lite_hrnet.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is based on
https://github.com/HRNet/Lite-HRNet/blob/hrnet/models/backbones/litehrnet.py
"""

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from numbers import Integral
from paddle import ParamAttr
from paddle.regularizer import L2Decay
from paddle.nn.initializer import Normal, Constant

from paddleseg.cvlibs import manager
from paddleseg import utils

__all__ = [
    "Lite_HRNet_18", "Lite_HRNet_30", "Lite_HRNet_naive",
    "Lite_HRNet_wider_naive", "LiteHRNet"
]


def Conv2d(in_channels,
           out_channels,
           kernel_size,
           stride=1,
           padding=0,
           dilation=1,
           groups=1,
           bias=True,
           weight_init=Normal(std=0.001),
           bias_init=Constant(0.)):
    weight_attr = paddle.framework.ParamAttr(initializer=weight_init)
    if bias:
        bias_attr = paddle.framework.ParamAttr(initializer=bias_init)
    else:
        bias_attr = False
    conv = nn.Conv2D(in_channels,
                     out_channels,
                     kernel_size,
                     stride,
                     padding,
                     dilation,
                     groups,
                     weight_attr=weight_attr,
                     bias_attr=bias_attr)
    return conv


def channel_shuffle(x, groups):
    x_shape = x.shape
    batch_size, height, width = x_shape[0], x_shape[2], x_shape[3]
    num_channels = x.shape[1]
    channels_per_group = num_channels // groups

    x = paddle.reshape(
        x=x, shape=[batch_size, groups, channels_per_group, height, width])
    x = paddle.transpose(x=x, perm=[0, 2, 1, 3, 4])
    x = paddle.reshape(x=x, shape=[batch_size, num_channels, height, width])

    return x


class ConvNormLayer(nn.Layer):

    def __init__(self,
                 ch_in,
                 ch_out,
                 filter_size,
                 stride=1,
                 groups=1,
                 norm_type=None,
                 norm_groups=32,
                 norm_decay=0.,
                 freeze_norm=False,
                 act=None):
        super(ConvNormLayer, self).__init__()
        self.act = act
        norm_lr = 0. if freeze_norm else 1.
        if norm_type is not None:
            assert norm_type in ['bn', 'sync_bn', 'gn'], \
                "norm_type should be one of ['bn', 'sync_bn', 'gn'], but got {}".format(norm_type)
            param_attr = ParamAttr(
                initializer=Constant(1.0),
                learning_rate=norm_lr,
                regularizer=L2Decay(norm_decay),
            )
            bias_attr = ParamAttr(learning_rate=norm_lr,
                                  regularizer=L2Decay(norm_decay))
            global_stats = True if freeze_norm else None
            if norm_type in ['bn', 'sync_bn']:
                self.norm = nn.BatchNorm2D(
                    ch_out,
                    weight_attr=param_attr,
                    bias_attr=bias_attr,
                    use_global_stats=global_stats,
                )
            elif norm_type == 'gn':
                self.norm = nn.GroupNorm(num_groups=norm_groups,
                                         num_channels=ch_out,
                                         weight_attr=param_attr,
                                         bias_attr=bias_attr)
            norm_params = self.norm.parameters()
            if freeze_norm:
                for param in norm_params:
                    param.stop_gradient = True
            conv_bias_attr = False
        else:
            conv_bias_attr = True
            self.norm = None

        self.conv = nn.Conv2D(
            in_channels=ch_in,
            out_channels=ch_out,
            kernel_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            weight_attr=ParamAttr(initializer=Normal(mean=0., std=0.001)),
            bias_attr=conv_bias_attr)

    def forward(self, inputs):
        out = self.conv(inputs)
        if self.norm is not None:
            out = self.norm(out)

        if self.act == 'relu':
            out = F.relu(out)
        elif self.act == 'sigmoid':
            out = F.sigmoid(out)
        return out


