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# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out LICENSE.md
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from imaginaire.utils.distributed import get_world_size, get_rank, \
dist_all_reduce_tensor
class GatherLayer(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
output = [torch.zeros_like(input) for _ in range(dist.get_world_size())]
dist.all_gather(output, input)
return tuple(output)
@staticmethod
def backward(ctx, *grads):
input, = ctx.saved_tensors
grad_out = torch.zeros_like(input)
all_grads = torch.stack(grads)
all_grads = dist_all_reduce_tensor(all_grads, reduce='sum')
grad_out[:] = all_grads[get_rank()]
return grad_out
class InfoNCELoss(nn.Module):
def __init__(self,
temperature=0.07,
gather_distributed=True,
learn_temperature=True,
single_direction=False,
flatten=True):
super(InfoNCELoss, self).__init__()
self.logit_scale = nn.Parameter(torch.tensor([math.log(1/temperature)]))
self.logit_scale.requires_grad = learn_temperature
self.gather_distributed = gather_distributed
self.single_direction = single_direction
self.flatten = flatten
def forward(self, features_a, features_b, gather_distributed=None, eps=1e-8):
if gather_distributed is None:
gather_distributed = self.gather_distributed
if features_a is None or features_b is None:
return torch.tensor(0, device='cuda'), torch.tensor(0, device='cuda')
bs_a, bs_b = features_a.size(0), features_b.size(0)
if self.flatten:
features_a, features_b = features_a.reshape(bs_a, -1), features_b.reshape(bs_b, -1)
else:
features_a = features_a.reshape(bs_a, features_a.size(1), -1).mean(-1)
features_b = features_b.reshape(bs_b, features_b.size(1), -1).mean(-1)
# Temperature clipping.
self.logit_scale.data = torch.clamp(self.logit_scale.data, 0, 4.6052)
# normalized features
features_a = features_a / (features_a.norm(dim=1, keepdim=True) + eps)
features_b = features_b / (features_b.norm(dim=1, keepdim=True) + eps)
loss_a = self._forward_single_direction(features_a, features_b, gather_distributed)
if self.single_direction:
return loss_a
else:
loss_b = self._forward_single_direction(features_b, features_a, gather_distributed)
return loss_a + loss_b
def _forward_single_direction(
self, features_a, features_b, gather_distributed):
bs_a = features_a.shape[0]
logit_scale = self.logit_scale.exp()
if get_world_size() > 1 and gather_distributed:
gather_features_b = torch.cat(GatherLayer.apply(features_b))
gather_labels_a = torch.arange(bs_a, device='cuda') + get_rank() * bs_a
logits_a = logit_scale * features_a @ gather_features_b.t()
else:
gather_labels_a = torch.arange(bs_a, device='cuda')
logits_a = logit_scale * features_a @ features_b.t()
loss_a = F.cross_entropy(logits_a, gather_labels_a)
return loss_a
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