# https://github.com/tonylins/pytorch-mobilenet-v2
import torch.nn as nn
import math
import numpy as np
import torch.utils.model_zoo as model_zoo

BN_MOMENTUM = 0.1


def conv_bn(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU6(inplace=True)
    )


def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU6(inplace=True)
    )


def make_divisible(x, divisible_by=8):
    return int(np.ceil(x * 1. / divisible_by) * divisible_by)


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

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

        if expand_ratio == 1:
            self.conv = nn.Sequential(
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                # pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )

    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, heads, head_conv, input_size=224, width_mult=1.):
        super(MobileNetV2, self).__init__()

        self.heads = heads
        self.deconv_with_bias = False

        block = InvertedResidual
        input_channel = 32
        last_channel = 1280
        interverted_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],
        ]

        # building first layer
        assert input_size % 32 == 0
        # input_channel = make_divisible(input_channel * width_mult)  # first channel is always 32!
        self.last_channel = make_divisible(last_channel * width_mult) if width_mult > 1.0 else last_channel
        self.inplanes = last_channel
        self.features = [conv_bn(3, input_channel, 2)]
        # building inverted residual blocks
        for t, c, n, s in interverted_residual_setting:
            output_channel = make_divisible(c * width_mult) if t > 1 else c
            for i in range(n):
                if i == 0:
                    self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
                else:
                    self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
                input_channel = output_channel
        # building last several layers
        self.features.append(conv_1x1_bn(input_channel, self.last_channel))
        # make it nn.Sequential
        self.features = nn.Sequential(*self.features)

        # building classifier
        # self.classifier = nn.Linear(self.last_channel, n_class)
        # used for deconv layers
        self.deconv_layers = self._make_deconv_layer(
            3,
            [256, 256, 256],
            [4, 4, 4],
        )
        # self.final_layer = []
        for head in sorted(self.heads):
            num_output = self.heads[head]
            if head_conv > 0:
                fc = nn.Sequential(
                    nn.Conv2d(256, head_conv,
                              kernel_size=3, padding=1, bias=True),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(head_conv, num_output,
                              kernel_size=1, stride=1, padding=0))
            else:
                fc = nn.Conv2d(
                    in_channels=256,
                    out_channels=num_output,
                    kernel_size=1,
                    stride=1,
                    padding=0
                )
            self.__setattr__(head, fc)
        # self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x = self.deconv_layers(x)
        ret = {}
        for head in self.heads:
            ret[head] = self.__getattr__(head)(x)
        return [ret]

    def _get_deconv_cfg(self, deconv_kernel, index):
        if deconv_kernel == 4:
            padding = 1
            output_padding = 0
        elif deconv_kernel == 3:
            padding = 1
            output_padding = 1
        elif deconv_kernel == 2:
            padding = 0
            output_padding = 0

        return deconv_kernel, padding, output_padding

    def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
        assert num_layers == len(num_filters), \
            'ERROR: num_deconv_layers is different len(num_deconv_filters)'
        assert num_layers == len(num_kernels), \
            'ERROR: num_deconv_layers is different len(num_deconv_filters)'

        layers = []
        for i in range(num_layers):
            kernel, padding, output_padding = \
                self._get_deconv_cfg(num_kernels[i], i)

            planes = num_filters[i]
            layers.append(
                nn.ConvTranspose2d(
                    in_channels=self.inplanes,
                    out_channels=planes,
                    kernel_size=kernel,
                    stride=2,
                    padding=padding,
                    output_padding=output_padding,
                    bias=self.deconv_with_bias))
            layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
            layers.append(nn.ReLU(inplace=True))
            self.inplanes = planes

        return nn.Sequential(*layers)