class DepthWiseSeparableConvNormLayer(nn.Layer):

    def __init__(self,
                 ch_in,
                 ch_out,
                 filter_size,
                 stride=1,
                 dw_norm_type=None,
                 pw_norm_type=None,
                 norm_decay=0.,
                 freeze_norm=False,
                 dw_act=None,
                 pw_act=None):
        super(DepthWiseSeparableConvNormLayer, self).__init__()
        self.depthwise_conv = ConvNormLayer(
            ch_in=ch_in,
            ch_out=ch_in,
            filter_size=filter_size,
            stride=stride,
            groups=ch_in,
            norm_type=dw_norm_type,
            act=dw_act,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
        )
        self.pointwise_conv = ConvNormLayer(
            ch_in=ch_in,
            ch_out=ch_out,
            filter_size=1,
            stride=1,
            norm_type=pw_norm_type,
            act=pw_act,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
        )

    def forward(self, x):
        x = self.depthwise_conv(x)
        x = self.pointwise_conv(x)
        return x


class CrossResolutionWeightingModule(nn.Layer):

    def __init__(self,
                 channels,
                 ratio=16,
                 norm_type='bn',
                 freeze_norm=False,
                 norm_decay=0.):
        super(CrossResolutionWeightingModule, self).__init__()
        self.channels = channels
        total_channel = sum(channels)
        self.conv1 = ConvNormLayer(ch_in=total_channel,
                                   ch_out=total_channel // ratio,
                                   filter_size=1,
                                   stride=1,
                                   norm_type=norm_type,
                                   act='relu',
                                   freeze_norm=freeze_norm,
                                   norm_decay=norm_decay)
        self.conv2 = ConvNormLayer(ch_in=total_channel // ratio,
                                   ch_out=total_channel,
                                   filter_size=1,
                                   stride=1,
                                   norm_type=norm_type,
                                   act='sigmoid',
                                   freeze_norm=freeze_norm,
                                   norm_decay=norm_decay)

    def forward(self, x):
        out = []
        for idx, xi in enumerate(x[:-1]):
            kernel_size = stride = pow(2, len(x) - idx - 1)
            xi = F.avg_pool2d(xi, kernel_size=kernel_size, stride=stride)
            out.append(xi)
        out.append(x[-1])

        out = paddle.concat(out, 1)
        out = self.conv1(out)
        out = self.conv2(out)
        out = paddle.split(out, self.channels, 1)
        out = [
            s * F.interpolate(a, s.shape[-2:], mode='nearest')
            for s, a in zip(x, out)
        ]
        return out


class SpatialWeightingModule(nn.Layer):

    def __init__(self, in_channel, ratio=16, freeze_norm=False, norm_decay=0.):
        super(SpatialWeightingModule, self).__init__()
        self.global_avgpooling = nn.AdaptiveAvgPool2D(1)
        self.conv1 = ConvNormLayer(ch_in=in_channel,
                                   ch_out=in_channel // ratio,
                                   filter_size=1,
                                   stride=1,
                                   act='relu',
                                   freeze_norm=freeze_norm,
                                   norm_decay=norm_decay)
        self.conv2 = ConvNormLayer(ch_in=in_channel // ratio,
                                   ch_out=in_channel,
                                   filter_size=1,
                                   stride=1,
                                   act='sigmoid',
                                   freeze_norm=freeze_norm,
                                   norm_decay=norm_decay)

    def forward(self, x):
        out = self.global_avgpooling(x)
        out = self.conv1(out)
        out = self.conv2(out)
        return x * out


class ConditionalChannelWeightingBlock(nn.Layer):

    def __init__(self,
                 in_channels,
                 stride,
                 reduce_ratio,
                 norm_type='bn',
                 freeze_norm=False,
                 norm_decay=0.):
        super(ConditionalChannelWeightingBlock, self).__init__()
        assert stride in [1, 2]
        branch_channels = [channel // 2 for channel in in_channels]

