import sys
_module = sys.modules[__name__]
del sys
litehrnet_18_coco_256x192 = _module
litehrnet_18_coco_384x288 = _module
litehrnet_30_coco_256x192 = _module
litehrnet_30_coco_384x288 = _module
litehrnet_18_mpii_256x256 = _module
litehrnet_30_mpii_256x256 = _module
naive_litehrnet_18_coco_256x192 = _module
wider_naive_litehrnet_18_coco_256x192 = _module
naive_litehrnet_18_mpii_256x256 = _module
wider_naive_litehrnet_18_mpii_256x256 = _module
models = _module
backbones = _module
litehrnet = _module
builder = _module
summary_network = _module
test = _module
torchstat = _module
analyzer = _module
compute_flops = _module
compute_madd = _module
compute_memory = _module
reporter = _module
stat_tree = _module
torchstat_utils = _module
train = _module

from _paritybench_helpers import _mock_config, patch_functional
from unittest.mock import mock_open, MagicMock
from torch.autograd import Function
from torch.nn import Module
import abc, collections, copy, enum, functools, inspect, itertools, logging, math, matplotlib, numbers, numpy, pandas, queue, random, re, scipy, sklearn, string, tensorflow, time, torch, torchaudio, torchtext, torchvision, types, typing, uuid, warnings
import numpy as np
from torch import Tensor
patch_functional()
open = mock_open()
yaml = logging = sys = argparse = MagicMock()
ArgumentParser = argparse.ArgumentParser
_global_config = args = argv = cfg = config = params = _mock_config()
argparse.ArgumentParser.return_value.parse_args.return_value = _global_config
yaml.load.return_value = _global_config
sys.argv = _global_config
__version__ = '1.0.0'
xrange = range
wraps = functools.wraps


import torch


import torch.nn as nn


import torch.nn.functional as F


from torch.nn.modules.batchnorm import _BatchNorm


import torch.utils.checkpoint as cp


from torch import nn


import time


from collections import OrderedDict


from typing import Dict


from typing import Sequence


import functools


import itertools


import numpy as np


import math


import copy


class SpatialWeighting(nn.Module):

    def __init__(self, channels, ratio=16, conv_cfg=None, act_cfg=(dict(type='ReLU'), dict(type='Sigmoid'))):
        super().__init__()
        if isinstance(act_cfg, dict):
            act_cfg = act_cfg, act_cfg
        assert len(act_cfg) == 2
        assert mmcv.is_tuple_of(act_cfg, dict)
        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
        self.conv1 = ConvModule(in_channels=channels, out_channels=int(channels / ratio), kernel_size=1, stride=1, conv_cfg=conv_cfg, act_cfg=act_cfg[0])
        self.conv2 = ConvModule(in_channels=int(channels / ratio), out_channels=channels, kernel_size=1, stride=1, conv_cfg=conv_cfg, act_cfg=act_cfg[1])

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


class CrossResolutionWeighting(nn.Module):

    def __init__(self, channels, ratio=16, conv_cfg=None, norm_cfg=None, act_cfg=(dict(type='ReLU'), dict(type='Sigmoid'))):
        super().__init__()
        if isinstance(act_cfg, dict):
            act_cfg = act_cfg, act_cfg
        assert len(act_cfg) == 2
        assert mmcv.is_tuple_of(act_cfg, dict)
        self.channels = channels
        total_channel = sum(channels)
        self.conv1 = ConvModule(in_channels=total_channel, out_channels=int(total_channel / ratio), kernel_size=1, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg[0])
        self.conv2 = ConvModule(in_channels=int(total_channel / ratio), out_channels=total_channel, kernel_size=1, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg[1])

    def forward(self, x):
        mini_size = x[-1].size()[-2:]
        out = [F.adaptive_avg_pool2d(s, mini_size) for s in x[:-1]] + [x[-1]]
        out = torch.cat(out, dim=1)
        out = self.conv1(out)
        out = self.conv2(out)
        out = torch.split(out, self.channels, dim=1)
        out = [(s * F.interpolate(a, size=s.size()[-2:], mode='nearest')) for s, a in zip(x, out)]
        return out


class ConditionalChannelWeighting(nn.Module):