    def initialize_weights(self, pretrained):
        for _, m in self.deconv_layers.named_modules():
            if isinstance(m, nn.ConvTranspose2d):
                nn.init.normal_(m.weight, std=0.001)
                if self.deconv_with_bias:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        for head in self.heads:
            final_layer = self.__getattr__(head)
            for i, m in enumerate(final_layer.modules()):
                if isinstance(m, nn.Conv2d):
                    if m.weight.shape[0] == self.heads[head]:
                        if 'hm' in head:
                            nn.init.constant_(m.bias, -2.19)
                        else:
                            nn.init.normal_(m.weight, std=0.001)
                            nn.init.constant_(m.bias, 0)
        if pretrained:
            url = 'https://www.dropbox.com/s/47tyzpofuuyyv1b/mobilenetv2_1.0-f2a8633.pth.tar?dl=1'
            pretrained_state_dict = model_zoo.load_url(url)
            print('=> loading pretrained model {}'.format(url))
            self.load_state_dict(pretrained_state_dict, strict=False)
        else:
            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))
                    if m.bias is not None:
                        m.bias.data.zero_()
                elif isinstance(m, nn.BatchNorm2d):
                    m.weight.data.fill_(1)
                    m.bias.data.zero_()
                elif isinstance(m, nn.Linear):
                    n = m.weight.size(1)
                    m.weight.data.normal_(0, 0.01)
                    m.bias.data.zero_()


# def mobilenet_v2(pretrained=True):
#     model = MobileNetV2(width_mult=1)
#
#     if pretrained:
#         try:
#             from torch.hub import load_state_dict_from_url
#         except ImportError:
#             from torch.utils.model_zoo import load_url as load_state_dict_from_url
#         state_dict = load_state_dict_from_url(
#             'https://www.dropbox.com/s/47tyzpofuuyyv1b/mobilenetv2_1.0-f2a8633.pth.tar?dl=1', progress=True)
#         model.load_state_dict(state_dict)
#     return model


def get_mobilenet_v2_pose_net(num_layers, heads, head_conv):
    model = MobileNetV2(heads, head_conv=head_conv, input_size=384, width_mult=1)
    model.initialize_weights(pretrained=True)
    return model


# if __name__ == '__main__':
#     net = mobilenet_v2(True)

# https://github.com/tonylins/pytorch-mobilenet-v2
import torch.nn as nn
import math
import numpy as np
import torch.utils.model_zoo as model_zoo

BN_MOMENTUM = 0.1


def conv_bn(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU6(inplace=True)
    )


def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU6(inplace=True)
    )


def make_divisible(x, divisible_by=8):
    return int(np.ceil(x * 1. / divisible_by) * divisible_by)


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

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

        if expand_ratio == 1:
            self.conv = nn.Sequential(
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                # pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )

    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, heads, head_conv, input_size=224, width_mult=1.):
        super(MobileNetV2, self).__init__()

        self.heads = heads
        self.deconv_with_bias = False

        block = InvertedResidual
        input_channel = 32
        last_channel = 1280
        interverted_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],
        ]

        # building first layer
        assert input_size % 32 == 0
        # input_channel = make_divisible(input_channel * width_mult)  # first channel is always 32!
        self.last_channel = make_divisible(last_channel * width_mult) if width_mult > 1.0 else last_channel
        self.inplanes = last_channel
        self.features = [conv_bn(3, input_channel, 2)]
        # building inverted residual blocks
        for t, c, n, s in interverted_residual_setting:
            output_channel = make_divisible(c * width_mult) if t > 1 else c
            for i in range(n):
                if i == 0:
                    self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
                else:
                    self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
                input_channel = output_channel
        # building last several layers
        self.features.append(conv_1x1_bn(input_channel, self.last_channel))
        # make it nn.Sequential
        self.features = nn.Sequential(*self.features)

        # building classifier
        # self.classifier = nn.Linear(self.last_channel, n_class)
        # used for deconv layers
        self.deconv_layers = self._make_deconv_layer(
            3,
            [256, 256, 256],
            [4, 4, 4],
        )
        # self.final_layer = []
        for head in sorted(self.heads):
            num_output = self.heads[head]
            if head_conv > 0:
                fc = nn.Sequential(
                    nn.Conv2d(256, head_conv,
                              kernel_size=3, padding=1, bias=True),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(head_conv, num_output,
                              kernel_size=1, stride=1, padding=0))
            else:
                fc = nn.Conv2d(
                    in_channels=256,
                    out_channels=num_output,
                    kernel_size=1,
                    stride=1,
                    padding=0
                )
            self.__setattr__(head, fc)
        # self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x = self.deconv_layers(x)
        ret = {}
        for head in self.heads:
            ret[head] = self.__getattr__(head)(x)
        return [ret]

    def _get_deconv_cfg(self, deconv_kernel, index):
        if deconv_kernel == 4:
            padding = 1
            output_padding = 0
        elif deconv_kernel == 3:
            padding = 1
            output_padding = 1
        elif deconv_kernel == 2:
            padding = 0
            output_padding = 0