        self.cross_resolution_weighting = CrossResolutionWeightingModule(
            branch_channels,
            ratio=reduce_ratio,
            norm_type=norm_type,
            freeze_norm=freeze_norm,
            norm_decay=norm_decay)
        self.depthwise_convs = nn.LayerList([
            ConvNormLayer(channel,
                          channel,
                          filter_size=3,
                          stride=stride,
                          groups=channel,
                          norm_type=norm_type,
                          freeze_norm=freeze_norm,
                          norm_decay=norm_decay) for channel in branch_channels
        ])

        self.spatial_weighting = nn.LayerList([
            SpatialWeightingModule(channel,
                                   ratio=4,
                                   freeze_norm=freeze_norm,
                                   norm_decay=norm_decay)
            for channel in branch_channels
        ])

    def forward(self, x):
        x = [s.chunk(2, axis=1) for s in x]
        x1 = [s[0] for s in x]
        x2 = [s[1] for s in x]

        x2 = self.cross_resolution_weighting(x2)
        x2 = [dw(s) for s, dw in zip(x2, self.depthwise_convs)]
        x2 = [sw(s) for s, sw in zip(x2, self.spatial_weighting)]

        out = [paddle.concat([s1, s2], axis=1) for s1, s2 in zip(x1, x2)]
        out = [channel_shuffle(s, groups=2) for s in out]
        return out


class ShuffleUnit(nn.Layer):

    def __init__(self,
                 in_channel,
                 out_channel,
                 stride,
                 norm_type='bn',
                 freeze_norm=False,
                 norm_decay=0.):
        super(ShuffleUnit, self).__init__()
        branch_channel = out_channel // 2
        self.stride = stride
        if self.stride == 1:
            assert in_channel == branch_channel * 2, \
                "when stride=1, in_channel {} should equal to branch_channel*2 {}".format(in_channel, branch_channel * 2)
        if stride > 1:
            self.branch1 = nn.Sequential(
                ConvNormLayer(ch_in=in_channel,
                              ch_out=in_channel,
                              filter_size=3,
                              stride=self.stride,
                              groups=in_channel,
                              norm_type=norm_type,
                              freeze_norm=freeze_norm,
                              norm_decay=norm_decay),
                ConvNormLayer(ch_in=in_channel,
                              ch_out=branch_channel,
                              filter_size=1,
                              stride=1,
                              norm_type=norm_type,
                              act='relu',
                              freeze_norm=freeze_norm,
                              norm_decay=norm_decay),
            )
        self.branch2 = nn.Sequential(
            ConvNormLayer(ch_in=branch_channel if stride == 1 else in_channel,
                          ch_out=branch_channel,
                          filter_size=1,
                          stride=1,
                          norm_type=norm_type,
                          act='relu',
                          freeze_norm=freeze_norm,
                          norm_decay=norm_decay),
            ConvNormLayer(ch_in=branch_channel,
                          ch_out=branch_channel,
                          filter_size=3,
                          stride=self.stride,
                          groups=branch_channel,
                          norm_type=norm_type,
                          freeze_norm=freeze_norm,
                          norm_decay=norm_decay),
            ConvNormLayer(ch_in=branch_channel,
                          ch_out=branch_channel,
                          filter_size=1,
                          stride=1,
                          norm_type=norm_type,
                          act='relu',
                          freeze_norm=freeze_norm,
                          norm_decay=norm_decay),
        )

    def forward(self, x):
        if self.stride > 1:
            x1 = self.branch1(x)
            x2 = self.branch2(x)
        else:
            x1, x2 = x.chunk(2, axis=1)
            x2 = self.branch2(x2)
        out = paddle.concat([x1, x2], axis=1)
        out = channel_shuffle(out, groups=2)
        return out


class IterativeHead(nn.Layer):

