# Copyright (c) Alibaba, Inc. and its affiliates.
import torch
import torch.nn as nn
from easycv.utils.registry import Registry, build_from_cfg
ACTIVATION_LAYERS = Registry('activation layer')
for module in [
nn.ReLU, nn.LeakyReLU, nn.PReLU, nn.RReLU, nn.ReLU6, nn.ELU,
nn.Sigmoid, nn.Tanh
]:
ACTIVATION_LAYERS.register_module(module)
[docs]@ACTIVATION_LAYERS.register_module()
class FReLU(nn.Module):
[docs] def __init__(self, in_channel):
super().__init__()
self.depthwise_conv_bn = nn.Sequential(
nn.Conv2d(
in_channel,
in_channel,
3,
padding=1,
groups=in_channel,
bias=False), nn.BatchNorm2d(in_channel))
[docs] def forward(self, x):
funnel_x = self.depthwise_conv_bn(x)
return torch.max(x, funnel_x)
[docs]def build_activation_layer(cfg):
"""Build activation layer.
Args:
cfg (dict): The activation layer config, which should contain:
- type (str): Layer type.
- layer args: Args needed to instantiate an activation layer.
Returns:
nn.Module: Created activation layer.
"""
return build_from_cfg(cfg, ACTIVATION_LAYERS)