Text2Human/models/archs/unet_arch.py

import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (UPSAMPLE_LAYERS, ConvModule, build_activation_layer,
                      build_norm_layer, build_upsample_layer, constant_init,
                      kaiming_init)
from mmcv.runner import load_checkpoint
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmseg.utils import get_root_logger


class UpConvBlock(nn.Module):
    """Upsample convolution block in decoder for UNet.

    This upsample convolution block consists of one upsample module
    followed by one convolution block. The upsample module expands the
    high-level low-resolution feature map and the convolution block fuses
    the upsampled high-level low-resolution feature map and the low-level
    high-resolution feature map from encoder.

    Args:
        conv_block (nn.Sequential): Sequential of convolutional layers.
        in_channels (int): Number of input channels of the high-level
        skip_channels (int): Number of input channels of the low-level
        high-resolution feature map from encoder.
        out_channels (int): Number of output channels.
        num_convs (int): Number of convolutional layers in the conv_block.
            Default: 2.
        stride (int): Stride of convolutional layer in conv_block. Default: 1.
        dilation (int): Dilation rate of convolutional layer in conv_block.
            Default: 1.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        conv_cfg (dict | None): Config dict for convolution layer.
            Default: None.
        norm_cfg (dict | None): Config dict for normalization layer.
            Default: dict(type='BN').
        act_cfg (dict | None): Config dict for activation layer in ConvModule.
            Default: dict(type='ReLU').
        upsample_cfg (dict): The upsample config of the upsample module in
            decoder. Default: dict(type='InterpConv'). If the size of
            high-level feature map is the same as that of skip feature map
            (low-level feature map from encoder), it does not need upsample the
            high-level feature map and the upsample_cfg is None.
        dcn (bool): Use deformable convoluton in convolutional layer or not.
            Default: None.
        plugins (dict): plugins for convolutional layers. Default: None.
    """

    def __init__(self,
                 conv_block,
                 in_channels,
                 skip_channels,
                 out_channels,
                 num_convs=2,
                 stride=1,
                 dilation=1,
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 upsample_cfg=dict(type='InterpConv'),
                 dcn=None,
                 plugins=None):
        super(UpConvBlock, self).__init__()
        assert dcn is None, 'Not implemented yet.'
        assert plugins is None, 'Not implemented yet.'

        self.conv_block = conv_block(
            in_channels=2 * skip_channels,
            out_channels=out_channels,
            num_convs=num_convs,
            stride=stride,
            dilation=dilation,
            with_cp=with_cp,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg,
            act_cfg=act_cfg,
            dcn=None,
            plugins=None)
        if upsample_cfg is not None:
            self.upsample = build_upsample_layer(
                cfg=upsample_cfg,
                in_channels=in_channels,
                out_channels=skip_channels,
                with_cp=with_cp,
                norm_cfg=norm_cfg,
                act_cfg=act_cfg)
        else:
            self.upsample = ConvModule(
                in_channels,
                skip_channels,
                kernel_size=1,
                stride=1,
                padding=0,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg,
                act_cfg=act_cfg)

    def forward(self, skip, x):
        """Forward function."""

        x = self.upsample(x)
        out = torch.cat([skip, x], dim=1)
        out = self.conv_block(out)

        return out


class BasicConvBlock(nn.Module):
    """Basic convolutional block for UNet.

    This module consists of several plain convolutional layers.

    Args:
        in_channels (int): Number of input channels.
        out_channels (int): Number of output channels.
        num_convs (int): Number of convolutional layers. Default: 2.
        stride (int): Whether use stride convolution to downsample
            the input feature map. If stride=2, it only uses stride convolution
            in the first convolutional layer to downsample the input feature
            map. Options are 1 or 2. Default: 1.
        dilation (int): Whether use dilated convolution to expand the
            receptive field. Set dilation rate of each convolutional layer and
            the dilation rate of the first convolutional layer is always 1.
            Default: 1.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        conv_cfg (dict | None): Config dict for convolution layer.
            Default: None.
        norm_cfg (dict | None): Config dict for normalization layer.
            Default: dict(type='BN').
        act_cfg (dict | None): Config dict for activation layer in ConvModule.
            Default: dict(type='ReLU').
        dcn (bool): Use deformable convoluton in convolutional layer or not.
            Default: None.
        plugins (dict): plugins for convolutional layers. Default: None.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 num_convs=2,
                 stride=1,
                 dilation=1,
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 dcn=None,
                 plugins=None):
        super(BasicConvBlock, self).__init__()
        assert dcn is None, 'Not implemented yet.'
        assert plugins is None, 'Not implemented yet.'

        self.with_cp = with_cp
        convs = []
        for i in range(num_convs):
            convs.append(
                ConvModule(
                    in_channels=in_channels if i == 0 else out_channels,
                    out_channels=out_channels,
                    kernel_size=3,
                    stride=stride if i == 0 else 1,
                    dilation=1 if i == 0 else dilation,
                    padding=1 if i == 0 else dilation,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    act_cfg=act_cfg))

        self.convs = nn.Sequential(*convs)

    def forward(self, x):
        """Forward function."""