    def __init__(self,
                 in_channels,
                 norm_type='bn',
                 freeze_norm=False,
                 norm_decay=0.):
        super(IterativeHead, self).__init__()
        num_branches = len(in_channels)
        self.in_channels = in_channels[::-1]

        projects = []
        for i in range(num_branches):
            if i != num_branches - 1:
                projects.append(
                    DepthWiseSeparableConvNormLayer(ch_in=self.in_channels[i],
                                                    ch_out=self.in_channels[i +
                                                                            1],
                                                    filter_size=3,
                                                    stride=1,
                                                    dw_act=None,
                                                    pw_act='relu',
                                                    dw_norm_type=norm_type,
                                                    pw_norm_type=norm_type,
                                                    freeze_norm=freeze_norm,
                                                    norm_decay=norm_decay))
            else:
                projects.append(
                    DepthWiseSeparableConvNormLayer(ch_in=self.in_channels[i],
                                                    ch_out=self.in_channels[i],
                                                    filter_size=3,
                                                    stride=1,
                                                    dw_act=None,
                                                    pw_act='relu',
                                                    dw_norm_type=norm_type,
                                                    pw_norm_type=norm_type,
                                                    freeze_norm=freeze_norm,
                                                    norm_decay=norm_decay))
        self.projects = nn.LayerList(projects)

    def forward(self, x):
        x = x[::-1]
        y = []
        last_x = None
        for i, s in enumerate(x):
            if last_x is not None:
                last_x = F.interpolate(last_x,
                                       size=s.shape[-2:],
                                       mode='bilinear',
                                       align_corners=True)
                s = s + last_x
            s = self.projects[i](s)
            y.append(s)
            last_x = s

        return y[::-1]


class Stem(nn.Layer):

    def __init__(self,
                 in_channel,
                 stem_channel,
                 out_channel,
                 expand_ratio,
                 norm_type='bn',
                 freeze_norm=False,
                 norm_decay=0.):
        super(Stem, self).__init__()
        self.conv1 = ConvNormLayer(in_channel,
                                   stem_channel,
                                   filter_size=3,
                                   stride=2,
                                   norm_type=norm_type,
                                   act='relu',
                                   freeze_norm=freeze_norm,
                                   norm_decay=norm_decay)
        mid_channel = int(round(stem_channel * expand_ratio))
        branch_channel = stem_channel // 2
        if stem_channel == out_channel:
            inc_channel = out_channel - branch_channel
        else:
            inc_channel = out_channel - stem_channel
        self.branch1 = nn.Sequential(
            ConvNormLayer(ch_in=branch_channel,
                          ch_out=branch_channel,
                          filter_size=3,
                          stride=2,
                          groups=branch_channel,
                          norm_type=norm_type,
                          freeze_norm=freeze_norm,
                          norm_decay=norm_decay),
            ConvNormLayer(ch_in=branch_channel,
                          ch_out=inc_channel,
                          filter_size=1,
                          stride=1,
                          norm_type=norm_type,
                          act='relu',
                          freeze_norm=freeze_norm,
                          norm_decay=norm_decay),
        )
        self.expand_conv = ConvNormLayer(ch_in=branch_channel,
                                         ch_out=mid_channel,
                                         filter_size=1,
                                         stride=1,
                                         norm_type=norm_type,
                                         act='relu',
                                         freeze_norm=freeze_norm,
                                         norm_decay=norm_decay)
        self.depthwise_conv = ConvNormLayer(ch_in=mid_channel,
                                            ch_out=mid_channel,
                                            filter_size=3,
                                            stride=2,
                                            groups=mid_channel,
                                            norm_type=norm_type,
                                            freeze_norm=freeze_norm,
                                            norm_decay=norm_decay)
        self.linear_conv = ConvNormLayer(ch_in=mid_channel,
                                         ch_out=branch_channel if stem_channel
                                         == out_channel else stem_channel,
                                         filter_size=1,
                                         stride=1,
                                         norm_type=norm_type,
                                         act='relu',
                                         freeze_norm=freeze_norm,
                                         norm_decay=norm_decay)

