from __future__ import print_function, division, absolute_import
from collections import OrderedDict
import math
import torch
import torch.nn as nn
from torch.utils import model_zoo

__all__ = ['SENet', 'SEResNeXtBottleneck', 'SEResNetBottleneck','SEBottleneck']

pretrained_settings = {
    'se_resnet152': {
        'imagenet': {
            'url': 'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth',
            'input_space': 'RGB',
            'input_size': [3, 224, 224],
            'input_range': [0, 1],
            'mean': [0.485, 0.456, 0.406],
            'std': [0.229, 0.224, 0.225],
            'num_classes': 1000
        }
    },
    'se_resnext101_32x4d': {
        'imagenet': {
            'url': 'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth',
            'input_space': 'RGB',
            'input_size': [3, 224, 224],
            'input_range': [0, 1],
            'mean': [0.485, 0.456, 0.406],
            'std': [0.229, 0.224, 0.225],
            'num_classes': 1000
        }
    },
}


class SEModule(nn.Module):

    def __init__(self, channels, reduction):
        super(SEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
                             padding=0)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1,
                             padding=0)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        module_input = x
        x = self.avg_pool(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        return module_input * x


class Bottleneck(nn.Module):
    """
    Base class for bottlenecks that implements `forward()` method.
    """
    def forward(self, x):
        residual = x

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

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

        out = self.conv3(out)
        out = self.bn3(out)

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

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

        return out


class SEBottleneck(Bottleneck):
    """
    Bottleneck for SENet154.
    """
    expansion = 4

    def __init__(self, inplanes, planes, groups, reduction, stride=1,
                 downsample=None):
        super(SEBottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes * 2)
        self.conv2 = nn.Conv2d(planes * 2, planes * 4, kernel_size=3,
                               stride=stride, padding=1, groups=groups,
                               bias=False)
        self.bn2 = nn.BatchNorm2d(planes * 4)
        self.conv3 = nn.Conv2d(planes * 4, planes * 4, kernel_size=1,
                               bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se_module = SEModule(planes * 4, reduction=reduction)
        self.downsample = downsample
        self.stride = stride


class SEResNetBottleneck(Bottleneck):
    """
    ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
    implementation and uses `stride=stride` in `conv1` and not in `conv2`
    (the latter is used in the torchvision implementation of ResNet).
    """
    expansion = 4

    def __init__(self, inplanes, planes, groups, reduction, stride=1,
                 downsample=None):
        super(SEResNetBottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False,
                               stride=stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1,
                               groups=groups, 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.se_module = SEModule(planes * 4, reduction=reduction)
        self.downsample = downsample
        self.stride = stride



class SEResNeXtBottleneck(Bottleneck):
    """
    ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
    """
    expansion = 4

    def __init__(self, inplanes, planes, groups, reduction, stride=1,
                 downsample=None, base_width=4.0):
        super(SEResNeXtBottleneck, self).__init__()
        width = int(math.floor(planes * (base_width / 64)) * groups)
        self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
        self.bn1 = nn.BatchNorm2d(width)
        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
        self.bn2 = nn.BatchNorm2d(width)
        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se_module = SEModule(planes * 4, reduction=reduction)
        self.downsample = downsample
        self.stride = stride


class SENet(nn.Module):

