d30f4d1dfd5237d89834363ce2cff9de4ee92811,torch/nn/modules/_functions.py,SyncBatchNorm,forward,#SyncBatchNorm#Any#Any#Any#Any#Any#Any#Any#Any#Any#,9

Before Change

        // world_size * (2C + 1) -> world_size * C, world_size * C, world_size * 1
        mean_all, invstd_all, count_all = torch.split(combined, num_channels, dim=1)

        size = count_all.view(-1).long().sum()
        if size == 1:
            raise ValueError("Expected more than 1 value per channel when training, got input size {}".format(size))

        // calculate global mean & invstd

After Change

            count_all.view(-1)
        )

        self.save_for_backward(input, weight, mean, invstd, count_all.to(torch.int32))
        self.process_group = process_group

        // apply element-wise normalization

Instances