    def __init__(self, in_channels, stride, reduce_ratio, conv_cfg=None, norm_cfg=dict(type='BN'), with_cp=False):
        super().__init__()
        self.with_cp = with_cp
        self.stride = stride
        assert stride in [1, 2]
        branch_channels = [(channel // 2) for channel in in_channels]
        self.cross_resolution_weighting = CrossResolutionWeighting(branch_channels, ratio=reduce_ratio, conv_cfg=conv_cfg, norm_cfg=norm_cfg)
        self.depthwise_convs = nn.ModuleList([ConvModule(channel, channel, kernel_size=3, stride=self.stride, padding=1, groups=channel, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None) for channel in branch_channels])
        self.spatial_weighting = nn.ModuleList([SpatialWeighting(channels=channel, ratio=4) for channel in branch_channels])

    def forward(self, x):

        def _inner_forward(x):
            x = [s.chunk(2, dim=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 = [torch.cat([s1, s2], dim=1) for s1, s2 in zip(x1, x2)]
            out = [channel_shuffle(s, 2) for s in out]
            return out
        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(_inner_forward, x)
        else:
            out = _inner_forward(x)
        return out


class Stem(nn.Module):

    def __init__(self, in_channels, stem_channels, out_channels, expand_ratio, conv_cfg=None, norm_cfg=dict(type='BN'), with_cp=False):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.with_cp = with_cp
        self.conv1 = ConvModule(in_channels=in_channels, out_channels=stem_channels, kernel_size=3, stride=2, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=dict(type='ReLU'))
        mid_channels = int(round(stem_channels * expand_ratio))
        branch_channels = stem_channels // 2
        if stem_channels == self.out_channels:
            inc_channels = self.out_channels - branch_channels
        else:
            inc_channels = self.out_channels - stem_channels
        self.branch1 = nn.Sequential(ConvModule(branch_channels, branch_channels, kernel_size=3, stride=2, padding=1, groups=branch_channels, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None), ConvModule(branch_channels, inc_channels, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=dict(type='ReLU')))
        self.expand_conv = ConvModule(branch_channels, mid_channels, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=dict(type='ReLU'))
        self.depthwise_conv = ConvModule(mid_channels, mid_channels, kernel_size=3, stride=2, padding=1, groups=mid_channels, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None)
        self.linear_conv = ConvModule(mid_channels, branch_channels if stem_channels == self.out_channels else stem_channels, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=dict(type='ReLU'))

    def forward(self, x):

        def _inner_forward(x):
            x = self.conv1(x)
            x1, x2 = x.chunk(2, dim=1)
            x2 = self.expand_conv(x2)
            x2 = self.depthwise_conv(x2)
            x2 = self.linear_conv(x2)
            out = torch.cat((self.branch1(x1), x2), dim=1)
            out = channel_shuffle(out, 2)
            return out
        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(_inner_forward, x)
        else:
            out = _inner_forward(x)
        return out


class IterativeHead(nn.Module):

    def __init__(self, in_channels, conv_cfg=None, norm_cfg=dict(type='BN')):
        super().__init__()
        projects = []
        num_branchs = len(in_channels)
        self.in_channels = in_channels[::-1]
        for i in range(num_branchs):
            if i != num_branchs - 1:
                projects.append(DepthwiseSeparableConvModule(in_channels=self.in_channels[i], out_channels=self.in_channels[i + 1], kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg, act_cfg=dict(type='ReLU'), dw_act_cfg=None, pw_act_cfg=dict(type='ReLU')))
            else:
                projects.append(DepthwiseSeparableConvModule(in_channels=self.in_channels[i], out_channels=self.in_channels[i], kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg, act_cfg=dict(type='ReLU'), dw_act_cfg=None, pw_act_cfg=dict(type='ReLU')))
        self.projects = nn.ModuleList(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.size()[-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 ShuffleUnit(nn.Module):
    """InvertedResidual block for ShuffleNetV2 backbone.

