# BSD 3-Clause License

# Copyright (c) Soumith Chintala 2016,
# All rights reserved.

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# Adaptation of the PyTorch torchvision MobileNetV2 without a classifier.
# See source here: https://pytorch.org/vision/0.8/_modules/torchvision/models/mobilenet.html#mobilenet_v2
from torch import nn


def _make_divisible(v, divisor, min_value=None):
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    :param v:
    :param divisor:
    :param min_value:
    :return:
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class ConvBNReLU(nn.Sequential):
    def __init__(
        self, in_planes, out_planes, kernel_size=3, stride=1, groups=1, norm_layer=None
    ):
        padding = (kernel_size - 1) // 2
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        super(ConvBNReLU, self).__init__(
            nn.Conv2d(
                in_planes,
                out_planes,
                kernel_size,
                stride,
                padding,
                groups=groups,
                bias=False,
            ),
            norm_layer(out_planes),
            nn.ReLU6(inplace=True),
        )


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio, norm_layer=None):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

        if norm_layer is None:
            norm_layer = nn.BatchNorm2d

        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = self.stride == 1 and inp == oup

        layers = []
        if expand_ratio != 1:
            # pw
            layers.append(
                ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer)
            )
        layers.extend(
            [
                # dw
                ConvBNReLU(
                    hidden_dim,
                    hidden_dim,
                    stride=stride,
                    groups=hidden_dim,
                    norm_layer=norm_layer,
                ),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                norm_layer(oup),
            ]
        )
        self.conv = nn.Sequential(*layers)

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MobileNetV2(nn.Module):
    def __init__(
        self,
        embed_dim=1028,
        width_mult=1.0,
        inverted_residual_setting=None,
        round_nearest=8,
        block=None,
        norm_layer=None,
    ):
        """
        MobileNet V2 main class

        Args:
            embed_dim (int): Number of channels in the final output.
            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
            inverted_residual_setting: Network structure
            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
            Set to 1 to turn off rounding
            block: Module specifying inverted residual building block for mobilenet
            norm_layer: Module specifying the normalization layer to use

        """
        super(MobileNetV2, self).__init__()

        if block is None:
            block = InvertedResidual

        if norm_layer is None:
            norm_layer = nn.BatchNorm2d

        input_channel = 32
        last_channel = embed_dim / width_mult

        if inverted_residual_setting is None:
            inverted_residual_setting = [
                # t, c, n, s
                [1, 16, 1, 1],
                [6, 24, 2, 2],
                [6, 32, 3, 2],
                [6, 64, 4, 2],
                [6, 96, 3, 1],
                [6, 160, 3, 2],
                [6, 320, 1, 1],
            ]

        # only check the first element, assuming user knows t,c,n,s are required
        if (
            len(inverted_residual_setting) == 0
            or len(inverted_residual_setting[0]) != 4
        ):
            raise ValueError(
                "inverted_residual_setting should be non-empty "
                "or a 4-element list, got {}".format(inverted_residual_setting)
            )

        # building first layer
        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
        self.last_channel = _make_divisible(
            last_channel * max(1.0, width_mult), round_nearest
        )
        features = [ConvBNReLU(3, input_channel, stride=2, norm_layer=norm_layer)]
        # building inverted residual blocks
        for t, c, n, s in inverted_residual_setting:
            output_channel = _make_divisible(c * width_mult, round_nearest)
            for i in range(n):
                stride = s if i == 0 else 1
                features.append(
                    block(
                        input_channel,
                        output_channel,
                        stride,
                        expand_ratio=t,
                        norm_layer=norm_layer,
                    )
                )
                input_channel = output_channel
        # building last several layers
        features.append(
            ConvBNReLU(
                input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer
            )
        )
        # make it nn.Sequential
        self.features = nn.Sequential(*features)

        # weight initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out")
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)

    def _forward_impl(self, x):
        # This exists since TorchScript doesn't support inheritance, so the superclass method
        # (this one) needs to have a name other than `forward` that can be accessed in a subclass
        return self.features(x)
        # return the features directly, no classifier or pooling

    def forward(self, x):
        return self._forward_impl(x)
