| 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 |
|
|
| |
| self.prev_num_logits = 0 |
| self.labels = {} |
|
|
| def get_ground_truth(self, device, num_logits) -> torch.Tensor: |
| |
| 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 |
|
|
