nn.Linear(3, 1, bias=bias),
         )
 
     def forward(self, x):
-        # Pass the input through the network
-        # a = self.a(x)
-        # b = self.b(x)
-        # a = torch.sigmoid(a)
-        # b = torch.sigmoid(b)
-        # x = torch.cat([x, a, b], dim=-1)
         x = self.network(x)
         # Circular mapping
         # x = (torch.sin(x) + 1) / 2

newmain.py

@@ -0,0 +1,28 @@
+from lightning.pytorch.cli import LightningCLI
+
+# from callbacks import SaveImageCallback
+from datamodule import ColorDataModule
+from model import ColorTransformerModel
+
+
+def cli_main():
+    cli = LightningCLI(ColorTransformerModel, ColorDataModule)  # noqa: F841
+    # note: don't call fit!!
+
+
+if __name__ == "__main__":
+    cli_main()
+    # note: it is good practice to implement the CLI in a function and call it in the main if block
+
+
+# save_img_callback = SaveImageCallback(
+#     save_interval=0,
+#     final_dir="out",
+# )
+
+# trainer = pl.Trainer(
+#     callbacks=[save_img_callback],
+# )
+
+# data = ColorDataModule()
+# trainer.fit(model, data)