        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(self.convs, x)
        else:
            out = self.convs(x)
        return out


class DeconvModule(nn.Module):
    """Deconvolution upsample module in decoder for UNet (2X upsample).

    This module uses deconvolution to upsample feature map in the decoder
    of UNet.

    Args:
        in_channels (int): Number of input channels.
        out_channels (int): Number of output channels.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        norm_cfg (dict | None): Config dict for normalization layer.
            Default: dict(type='BN').
        act_cfg (dict | None): Config dict for activation layer in ConvModule.
            Default: dict(type='ReLU').
        kernel_size (int): Kernel size of the convolutional layer. Default: 4.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 with_cp=False,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 *,
                 kernel_size=4,
                 scale_factor=2):
        super(DeconvModule, self).__init__()

        assert (kernel_size - scale_factor >= 0) and\
               (kernel_size - scale_factor) % 2 == 0,\
               f'kernel_size should be greater than or equal to scale_factor '\
               f'and (kernel_size - scale_factor) should be even numbers, '\
               f'while the kernel size is {kernel_size} and scale_factor is '\
               f'{scale_factor}.'

        stride = scale_factor
        padding = (kernel_size - scale_factor) // 2
        self.with_cp = with_cp
        deconv = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding)

        norm_name, norm = build_norm_layer(norm_cfg, out_channels)
        activate = build_activation_layer(act_cfg)
        self.deconv_upsamping = nn.Sequential(deconv, norm, activate)

    def forward(self, x):
        """Forward function."""

        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(self.deconv_upsamping, x)
        else:
            out = self.deconv_upsamping(x)
        return out


@UPSAMPLE_LAYERS.register_module()
class InterpConv(nn.Module):
    """Interpolation upsample module in decoder for UNet.

    This module uses interpolation to upsample feature map in the decoder
    of UNet. It consists of one interpolation upsample layer and one
    convolutional layer. It can be one interpolation upsample layer followed
    by one convolutional layer (conv_first=False) or one convolutional layer
    followed by one interpolation upsample layer (conv_first=True).

    Args:
        in_channels (int): Number of input channels.
        out_channels (int): Number of output channels.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        norm_cfg (dict | None): Config dict for normalization layer.
            Default: dict(type='BN').
        act_cfg (dict | None): Config dict for activation layer in ConvModule.
            Default: dict(type='ReLU').
        conv_cfg (dict | None): Config dict for convolution layer.
            Default: None.
        conv_first (bool): Whether convolutional layer or interpolation
            upsample layer first. Default: False. It means interpolation
            upsample layer followed by one convolutional layer.
        kernel_size (int): Kernel size of the convolutional layer. Default: 1.
        stride (int): Stride of the convolutional layer. Default: 1.
        padding (int): Padding of the convolutional layer. Default: 1.
        upsampe_cfg (dict): Interpolation config of the upsample layer.
            Default: dict(
                scale_factor=2, mode='bilinear', align_corners=False).
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 with_cp=False,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 *,
                 conv_cfg=None,
                 conv_first=False,
                 kernel_size=1,
                 stride=1,
                 padding=0,
                 upsampe_cfg=dict(
                     scale_factor=2, mode='bilinear', align_corners=False)):
        super(InterpConv, self).__init__()

        self.with_cp = with_cp
        conv = ConvModule(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg,
            act_cfg=act_cfg)
        upsample = nn.Upsample(**upsampe_cfg)
        if conv_first:
            self.interp_upsample = nn.Sequential(conv, upsample)
        else:
            self.interp_upsample = nn.Sequential(upsample, conv)

    def forward(self, x):
        """Forward function."""

