mmcv.cnn.utils.fuse_conv_bn 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn

def _fuse_conv_bn(conv, bn):
    """Fuse conv and bn into one module.

        conv (nn.Module): Conv to be fused.
        bn (nn.Module): BN to be fused.

        nn.Module: Fused module.
    conv_w = conv.weight
    conv_b = conv.bias if conv.bias is not None else torch.zeros_like(

    factor = bn.weight / torch.sqrt(bn.running_var + bn.eps)
    conv.weight = nn.Parameter(conv_w *
                               factor.reshape([conv.out_channels, 1, 1, 1]))
    conv.bias = nn.Parameter((conv_b - bn.running_mean) * factor + bn.bias)
    return conv

[文档]def fuse_conv_bn(module): """Recursively fuse conv and bn in a module. During inference, the functionary of batch norm layers is turned off but only the mean and var alone channels are used, which exposes the chance to fuse it with the preceding conv layers to save computations and simplify network structures. Args: module (nn.Module): Module to be fused. Returns: nn.Module: Fused module. """ last_conv = None last_conv_name = None for name, child in module.named_children(): if isinstance(child, (nn.modules.batchnorm._BatchNorm, nn.SyncBatchNorm)): if last_conv is None: # only fuse BN that is after Conv continue fused_conv = _fuse_conv_bn(last_conv, child) module._modules[last_conv_name] = fused_conv # To reduce changes, set BN as Identity instead of deleting it. module._modules[name] = nn.Identity() last_conv = None elif isinstance(child, nn.Conv2d): last_conv = child last_conv_name = name else: fuse_conv_bn(child) return module