# 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.

import math
import os

import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
from paddle.fluid.initializer import Normal
from paddle.nn import SyncBatchNorm as BatchNorm

from dygraph.cvlibs import manager
from dygraph.utils import utils
from dygraph.cvlibs import param_init

__all__ = [
    "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", "HRNet_W18", "HRNet_W30",
    "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", "HRNet_W60", "HRNet_W64"
]


class HRNet(fluid.dygraph.Layer):
    """
    HRNet：Deep High-Resolution Representation Learning for Visual Recognition
    https://arxiv.org/pdf/1908.07919.pdf.

    Args:
        backbone_pretrained (str): the path of pretrained model.
        stage1_num_modules (int): number of modules for stage1. Default 1.
        stage1_num_blocks (list): number of blocks per module for stage1. Default [4].
        stage1_num_channels (list): number of channels per branch for stage1. Default [64].
        stage2_num_modules (int): number of modules for stage2. Default 1.
        stage2_num_blocks (list): number of blocks per module for stage2. Default [4, 4]
        stage2_num_channels (list): number of channels per branch for stage2. Default [18, 36].
        stage3_num_modules (int): number of modules for stage3. Default 4.
        stage3_num_blocks (list): number of blocks per module for stage3. Default [4, 4, 4]
        stage3_num_channels (list): number of channels per branch for stage3. Default [18, 36, 72].
        stage4_num_modules (int): number of modules for stage4. Default 3.
        stage4_num_blocks (list): number of blocks per module for stage4. Default [4, 4, 4, 4]
        stage4_num_channels (list): number of channels per branch for stage4. Default [18, 36, 72. 144].
        has_se (bool): whether to use Squeeze-and-Excitation module. Default False.
    """

    def __init__(self,
                 backbone_pretrained=None,
                 stage1_num_modules=1,
                 stage1_num_blocks=[4],
                 stage1_num_channels=[64],
                 stage2_num_modules=1,
                 stage2_num_blocks=[4, 4],
                 stage2_num_channels=[18, 36],
                 stage3_num_modules=4,
                 stage3_num_blocks=[4, 4, 4],
                 stage3_num_channels=[18, 36, 72],
                 stage4_num_modules=3,
                 stage4_num_blocks=[4, 4, 4, 4],
                 stage4_num_channels=[18, 36, 72, 144],
                 has_se=False):
        super(HRNet, self).__init__()

        self.stage1_num_modules = stage1_num_modules
        self.stage1_num_blocks = stage1_num_blocks
        self.stage1_num_channels = stage1_num_channels
        self.stage2_num_modules = stage2_num_modules
        self.stage2_num_blocks = stage2_num_blocks
        self.stage2_num_channels = stage2_num_channels
        self.stage3_num_modules = stage3_num_modules
        self.stage3_num_blocks = stage3_num_blocks
        self.stage3_num_channels = stage3_num_channels
        self.stage4_num_modules = stage4_num_modules
        self.stage4_num_blocks = stage4_num_blocks
        self.stage4_num_channels = stage4_num_channels
        self.has_se = has_se

        self.conv_layer1_1 = ConvBNLayer(
            num_channels=3,
            num_filters=64,
            filter_size=3,
            stride=2,
            act='relu',
            name="layer1_1")

        self.conv_layer1_2 = ConvBNLayer(
            num_channels=64,
            num_filters=64,
            filter_size=3,
            stride=2,
            act='relu',
            name="layer1_2")

        self.la1 = Layer1(
            num_channels=64,
            num_blocks=self.stage1_num_blocks[0],
            num_filters=self.stage1_num_channels[0],
            has_se=has_se,
            name="layer2")

        self.tr1 = TransitionLayer(
            in_channels=[self.stage1_num_channels[0] * 4],
            out_channels=self.stage2_num_channels,
            name="tr1")

        self.st2 = Stage(
            num_channels=self.stage2_num_channels,
            num_modules=self.stage2_num_modules,
            num_blocks=self.stage2_num_blocks,
            num_filters=self.stage2_num_channels,
            has_se=self.has_se,
            name="st2")

