<gh_stars>0
import torch
import torch.nn as nn
import torch.nn.functional as F


class CReLU6(nn.Module):
    def __init__(self):
        super(CReLU6, self).__init__()

    def forward(self, x):
        return torch.cat((F.relu6(x), F.relu6(-x)), 1)


def conv_bn(inp, oup, stride, crelu=False):
    if crelu:
        return nn.Sequential(
            nn.Conv2d(inp, oup//2, 3, stride, 1, bias=False),
            nn.BatchNorm2d(oup//2),
            CReLU6()
        )
    else:
        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)
    )


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

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

        if expand_ratio == 1:
            if crelu:
                self.conv = nn.Sequential(
                    # dw
                    nn.Conv2d(hidden_dim, hidden_dim//2, 3, stride, 1, groups=hidden_dim, bias=False),
                    nn.BatchNorm2d(hidden_dim//2),
                    CReLU6(),
                    # pw-linear
                    nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                    nn.BatchNorm2d(oup),
                )
            else:
                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:
            if crelu:
                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//2, 3, stride, 1, groups=hidden_dim, bias=False),
                    nn.BatchNorm2d(hidden_dim//2),
                    CReLU6(),
                    # 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, n_class=1000, input_size=224, width_mult=1., dropout_rate=0.2, grayscale=False, crelu=False):
        super(MobileNetV2, self).__init__()
        self.n_class = n_class
        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 = int(input_channel * width_mult)
        self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel
        self.features = [conv_bn(3 if not grayscale else 1, input_channel, 2, crelu=crelu)]
        # building inverted residual blocks
        for t, c, n, s in interverted_residual_setting:
            output_channel = int(c * width_mult)
            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.Sequential(
            nn.Dropout2d(dropout_rate),
            nn.Conv2d(self.last_channel, n_class, 1)
        )

        self._initialize_weights()

    def forward(self, x, heatmap=False):
        x = self.features(x)
        if not heatmap:
            x = F.adaptive_avg_pool2d(x, (1, 1))
            x = self.classifier(x).view(x.size(0), -1)
            return x
        else:
            x = self.classifier(x)
            return F.adaptive_avg_pool2d(x, (1, 1)).view(x.size(0), -1), x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
                if m.bias is not None:
                    m.weight.data.normal_(0, 0.01)
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()
    
    def load_state_dict(self, state_dict, strict=True, transfer=False):
        keys = []
        for key in state_dict:
            keys.append(key)
        
        for key in keys:
            if key.startswith('module.'):
                state_dict[key[7:]] = state_dict[key]
                del state_dict[key]
        
        keys = []
        for key in state_dict:
            keys.append(key)
        
        if transfer:
            for key in keys:
                if key.startswith('classifier.'):
                    del state_dict[key]

        if strict and transfer:
            print('load_state_dict warning: strict must be False when transfer is True')
        
        super(MobileNetV2, self).load_state_dict(state_dict, strict=(strict and not transfer))

import torch
import torch.nn as nn
import torch.nn.functional as F


class CReLU6(nn.Module):
    def __init__(self):
        super(CReLU6, self).__init__()

    def forward(self, x):
        return torch.cat((F.relu6(x), F.relu6(-x)), 1)


def conv_bn(inp, oup, stride, crelu=False):
    if crelu:
        return nn.Sequential(
            nn.Conv2d(inp, oup//2, 3, stride, 1, bias=False),
            nn.BatchNorm2d(oup//2),
            CReLU6()
        )
    else:
        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)
    )


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

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

        if expand_ratio == 1:
            if crelu:
                self.conv = nn.Sequential(
                    # dw
                    nn.Conv2d(hidden_dim, hidden_dim//2, 3, stride, 1, groups=hidden_dim, bias=False),
                    nn.BatchNorm2d(hidden_dim//2),
                    CReLU6(),
                    # pw-linear
                    nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                    nn.BatchNorm2d(oup),
                )
            else:
                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:
            if crelu:
                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//2, 3, stride, 1, groups=hidden_dim, bias=False),
                    nn.BatchNorm2d(hidden_dim//2),
                    CReLU6(),
                    # 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, n_class=1000, input_size=224, width_mult=1., dropout_rate=0.2, grayscale=False, crelu=False):
        super(MobileNetV2, self).__init__()
        self.n_class = n_class
        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 = int(input_channel * width_mult)
        self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel
        self.features = [conv_bn(3 if not grayscale else 1, input_channel, 2, crelu=crelu)]
        # building inverted residual blocks
        for t, c, n, s in interverted_residual_setting:
            output_channel = int(c * width_mult)
            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.Sequential(
            nn.Dropout2d(dropout_rate),
            nn.Conv2d(self.last_channel, n_class, 1)
        )

        self._initialize_weights()

    def forward(self, x, heatmap=False):
        x = self.features(x)
        if not heatmap:
            x = F.adaptive_avg_pool2d(x, (1, 1))
            x = self.classifier(x).view(x.size(0), -1)
            return x
        else:
            x = self.classifier(x)
            return F.adaptive_avg_pool2d(x, (1, 1)).view(x.size(0), -1), x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
                if m.bias is not None:
                    m.weight.data.normal_(0, 0.01)
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()
    
    def load_state_dict(self, state_dict, strict=True, transfer=False):
        keys = []
        for key in state_dict:
            keys.append(key)
        
        for key in keys:
            if key.startswith('module.'):
                state_dict[key[7:]] = state_dict[key]
                del state_dict[key]
        
        keys = []
        for key in state_dict:
            keys.append(key)
        
        if transfer:
            for key in keys:
                if key.startswith('classifier.'):
                    del state_dict[key]

        if strict and transfer:
            print('load_state_dict warning: strict must be False when transfer is True')
        
        super(MobileNetV2, self).load_state_dict(state_dict, strict=(strict and not transfer))
# dw # pw-linear # dw # pw-linear # pw # dw # pw-linear # pw # dw # pw-linear # t, c, n, s # building first layer # building inverted residual blocks # building last several layers # make it nn.Sequential # building classifier