# The implementation here is modified based on nanodet,
# originally Apache 2.0 License and publicly avaialbe at https://github.com/RangiLyu/nanodet

import torch
import torch.nn as nn

from .utils import act_layers


def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()
    channels_per_group = num_channels // groups

    x = x.view(batchsize, groups, channels_per_group, height, width)

    x = torch.transpose(x, 1, 2).contiguous()

    x = x.view(batchsize, -1, height, width)

    return x


class ShuffleV2Block(nn.Module):

    def __init__(self, inp, oup, stride, activation='ReLU'):
        super(ShuffleV2Block, self).__init__()

        if not (1 <= stride <= 3):
            raise ValueError('illegal stride value')
        self.stride = stride

        branch_features = oup // 2
        assert (self.stride != 1) or (inp == branch_features << 1)

        if self.stride > 1:
            self.branch1 = nn.Sequential(
                self.depthwise_conv(
                    inp, inp, kernel_size=3, stride=self.stride, padding=1),
                nn.BatchNorm2d(inp),
                nn.Conv2d(
                    inp,
                    branch_features,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    bias=False),
                nn.BatchNorm2d(branch_features),
                act_layers(activation),
            )
        else:
            self.branch1 = nn.Sequential()

        self.branch2 = nn.Sequential(
            nn.Conv2d(
                inp if (self.stride > 1) else branch_features,
                branch_features,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=False,
            ),
            nn.BatchNorm2d(branch_features),
            act_layers(activation),
            self.depthwise_conv(
                branch_features,
                branch_features,
                kernel_size=3,
                stride=self.stride,
                padding=1,
            ),
            nn.BatchNorm2d(branch_features),
            nn.Conv2d(
                branch_features,
                branch_features,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=False,
            ),
            nn.BatchNorm2d(branch_features),
            act_layers(activation),
        )

    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(
            i, o, kernel_size, stride, padding, bias=bias, groups=i)

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)

        out = channel_shuffle(out, 2)

        return out


class ShuffleNetV2(nn.Module):

    def __init__(
        self,
        model_size='1.5x',
        out_stages=(2, 3, 4),
        with_last_conv=False,
        kernal_size=3,
        activation='ReLU',
        pretrain=True,
    ):
        super(ShuffleNetV2, self).__init__()
        assert set(out_stages).issubset((2, 3, 4))

        print('model size is ', model_size)

        self.stage_repeats = [4, 8, 4]
        self.model_size = model_size
        self.out_stages = out_stages
        self.with_last_conv = with_last_conv
        self.kernal_size = kernal_size
        self.activation = activation
        if model_size == '0.5x':
            self._stage_out_channels = [24, 48, 96, 192, 1024]
        elif model_size == '1.0x':
            self._stage_out_channels = [24, 116, 232, 464, 1024]
        elif model_size == '1.5x':
            self._stage_out_channels = [24, 176, 352, 704, 1024]
        elif model_size == '2.0x':
            self._stage_out_channels = [24, 244, 488, 976, 2048]
        else:
            raise NotImplementedError

        # building first layer
        input_channels = 3
        output_channels = self._stage_out_channels[0]
        self.conv1 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
            nn.BatchNorm2d(output_channels),
            act_layers(activation),
        )
        input_channels = output_channels

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(
                stage_names, self.stage_repeats, self._stage_out_channels[1:]):
            seq = [
                ShuffleV2Block(
                    input_channels, output_channels, 2, activation=activation)
            ]
            for i in range(repeats - 1):
                seq.append(
                    ShuffleV2Block(
                        output_channels,
                        output_channels,
                        1,
                        activation=activation))
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels
        output_channels = self._stage_out_channels[-1]
        if self.with_last_conv:
            conv5 = nn.Sequential(
                nn.Conv2d(
                    input_channels, output_channels, 1, 1, 0, bias=False),
                nn.BatchNorm2d(output_channels),
                act_layers(activation),
            )
            self.stage4.add_module('conv5', conv5)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        output = []

        for i in range(2, 5):
            stage = getattr(self, 'stage{}'.format(i))
            x = stage(x)
            if i in self.out_stages:
                output.append(x)
        return tuple(output)

b7ae215d1db3ec45f7e5c1e00fd0170b16b2dffc
<mask token>


class ShuffleV2Block(nn.Module):
    <mask token>

    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias,
            groups=i)