        self.tr2 = TransitionLayer(
            in_channels=self.stage2_num_channels,
            out_channels=self.stage3_num_channels,
            name="tr2")
        self.st3 = Stage(
            num_channels=self.stage3_num_channels,
            num_modules=self.stage3_num_modules,
            num_blocks=self.stage3_num_blocks,
            num_filters=self.stage3_num_channels,
            has_se=self.has_se,
            name="st3")

        self.tr3 = TransitionLayer(
            in_channels=self.stage3_num_channels,
            out_channels=self.stage4_num_channels,
            name="tr3")
        self.st4 = Stage(
            num_channels=self.stage4_num_channels,
            num_modules=self.stage4_num_modules,
            num_blocks=self.stage4_num_blocks,
            num_filters=self.stage4_num_channels,
            has_se=self.has_se,
            name="st4")

        if self.training:
            self.init_weight(backbone_pretrained)

    def forward(self, x, label=None, mode='train'):
        input_shape = x.shape[2:]
        conv1 = self.conv_layer1_1(x)
        conv2 = self.conv_layer1_2(conv1)

        la1 = self.la1(conv2)

        tr1 = self.tr1([la1])
        st2 = self.st2(tr1)

        tr2 = self.tr2(st2)
        st3 = self.st3(tr2)

        tr3 = self.tr3(st3)
        st4 = self.st4(tr3)

        x0_h, x0_w = st4[0].shape[2:]
        x1 = fluid.layers.resize_bilinear(st4[1], out_shape=(x0_h, x0_w))
        x2 = fluid.layers.resize_bilinear(st4[2], out_shape=(x0_h, x0_w))
        x3 = fluid.layers.resize_bilinear(st4[3], out_shape=(x0_h, x0_w))
        x = fluid.layers.concat([st4[0], x1, x2, x3], axis=1)

        return [x]

    def init_weight(self, pretrained_model=None):
        """
        Initialize the parameters of model parts.
        Args:
            pretrained_model ([str], optional): the path of pretrained model. Defaults to None.
        """
        params = self.parameters()
        for param in params:
            param_name = param.name
            if 'batch_norm' in param_name:
                if 'w_0' in param_name:
                    param_init.constant_init(param, 1.0)
                elif 'b_0' in param_name:
                    param_init.constant_init(param, 0.0)
            if 'conv' in param_name and 'w_0' in param_name:
                param_init.normal_init(param, scale=0.001)

        if pretrained_model is not None:
            if os.path.exists(pretrained_model):
                utils.load_pretrained_model(self, pretrained_model)
            else:
                raise Exception('Pretrained model is not found: {}'.format(
                    pretrained_model))


class ConvBNLayer(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 groups=1,
                 act="relu",
                 name=None):
        super(ConvBNLayer, self).__init__()

        self._conv = Conv2D(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            bias_attr=False)
        self._batch_norm = BatchNorm(num_filters)
        self.act = act

    def forward(self, input):
        y = self._conv(input)
        y = self._batch_norm(y)
        if self.act == 'relu':
            y = fluid.layers.relu(y)
        return y


class Layer1(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 num_blocks,
                 has_se=False,
                 name=None):
        super(Layer1, self).__init__()

        self.bottleneck_block_list = []

        for i in range(num_blocks):
            bottleneck_block = self.add_sublayer(
                "bb_{}_{}".format(name, i + 1),
                BottleneckBlock(
                    num_channels=num_channels if i == 0 else num_filters * 4,
                    num_filters=num_filters,
                    has_se=has_se,
                    stride=1,
                    downsample=True if i == 0 else False,
                    name=name + '_' + str(i + 1)))
            self.bottleneck_block_list.append(bottleneck_block)

    def forward(self, input):
        conv = input
        for block_func in self.bottleneck_block_list:
            conv = block_func(conv)
        return conv


class TransitionLayer(fluid.dygraph.Layer):
    def __init__(self, in_channels, out_channels, name=None):
        super(TransitionLayer, self).__init__()