        return deconv_kernel, padding, output_padding

    def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
        assert num_layers == len(num_filters), \
            'ERROR: num_deconv_layers is different len(num_deconv_filters)'
        assert num_layers == len(num_kernels), \
            'ERROR: num_deconv_layers is different len(num_deconv_filters)'

        layers = []
        for i in range(num_layers):
            kernel, padding, output_padding = \
                self._get_deconv_cfg(num_kernels[i], i)

            planes = num_filters[i]
            layers.append(
                nn.ConvTranspose2d(
                    in_channels=self.inplanes,
                    out_channels=planes,
                    kernel_size=kernel,
                    stride=2,
                    padding=padding,
                    output_padding=output_padding,
                    bias=self.deconv_with_bias))
            layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
            layers.append(nn.ReLU(inplace=True))
            self.inplanes = planes

        return nn.Sequential(*layers)

    def initialize_weights(self, pretrained):
        for _, m in self.deconv_layers.named_modules():
            if isinstance(m, nn.ConvTranspose2d):
                nn.init.normal_(m.weight, std=0.001)
                if self.deconv_with_bias:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        for head in self.heads:
            final_layer = self.__getattr__(head)
            for i, m in enumerate(final_layer.modules()):
                if isinstance(m, nn.Conv2d):
                    if m.weight.shape[0] == self.heads[head]:
                        if 'hm' in head:
                            nn.init.constant_(m.bias, -2.19)
                        else:
                            nn.init.normal_(m.weight, std=0.001)
                            nn.init.constant_(m.bias, 0)
        if pretrained:
            url = 'https://www.dropbox.com/s/47tyzpofuuyyv1b/mobilenetv2_1.0-f2a8633.pth.tar?dl=1'
            pretrained_state_dict = model_zoo.load_url(url)
            print('=> loading pretrained model {}'.format(url))
            self.load_state_dict(pretrained_state_dict, strict=False)
        else:
            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))
                    if m.bias is not None:
                        m.bias.data.zero_()
                elif isinstance(m, nn.BatchNorm2d):
                    m.weight.data.fill_(1)
                    m.bias.data.zero_()
                elif isinstance(m, nn.Linear):
                    n = m.weight.size(1)
                    m.weight.data.normal_(0, 0.01)
                    m.bias.data.zero_()


# def mobilenet_v2(pretrained=True):
#     model = MobileNetV2(width_mult=1)
#
#     if pretrained:
#         try:
#             from torch.hub import load_state_dict_from_url
#         except ImportError:
#             from torch.utils.model_zoo import load_url as load_state_dict_from_url
#         state_dict = load_state_dict_from_url(
#             'https://www.dropbox.com/s/47tyzpofuuyyv1b/mobilenetv2_1.0-f2a8633.pth.tar?dl=1', progress=True)
#         model.load_state_dict(state_dict)
#     return model


def get_mobilenet_v2_pose_net(num_layers, heads, head_conv):
    model = MobileNetV2(heads, head_conv=head_conv, input_size=384, width_mult=1)
    model.initialize_weights(pretrained=True)
    return model


# if __name__ == '__main__':
#     net = mobilenet_v2(True)

# https://github.com/tonylins/pytorch-mobilenet-v2 # dw # pw-linear # pw # dw # pw-linear # t, c, n, s # building first layer # input_channel = make_divisible(input_channel * width_mult)  # first channel is always 32! # building inverted residual blocks # building last several layers # make it nn.Sequential # building classifier # self.classifier = nn.Linear(self.last_channel, n_class) # used for deconv layers # self.final_layer = [] # self._initialize_weights() # def mobilenet_v2(pretrained=True): #     model = MobileNetV2(width_mult=1) # #     if pretrained: #         try: #             from torch.hub import load_state_dict_from_url #         except ImportError: #             from torch.utils.model_zoo import load_url as load_state_dict_from_url #         state_dict = load_state_dict_from_url( #             'https://www.dropbox.com/s/47tyzpofuuyyv1b/mobilenetv2_1.0-f2a8633.pth.tar?dl=1', progress=True) #         model.load_state_dict(state_dict) #     return model # if __name__ == '__main__': #     net = mobilenet_v2(True)