loss.py

import torch
import torch.nn.functional as F
import torch.nn as nn

class SatCLIPLoss(nn.Module):

    def __init__(
            self,
            local_loss=False,
            cache_labels=False,
            rank=0,
            world_size=1,
    ):
        super().__init__()
        self.local_loss = local_loss
        self.cache_labels = cache_labels
        self.rank = rank
        self.world_size = world_size

        # cache state
        self.prev_num_logits = 0
        self.labels = {}

    def get_ground_truth(self, device, num_logits) -> torch.Tensor:
        # calculated ground-truth and cache if enabled
        if self.prev_num_logits != num_logits or device not in self.labels:
            labels = torch.arange(num_logits, device=device, dtype=torch.long)
            if self.world_size > 1 and self.local_loss:
                labels = labels + num_logits * self.rank
            if self.cache_labels:
                self.labels[device] = labels
                self.prev_num_logits = num_logits
        else:
            labels = self.labels[device]
        return labels

    def forward(self, logits_per_image, logits_per_coord, output_dict=False):
        device = logits_per_image.device

        labels = self.get_ground_truth(device, logits_per_image.shape[0])

        total_loss = (
            F.cross_entropy(logits_per_image, labels) +
            F.cross_entropy(logits_per_coord, labels)
        ) / 2

        return {"contrastive_loss": total_loss} if output_dict else total_loss