"""
Modified from https://github.com/pytorch/vision.git
"""
import math

import torch.nn as nn
import torch.nn.init as init

__all__ = [
    "VGG",
    "vgg11",
    "vgg11_bn",
    "vgg13",
    "vgg13_bn",
    "vgg16",
    "vgg16_bn",
    "vgg19_bn",
    "vgg19",
]


class VGG(nn.Module):
    """
    VGG model
    """

    def __init__(self, features):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(512, 512),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(512, 512),
            nn.ReLU(True),
            nn.Linear(512, 10),
        )
        # Initialize weights
        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(2.0 / n))
                m.bias.data.zero_()

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == "M":
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


cfg = {
    "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
    "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
    "D": [
        64,
        64,
        "M",
        128,
        128,
        "M",
        256,
        256,
        256,
        "M",
        512,
        512,
        512,
        "M",
        512,
        512,
        512,
        "M",
    ],
    "E": [
        64,
        64,
        "M",
        128,
        128,
        "M",
        256,
        256,
        256,
        256,
        "M",
        512,
        512,
        512,
        512,
        "M",
        512,
        512,
        512,
        512,
        "M",
    ],
}


def vgg11():
    """VGG 11-layer model (configuration "A")"""
    return VGG(make_layers(cfg["A"]))


def vgg11_bn():
    """VGG 11-layer model (configuration "A") with batch normalization"""
    return VGG(make_layers(cfg["A"], batch_norm=True))


def vgg13():
    """VGG 13-layer model (configuration "B")"""
    return VGG(make_layers(cfg["B"]))


def vgg13_bn():
    """VGG 13-layer model (configuration "B") with batch normalization"""
    return VGG(make_layers(cfg["B"], batch_norm=True))


def vgg16():
    """VGG 16-layer model (configuration "D")"""
    return VGG(make_layers(cfg["D"]))


def vgg16_bn():
    """VGG 16-layer model (configuration "D") with batch normalization"""
    return VGG(make_layers(cfg["D"], batch_norm=True))


def vgg19():
    """VGG 19-layer model (configuration "E")"""
    return VGG(make_layers(cfg["E"]))


def vgg19_bn():
    """VGG 19-layer model (configuration 'E') with batch normalization"""
    return VGG(make_layers(cfg["E"], batch_norm=True))