        num_in = len(in_channels)
        num_out = len(out_channels)
        self.conv_bn_func_list = []
        for i in range(num_out):
            residual = None
            if i < num_in:
                if in_channels[i] != out_channels[i]:
                    residual = self.add_sublayer(
                        "transition_{}_layer_{}".format(name, i + 1),
                        ConvBNLayer(
                            num_channels=in_channels[i],
                            num_filters=out_channels[i],
                            filter_size=3,
                            name=name + '_layer_' + str(i + 1)))
            else:
                residual = self.add_sublayer(
                    "transition_{}_layer_{}".format(name, i + 1),
                    ConvBNLayer(
                        num_channels=in_channels[-1],
                        num_filters=out_channels[i],
                        filter_size=3,
                        stride=2,
                        name=name + '_layer_' + str(i + 1)))
            self.conv_bn_func_list.append(residual)

    def forward(self, input):
        outs = []
        for idx, conv_bn_func in enumerate(self.conv_bn_func_list):
            if conv_bn_func is None:
                outs.append(input[idx])
            else:
                if idx < len(input):
                    outs.append(conv_bn_func(input[idx]))
                else:
                    outs.append(conv_bn_func(input[-1]))
        return outs


class Branches(fluid.dygraph.Layer):
    def __init__(self,
                 num_blocks,
                 in_channels,
                 out_channels,
                 has_se=False,
                 name=None):
        super(Branches, self).__init__()

        self.basic_block_list = []

        for i in range(len(out_channels)):
            self.basic_block_list.append([])
            for j in range(num_blocks[i]):
                in_ch = in_channels[i] if j == 0 else out_channels[i]
                basic_block_func = self.add_sublayer(
                    "bb_{}_branch_layer_{}_{}".format(name, i + 1, j + 1),
                    BasicBlock(
                        num_channels=in_ch,
                        num_filters=out_channels[i],
                        has_se=has_se,
                        name=name + '_branch_layer_' + str(i + 1) + '_' +
                        str(j + 1)))
                self.basic_block_list[i].append(basic_block_func)

    def forward(self, inputs):
        outs = []
        for idx, input in enumerate(inputs):
            conv = input
            for basic_block_func in self.basic_block_list[idx]:
                conv = basic_block_func(conv)
            outs.append(conv)
        return outs


class BottleneckBlock(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 has_se,
                 stride=1,
                 downsample=False,
                 name=None):
        super(BottleneckBlock, self).__init__()

        self.has_se = has_se
        self.downsample = downsample

        self.conv1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            act="relu",
            name=name + "_conv1",
        )
        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act="relu",
            name=name + "_conv2")
        self.conv3 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters * 4,
            filter_size=1,
            act=None,
            name=name + "_conv3")

        if self.downsample:
            self.conv_down = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters * 4,
                filter_size=1,
                act=None,
                name=name + "_downsample")

        if self.has_se:
            self.se = SELayer(
                num_channels=num_filters * 4,
                num_filters=num_filters * 4,
                reduction_ratio=16,
                name=name + '_fc')

    def forward(self, input):
        residual = input
        conv1 = self.conv1(input)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)

        if self.downsample:
            residual = self.conv_down(input)

        if self.has_se:
            conv3 = self.se(conv3)

        y = fluid.layers.elementwise_add(x=conv3, y=residual, act="relu")
        return y


class BasicBlock(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride=1,
                 has_se=False,
                 downsample=False,
                 name=None):
        super(BasicBlock, self).__init__()

        self.has_se = has_se
        self.downsample = downsample

        self.conv1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act="relu",
            name=name + "_conv1")
        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            stride=1,
            act=None,
            name=name + "_conv2")

        if self.downsample:
            self.conv_down = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters * 4,
                filter_size=1,
                act="relu",
                name=name + "_downsample")

        if self.has_se:
            self.se = SELayer(
                num_channels=num_filters,
                num_filters=num_filters,
                reduction_ratio=16,
                name=name + '_fc')

    def forward(self, input):
        residual = input
        conv1 = self.conv1(input)
        conv2 = self.conv2(conv1)

        if self.downsample:
            residual = self.conv_down(input)

        if self.has_se:
            conv2 = self.se(conv2)

        y = fluid.layers.elementwise_add(x=conv2, y=residual, act="relu")
        return y


class SELayer(fluid.dygraph.Layer):
    def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
        super(SELayer, self).__init__()

