colors/model.py

import pytorch_lightning as pl
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import ReduceLROnPlateau

from losses import preservation_loss, smoothness_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),
            nn.ReLU(),
            nn.Linear(128, 128),
            nn.ReLU(),
            nn.Linear(128, 1),
        )

    def forward(self, x):
        x = self.layers(x)
        x = (torch.sin(x) + 1) / 2
        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)
        s_loss = smoothness_loss(outputs)
        p_loss = preservation_loss(
            inputs,
            outputs,
        )
        alpha = self.hparams.alpha
        loss = p_loss + alpha * s_loss
        self.log("hp_metric", p_loss)
        self.log("train_loss", loss)
        return loss

    def configure_optimizers(self):
        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
            },
        }