import torch
import torch.nn as nn
from torch import Tensor


class Fire(nn.Module):

    def __init__(self, inplanes: int, squeeze_planes: int, expand1x1_planes: int, expand3x3_planes: int):
        super(Fire, self).__init__()
        self.inplanes = inplanes
        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
        self.squeeze_activation = nn.ReLU(inplace=True)
        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1)
        self.expand1x1_activation = nn.ReLU(inplace=True)
        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes, kernel_size=3, padding=1)
        self.expand3x3_activation = nn.ReLU(inplace=True)

    def forward(self, x: Tensor) -> Tensor:
        x = self.squeeze_activation(self.squeeze(x))
        return torch.cat([self.expand1x1_activation(self.expand1x1(x)),
                          self.expand3x3_activation(self.expand3x3(x))], 1)
