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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, build_norm_layer
from mmengine.model import BaseModule
from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
from mmocr.registry import MODELS
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 convolution 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().__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:
upsample_cfg.update(
dict(
in_channels=in_channels,
out_channels=skip_channels,
with_cp=with_cp,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.upsample = MODELS.build(upsample_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 convolution 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().__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
@MODELS.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().__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 = build_norm_layer(norm_cfg, out_channels)
activate = MODELS.build(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
@MODELS.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.
upsample_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,
upsample_cfg=dict(
scale_factor=2, mode='bilinear', align_corners=False)):
super().__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(**upsample_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
@MODELS.register_module()
class UNet(BaseModule):
"""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 divisible by the whole downsample rate
of the encoder. More detail of the whole downsample rate can be found
in UNet._check_input_divisible.
"""
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,
init_cfg=[
dict(type='Kaiming', layer='Conv2d'),
dict(
type='Constant',
layer=['_BatchNorm', 'GroupNorm'],
val=1)
]):
super().__init__(init_cfg=init_cfg)
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.base_channels = base_channels
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_divisible(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().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_divisible(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 divisible 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}.')