    Args:
        in_channels (int): The input channels of the block.
        out_channels (int): The output channels of the block.
        stride (int): Stride of the 3x3 convolution layer. Default: 1
        conv_cfg (dict): Config dict for convolution layer.
            Default: None, which means using conv2d.
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='BN').
        act_cfg (dict): Config dict for activation layer.
            Default: dict(type='ReLU').
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
    """

    def __init__(self, in_channels, out_channels, stride=1, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'), with_cp=False):
        super().__init__()
        self.stride = stride
        self.with_cp = with_cp
        branch_features = out_channels // 2
        if self.stride == 1:
            assert in_channels == branch_features * 2, f'in_channels ({in_channels}) should equal to branch_features * 2 ({branch_features * 2}) when stride is 1'
        if in_channels != branch_features * 2:
            assert self.stride != 1, f'stride ({self.stride}) should not equal 1 when in_channels != branch_features * 2'
        if self.stride > 1:
            self.branch1 = nn.Sequential(ConvModule(in_channels, in_channels, kernel_size=3, stride=self.stride, padding=1, groups=in_channels, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None), ConvModule(in_channels, branch_features, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg))
        self.branch2 = nn.Sequential(ConvModule(in_channels if self.stride > 1 else branch_features, branch_features, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg), ConvModule(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1, groups=branch_features, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None), ConvModule(branch_features, branch_features, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg))

    def forward(self, x):

        def _inner_forward(x):
            if self.stride > 1:
                out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
            else:
                x1, x2 = x.chunk(2, dim=1)
                out = torch.cat((x1, self.branch2(x2)), dim=1)
            out = channel_shuffle(out, 2)
            return out
        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(_inner_forward, x)
        else:
            out = _inner_forward(x)
        return out


class LiteHRModule(nn.Module):

    def __init__(self, num_branches, num_blocks, in_channels, reduce_ratio, module_type, multiscale_output=False, with_fuse=True, conv_cfg=None, norm_cfg=dict(type='BN'), with_cp=False):
        super().__init__()
        self._check_branches(num_branches, in_channels)
        self.in_channels = in_channels
        self.num_branches = num_branches
        self.module_type = module_type
        self.multiscale_output = multiscale_output
        self.with_fuse = with_fuse
        self.norm_cfg = norm_cfg
        self.conv_cfg = conv_cfg
        self.with_cp = with_cp
        if self.module_type == 'LITE':
            self.layers = self._make_weighting_blocks(num_blocks, reduce_ratio)
        elif self.module_type == 'NAIVE':
            self.layers = self._make_naive_branches(num_branches, num_blocks)
        if self.with_fuse:
            self.fuse_layers = self._make_fuse_layers()
            self.relu = nn.ReLU()

    def _check_branches(self, num_branches, in_channels):
        """Check input to avoid ValueError."""
        if num_branches != len(in_channels):
            error_msg = f'NUM_BRANCHES({num_branches}) != NUM_INCHANNELS({len(in_channels)})'
            raise ValueError(error_msg)

    def _make_weighting_blocks(self, num_blocks, reduce_ratio, stride=1):
        layers = []
        for i in range(num_blocks):
            layers.append(ConditionalChannelWeighting(self.in_channels, stride=stride, reduce_ratio=reduce_ratio, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, with_cp=self.with_cp))
        return nn.Sequential(*layers)

    def _make_one_branch(self, branch_index, num_blocks, stride=1):
        """Make one branch."""
        layers = []
        layers.append(ShuffleUnit(self.in_channels[branch_index], self.in_channels[branch_index], stride=stride, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=dict(type='ReLU'), with_cp=self.with_cp))
        for i in range(1, num_blocks):
            layers.append(ShuffleUnit(self.in_channels[branch_index], self.in_channels[branch_index], stride=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=dict(type='ReLU'), with_cp=self.with_cp))
        return nn.Sequential(*layers)

    def _make_naive_branches(self, num_branches, num_blocks):
        """Make branches."""
        branches = []
        for i in range(num_branches):
            branches.append(self._make_one_branch(i, num_blocks))
        return nn.ModuleList(branches)

