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| import math | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| try: | |
| import torch.distributed.nn | |
| from torch import distributed as dist | |
| has_distributed = True | |
| except ImportError: | |
| has_distributed = False | |
| try: | |
| import horovod.torch as hvd | |
| except ImportError: | |
| hvd = None | |
| from timm.loss import LabelSmoothingCrossEntropy | |
| def gather_features( | |
| image_features, | |
| text_features, | |
| local_loss=False, | |
| gather_with_grad=False, | |
| rank=0, | |
| world_size=1, | |
| use_horovod=False | |
| ): | |
| assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.' | |
| if use_horovod: | |
| assert hvd is not None, 'Please install horovod' | |
| if gather_with_grad: | |
| all_image_features = hvd.allgather(image_features) | |
| all_text_features = hvd.allgather(text_features) | |
| else: | |
| with torch.no_grad(): | |
| all_image_features = hvd.allgather(image_features) | |
| all_text_features = hvd.allgather(text_features) | |
| if not local_loss: | |
| # ensure grads for local rank when all_* features don't have a gradient | |
| gathered_image_features = list(all_image_features.chunk(world_size, dim=0)) | |
| gathered_text_features = list(all_text_features.chunk(world_size, dim=0)) | |
| gathered_image_features[rank] = image_features | |
| gathered_text_features[rank] = text_features | |
| all_image_features = torch.cat(gathered_image_features, dim=0) | |
| all_text_features = torch.cat(gathered_text_features, dim=0) | |
| else: | |
| # We gather tensors from all gpus | |
| if gather_with_grad: | |
| all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0) | |
| all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) | |
| # all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features, async_op=True), dim=0) | |
| # all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features, async_op=True), dim=0) | |
| else: | |
| gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)] | |
| gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)] | |
| dist.all_gather(gathered_image_features, image_features) | |
| dist.all_gather(gathered_text_features, text_features) | |
| if not local_loss: | |
| # ensure grads for local rank when all_* features don't have a gradient | |
| gathered_image_features[rank] = image_features | |
| gathered_text_features[rank] = text_features | |
| all_image_features = torch.cat(gathered_image_features, dim=0) | |
| all_text_features = torch.cat(gathered_text_features, dim=0) | |
| return all_image_features, all_text_features | |
| class ClipLoss(nn.Module): | |
| def __init__( | |
| self, | |
| local_loss=False, | |
| gather_with_grad=False, | |
| cache_labels=False, | |
| rank=0, | |
| world_size=1, | |
| use_horovod=False, | |
| smoothing=0., | |
| ): | |
| super().__init__() | |
| self.local_loss = local_loss | |
| self.gather_with_grad = gather_with_grad | |
| self.cache_labels = cache_labels | |
| self.rank = rank | |
| self.world_size = world_size | |
| self.use_horovod = use_horovod | |
| self.label_smoothing_cross_entropy = LabelSmoothingCrossEntropy(smoothing=smoothing) if smoothing > 0 else None | |
| # cache state | |
| self.prev_num_logits = 0 | |
| self.labels = {} | |
| def forward(self, image_features, text_features, logit_scale=1.): | |
| device = image_features.device | |
| if self.world_size > 1: | |
| all_image_features, all_text_features = gather_features( | |
| image_features, text_features, | |
| self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) | |
| if self.local_loss: | |
| logits_per_image = logit_scale * image_features @ all_text_features.T | |
| logits_per_text = logit_scale * text_features @ all_image_features.T | |
| else: | |
| logits_per_image = logit_scale * all_image_features @ all_text_features.T | |
| logits_per_text = logits_per_image.T | |
| else: | |
| logits_per_image = logit_scale * image_features @ text_features.T | |
| logits_per_text = logit_scale * text_features @ image_features.T | |
| # calculated ground-truth and cache if enabled | |
| num_logits = logits_per_image.shape[0] | |
| 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] | |
| if self.label_smoothing_cross_entropy: | |
| total_loss = ( | |
| self.label_smoothing_cross_entropy(logits_per_image, labels) + | |
| self.label_smoothing_cross_entropy(logits_per_text, labels) | |
| ) / 2 | |
| else: | |
| total_loss = ( | |
| F.cross_entropy(logits_per_image, labels) + | |
| F.cross_entropy(logits_per_text, labels) | |
| ) / 2 | |
| acc = None | |
| i2t_acc = (logits_per_image.argmax(-1) == labels).sum() / len(logits_per_image) | |
| t2i_acc = (logits_per_text.argmax(-1) == labels).sum() / len(logits_per_text) | |
| acc = {"i2t": i2t_acc, "t2i": t2i_acc} | |
| return total_loss, acc |