common.py

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

class Hswish(nn.Module):
    def __init__(self, inplace=True):
        super(Hswish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return x * F.relu6(x + 3., inplace=self.inplace) / 6.

# out = max(0, min(1, slop*x+offset))
# paddle.fluid.layers.hard_sigmoid(x, slope=0.2, offset=0.5, name=None)
class Hsigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(Hsigmoid, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        # torch: F.relu6(x + 3., inplace=self.inplace) / 6.
        # paddle: F.relu6(1.2 * x + 3., inplace=self.inplace) / 6.
        return F.relu6(1.2 * x + 3., inplace=self.inplace) / 6.


class Activation(nn.Module):
    def __init__(self, act_type, inplace=True):
        super(Activation, self).__init__()
        act_type = act_type.lower()
        if act_type == 'relu':
            self.act = nn.ReLU(inplace=inplace)
        elif act_type == 'relu6':
            self.act = nn.ReLU6(inplace=inplace)
        elif act_type == 'sigmoid':
            raise NotImplementedError
        elif act_type == 'hard_sigmoid':
            self.act = Hsigmoid(inplace)
        elif act_type == 'hard_swish':
            self.act = Hswish(inplace=inplace)
        elif act_type == 'leakyrelu':
            self.act = nn.LeakyReLU(inplace=inplace)
        else:
            raise NotImplementedError

    def forward(self, inputs):
        return self.act(inputs)