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import torch
import torch.nn as nn
import torch.nn.functional as F
def is_dist_initialized():
return torch.distributed.is_initialized()
def get_world_size():
if is_dist_initialized():
return torch.distributed.get_world_size()
return 1
def all_gather_grad(x):
if get_world_size() > 1:
all_x = [torch.zeros_like(x) for _ in range(get_world_size())]
torch.distributed.all_gather(all_x, x)
all_x[torch.distributed.get_rank()] = x
x = torch.cat(all_x, dim=0)
return x
@torch.no_grad()
def all_gather_nograd(tensor):
# from albef
"""
Performs all_gather operation on the provided tensors.
*** Warning ***: torch.distributed.all_gather has no gradient.
"""
if get_world_size() > 1:
tensors_gather = [torch.ones_like(tensor)
for _ in range(torch.distributed.get_world_size())]
torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
tensor = torch.cat(tensors_gather, dim=0)
return tensor
def image_text_contrastive_loss(image_feat, text_feat, temperature, image_id=None, text_id=None):
# add the following 4 lines
image_feat = all_gather_grad(image_feat)
text_feat = all_gather_grad(text_feat)
logits = torch.matmul(image_feat, text_feat.t())
logits /= temperature
if image_id is None and text_id is None:
gt = torch.arange(logits.shape[0], device=logits.device)
loss1 = F.cross_entropy(logits, gt)
loss2 = F.cross_entropy(logits.t(), gt)
else:
image_id = all_gather_grad(image_id)
text_id = all_gather_grad(text_id)
gt_image = image_id.reshape((-1, 1)) == image_id.reshape((1, -1))
gt_text = text_id.reshape((-1, 1)) == text_id.reshape((1, -1))
gt = torch.logical_or(gt_image, gt_text)
loss1 = -torch.sum(gt * F.log_softmax(logits, dim=1)) / gt.sum()
loss2 = -torch.sum(gt.t() * F.log_softmax(logits.t(), dim=1)) / gt.sum()
return (loss1 + loss2) / 2 * get_world_size() # scale it up by the number of GPUs
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