# coding=UTF-8
import torchvision.models as models
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo

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):
        super(BasicBlock, self).__init__()


        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)


        self.downsample = downsample
        self.stride = stride
    def forward(self, x):
        #print('1',x.size())
        residual = x
        out = self.bn1(x)
        print('2',out.size())
        out = self.relu(out)
        print('3',out.size())
        out = self.conv1(out)

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

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

        return out
class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=10):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # self.avgpool = nn.AvgPool2d(7, stride=1)
        # 新增层
        self.classifier = nn.Sequential(
            nn.Dropout(p=0.75),
            nn.Linear(in_features=512 *7*7, out_features=num_classes),
            nn.Softmax())

        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 _make_layer(self, block, planes, blocks, 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))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

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

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        # x = self.avgpool(x)
        # 新加层的forward
        x = x.view(x.size(0), -1)
        out = self.classifier(x)
        return out


def single_emd_loss(p, q, r=2):
    """
    Earth Mover's Distance of one sample

    Args:
        p: true distribution of shape num_classes × 1
        q: estimated distribution of shape num_classes × 1
        r: norm parameter
    """
    assert p.shape == q.shape, "Length of the two distribution must be the same"
    length = p.shape[0]
    emd_loss = 0.0
    for i in range(1, length + 1):
        emd_loss += sum(torch.abs(p[:i] - q[:i])) ** r
    return (emd_loss / length) ** (1. / r)


def emd_loss(p, q, r=2):
    """
    Earth Mover's Distance on a batch

    Args:
        p: true distribution of shape mini_batch_size × num_classes × 1
        q: estimated distribution of shape mini_batch_size × num_classes × 1
        r: norm parameters
    """
    assert p.shape == q.shape, "Shape of the two distribution batches must be the same."
    mini_batch_size = p.shape[0]
    loss_vector = []
    for i in range(mini_batch_size):
        loss_vector.append(single_emd_loss(p[i], q[i], r=r))
    return sum(loss_vector) / mini_batch_size

