from __future__ import absolute_import

'''Resnet for cifar dataset.
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, Wei
'''
import torch.nn as nn
import math


__all__ = ['resnet']

def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, stepsize=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

        self.stepsize = stepsize

    def forward(self, x):
        out = x

        residual = self.conv1(x)
        residual = self.bn1(residual)
        residual = self.relu(residual)

        residual = self.conv2(residual)
        residual = self.bn2(residual)

        residual *= self.stepsize

        if self.downsample is not None:
            out = self.downsample(x)

        # out += residual
        # out = self.relu(out)

        return self.relu(out + residual)


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        out = x

        residual = self.conv1(x)
        residual = self.bn1(residual)
        residual = self.relu(residual)

        residual = self.conv2(residual)
        residual = self.bn2(residual)
        residual = self.relu(residual)

        residual = self.conv3(residual)
        residual = self.bn3(residual)

        if self.downsample is not None:
            out = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    """
        todo: zero_init_residual trick, see https://pytorch.org/docs/stable/_modules/torchvision/models/resnet.html#resnet18
    """

    num_layers = 4

    def __init__(self, depth=None, num_classes=1000, block_name='BasicBlock', archs=None):
        super(ResNet, self).__init__()
        if not depth:
            assert archs, 'Architecture must be given for unknown depth!'

        assert block_name == 'BasicBlock', 'Block type other than BasicBlock not supported yet!'
        block = BasicBlock

        if depth:
            assert (depth - 2) % (self.num_layers * 2) == 0, 'When use basicblock, depth should be 8n+2. e.g. 18'
            num_blocks_per_layer = (depth - 2) // (self.num_layers * 2)

        # # growing blocks
        if not archs:
            archs = [[1.0 for _ in range(num_blocks_per_layer)] for _ in range(self.num_layers)]

        self.inplanes = 64 # 16
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, archs[0])
        self.layer2 = self._make_layer(block, 128, archs[1], stride=2)
        self.layer3 = self._make_layer(block, 256, archs[2], stride=2)
        self.layer4 = self._make_layer(block, 512, archs[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                # m.weight.data.normal_(0, math.sqrt(2. / n))
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                # m.weight.data.fill_(1)
                # m.bias.data.zero_()
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)


    def _make_layer(self, block, planes, arch, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, stepsize=arch[0]))
        self.inplanes = planes * block.expansion
        # for i in range(1, blocks):
        for stepsize in arch[1:]:
            # layers.append(block(self.inplanes, planes))
            layers.append(block(self.inplanes, planes, stepsize=stepsize))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)    # 32x32
        x = self.maxpool(x)

        x = self.layer1(x)  # 32x32
        x = self.layer2(x)  # 16x16
        x = self.layer3(x)  # 8x8
        x = self.layer4(x)  # 8x8

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def resnet(**kwargs):
    """
    Constructs a ResNet model.
    """
    return ResNet(**kwargs)
