Source code for fairseq.modules.fp32_instance_norm

# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
Layer norm done in fp32 (for fp16 training)

import torch.nn as nn
import torch.nn.functional as F

[docs]class Fp32InstanceNorm(nn.InstanceNorm1d): def __init__(self, *args, **kwargs): self.transpose_last = "transpose_last" in kwargs and kwargs["transpose_last"] if "transpose_last" in kwargs: del kwargs["transpose_last"] super().__init__(*args, **kwargs)
[docs] def forward(self, input): if self.transpose_last: input = input.transpose(1, 2) output = F.instance_norm( input.float(), running_mean=self.running_mean, running_var=self.running_var, weight=self.weight.float() if self.weight is not None else None, bias=self.bias.float() if self.bias is not None else None, or not self.track_running_stats, momentum=self.momentum, eps=self.eps, ) if self.transpose_last: output = output.transpose(1, 2) return output.type_as(input)