        self.pool2d_gap = Pool2D(pool_type='avg', global_pooling=True)

        self._num_channels = num_channels

        med_ch = int(num_channels / reduction_ratio)
        stdv = 1.0 / math.sqrt(num_channels * 1.0)
        self.squeeze = Linear(
            num_channels,
            med_ch,
            act="relu",
            param_attr=ParamAttr(
                initializer=fluid.initializer.Uniform(-stdv, stdv),
                name=name + "_sqz_weights"),
            bias_attr=ParamAttr(name=name + '_sqz_offset'))

        stdv = 1.0 / math.sqrt(med_ch * 1.0)
        self.excitation = Linear(
            med_ch,
            num_filters,
            act="sigmoid",
            param_attr=ParamAttr(
                initializer=fluid.initializer.Uniform(-stdv, stdv),
                name=name + "_exc_weights"),
            bias_attr=ParamAttr(name=name + '_exc_offset'))

    def forward(self, input):
        pool = self.pool2d_gap(input)
        pool = fluid.layers.reshape(pool, shape=[-1, self._num_channels])
        squeeze = self.squeeze(pool)
        excitation = self.excitation(squeeze)
        excitation = fluid.layers.reshape(
            excitation, shape=[-1, self._num_channels, 1, 1])
        out = input * excitation
        return out


class Stage(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_modules,
                 num_blocks,
                 num_filters,
                 has_se=False,
                 multi_scale_output=True,
                 name=None):
        super(Stage, self).__init__()

        self._num_modules = num_modules

        self.stage_func_list = []
        for i in range(num_modules):
            if i == num_modules - 1 and not multi_scale_output:
                stage_func = self.add_sublayer(
                    "stage_{}_{}".format(name, i + 1),
                    HighResolutionModule(
                        num_channels=num_channels,
                        num_blocks=num_blocks,
                        num_filters=num_filters,
                        has_se=has_se,
                        multi_scale_output=False,
                        name=name + '_' + str(i + 1)))
            else:
                stage_func = self.add_sublayer(
                    "stage_{}_{}".format(name, i + 1),
                    HighResolutionModule(
                        num_channels=num_channels,
                        num_blocks=num_blocks,
                        num_filters=num_filters,
                        has_se=has_se,
                        name=name + '_' + str(i + 1)))

            self.stage_func_list.append(stage_func)

    def forward(self, input):
        out = input
        for idx in range(self._num_modules):
            out = self.stage_func_list[idx](out)
        return out


class HighResolutionModule(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_blocks,
                 num_filters,
                 has_se=False,
                 multi_scale_output=True,
                 name=None):
        super(HighResolutionModule, self).__init__()

        self.branches_func = Branches(
            num_blocks=num_blocks,
            in_channels=num_channels,
            out_channels=num_filters,
            has_se=has_se,
            name=name)

        self.fuse_func = FuseLayers(
            in_channels=num_filters,
            out_channels=num_filters,
            multi_scale_output=multi_scale_output,
            name=name)

    def forward(self, input):
        out = self.branches_func(input)
        out = self.fuse_func(out)
        return out


class FuseLayers(fluid.dygraph.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 multi_scale_output=True,
                 name=None):
        super(FuseLayers, self).__init__()

        self._actual_ch = len(in_channels) if multi_scale_output else 1
        self._in_channels = in_channels