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
        out = channel_shuffle(out, 2)
        return out


class ShuffleNetV2(nn.Module):

    def __init__(self, model_size='1.5x', out_stages=(2, 3, 4),
        with_last_conv=False, kernal_size=3, activation='ReLU', pretrain=True):
        super(ShuffleNetV2, self).__init__()
        assert set(out_stages).issubset((2, 3, 4))
        print('model size is ', model_size)
        self.stage_repeats = [4, 8, 4]
        self.model_size = model_size
        self.out_stages = out_stages
        self.with_last_conv = with_last_conv
        self.kernal_size = kernal_size
        self.activation = activation
        if model_size == '0.5x':
            self._stage_out_channels = [24, 48, 96, 192, 1024]
        elif model_size == '1.0x':
            self._stage_out_channels = [24, 116, 232, 464, 1024]
        elif model_size == '1.5x':
            self._stage_out_channels = [24, 176, 352, 704, 1024]
        elif model_size == '2.0x':
            self._stage_out_channels = [24, 244, 488, 976, 2048]
        else:
            raise NotImplementedError
        input_channels = 3
        output_channels = self._stage_out_channels[0]
        self.conv1 = nn.Sequential(nn.Conv2d(input_channels,
            output_channels, 3, 2, 1, bias=False), nn.BatchNorm2d(
            output_channels), act_layers(activation))
        input_channels = output_channels
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(stage_names, self.
            stage_repeats, self._stage_out_channels[1:]):
            seq = [ShuffleV2Block(input_channels, output_channels, 2,
                activation=activation)]
            for i in range(repeats - 1):
                seq.append(ShuffleV2Block(output_channels, output_channels,
                    1, activation=activation))
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels
        output_channels = self._stage_out_channels[-1]
        if self.with_last_conv:
            conv5 = nn.Sequential(nn.Conv2d(input_channels, output_channels,
                1, 1, 0, bias=False), nn.BatchNorm2d(output_channels),
                act_layers(activation))
            self.stage4.add_module('conv5', conv5)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        output = []
        for i in range(2, 5):
            stage = getattr(self, 'stage{}'.format(i))
            x = stage(x)
            if i in self.out_stages:
                output.append(x)
        return tuple(output)

<mask token>


class ShuffleV2Block(nn.Module):

    def __init__(self, inp, oup, stride, activation='ReLU'):
        super(ShuffleV2Block, self).__init__()
        if not 1 <= stride <= 3:
            raise ValueError('illegal stride value')
        self.stride = stride
        branch_features = oup // 2
        assert self.stride != 1 or inp == branch_features << 1
        if self.stride > 1:
            self.branch1 = nn.Sequential(self.depthwise_conv(inp, inp,
                kernel_size=3, stride=self.stride, padding=1), nn.
                BatchNorm2d(inp), nn.Conv2d(inp, branch_features,
                kernel_size=1, stride=1, padding=0, bias=False), nn.
                BatchNorm2d(branch_features), act_layers(activation))
        else:
            self.branch1 = nn.Sequential()
        self.branch2 = nn.Sequential(nn.Conv2d(inp if self.stride > 1 else
            branch_features, branch_features, kernel_size=1, stride=1,
            padding=0, bias=False), nn.BatchNorm2d(branch_features),
            act_layers(activation), self.depthwise_conv(branch_features,
            branch_features, kernel_size=3, stride=self.stride, padding=1),
            nn.BatchNorm2d(branch_features), nn.Conv2d(branch_features,
            branch_features, kernel_size=1, stride=1, padding=0, bias=False
            ), nn.BatchNorm2d(branch_features), act_layers(activation))

    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias,
            groups=i)

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
        out = channel_shuffle(out, 2)
        return out


class ShuffleNetV2(nn.Module):

