import torch.nn as nn import torch.utils.checkpoint as cp from mmcv.cnn import (UPSAMPLE_LAYERS, ConvModule, build_activation_layer, build_norm_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 from ..builder import BACKBONES from ..utils import UpConvBlock 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 @UPSAMPLE_LAYERS.register_module() 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 @BACKBONES.register_module() 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 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, 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): self._check_input_devisible(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 train(self, mode=True): """Convert the model into training mode while keep normalization layer freezed.""" super(UNet, self).train(mode) if mode and self.norm_eval: for m in self.modules(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): m.eval() def _check_input_devisible(self, x): h, w = x.shape[-2:] whole_downsample_rate = 1 for i in range(1, self.num_stages): if self.strides[i] == 2 or self.downsamples[i - 1]: whole_downsample_rate *= 2 assert (h % whole_downsample_rate == 0) \ and (w % whole_downsample_rate == 0),\ f'The input image size {(h, w)} should be devisible by the whole '\ f'downsample rate {whole_downsample_rate}, when num_stages is '\ f'{self.num_stages}, strides is {self.strides}, and downsamples '\ f'is {self.downsamples}.' 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')