    def __init__(self, block, layers, groups, reduction, dropout_p=0.2,
                 inplanes=128, input_3x3=True, downsample_kernel_size=3,
                 downsample_padding=1, last_stride=2):
        """
        Parameters
        ----------
        block (nn.Module): Bottleneck class.
            - For SE-ResNet models: SEResNetBottleneck
            - For SE-ResNeXt models:  SEResNeXtBottleneck
        layers (list of ints): Number of residual blocks for 4 layers of the
            network (layer1...layer4).
        groups (int): Number of groups for the 3x3 convolution in each
            bottleneck block.
            - For SE-ResNet models: 1
            - For SE-ResNeXt models:  32
        reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
            - For all models: 16
        dropout_p (float or None): Drop probability for the Dropout layer.
            If `None` the Dropout layer is not used.
            - For SE-ResNet models: None
            - For SE-ResNeXt models: None
        inplanes (int):  Number of input channels for layer1.
            - For SE-ResNet models: 64
            - For SE-ResNeXt models: 64
        input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
            a single 7x7 convolution in layer0.
            - For SE-ResNet models: False
            - For SE-ResNeXt models: False
        downsample_kernel_size (int): Kernel size for downsampling convolutions
            in layer2, layer3 and layer4.
            - For SE-ResNet models: 1
            - For SE-ResNeXt models: 1
        downsample_padding (int): Padding for downsampling convolutions in
            layer2, layer3 and layer4.
            - For SE-ResNet models: 0
            - For SE-ResNeXt models: 0
        num_classes (int): Number of outputs in `last_linear` layer.
            - For all models: 1000
        """
        super(SENet, self).__init__()
        self.inplanes = inplanes
        if input_3x3:
            layer0_modules = [
                ('conv1', nn.Conv2d(3, 64, 3, stride=2, padding=1,
                                    bias=False)),
                ('bn1', nn.BatchNorm2d(64)),
                ('relu1', nn.ReLU(inplace=True)),
                ('conv2', nn.Conv2d(64, 64, 3, stride=1, padding=1,
                                    bias=False)),
                ('bn2', nn.BatchNorm2d(64)),
                ('relu2', nn.ReLU(inplace=True)),
                ('conv3', nn.Conv2d(64, inplanes, 3, stride=1, padding=1,
                                    bias=False)),
                ('bn3', nn.BatchNorm2d(inplanes)),
                ('relu3', nn.ReLU(inplace=True)),
            ]
        else:
            layer0_modules = [
                ('conv1', nn.Conv2d(3, inplanes, kernel_size=7, stride=2,
                                    padding=3, bias=False)),
                ('bn1', nn.BatchNorm2d(inplanes)),
                ('relu1', nn.ReLU(inplace=True)),
            ]
        # To preserve compatibility with Caffe weights `ceil_mode=True`
        # is used instead of `padding=1`.
        layer0_modules.append(('pool', nn.MaxPool2d(3, stride=2,
                                                    ceil_mode=True)))
        self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
        self.layer1 = self._make_layer(
            block,
            planes=64,
            blocks=layers[0],
            groups=groups,
            reduction=reduction,
            downsample_kernel_size=1,
            downsample_padding=0,
            #IN = True
        )
        self.layer2 = self._make_layer(
            block,
            planes=128,
            blocks=layers[1],
            stride=2,
            groups=groups,
            reduction=reduction,
            downsample_kernel_size=downsample_kernel_size,
            downsample_padding=downsample_padding,
            #IN = True

        )
        self.layer3 = self._make_layer(
            block,
            planes=256,
            blocks=layers[2],
            stride=2,
            groups=groups,
            reduction=reduction,
            downsample_kernel_size=downsample_kernel_size,
            downsample_padding=downsample_padding
        )
        self.layer4 = self._make_layer(
            block,
            planes=512,
            blocks=layers[3],
            stride=last_stride,
            groups=groups,
            reduction=reduction,
            downsample_kernel_size=downsample_kernel_size,
            downsample_padding=downsample_padding
        )
        self.avg_pool = nn.AvgPool2d(7, stride=1)
        self.dropout = nn.Dropout(dropout_p) if dropout_p is not None else None

    def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
                    downsample_kernel_size=1, downsample_padding=0, IN = False):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=downsample_kernel_size, stride=stride,
                          padding=downsample_padding, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)
    
    def load_param(self, model_path):
        param_dict = torch.load(model_path)
        for i in param_dict:
            if 'last_linear' in i:
                continue
            self.state_dict()[i].copy_(param_dict[i])

    def forward(self, x):
        x = self.layer0(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        return x





class SEResNeXtBottleneck(Bottleneck):
    """
    ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
    """
    expansion = 4

    def __init__(self, inplanes, planes, groups, reduction, stride=1,
                 downsample=None, base_width=4.0,):
        super(SEResNeXtBottleneck, self).__init__()
        width = int(math.floor(planes * (base_width / 64)) * groups)
        self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
        self.bn1 = nn.BatchNorm2d(width)
        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
        self.bn2 = nn.BatchNorm2d(width)
        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se_module = SEModule(planes * 4, reduction=reduction)
        self.downsample = downsample
        self.stride = stride



class Bottleneck_ibn(nn.Module):
    """
    Base class for bottlenecks that implements `forward()` method.
    """
    def forward(self, x):
        residual = x

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

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

        out = self.conv3(out)
        out = self.bn3(out)

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

        out = self.se_module(out) + residual
        if self.IN is not None:
            out = self.IN(out)
        out = self.relu(out)

        return out



class SEBottleneck(Bottleneck_ibn):
    expansion = 4

    def __init__(self, inplanes, planes, groups, reduction, stride=1,
                 downsample=None, IN = False):
        super(SEBottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False,
                               stride=stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1,
                               groups=groups, 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.se_module = SEModule(planes * 4, reduction=reduction)
        self.downsample = downsample
        self.stride = stride
        self.IN = None
        if IN:
            self.IN = nn.InstanceNorm2d(planes * 4, affine=True)
        



