transformers - 💡(How to fix) Fix Bring Loss classes back to modeling file and leverage modular [1 comments, 2 participants]

Problem Summary

Move custom losses from the loss folder to the modeling file.

Root Cause Analysis

The issue is that custom losses are scattered across different files, making it difficult to understand how they are computed.

Fix Plan

Step 1: Identify Custom Losses

• Check the LOSS_MAPPING dictionary in loss_utils.py to identify custom losses.
• For example:

LOSS_MAPPING = {
    "LwDetr": "transformers.loss.lw_detr_loss.LwDetrLoss",
    "RfDetr": "transformers.loss.rf_detr_loss.RfDetrLoss",
}

Step 2: Move Custom Losses to Modeling File

• Create a new file for each custom loss in the modeling file (e.g., model.py).
• For example, create a new file lw_detr_loss.py with the following content:

class LwDetrLoss:
    def __init__(self, args):
        # Initialize loss components
        self.loss_components = {}

    def forward(self, inputs):
        # Compute loss
        loss = 0
        for component in self.loss_components.values():
            loss += component(inputs)
        return loss

Step 3: Update LOSS_MAPPING Dictionary

• Update the LOSS_MAPPING dictionary to point to the new custom loss files.
• For example:

LOSS_MAPPING = {
    "LwDetr": "model.lw_detr_loss.LwDetrLoss",
    "RfDetr": "model.rf_detr_loss.RfDetrLoss",
}

Step 4: Refactor Modular Code

• Refactor the modular code to leverage the new custom loss files.
• For example:

class Model:
    def __init__(self, args):
        # Initialize custom losses
        self.losses = {
            "L