        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(self.interp_upsample, x)
        else:
            out = self.interp_upsample(x)
        return out


class UNet(nn.Module):
    """UNet backbone.
    U-Net: Convolutional Networks for Biomedical Image Segmentation.
    https://arxiv.org/pdf/1505.04597.pdf

    Args:
        in_channels (int): Number of input image channels. Default" 3.
        base_channels (int): Number of base channels of each stage.
            The output channels of the first stage. Default: 64.
        num_stages (int): Number of stages in encoder, normally 5. Default: 5.
        strides (Sequence[int 1 | 2]): Strides of each stage in encoder.
            len(strides) is equal to num_stages. Normally the stride of the
            first stage in encoder is 1. If strides[i]=2, it uses stride
            convolution to downsample in the correspondence encoder stage.
            Default: (1, 1, 1, 1, 1).
        enc_num_convs (Sequence[int]): Number of convolutional layers in the
            convolution block of the correspondence encoder stage.
            Default: (2, 2, 2, 2, 2).
        dec_num_convs (Sequence[int]): Number of convolutional layers in the
            convolution block of the correspondence decoder stage.
            Default: (2, 2, 2, 2).
        downsamples (Sequence[int]): Whether use MaxPool to downsample the
            feature map after the first stage of encoder
            (stages: [1, num_stages)). If the correspondence encoder stage use
            stride convolution (strides[i]=2), it will never use MaxPool to
            downsample, even downsamples[i-1]=True.
            Default: (True, True, True, True).
        enc_dilations (Sequence[int]): Dilation rate of each stage in encoder.
            Default: (1, 1, 1, 1, 1).
        dec_dilations (Sequence[int]): Dilation rate of each stage in decoder.
            Default: (1, 1, 1, 1).
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        conv_cfg (dict | None): Config dict for convolution layer.
            Default: None.
        norm_cfg (dict | None): Config dict for normalization layer.
            Default: dict(type='BN').
        act_cfg (dict | None): Config dict for activation layer in ConvModule.
            Default: dict(type='ReLU').
        upsample_cfg (dict): The upsample config of the upsample module in
            decoder. Default: dict(type='InterpConv').
        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.
        dcn (bool): Use deformable convolution in convolutional layer or not.
            Default: None.
        plugins (dict): plugins for convolutional layers. Default: None.

    Notice:
        The input image size should be devisible by the whole downsample rate
        of the encoder. More detail of the whole downsample rate can be found
        in UNet._check_input_devisible.

    """

    def __init__(self,
                 in_channels=3,
                 base_channels=64,
                 num_stages=5,
                 strides=(1, 1, 1, 1, 1),
                 enc_num_convs=(2, 2, 2, 2, 2),
                 dec_num_convs=(2, 2, 2, 2),
                 downsamples=(True, True, True, True),
                 enc_dilations=(1, 1, 1, 1, 1),
                 dec_dilations=(1, 1, 1, 1),
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 upsample_cfg=dict(type='InterpConv'),
                 norm_eval=False,
                 dcn=None,
                 plugins=None):
        super(UNet, self).__init__()
        assert dcn is None, 'Not implemented yet.'
        assert plugins is None, 'Not implemented yet.'
        assert len(strides) == num_stages, \
            'The length of strides should be equal to num_stages, '\
            f'while the strides is {strides}, the length of '\
            f'strides is {len(strides)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(enc_num_convs) == num_stages, \
            'The length of enc_num_convs should be equal to num_stages, '\
            f'while the enc_num_convs is {enc_num_convs}, the length of '\
            f'enc_num_convs is {len(enc_num_convs)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(dec_num_convs) == (num_stages-1), \
            'The length of dec_num_convs should be equal to (num_stages-1), '\
            f'while the dec_num_convs is {dec_num_convs}, the length of '\
            f'dec_num_convs is {len(dec_num_convs)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(downsamples) == (num_stages-1), \
            'The length of downsamples should be equal to (num_stages-1), '\
            f'while the downsamples is {downsamples}, the length of '\
            f'downsamples is {len(downsamples)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(enc_dilations) == num_stages, \
            'The length of enc_dilations should be equal to num_stages, '\
            f'while the enc_dilations is {enc_dilations}, the length of '\
            f'enc_dilations is {len(enc_dilations)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(dec_dilations) == (num_stages-1), \
            'The length of dec_dilations should be equal to (num_stages-1), '\
            f'while the dec_dilations is {dec_dilations}, the length of '\
            f'dec_dilations is {len(dec_dilations)}, and the num_stages is '\
            f'{num_stages}.'
        self.num_stages = num_stages
        self.strides = strides
        self.downsamples = downsamples
        self.norm_eval = norm_eval