        self.residual_func_list = []
        for i in range(self._actual_ch):
            for j in range(len(in_channels)):
                residual_func = None
                if j > i:
                    residual_func = self.add_sublayer(
                        "residual_{}_layer_{}_{}".format(name, i + 1, j + 1),
                        ConvBNLayer(
                            num_channels=in_channels[j],
                            num_filters=out_channels[i],
                            filter_size=1,
                            stride=1,
                            act=None,
                            name=name + '_layer_' + str(i + 1) + '_' +
                            str(j + 1)))
                    self.residual_func_list.append(residual_func)
                elif j < i:
                    pre_num_filters = in_channels[j]
                    for k in range(i - j):
                        if k == i - j - 1:
                            residual_func = self.add_sublayer(
                                "residual_{}_layer_{}_{}_{}".format(
                                    name, i + 1, j + 1, k + 1),
                                ConvBNLayer(
                                    num_channels=pre_num_filters,
                                    num_filters=out_channels[i],
                                    filter_size=3,
                                    stride=2,
                                    act=None,
                                    name=name + '_layer_' + str(i + 1) + '_' +
                                    str(j + 1) + '_' + str(k + 1)))
                            pre_num_filters = out_channels[i]
                        else:
                            residual_func = self.add_sublayer(
                                "residual_{}_layer_{}_{}_{}".format(
                                    name, i + 1, j + 1, k + 1),
                                ConvBNLayer(
                                    num_channels=pre_num_filters,
                                    num_filters=out_channels[j],
                                    filter_size=3,
                                    stride=2,
                                    act="relu",
                                    name=name + '_layer_' + str(i + 1) + '_' +
                                    str(j + 1) + '_' + str(k + 1)))
                            pre_num_filters = out_channels[j]
                        self.residual_func_list.append(residual_func)

    def forward(self, input):
        outs = []
        residual_func_idx = 0
        for i in range(self._actual_ch):
            residual = input[i]
            residual_shape = residual.shape[-2:]
            for j in range(len(self._in_channels)):
                if j > i:
                    y = self.residual_func_list[residual_func_idx](input[j])
                    residual_func_idx += 1

                    y = fluid.layers.resize_bilinear(
                        input=y, out_shape=residual_shape)
                    residual = fluid.layers.elementwise_add(
                        x=residual, y=y, act=None)
                elif j < i:
                    y = input[j]
                    for k in range(i - j):
                        y = self.residual_func_list[residual_func_idx](y)
                        residual_func_idx += 1

                    residual = fluid.layers.elementwise_add(
                        x=residual, y=y, act=None)

            layer_helper = LayerHelper(self.full_name(), act='relu')
            residual = layer_helper.append_activation(residual)
            outs.append(residual)

        return outs


class LastClsOut(fluid.dygraph.Layer):
    def __init__(self,
                 num_channel_list,
                 has_se,
                 num_filters_list=[32, 64, 128, 256],
                 name=None):
        super(LastClsOut, self).__init__()

        self.func_list = []
        for idx in range(len(num_channel_list)):
            func = self.add_sublayer(
                "conv_{}_conv_{}".format(name, idx + 1),
                BottleneckBlock(
                    num_channels=num_channel_list[idx],
                    num_filters=num_filters_list[idx],
                    has_se=has_se,
                    downsample=True,
                    name=name + 'conv_' + str(idx + 1)))
            self.func_list.append(func)

    def forward(self, inputs):
        outs = []
        for idx, input in enumerate(inputs):
            out = self.func_list[idx](input)
            outs.append(out)
        return outs


@manager.BACKBONES.add_component
def HRNet_W18_Small_V1(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[1],
        stage1_num_channels=[32],
        stage2_num_modules=1,
        stage2_num_blocks=[2, 2],
        stage2_num_channels=[16, 32],
        stage3_num_modules=1,
        stage3_num_blocks=[2, 2, 2],
        stage3_num_channels=[16, 32, 64],
        stage4_num_modules=1,
        stage4_num_blocks=[2, 2, 2, 2],
        stage4_num_channels=[16, 32, 64, 128],
        **kwargs)
    return model


@manager.BACKBONES.add_component
def HRNet_W18_Small_V2(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[2],
        stage1_num_channels=[64],
        stage2_num_modules=1,
        stage2_num_blocks=[2, 2],
        stage2_num_channels=[18, 36],
        stage3_num_modules=1,
        stage3_num_blocks=[2, 2, 2],
        stage3_num_channels=[18, 36, 72],
        stage4_num_modules=1,
        stage4_num_blocks=[2, 2, 2, 2],
        stage4_num_channels=[18, 36, 72, 144],
        **kwargs)
    return model


@manager.BACKBONES.add_component
def HRNet_W18(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[4],
        stage1_num_channels=[64],
        stage2_num_modules=1,
        stage2_num_blocks=[4, 4],
        stage2_num_channels=[18, 36],
        stage3_num_modules=4,
        stage3_num_blocks=[4, 4, 4],
        stage3_num_channels=[18, 36, 72],
        stage4_num_modules=3,
        stage4_num_blocks=[4, 4, 4, 4],
        stage4_num_channels=[18, 36, 72, 144],
        **kwargs)
    return model