    def forward(self, x):
        x = self.conv1(x)
        x1, x2 = x.chunk(2, axis=1)
        x1 = self.branch1(x1)
        x2 = self.expand_conv(x2)
        x2 = self.depthwise_conv(x2)
        x2 = self.linear_conv(x2)
        out = paddle.concat([x1, x2], axis=1)
        out = channel_shuffle(out, groups=2)

        return out


class LiteHRNetModule(nn.Layer):

    def __init__(self,
                 num_branches,
                 num_blocks,
                 in_channels,
                 reduce_ratio,
                 module_type,
                 multiscale_output=False,
                 with_fuse=True,
                 norm_type='bn',
                 freeze_norm=False,
                 norm_decay=0.):
        super(LiteHRNetModule, self).__init__()
        assert num_branches == len(in_channels),\
            "num_branches {} should equal to num_in_channels {}".format(num_branches, len(in_channels))
        assert module_type in [
            'LITE', 'NAIVE'
        ], "module_type should be one of ['LITE', 'NAIVE']"
        self.num_branches = num_branches
        self.in_channels = in_channels
        self.multiscale_output = multiscale_output
        self.with_fuse = with_fuse
        self.norm_type = 'bn'
        self.module_type = module_type

        if self.module_type == 'LITE':
            self.layers = self._make_weighting_blocks(num_blocks,
                                                      reduce_ratio,
                                                      freeze_norm=freeze_norm,
                                                      norm_decay=norm_decay)
        elif self.module_type == 'NAIVE':
            self.layers = self._make_naive_branches(num_branches,
                                                    num_blocks,
                                                    freeze_norm=freeze_norm,
                                                    norm_decay=norm_decay)

        if self.with_fuse:
            self.fuse_layers = self._make_fuse_layers(freeze_norm=freeze_norm,
                                                      norm_decay=norm_decay)
            self.relu = nn.ReLU()

    def _make_weighting_blocks(self,
                               num_blocks,
                               reduce_ratio,
                               stride=1,
                               freeze_norm=False,
                               norm_decay=0.):
        layers = []
        for i in range(num_blocks):
            layers.append(
                ConditionalChannelWeightingBlock(self.in_channels,
                                                 stride=stride,
                                                 reduce_ratio=reduce_ratio,
                                                 norm_type=self.norm_type,
                                                 freeze_norm=freeze_norm,
                                                 norm_decay=norm_decay))
        return nn.Sequential(*layers)

    def _make_naive_branches(self,
                             num_branches,
                             num_blocks,
                             freeze_norm=False,
                             norm_decay=0.):
        branches = []
        for branch_idx in range(num_branches):
            layers = []
            for i in range(num_blocks):
                layers.append(
                    ShuffleUnit(self.in_channels[branch_idx],
                                self.in_channels[branch_idx],
                                stride=1,
                                norm_type=self.norm_type,
                                freeze_norm=freeze_norm,
                                norm_decay=norm_decay))
            branches.append(nn.Sequential(*layers))
        return nn.LayerList(branches)