    def __init__(self, model_size='1.5x', out_stages=(2, 3, 4),
        with_last_conv=False, kernal_size=3, activation='ReLU', pretrain=True):
        super(ShuffleNetV2, self).__init__()
        assert set(out_stages).issubset((2, 3, 4))
        print('model size is ', model_size)
        self.stage_repeats = [4, 8, 4]
        self.model_size = model_size
        self.out_stages = out_stages
        self.with_last_conv = with_last_conv
        self.kernal_size = kernal_size
        self.activation = activation
        if model_size == '0.5x':
            self._stage_out_channels = [24, 48, 96, 192, 1024]
        elif model_size == '1.0x':
            self._stage_out_channels = [24, 116, 232, 464, 1024]
        elif model_size == '1.5x':
            self._stage_out_channels = [24, 176, 352, 704, 1024]
        elif model_size == '2.0x':
            self._stage_out_channels = [24, 244, 488, 976, 2048]
        else:
            raise NotImplementedError
        input_channels = 3
        output_channels = self._stage_out_channels[0]
        self.conv1 = nn.Sequential(nn.Conv2d(input_channels,
            output_channels, 3, 2, 1, bias=False), nn.BatchNorm2d(
            output_channels), act_layers(activation))
        input_channels = output_channels
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(stage_names, self.
            stage_repeats, self._stage_out_channels[1:]):
            seq = [ShuffleV2Block(input_channels, output_channels, 2,
                activation=activation)]
            for i in range(repeats - 1):
                seq.append(ShuffleV2Block(output_channels, output_channels,
                    1, activation=activation))
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels
        output_channels = self._stage_out_channels[-1]
        if self.with_last_conv:
            conv5 = nn.Sequential(nn.Conv2d(input_channels, output_channels,
                1, 1, 0, bias=False), nn.BatchNorm2d(output_channels),
                act_layers(activation))
            self.stage4.add_module('conv5', conv5)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        output = []
        for i in range(2, 5):
            stage = getattr(self, 'stage{}'.format(i))
            x = stage(x)
            if i in self.out_stages:
                output.append(x)
        return tuple(output)

<mask token>


def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()
    channels_per_group = num_channels // groups
    x = x.view(batchsize, groups, channels_per_group, height, width)
    x = torch.transpose(x, 1, 2).contiguous()
    x = x.view(batchsize, -1, height, width)
    return x


class ShuffleV2Block(nn.Module):

    def __init__(self, inp, oup, stride, activation='ReLU'):
        super(ShuffleV2Block, self).__init__()
        if not 1 <= stride <= 3:
            raise ValueError('illegal stride value')
        self.stride = stride
        branch_features = oup // 2
        assert self.stride != 1 or inp == branch_features << 1
        if self.stride > 1:
            self.branch1 = nn.Sequential(self.depthwise_conv(inp, inp,
                kernel_size=3, stride=self.stride, padding=1), nn.
                BatchNorm2d(inp), nn.Conv2d(inp, branch_features,
                kernel_size=1, stride=1, padding=0, bias=False), nn.
                BatchNorm2d(branch_features), act_layers(activation))
        else:
            self.branch1 = nn.Sequential()
        self.branch2 = nn.Sequential(nn.Conv2d(inp if self.stride > 1 else
            branch_features, branch_features, kernel_size=1, stride=1,
            padding=0, bias=False), nn.BatchNorm2d(branch_features),
            act_layers(activation), self.depthwise_conv(branch_features,
            branch_features, kernel_size=3, stride=self.stride, padding=1),
            nn.BatchNorm2d(branch_features), nn.Conv2d(branch_features,
            branch_features, kernel_size=1, stride=1, padding=0, bias=False
            ), nn.BatchNorm2d(branch_features), act_layers(activation))

    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias,
            groups=i)

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
        out = channel_shuffle(out, 2)
        return out


class ShuffleNetV2(nn.Module):

    def __init__(self, model_size='1.5x', out_stages=(2, 3, 4),
        with_last_conv=False, kernal_size=3, activation='ReLU', pretrain=True):
        super(ShuffleNetV2, self).__init__()
        assert set(out_stages).issubset((2, 3, 4))
        print('model size is ', model_size)
        self.stage_repeats = [4, 8, 4]
        self.model_size = model_size
        self.out_stages = out_stages
        self.with_last_conv = with_last_conv
        self.kernal_size = kernal_size
        self.activation = activation
        if model_size == '0.5x':
            self._stage_out_channels = [24, 48, 96, 192, 1024]
        elif model_size == '1.0x':
            self._stage_out_channels = [24, 116, 232, 464, 1024]
        elif model_size == '1.5x':
            self._stage_out_channels = [24, 176, 352, 704, 1024]
        elif model_size == '2.0x':
            self._stage_out_channels = [24, 244, 488, 976, 2048]
        else:
            raise NotImplementedError
        input_channels = 3
        output_channels = self._stage_out_channels[0]
        self.conv1 = nn.Sequential(nn.Conv2d(input_channels,
            output_channels, 3, 2, 1, bias=False), nn.BatchNorm2d(
            output_channels), act_layers(activation))
        input_channels = output_channels
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(stage_names, self.
            stage_repeats, self._stage_out_channels[1:]):
            seq = [ShuffleV2Block(input_channels, output_channels, 2,
                activation=activation)]
            for i in range(repeats - 1):
                seq.append(ShuffleV2Block(output_channels, output_channels,
                    1, activation=activation))
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels
        output_channels = self._stage_out_channels[-1]
        if self.with_last_conv:
            conv5 = nn.Sequential(nn.Conv2d(input_channels, output_channels,
                1, 1, 0, bias=False), nn.BatchNorm2d(output_channels),
                act_layers(activation))
            self.stage4.add_module('conv5', conv5)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        output = []
        for i in range(2, 5):
            stage = getattr(self, 'stage{}'.format(i))
            x = stage(x)
            if i in self.out_stages:
                output.append(x)
        return tuple(output)

