"""
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Keras implementation. Achieves the following performance on the validation set:
Loss:0.9173 Prec@1:78.892 Prec@5:94.292
REMEMBER to set your image size to 3x299x299 for both test and validation
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
                                  std=[0.5, 0.5, 0.5])
The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.nn import init
import models.modified_linear as modified_linear

pretrained_settings = {
    'xception': {
        'imagenet': {
            'url': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-b5690688.pth',
            'input_space': 'RGB',
            'input_size': [3, 299, 299],
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1000,
            'scale': 0.8975 # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
        }
    }
}


class SeparableConv2d(nn.Module):
    def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False):
        super(SeparableConv2d,self).__init__()

        self.conv1 = nn.Conv2d(in_channels,in_channels,kernel_size,stride,padding,dilation,groups=in_channels,bias=bias)
        self.pointwise = nn.Conv2d(in_channels,out_channels,1,1,0,1,1,bias=bias)

    def forward(self,x):
        x = self.conv1(x)
        x = self.pointwise(x)
        return x


class Block(nn.Module):
    def __init__(self,in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True):
        super(Block, self).__init__()

        if out_filters != in_filters or strides!=1:
            self.skip = nn.Conv2d(in_filters,out_filters,1,stride=strides, bias=False)
            self.skipbn = nn.BatchNorm2d(out_filters)
        else:
            self.skip=None

        self.relu = nn.ReLU(inplace=True)
        rep=[]

        filters=in_filters
        if grow_first:
            rep.append(self.relu)
            rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False))
            rep.append(nn.BatchNorm2d(out_filters))
            filters = out_filters

        for i in range(reps-1):
            rep.append(self.relu)
            rep.append(SeparableConv2d(filters,filters,3,stride=1,padding=1,bias=False))
            rep.append(nn.BatchNorm2d(filters))

        if not grow_first:
            rep.append(self.relu)
            rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False))
            rep.append(nn.BatchNorm2d(out_filters))

        if not start_with_relu:
            rep = rep[1:]
        else:
            rep[0] = nn.ReLU(inplace=False)

        if strides != 1:
            rep.append(nn.MaxPool2d(3,strides,1))
        self.rep = nn.Sequential(*rep)

    def forward(self,inp):
        x = self.rep(inp)

        if self.skip is not None:
            skip = self.skip(inp)
            skip = self.skipbn(skip)
        else:
            skip = inp

        x+=skip
        return x


class Xception(nn.Module):
    """
    Xception optimized for the ImageNet dataset, as specified in
    https://arxiv.org/pdf/1610.02357.pdf
    """
    def __init__(self, num_classes=1000):
        """ Constructor
        Args:
            num_classes: number of classes
        """
        super(Xception, self).__init__()
        self.num_classes = num_classes

        self.conv1 = nn.Conv2d(3, 32, 3,2, 0, bias=False)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(32,64,3,bias=False)
        self.bn2 = nn.BatchNorm2d(64)
        #do relu here

        self.block1=Block(64,128,2,2,start_with_relu=False,grow_first=True)
        self.block2=Block(128,256,2,2,start_with_relu=True,grow_first=True)
        self.block3=Block(256,728,2,2,start_with_relu=True,grow_first=True)

        self.block4=Block(728,728,3,1,start_with_relu=True,grow_first=True)
        self.block5=Block(728,728,3,1,start_with_relu=True,grow_first=True)
        self.block6=Block(728,728,3,1,start_with_relu=True,grow_first=True)
        self.block7=Block(728,728,3,1,start_with_relu=True,grow_first=True)

        self.block8=Block(728,728,3,1,start_with_relu=True,grow_first=True)
        self.block9=Block(728,728,3,1,start_with_relu=True,grow_first=True)
        self.block10=Block(728,728,3,1,start_with_relu=True,grow_first=True)
        self.block11=Block(728,728,3,1,start_with_relu=True,grow_first=True)

        self.block12=Block(728,1024,2,2,start_with_relu=True,grow_first=False)

        self.conv3 = SeparableConv2d(1024,1536,3,1,1)
        self.bn3 = nn.BatchNorm2d(1536)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        #do relu here
        self.conv4 = SeparableConv2d(1536,2048,3,1,1)
        self.bn4 = nn.BatchNorm2d(2048)

        self.fc = modified_linear.CosineLinear(2048, num_classes)
        self._init_weight()

    def _init_weight(self):
        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))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

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

        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.block4(x)
        x = self.block5(x)
        x = self.block6(x)
        x = self.block7(x)
        x = self.block8(x)
        x = self.block9(x)
        x = self.block10(x)
        x = self.block11(x)
        x = self.block12(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)

        x = self.conv4(x)
        x = self.bn4(x)

        x = self.avgpool(self.relu(x))
        features = torch.flatten(x, 1)
        x = self.fc(features)
        return {'logits': x, 'features': features,}

def xception(num_classes=1000, pretrained=True):
    model = Xception(num_classes=num_classes)
    if pretrained:
        # import pdb;pdb.set_trace()
        model_dict = model.state_dict()
        pretrained_dict = torch.load("/home/yabin/workspace/GANIL/weights/xception-43020ad28.pth")
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if (k in model_dict and 'fc' not in k)}
        model_dict.update(pretrained_dict)
        model.load_state_dict(model_dict)
    return model