# MIT License

# Copyright (c) 2017 liukuang

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# https://github.com/kuangliu/pytorch-cifar

'''ResNet in PyTorch.

For Pre-activation ResNet, see 'preact_resnet.py'.

Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F

from torch.autograd import Variable


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, negative_slope=0.01):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.negative_slope = negative_slope

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.leaky_relu(self.bn1(self.conv1(x)), negative_slope=self.negative_slope)
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.leaky_relu(out, negative_slope=self.negative_slope)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, input_size=32, num_classes=10, negative_slope=0.01):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1, negative_slope=negative_slope)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2, negative_slope=negative_slope)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2, negative_slope=negative_slope)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2, negative_slope=negative_slope)
        self.negative_slope = negative_slope

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        self.linear = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride, negative_slope):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride, negative_slope=negative_slope))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.leaky_relu(self.bn1(self.conv1(x)), negative_slope=self.negative_slope)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)

        out = out.view(out.size(0), -1)
        out = self.linear(out)

        return out


def ResNet18(input_size=32, num_classes=10, negative_slope=0.01):
    return ResNet(BasicBlock, [2, 2, 2, 2], input_size=input_size, num_classes=num_classes,
                  negative_slope=negative_slope)