import torch
import torch.nn as nn
from .utils import act_layers


def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()
    channels_per_group = num_channels // groups
    x = x.view(batchsize, groups, channels_per_group, height, width)
    x = torch.transpose(x, 1, 2).contiguous()
    x = x.view(batchsize, -1, height, width)
    return x


class ShuffleV2Block(nn.Module):

    def __init__(self, inp, oup, stride, activation='ReLU'):
        super(ShuffleV2Block, self).__init__()
        if not 1 <= stride <= 3:
            raise ValueError('illegal stride value')
        self.stride = stride
        branch_features = oup // 2
        assert self.stride != 1 or inp == branch_features << 1
        if self.stride > 1:
            self.branch1 = nn.Sequential(self.depthwise_conv(inp, inp,
                kernel_size=3, stride=self.stride, padding=1), nn.
                BatchNorm2d(inp), nn.Conv2d(inp, branch_features,
                kernel_size=1, stride=1, padding=0, bias=False), nn.
                BatchNorm2d(branch_features), act_layers(activation))
        else:
            self.branch1 = nn.Sequential()
        self.branch2 = nn.Sequential(nn.Conv2d(inp if self.stride > 1 else
            branch_features, branch_features, kernel_size=1, stride=1,
            padding=0, bias=False), nn.BatchNorm2d(branch_features),
            act_layers(activation), self.depthwise_conv(branch_features,
            branch_features, kernel_size=3, stride=self.stride, padding=1),
            nn.BatchNorm2d(branch_features), nn.Conv2d(branch_features,
            branch_features, kernel_size=1, stride=1, padding=0, bias=False
            ), nn.BatchNorm2d(branch_features), act_layers(activation))

    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias,
            groups=i)

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
        out = channel_shuffle(out, 2)
        return out


class ShuffleNetV2(nn.Module):

    def __init__(self, model_size='1.5x', out_stages=(2, 3, 4),
        with_last_conv=False, kernal_size=3, activation='ReLU', pretrain=True):
        super(ShuffleNetV2, self).__init__()
        assert set(out_stages).issubset((2, 3, 4))
        print('model size is ', model_size)
        self.stage_repeats = [4, 8, 4]
        self.model_size = model_size
        self.out_stages = out_stages
        self.with_last_conv = with_last_conv
        self.kernal_size = kernal_size
        self.activation = activation
        if model_size == '0.5x':
            self._stage_out_channels = [24, 48, 96, 192, 1024]
        elif model_size == '1.0x':
            self._stage_out_channels = [24, 116, 232, 464, 1024]
        elif model_size == '1.5x':
            self._stage_out_channels = [24, 176, 352, 704, 1024]
        elif model_size == '2.0x':
            self._stage_out_channels = [24, 244, 488, 976, 2048]
        else:
            raise NotImplementedError
        input_channels = 3
        output_channels = self._stage_out_channels[0]
        self.conv1 = nn.Sequential(nn.Conv2d(input_channels,
            output_channels, 3, 2, 1, bias=False), nn.BatchNorm2d(
            output_channels), act_layers(activation))
        input_channels = output_channels
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(stage_names, self.
            stage_repeats, self._stage_out_channels[1:]):
            seq = [ShuffleV2Block(input_channels, output_channels, 2,
                activation=activation)]
            for i in range(repeats - 1):
                seq.append(ShuffleV2Block(output_channels, output_channels,
                    1, activation=activation))
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels
        output_channels = self._stage_out_channels[-1]
        if self.with_last_conv:
            conv5 = nn.Sequential(nn.Conv2d(input_channels, output_channels,
                1, 1, 0, bias=False), nn.BatchNorm2d(output_channels),
                act_layers(activation))
            self.stage4.add_module('conv5', conv5)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        output = []
        for i in range(2, 5):
            stage = getattr(self, 'stage{}'.format(i))
            x = stage(x)
            if i in self.out_stages:
                output.append(x)
        return tuple(output)