    def _make_fuse_layers(self, freeze_norm=False, norm_decay=0.):
        if self.num_branches == 1:
            return None
        fuse_layers = []
        num_out_branches = self.num_branches if self.multiscale_output else 1
        for i in range(num_out_branches):
            fuse_layer = []
            for j in range(self.num_branches):
                if j > i:
                    fuse_layer.append(
                        nn.Sequential(
                            Conv2d(
                                self.in_channels[j],
                                self.in_channels[i],
                                kernel_size=1,
                                stride=1,
                                padding=0,
                                bias=False,
                            ), nn.BatchNorm2D(self.in_channels[i]),
                            nn.Upsample(scale_factor=2**(j - i),
                                        mode='nearest')))
                elif j == i:
                    fuse_layer.append(None)
                else:
                    conv_downsamples = []
                    for k in range(i - j):
                        if k == i - j - 1:
                            conv_downsamples.append(
                                nn.Sequential(
                                    Conv2d(
                                        self.in_channels[j],
                                        self.in_channels[j],
                                        kernel_size=3,
                                        stride=2,
                                        padding=1,
                                        groups=self.in_channels[j],
                                        bias=False,
                                    ), nn.BatchNorm2D(self.in_channels[j]),
                                    Conv2d(
                                        self.in_channels[j],
                                        self.in_channels[i],
                                        kernel_size=1,
                                        stride=1,
                                        padding=0,
                                        bias=False,
                                    ), nn.BatchNorm2D(self.in_channels[i])))
                        else:
                            conv_downsamples.append(
                                nn.Sequential(
                                    Conv2d(
                                        self.in_channels[j],
                                        self.in_channels[j],
                                        kernel_size=3,
                                        stride=2,
                                        padding=1,
                                        groups=self.in_channels[j],
                                        bias=False,
                                    ), nn.BatchNorm2D(self.in_channels[j]),
                                    Conv2d(
                                        self.in_channels[j],
                                        self.in_channels[j],
                                        kernel_size=1,
                                        stride=1,
                                        padding=0,
                                        bias=False,
                                    ), nn.BatchNorm2D(self.in_channels[j]),
                                    nn.ReLU()))

                    fuse_layer.append(nn.Sequential(*conv_downsamples))
            fuse_layers.append(nn.LayerList(fuse_layer))

        return nn.LayerList(fuse_layers)

    def forward(self, x):
        if self.num_branches == 1:
            return [self.layers[0](x[0])]
        if self.module_type == 'LITE':
            out = self.layers(x)
        elif self.module_type == 'NAIVE':
            for i in range(self.num_branches):
                x[i] = self.layers[i](x[i])
            out = x
        if self.with_fuse:
            out_fuse = []
            for i in range(len(self.fuse_layers)):
                y = out[0] if i == 0 else self.fuse_layers[i][0](out[0])
                for j in range(self.num_branches):
                    if j == 0:
                        y += y
                    elif i == j:
                        y += out[j]
                    else:
                        y += self.fuse_layers[i][j](out[j])
                    if i == 0:
                        out[i] = y
                out_fuse.append(self.relu(y))
            out = out_fuse
        elif not self.multiscale_output:
            out = [out[0]]
        return out


class LiteHRNet(nn.Layer):
    """
    @inproceedings{Yulitehrnet21,
    title={Lite-HRNet: A Lightweight High-Resolution Network},
        author={Yu, Changqian and Xiao, Bin and Gao, Changxin and Yuan, Lu and Zhang, Lei and Sang, Nong and Wang, Jingdong},
        booktitle={CVPR},year={2021}
    }

    Args:
        network_type (str): the network_type should be one of ["lite_18", "lite_30", "naive", "wider_naive"],
            "naive": Simply combining the shuffle block in ShuffleNet and the highresolution design pattern in HRNet.
            "wider_naive": Naive network with wider channels in each block.
            "lite_18": Lite-HRNet-18, which replaces the pointwise convolution in a shuffle block by conditional channel weighting.
            "lite_30": Lite-HRNet-30, with more blocks compared with Lite-HRNet-18.
        in_channels (int, optional): The channels of input image. Default: 3.
        freeze_at (int): the stage to freeze
        freeze_norm (bool): whether to freeze norm in HRNet
        norm_decay (float): weight decay for normalization layer weights
        return_idx (List): the stage to return
    """