        self.encoder = nn.ModuleList()
        self.decoder = nn.ModuleList()

        for i in range(num_stages):
            enc_conv_block = []
            if i != 0:
                if strides[i] == 1 and downsamples[i - 1]:
                    enc_conv_block.append(nn.MaxPool2d(kernel_size=2))
                upsample = (strides[i] != 1 or downsamples[i - 1])
                self.decoder.append(
                    UpConvBlock(
                        conv_block=BasicConvBlock,
                        in_channels=base_channels * 2**i,
                        skip_channels=base_channels * 2**(i - 1),
                        out_channels=base_channels * 2**(i - 1),
                        num_convs=dec_num_convs[i - 1],
                        stride=1,
                        dilation=dec_dilations[i - 1],
                        with_cp=with_cp,
                        conv_cfg=conv_cfg,
                        norm_cfg=norm_cfg,
                        act_cfg=act_cfg,
                        upsample_cfg=upsample_cfg if upsample else None,
                        dcn=None,
                        plugins=None))

            enc_conv_block.append(
                BasicConvBlock(
                    in_channels=in_channels,
                    out_channels=base_channels * 2**i,
                    num_convs=enc_num_convs[i],
                    stride=strides[i],
                    dilation=enc_dilations[i],
                    with_cp=with_cp,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    act_cfg=act_cfg,
                    dcn=None,
                    plugins=None))
            self.encoder.append((nn.Sequential(*enc_conv_block)))
            in_channels = base_channels * 2**i

    def forward(self, x):
        enc_outs = []

        for enc in self.encoder:
            x = enc(x)
            enc_outs.append(x)
        dec_outs = [x]
        for i in reversed(range(len(self.decoder))):
            x = self.decoder[i](enc_outs[i], x)
            dec_outs.append(x)

        return dec_outs

    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):
                    kaiming_init(m)
                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
                    constant_init(m, 1)
        else:
            raise TypeError('pretrained must be a str or None')


class ShapeUNet(nn.Module):
    """ShapeUNet backbone with small modifications.
    U-Net: Convolutional Networks for Biomedical Image Segmentation.
    https://arxiv.org/pdf/1505.04597.pdf

    Args:
        in_channels (int): Number of input image channels. Default" 3.
        base_channels (int): Number of base channels of each stage.
            The output channels of the first stage. Default: 64.
        num_stages (int): Number of stages in encoder, normally 5. Default: 5.
        strides (Sequence[int 1 | 2]): Strides of each stage in encoder.
            len(strides) is equal to num_stages. Normally the stride of the
            first stage in encoder is 1. If strides[i]=2, it uses stride
            convolution to downsample in the correspondance encoder stage.
            Default: (1, 1, 1, 1, 1).
        enc_num_convs (Sequence[int]): Number of convolutional layers in the
            convolution block of the correspondance encoder stage.
            Default: (2, 2, 2, 2, 2).
        dec_num_convs (Sequence[int]): Number of convolutional layers in the
            convolution block of the correspondance decoder stage.
            Default: (2, 2, 2, 2).
        downsamples (Sequence[int]): Whether use MaxPool to downsample the
            feature map after the first stage of encoder
            (stages: [1, num_stages)). If the correspondance encoder stage use
            stride convolution (strides[i]=2), it will never use MaxPool to
            downsample, even downsamples[i-1]=True.
            Default: (True, True, True, True).
        enc_dilations (Sequence[int]): Dilation rate of each stage in encoder.
            Default: (1, 1, 1, 1, 1).
        dec_dilations (Sequence[int]): Dilation rate of each stage in decoder.
            Default: (1, 1, 1, 1).
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        conv_cfg (dict | None): Config dict for convolution layer.
            Default: None.
        norm_cfg (dict | None): Config dict for normalization layer.
            Default: dict(type='BN').
        act_cfg (dict | None): Config dict for activation layer in ConvModule.
            Default: dict(type='ReLU').
        upsample_cfg (dict): The upsample config of the upsample module in
            decoder. Default: dict(type='InterpConv').
        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.
        dcn (bool): Use deformable convoluton in convolutional layer or not.
            Default: None.
        plugins (dict): plugins for convolutional layers. Default: None.

    Notice:
        The input image size should be devisible by the whole downsample rate
        of the encoder. More detail of the whole downsample rate can be found
        in UNet._check_input_devisible.