# The implementation here is modified based on nanodet,
# originally Apache 2.0 License and publicly avaialbe at https://github.com/RangiLyu/nanodet

import torch
import torch.nn as nn

from .utils import act_layers


def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()
    channels_per_group = num_channels // groups

    x = x.view(batchsize, groups, channels_per_group, height, width)

    x = torch.transpose(x, 1, 2).contiguous()

    x = x.view(batchsize, -1, height, width)

    return x


class ShuffleV2Block(nn.Module):

    def __init__(self, inp, oup, stride, activation='ReLU'):
        super(ShuffleV2Block, self).__init__()

        if not (1 <= stride <= 3):
            raise ValueError('illegal stride value')
        self.stride = stride

        branch_features = oup // 2
        assert (self.stride != 1) or (inp == branch_features << 1)

        if self.stride > 1:
            self.branch1 = nn.Sequential(
                self.depthwise_conv(
                    inp, inp, kernel_size=3, stride=self.stride, padding=1),
                nn.BatchNorm2d(inp),
                nn.Conv2d(
                    inp,
                    branch_features,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    bias=False),
                nn.BatchNorm2d(branch_features),
                act_layers(activation),
            )
        else:
            self.branch1 = nn.Sequential()

        self.branch2 = nn.Sequential(
            nn.Conv2d(
                inp if (self.stride > 1) else branch_features,
                branch_features,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=False,
            ),
            nn.BatchNorm2d(branch_features),
            act_layers(activation),
            self.depthwise_conv(
                branch_features,
                branch_features,
                kernel_size=3,
                stride=self.stride,
                padding=1,
            ),
            nn.BatchNorm2d(branch_features),
            nn.Conv2d(
                branch_features,
                branch_features,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=False,
            ),
            nn.BatchNorm2d(branch_features),
            act_layers(activation),
        )

    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(
            i, o, kernel_size, stride, padding, bias=bias, groups=i)

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)

        out = channel_shuffle(out, 2)

        return out


class ShuffleNetV2(nn.Module):

    def __init__(
        self,
        model_size='1.5x',
        out_stages=(2, 3, 4),
        with_last_conv=False,
        kernal_size=3,
        activation='ReLU',
        pretrain=True,
    ):
        super(ShuffleNetV2, self).__init__()
        assert set(out_stages).issubset((2, 3, 4))

        print('model size is ', model_size)

        self.stage_repeats = [4, 8, 4]
        self.model_size = model_size
        self.out_stages = out_stages
        self.with_last_conv = with_last_conv
        self.kernal_size = kernal_size
        self.activation = activation
        if model_size == '0.5x':
            self._stage_out_channels = [24, 48, 96, 192, 1024]
        elif model_size == '1.0x':
            self._stage_out_channels = [24, 116, 232, 464, 1024]
        elif model_size == '1.5x':
            self._stage_out_channels = [24, 176, 352, 704, 1024]
        elif model_size == '2.0x':
            self._stage_out_channels = [24, 244, 488, 976, 2048]
        else:
            raise NotImplementedError

        # building first layer
        input_channels = 3
        output_channels = self._stage_out_channels[0]
        self.conv1 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
            nn.BatchNorm2d(output_channels),
            act_layers(activation),
        )
        input_channels = output_channels

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(
                stage_names, self.stage_repeats, self._stage_out_channels[1:]):
            seq = [
                ShuffleV2Block(
                    input_channels, output_channels, 2, activation=activation)
            ]
            for i in range(repeats - 1):
                seq.append(
                    ShuffleV2Block(
                        output_channels,
                        output_channels,
                        1,
                        activation=activation))
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels
        output_channels = self._stage_out_channels[-1]
        if self.with_last_conv:
            conv5 = nn.Sequential(
                nn.Conv2d(
                    input_channels, output_channels, 1, 1, 0, bias=False),
                nn.BatchNorm2d(output_channels),
                act_layers(activation),
            )
            self.stage4.add_module('conv5', conv5)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        output = []

        for i in range(2, 5):
            stage = getattr(self, 'stage{}'.format(i))
            x = stage(x)
            if i in self.out_stages:
                output.append(x)
        return tuple(output)