    def __init__(self,
                 network_type,
                 in_channels=3,
                 freeze_at=0,
                 freeze_norm=True,
                 norm_decay=0.,
                 return_idx=[0, 1, 2, 3],
                 use_head=False,
                 pretrained=None):
        super(LiteHRNet, self).__init__()
        if isinstance(return_idx, Integral):
            return_idx = [return_idx]
        assert network_type in ["lite_18", "lite_30", "naive", "wider_naive"], \
            "the network_type should be one of [lite_18, lite_30, naive, wider_naive]"
        assert len(return_idx) > 0, "need one or more return index"
        self.freeze_at = freeze_at
        self.freeze_norm = freeze_norm
        self.norm_decay = norm_decay
        self.return_idx = return_idx
        self.norm_type = 'bn'
        self.use_head = use_head
        self.pretrained = pretrained

        self.module_configs = {
            "lite_18": {
                "num_modules": [2, 4, 2],
                "num_branches": [2, 3, 4],
                "num_blocks": [2, 2, 2],
                "module_type": ["LITE", "LITE", "LITE"],
                "reduce_ratios": [8, 8, 8],
                "num_channels": [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
            },
            "lite_30": {
                "num_modules": [3, 8, 3],
                "num_branches": [2, 3, 4],
                "num_blocks": [2, 2, 2],
                "module_type": ["LITE", "LITE", "LITE"],
                "reduce_ratios": [8, 8, 8],
                "num_channels": [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
            },
            "naive": {
                "num_modules": [2, 4, 2],
                "num_branches": [2, 3, 4],
                "num_blocks": [2, 2, 2],
                "module_type": ["NAIVE", "NAIVE", "NAIVE"],
                "reduce_ratios": [1, 1, 1],
                "num_channels": [[30, 60], [30, 60, 120], [30, 60, 120, 240]],
            },
            "wider_naive": {
                "num_modules": [2, 4, 2],
                "num_branches": [2, 3, 4],
                "num_blocks": [2, 2, 2],
                "module_type": ["NAIVE", "NAIVE", "NAIVE"],
                "reduce_ratios": [1, 1, 1],
                "num_channels": [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
            },
        }

        self.stages_config = self.module_configs[network_type]

        self.stem = Stem(in_channels, 32, 32, 1)
        num_channels_pre_layer = [32]
        for stage_idx in range(3):
            num_channels = self.stages_config["num_channels"][stage_idx]
            setattr(
                self, 'transition{}'.format(stage_idx),
                self._make_transition_layer(num_channels_pre_layer,
                                            num_channels, self.freeze_norm,
                                            self.norm_decay))
            stage, num_channels_pre_layer = self._make_stage(
                self.stages_config, stage_idx, num_channels, True,
                self.freeze_norm, self.norm_decay)
            setattr(self, 'stage{}'.format(stage_idx), stage)

        num_channels = self.stages_config["num_channels"][-1]
        self.feat_channels = num_channels

        if self.use_head:
            self.head_layer = IterativeHead(num_channels_pre_layer, 'bn',
                                            self.freeze_norm, self.norm_decay)

            self.feat_channels = [num_channels[0]]
            for i in range(1, len(num_channels)):
                self.feat_channels.append(num_channels[i] // 2)

        self.init_weight()

    def init_weight(self):
        if self.pretrained is not None:
            utils.load_entire_model(self, self.pretrained)

