import math
import torch
import torch.nn as nn

__all__ = ['InceptionV4', 'inception_v4']


class BasicConv2d(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_planes,
                              out_planes,
                              kernel_size=kernel_size,
                              stride=stride,
                              padding=padding,
                              bias=False)  # verify bias false
        self.bn = nn.BatchNorm2d(
            out_planes,
            eps=0.001,  # value found in tensorflow
            momentum=0.1,  # default pytorch value
            affine=True)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class Mixed_3a(nn.Module):

    def __init__(self):
        super(Mixed_3a, self).__init__()
        self.maxpool = nn.MaxPool2d(3, stride=2)
        self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)

    def forward(self, x):
        x0 = self.maxpool(x)
        x1 = self.conv(x)
        out = torch.cat((x0, x1), 1)
        return out


class Mixed_4a(nn.Module):

    def __init__(self):
        super(Mixed_4a, self).__init__()

        self.branch0 = nn.Sequential(
            BasicConv2d(160, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1))

        self.branch1 = nn.Sequential(
            BasicConv2d(160, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
            BasicConv2d(64, 96, kernel_size=(3, 3), stride=1))

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), 1)
        return out


class Mixed_5a(nn.Module):

    def __init__(self):
        super(Mixed_5a, self).__init__()
        self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
        self.maxpool = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.conv(x)
        x1 = self.maxpool(x)
        out = torch.cat((x0, x1), 1)
        return out


class Inception_A(nn.Module):

    def __init__(self):
        super(Inception_A, self).__init__()
        self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(384, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1))

        self.branch2 = nn.Sequential(
            BasicConv2d(384, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1))

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1),
            BasicConv2d(384, 96, kernel_size=1, stride=1))

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class Reduction_A(nn.Module):

    def __init__(self):
        super(Reduction_A, self).__init__()
        self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)

        self.branch1 = nn.Sequential(
            BasicConv2d(384, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
            BasicConv2d(224, 256, kernel_size=3, stride=2))

        self.branch2 = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        return out


class Inception_B(nn.Module):

    def __init__(self):
        super(Inception_B, self).__init__()
        self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(1024, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 224, kernel_size=(1, 7), stride=1,
                        padding=(0, 3)),
            BasicConv2d(224, 256, kernel_size=(7, 1), stride=1,
                        padding=(3, 0)))

        self.branch2 = nn.Sequential(
            BasicConv2d(1024, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 192, kernel_size=(1, 7), stride=1,
                        padding=(0, 3)),
            BasicConv2d(192, 224, kernel_size=(7, 1), stride=1,
                        padding=(3, 0)),
            BasicConv2d(224, 224, kernel_size=(1, 7), stride=1,
                        padding=(0, 3)),
            BasicConv2d(224, 256, kernel_size=(7, 1), stride=1,
                        padding=(3, 0)))

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1),
            BasicConv2d(1024, 128, kernel_size=1, stride=1))

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class Reduction_B(nn.Module):

    def __init__(self):
        super(Reduction_B, self).__init__()

        self.branch0 = nn.Sequential(
            BasicConv2d(1024, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 192, kernel_size=3, stride=2))

        self.branch1 = nn.Sequential(
            BasicConv2d(1024, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 256, kernel_size=(1, 7), stride=1,
                        padding=(0, 3)),
            BasicConv2d(256, 320, kernel_size=(7, 1), stride=1,
                        padding=(3, 0)),
            BasicConv2d(320, 320, kernel_size=3, stride=2))

        self.branch2 = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        return out


class Inception_C(nn.Module):

    def __init__(self):
        super(Inception_C, self).__init__()

        self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)

        self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
        self.branch1_1a = BasicConv2d(384,
                                      256,
                                      kernel_size=(1, 3),
                                      stride=1,
                                      padding=(0, 1))
        self.branch1_1b = BasicConv2d(384,
                                      256,
                                      kernel_size=(3, 1),
                                      stride=1,
                                      padding=(1, 0))

        self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
        self.branch2_1 = BasicConv2d(384,
                                     448,
                                     kernel_size=(1, 3),
                                     stride=1,
                                     padding=(0, 1))
        self.branch2_2 = BasicConv2d(448,
                                     512,
                                     kernel_size=(3, 1),
                                     stride=1,
                                     padding=(1, 0))
        self.branch2_3a = BasicConv2d(512,
                                      256,
                                      kernel_size=(1, 3),
                                      stride=1,
                                      padding=(0, 1))
        self.branch2_3b = BasicConv2d(512,
                                      256,
                                      kernel_size=(3, 1),
                                      stride=1,
                                      padding=(1, 0))

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1),
            BasicConv2d(1536, 256, kernel_size=1, stride=1))

    def forward(self, x):
        x0 = self.branch0(x)

        x1_0 = self.branch1_0(x)
        x1_1a = self.branch1_1a(x1_0)
        x1_1b = self.branch1_1b(x1_0)
        x1 = torch.cat((x1_1a, x1_1b), 1)

        x2_0 = self.branch2_0(x)
        x2_1 = self.branch2_1(x2_0)
        x2_2 = self.branch2_2(x2_1)
        x2_3a = self.branch2_3a(x2_2)
        x2_3b = self.branch2_3b(x2_2)
        x2 = torch.cat((x2_3a, x2_3b), 1)

        x3 = self.branch3(x)

        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class InceptionV4(nn.Module):

    def __init__(self, num_classes=1000):
        super(InceptionV4, self).__init__()
        # Special attributs
        self.input_space = None
        self.input_size = (299, 299, 3)
        self.mean = None
        self.std = None
        # Modules
        self.features = nn.Sequential(
            BasicConv2d(3, 32, kernel_size=3, stride=2),
            BasicConv2d(32, 32, kernel_size=3, stride=1),
            BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
            Mixed_3a(),
            Mixed_4a(),
            Mixed_5a(),
            Inception_A(),
            Inception_A(),
            Inception_A(),
            Inception_A(),
            Reduction_A(),  # Mixed_6a
            Inception_B(),
            Inception_B(),
            Inception_B(),
            Inception_B(),
            Inception_B(),
            Inception_B(),
            Inception_B(),
            Reduction_B(),  # Mixed_7a
            Inception_C(),
            Inception_C(),
            Inception_C())
        self.avg_pool = nn.AvgPool2d(8)
        self.drop = nn.Dropout(p=0.2)
        self.last_linear = nn.Linear(1536, 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))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def logits(self, features):
        x = self.avg_pool(features)
        x = self.drop(x)
        x = x.view(x.size(0), -1)
        x = self.last_linear(x)
        return x

    def forward(self, input):
        x = self.features(input)
        x = self.logits(x)
        return x


def inception_v4(**kwargs):

    model = InceptionV4(**kwargs)
    return model