# Orignal source
# https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/inceptionv4.py

from __future__ import print_function, division, absolute_import

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo

__all__ = ["InceptionV4", "inceptionv4"]

pretrained_settings = {
    "inceptionv4": {
        "imagenet": {
            "url": "http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.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,
        },
        "imagenet+background": {
            "url": "http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.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": 1001,
        },
    }
}


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, count_include_pad=False),
            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=(7, 1), stride=1,
                        padding=(3, 0)),
            BasicConv2d(192, 224, kernel_size=(1, 7), stride=1,
                        padding=(0, 3)),
            BasicConv2d(224, 224, kernel_size=(7, 1), stride=1,
                        padding=(3, 0)),
            BasicConv2d(224, 256, kernel_size=(1, 7), stride=1,
                        padding=(0, 3)),
        )

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            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=(3, 1), stride=1, padding=(1, 0)
        )
        self.branch2_2 = BasicConv2d(
            448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1)
        )
        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, count_include_pad=False),
            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=1001):
        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),  # 0, layer0
            BasicConv2d(32, 32, kernel_size=3, stride=1),  # 1, layer0
            BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1), # 2
            Mixed_3a(),  # 3
            Mixed_4a(),  # 4
            Mixed_5a(),  # 5
            Inception_A(),  # 6
            Inception_A(),  # 7
            Inception_A(),  # 8
            Inception_A(),  # 9
            Reduction_A(),  # 10 # Mixed_6a
            Inception_B(),  # 11
            Inception_B(),  # 12
            Inception_B(),  # 13
            Inception_B(),  # 14
            Inception_B(),  # 15
            Inception_B(),  # 16
            Inception_B(),  # 17
            Reduction_B(),  # 18  # Mixed_7a
            Inception_C(),  # 19
            Inception_C(),  # 20
            Inception_C(),  # 21
        )
        self.last_linear = nn.Linear(1536, num_classes)

    def logits(self, features):
        # Allows image of any size to be processed
        adaptiveAvgPoolWidth = features.shape[2]
        x = F.avg_pool2d(features, kernel_size=adaptiveAvgPoolWidth)
        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 inceptionv4(num_classes=1000, pretrained="imagenet"):
    if pretrained:
        settings = pretrained_settings["inceptionv4"][pretrained]
        assert (num_classes == settings["num_classes"]), \
            "num_classes should be {}, but is {}".format(
                settings["num_classes"], num_classes
            )

        # both 'imagenet'&'imagenet+background' are loaded from same parameters
        model = InceptionV4(num_classes=1001)
        model.load_state_dict(model_zoo.load_url(settings["url"]))

        if pretrained == "imagenet":
            new_last_linear = nn.Linear(1536, 1000)
            new_last_linear.weight.data = model.last_linear.weight.data[1:]
            new_last_linear.bias.data = model.last_linear.bias.data[1:]
            model.last_linear = new_last_linear

        model.input_space = settings["input_space"]
        model.input_size = settings["input_size"]
        model.input_range = settings["input_range"]
        model.mean = settings["mean"]
        model.std = settings["std"]
    else:
        model = InceptionV4(num_classes=num_classes)
    return model