    def _make_transition_layer(self,
                               num_channels_pre_layer,
                               num_channels_cur_layer,
                               freeze_norm=False,
                               norm_decay=0.):
        num_branches_pre = len(num_channels_pre_layer)
        num_branches_cur = len(num_channels_cur_layer)
        transition_layers = []
        for i in range(num_branches_cur):
            if i < num_branches_pre:
                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                    transition_layers.append(
                        nn.Sequential(
                            Conv2d(num_channels_pre_layer[i],
                                   num_channels_pre_layer[i],
                                   kernel_size=3,
                                   stride=1,
                                   padding=1,
                                   groups=num_channels_pre_layer[i],
                                   bias=False),
                            nn.BatchNorm2D(num_channels_pre_layer[i]),
                            Conv2d(
                                num_channels_pre_layer[i],
                                num_channels_cur_layer[i],
                                kernel_size=1,
                                stride=1,
                                padding=0,
                                bias=False,
                            ), nn.BatchNorm2D(num_channels_cur_layer[i]),
                            nn.ReLU()))
                else:
                    transition_layers.append(None)
            else:
                conv_downsamples = []
                for j in range(i + 1 - num_branches_pre):
                    conv_downsamples.append(
                        nn.Sequential(
                            Conv2d(
                                num_channels_pre_layer[-1],
                                num_channels_pre_layer[-1],
                                groups=num_channels_pre_layer[-1],
                                kernel_size=3,
                                stride=2,
                                padding=1,
                                bias=False,
                            ), nn.BatchNorm2D(num_channels_pre_layer[-1]),
                            Conv2d(
                                num_channels_pre_layer[-1],
                                num_channels_cur_layer[i] if j == i -
                                num_branches_pre else
                                num_channels_pre_layer[-1],
                                kernel_size=1,
                                stride=1,
                                padding=0,
                                bias=False,
                            ),
                            nn.BatchNorm2D(num_channels_cur_layer[i] if j == i -
                                           num_branches_pre
                                           else num_channels_pre_layer[-1]),
                            nn.ReLU()))
                transition_layers.append(nn.Sequential(*conv_downsamples))
        return nn.LayerList(transition_layers)

    def _make_stage(self,
                    stages_config,
                    stage_idx,
                    in_channels,
                    multiscale_output,
                    freeze_norm=False,
                    norm_decay=0.):
        num_modules = stages_config["num_modules"][stage_idx]
        num_branches = stages_config["num_branches"][stage_idx]
        num_blocks = stages_config["num_blocks"][stage_idx]
        reduce_ratio = stages_config['reduce_ratios'][stage_idx]
        module_type = stages_config['module_type'][stage_idx]

        modules = []
        for i in range(num_modules):
            if not multiscale_output and i == num_modules - 1:
                reset_multiscale_output = False
            else:
                reset_multiscale_output = True
            modules.append(
                LiteHRNetModule(num_branches,
                                num_blocks,
                                in_channels,
                                reduce_ratio,
                                module_type,
                                multiscale_output=reset_multiscale_output,
                                with_fuse=True,
                                freeze_norm=freeze_norm,
                                norm_decay=norm_decay))
            in_channels = modules[-1].in_channels
        return nn.Sequential(*modules), in_channels

    def forward(self, x):
        x = self.stem(x)

        y_list = [x]
        for stage_idx in range(3):
            x_list = []
            transition = getattr(self, 'transition{}'.format(stage_idx))
            for j in range(self.stages_config["num_branches"][stage_idx]):
                if transition[j] is not None:
                    if j >= len(y_list):
                        x_list.append(transition[j](y_list[-1]))
                    else:
                        x_list.append(transition[j](y_list[j]))
                else:
                    x_list.append(y_list[j])
            y_list = getattr(self, 'stage{}'.format(stage_idx))(x_list)

        if self.use_head:
            y_list = self.head_layer(y_list)

        res = []
        for i, layer in enumerate(y_list):
            if i == self.freeze_at:
                layer.stop_gradient = True
            if i in self.return_idx:
                res.append(layer)
        return res


@manager.BACKBONES.add_component
def Lite_HRNet_18(**kwargs):
    model = LiteHRNet(network_type="lite_18", **kwargs)
    return model


@manager.BACKBONES.add_component
def Lite_HRNet_30(**kwargs):
    model = LiteHRNet(network_type="lite_30", **kwargs)
    return model


@manager.BACKBONES.add_component
def Lite_HRNet_naive(**kwargs):
    model = LiteHRNet(network_type="naive", **kwargs)
    return model


@manager.BACKBONES.add_component
def Lite_HRNet_wider_naive(**kwargs):
    model = LiteHRNet(network_type="wider_naive", **kwargs)
    return model