@manager.BACKBONES.add_component
def HRNet_W30(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[4],
        stage1_num_channels=[64],
        stage2_num_modules=1,
        stage2_num_blocks=[4, 4],
        stage2_num_channels=[30, 60],
        stage3_num_modules=4,
        stage3_num_blocks=[4, 4, 4],
        stage3_num_channels=[30, 60, 120],
        stage4_num_modules=3,
        stage4_num_blocks=[4, 4, 4, 4],
        stage4_num_channels=[30, 60, 120, 240],
        **kwargs)
    return model


@manager.BACKBONES.add_component
def HRNet_W32(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[4],
        stage1_num_channels=[64],
        stage2_num_modules=1,
        stage2_num_blocks=[4, 4],
        stage2_num_channels=[32, 64],
        stage3_num_modules=4,
        stage3_num_blocks=[4, 4, 4],
        stage3_num_channels=[32, 64, 128],
        stage4_num_modules=3,
        stage4_num_blocks=[4, 4, 4, 4],
        stage4_num_channels=[32, 64, 128, 256],
        **kwargs)
    return model


@manager.BACKBONES.add_component
def HRNet_W40(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[4],
        stage1_num_channels=[64],
        stage2_num_modules=1,
        stage2_num_blocks=[4, 4],
        stage2_num_channels=[40, 80],
        stage3_num_modules=4,
        stage3_num_blocks=[4, 4, 4],
        stage3_num_channels=[40, 80, 160],
        stage4_num_modules=3,
        stage4_num_blocks=[4, 4, 4, 4],
        stage4_num_channels=[40, 80, 160, 320],
        **kwargs)
    return model


@manager.BACKBONES.add_component
def HRNet_W44(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[4],
        stage1_num_channels=[64],
        stage2_num_modules=1,
        stage2_num_blocks=[4, 4],
        stage2_num_channels=[44, 88],
        stage3_num_modules=4,
        stage3_num_blocks=[4, 4, 4],
        stage3_num_channels=[44, 88, 176],
        stage4_num_modules=3,
        stage4_num_blocks=[4, 4, 4, 4],
        stage4_num_channels=[44, 88, 176, 352],
        **kwargs)
    return model


@manager.BACKBONES.add_component
def HRNet_W48(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[4],
        stage1_num_channels=[64],
        stage2_num_modules=1,
        stage2_num_blocks=[4, 4],
        stage2_num_channels=[48, 96],
        stage3_num_modules=4,
        stage3_num_blocks=[4, 4, 4],
        stage3_num_channels=[48, 96, 192],
        stage4_num_modules=3,
        stage4_num_blocks=[4, 4, 4, 4],
        stage4_num_channels=[48, 96, 192, 384],
        **kwargs)
    return model


@manager.BACKBONES.add_component
def HRNet_W60(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[4],
        stage1_num_channels=[64],
        stage2_num_modules=1,
        stage2_num_blocks=[4, 4],
        stage2_num_channels=[60, 120],
        stage3_num_modules=4,
        stage3_num_blocks=[4, 4, 4],
        stage3_num_channels=[60, 120, 240],
        stage4_num_modules=3,
        stage4_num_blocks=[4, 4, 4, 4],
        stage4_num_channels=[60, 120, 240, 480],
        **kwargs)
    return model


@manager.BACKBONES.add_component
def HRNet_W64(**kwargs):
    model = HRNet(
        stage1_num_modules=1,
        stage1_num_blocks=[4],
        stage1_num_channels=[64],
        stage2_num_modules=1,
        stage2_num_blocks=[4, 4],
        stage2_num_channels=[64, 128],
        stage3_num_modules=4,
        stage3_num_blocks=[4, 4, 4],
        stage3_num_channels=[64, 128, 256],
        stage4_num_modules=3,
        stage4_num_blocks=[4, 4, 4, 4],
        stage4_num_channels=[64, 128, 256, 512],
        **kwargs)
    return model