    def _make_fuse_layers(self):
        """Make fuse layer."""
        if self.num_branches == 1:
            return None
        num_branches = self.num_branches
        in_channels = self.in_channels
        fuse_layers = []
        num_out_branches = num_branches if self.multiscale_output else 1
        for i in range(num_out_branches):
            fuse_layer = []
            for j in range(num_branches):
                if j > i:
                    fuse_layer.append(nn.Sequential(build_conv_layer(self.conv_cfg, in_channels[j], in_channels[i], kernel_size=1, stride=1, padding=0, bias=False), build_norm_layer(self.norm_cfg, in_channels[i])[1], 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(build_conv_layer(self.conv_cfg, in_channels[j], in_channels[j], kernel_size=3, stride=2, padding=1, groups=in_channels[j], bias=False), build_norm_layer(self.norm_cfg, in_channels[j])[1], build_conv_layer(self.conv_cfg, in_channels[j], in_channels[i], kernel_size=1, stride=1, padding=0, bias=False), build_norm_layer(self.norm_cfg, in_channels[i])[1]))
                        else:
                            conv_downsamples.append(nn.Sequential(build_conv_layer(self.conv_cfg, in_channels[j], in_channels[j], kernel_size=3, stride=2, padding=1, groups=in_channels[j], bias=False), build_norm_layer(self.norm_cfg, in_channels[j])[1], build_conv_layer(self.conv_cfg, in_channels[j], in_channels[j], kernel_size=1, stride=1, padding=0, bias=False), build_norm_layer(self.norm_cfg, in_channels[j])[1], nn.ReLU(inplace=True)))
                    fuse_layer.append(nn.Sequential(*conv_downsamples))
            fuse_layers.append(nn.ModuleList(fuse_layer))
        return nn.ModuleList(fuse_layers)

    def forward(self, x):
        """Forward function."""
        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 i == j:
                        y += out[j]
                    else:
                        y += self.fuse_layers[i][j](out[j])
                out_fuse.append(self.relu(y))
            out = out_fuse
        elif not self.multiscale_output:
            out = [out[0]]
        return out


class LiteHRNet(nn.Module):
    """Lite-HRNet backbone.

    `High-Resolution Representations for Labeling Pixels and Regions
    <https://arxiv.org/abs/1904.04514>`_

    Args:
        extra (dict): detailed configuration for each stage of HRNet.
        in_channels (int): Number of input image channels. Default: 3.
        conv_cfg (dict): dictionary to construct and config conv layer.
        norm_cfg (dict): dictionary to construct and config norm layer.
        norm_eval (bool): Whether to set norm layers to eval mode, namely,
            freeze running stats (mean and var). Note: Effect on Batch Norm
            and its variants only. Default: False
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed.
        zero_init_residual (bool): whether to use zero init for last norm layer
            in resblocks to let them behave as identity.

    Example:
        >>> from mmpose.models import HRNet
        >>> import torch
        >>> extra = dict(
        >>>     stage1=dict(
        >>>         num_modules=1,
        >>>         num_branches=1,
        >>>         block='BOTTLENECK',
        >>>         num_blocks=(4, ),
        >>>         num_channels=(64, )),
        >>>     stage2=dict(
        >>>         num_modules=1,
        >>>         num_branches=2,
        >>>         block='BASIC',
        >>>         num_blocks=(4, 4),
        >>>         num_channels=(32, 64)),
        >>>     stage3=dict(
        >>>         num_modules=4,
        >>>         num_branches=3,
        >>>         block='BASIC',
        >>>         num_blocks=(4, 4, 4),
        >>>         num_channels=(32, 64, 128)),
        >>>     stage4=dict(
        >>>         num_modules=3,
        >>>         num_branches=4,
        >>>         block='BASIC',
        >>>         num_blocks=(4, 4, 4, 4),
        >>>         num_channels=(32, 64, 128, 256)))
        >>> self = HRNet(extra, in_channels=1)
        >>> self.eval()
        >>> inputs = torch.rand(1, 1, 32, 32)
        >>> level_outputs = self.forward(inputs)
        >>> for level_out in level_outputs:
        ...     print(tuple(level_out.shape))
        (1, 32, 8, 8)
        (1, 64, 4, 4)
        (1, 128, 2, 2)
        (1, 256, 1, 1)
    """