    """

    def __init__(self,
                 in_channels=3,
                 base_channels=64,
                 num_stages=5,
                 attr_embedding=128,
                 strides=(1, 1, 1, 1, 1),
                 enc_num_convs=(2, 2, 2, 2, 2),
                 dec_num_convs=(2, 2, 2, 2),
                 downsamples=(True, True, True, True),
                 enc_dilations=(1, 1, 1, 1, 1),
                 dec_dilations=(1, 1, 1, 1),
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 upsample_cfg=dict(type='InterpConv'),
                 norm_eval=False,
                 dcn=None,
                 plugins=None):
        super(ShapeUNet, self).__init__()
        assert dcn is None, 'Not implemented yet.'
        assert plugins is None, 'Not implemented yet.'
        assert len(strides) == num_stages, \
            'The length of strides should be equal to num_stages, '\
            f'while the strides is {strides}, the length of '\
            f'strides is {len(strides)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(enc_num_convs) == num_stages, \
            'The length of enc_num_convs should be equal to num_stages, '\
            f'while the enc_num_convs is {enc_num_convs}, the length of '\
            f'enc_num_convs is {len(enc_num_convs)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(dec_num_convs) == (num_stages-1), \
            'The length of dec_num_convs should be equal to (num_stages-1), '\
            f'while the dec_num_convs is {dec_num_convs}, the length of '\
            f'dec_num_convs is {len(dec_num_convs)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(downsamples) == (num_stages-1), \
            'The length of downsamples should be equal to (num_stages-1), '\
            f'while the downsamples is {downsamples}, the length of '\
            f'downsamples is {len(downsamples)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(enc_dilations) == num_stages, \
            'The length of enc_dilations should be equal to num_stages, '\
            f'while the enc_dilations is {enc_dilations}, the length of '\
            f'enc_dilations is {len(enc_dilations)}, and the num_stages is '\
            f'{num_stages}.'
        assert len(dec_dilations) == (num_stages-1), \
            'The length of dec_dilations should be equal to (num_stages-1), '\
            f'while the dec_dilations is {dec_dilations}, the length of '\
            f'dec_dilations is {len(dec_dilations)}, and the num_stages is '\
            f'{num_stages}.'
        self.num_stages = num_stages
        self.strides = strides
        self.downsamples = downsamples
        self.norm_eval = norm_eval

        self.encoder = nn.ModuleList()
        self.decoder = nn.ModuleList()

        for i in range(num_stages):
            enc_conv_block = []
            if i != 0:
                if strides[i] == 1 and downsamples[i - 1]:
                    enc_conv_block.append(nn.MaxPool2d(kernel_size=2))
                upsample = (strides[i] != 1 or downsamples[i - 1])
                self.decoder.append(
                    UpConvBlock(
                        conv_block=BasicConvBlock,
                        in_channels=base_channels * 2**i,
                        skip_channels=base_channels * 2**(i - 1),
                        out_channels=base_channels * 2**(i - 1),
                        num_convs=dec_num_convs[i - 1],
                        stride=1,
                        dilation=dec_dilations[i - 1],
                        with_cp=with_cp,
                        conv_cfg=conv_cfg,
                        norm_cfg=norm_cfg,
                        act_cfg=act_cfg,
                        upsample_cfg=upsample_cfg if upsample else None,
                        dcn=None,
                        plugins=None))

            enc_conv_block.append(
                BasicConvBlock(
                    in_channels=in_channels + attr_embedding,
                    out_channels=base_channels * 2**i,
                    num_convs=enc_num_convs[i],
                    stride=strides[i],
                    dilation=enc_dilations[i],
                    with_cp=with_cp,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    act_cfg=act_cfg,
                    dcn=None,
                    plugins=None))
            self.encoder.append((nn.Sequential(*enc_conv_block)))
            in_channels = base_channels * 2**i

    def forward(self, x, attr_embedding):
        enc_outs = []
        Be, Ce = attr_embedding.size()
        for enc in self.encoder:
            _, _, H, W = x.size()
            x = enc(
                torch.cat([
                    x,
                    attr_embedding.view(Be, Ce, 1, 1).expand((Be, Ce, H, W))
                ],
                          dim=1))
            enc_outs.append(x)
        dec_outs = [x]
        for i in reversed(range(len(self.decoder))):
            x = self.decoder[i](enc_outs[i], x)
            dec_outs.append(x)

        return dec_outs

    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):
                    kaiming_init(m)
                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
                    constant_init(m, 1)
        else:
            raise TypeError('pretrained must be a str or None')