    def __init__(self, extra, in_channels=3, conv_cfg=None, norm_cfg=dict(type='BN'), norm_eval=False, with_cp=False, zero_init_residual=False):
        super().__init__()
        self.extra = extra
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.norm_eval = norm_eval
        self.with_cp = with_cp
        self.zero_init_residual = zero_init_residual
        self.stem = Stem(in_channels, stem_channels=self.extra['stem']['stem_channels'], out_channels=self.extra['stem']['out_channels'], expand_ratio=self.extra['stem']['expand_ratio'], conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg)
        self.num_stages = self.extra['num_stages']
        self.stages_spec = self.extra['stages_spec']
        num_channels_last = [self.stem.out_channels]
        for i in range(self.num_stages):
            num_channels = self.stages_spec['num_channels'][i]
            num_channels = [num_channels[i] for i in range(len(num_channels))]
            setattr(self, 'transition{}'.format(i), self._make_transition_layer(num_channels_last, num_channels))
            stage, num_channels_last = self._make_stage(self.stages_spec, i, num_channels, multiscale_output=True)
            setattr(self, 'stage{}'.format(i), stage)
        self.with_head = self.extra['with_head']
        if self.with_head:
            self.head_layer = IterativeHead(in_channels=num_channels_last, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg)

    def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer):
        """Make transition layer."""
        num_branches_cur = len(num_channels_cur_layer)
        num_branches_pre = len(num_channels_pre_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(build_conv_layer(self.conv_cfg, 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), build_norm_layer(self.norm_cfg, num_channels_pre_layer[i])[1], build_conv_layer(self.conv_cfg, num_channels_pre_layer[i], num_channels_cur_layer[i], kernel_size=1, stride=1, padding=0, bias=False), build_norm_layer(self.norm_cfg, num_channels_cur_layer[i])[1], nn.ReLU()))
                else:
                    transition_layers.append(None)
            else:
                conv_downsamples = []
                for j in range(i + 1 - num_branches_pre):
                    in_channels = num_channels_pre_layer[-1]
                    out_channels = num_channels_cur_layer[i] if j == i - num_branches_pre else in_channels
                    conv_downsamples.append(nn.Sequential(build_conv_layer(self.conv_cfg, in_channels, in_channels, kernel_size=3, stride=2, padding=1, groups=in_channels, bias=False), build_norm_layer(self.norm_cfg, in_channels)[1], build_conv_layer(self.conv_cfg, in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False), build_norm_layer(self.norm_cfg, out_channels)[1], nn.ReLU()))
                transition_layers.append(nn.Sequential(*conv_downsamples))
        return nn.ModuleList(transition_layers)

    def _make_stage(self, stages_spec, stage_index, in_channels, multiscale_output=True):
        num_modules = stages_spec['num_modules'][stage_index]
        num_branches = stages_spec['num_branches'][stage_index]
        num_blocks = stages_spec['num_blocks'][stage_index]
        reduce_ratio = stages_spec['reduce_ratios'][stage_index]
        with_fuse = stages_spec['with_fuse'][stage_index]
        module_type = stages_spec['module_type'][stage_index]
        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(LiteHRModule(num_branches, num_blocks, in_channels, reduce_ratio, module_type, multiscale_output=reset_multiscale_output, with_fuse=with_fuse, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, with_cp=self.with_cp))
            in_channels = modules[-1].in_channels
        return nn.Sequential(*modules), in_channels

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.

        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """
        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint(self, pretrained, strict=False, logger=logger)
        elif pretrained is None:
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    normal_init(m, std=0.001)
                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
                    constant_init(m, 1)
            if self.zero_init_residual:
                for m in self.modules():
                    if isinstance(m, Bottleneck):
                        constant_init(m.norm3, 0)
                    elif isinstance(m, BasicBlock):
                        constant_init(m.norm2, 0)
        else:
            raise TypeError('pretrained must be a str or None')

    def forward(self, x):
        """Forward function."""
        x = self.stem(x)
        y_list = [x]
        for i in range(self.num_stages):
            x_list = []
            transition = getattr(self, 'transition{}'.format(i))
            for j in range(self.stages_spec['num_branches'][i]):
                if transition[j]:
                    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(i))(x_list)
        x = y_list
        if self.with_head:
            x = self.head_layer(x)
        return [x[0]]

    def train(self, mode=True):
        """Convert the model into training mode."""
        super().train(mode)
        if mode and self.norm_eval:
            for m in self.modules():
                if isinstance(m, _BatchNorm):
                    m.eval()

