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from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmcv.runner import auto_fp16, force_fp32
from mmseg.core import build_pixel_sampler
from mmseg.ops import resize
from ..builder import build_loss
from ..losses import accuracy
class BaseDecodeHead(nn.Module, metaclass=ABCMeta):
"""Base class for BaseDecodeHead.
Args:
in_channels (int|Sequence[int]): Input channels.
channels (int): Channels after modules, before conv_seg.
num_classes (int): Number of classes.
dropout_ratio (float): Ratio of dropout layer. Default: 0.1.
conv_cfg (dict|None): Config of conv layers. Default: None.
norm_cfg (dict|None): Config of norm layers. Default: None.
act_cfg (dict): Config of activation layers.
Default: dict(type='ReLU')
in_index (int|Sequence[int]): Input feature index. Default: -1
input_transform (str|None): Transformation type of input features.
Options: 'resize_concat', 'multiple_select', None.
'resize_concat': Multiple feature maps will be resize to the
same size as first one and than concat together.
Usually used in FCN head of HRNet.
'multiple_select': Multiple feature maps will be bundle into
a list and passed into decode head.
None: Only one select feature map is allowed.
Default: None.
loss_decode (dict): Config of decode loss.
Default: dict(type='CrossEntropyLoss').
ignore_index (int | None): The label index to be ignored. When using
masked BCE loss, ignore_index should be set to None. Default: 255
sampler (dict|None): The config of segmentation map sampler.
Default: None.
align_corners (bool): align_corners argument of F.interpolate.
Default: False.
"""
def __init__(self,
in_channels,
channels,
*,
num_classes,
dropout_ratio=0.1,
conv_cfg=None,
norm_cfg=None,
act_cfg=dict(type='ReLU'),
in_index=-1,
input_transform=None,
loss_decode=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
ignore_index=255,
sampler=None,
align_corners=False):
super(BaseDecodeHead, self).__init__()
self._init_inputs(in_channels, in_index, input_transform)
self.channels = channels
self.num_classes = num_classes
self.dropout_ratio = dropout_ratio
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.in_index = in_index
self.loss_decode = build_loss(loss_decode)
self.ignore_index = ignore_index
self.align_corners = align_corners
if sampler is not None:
self.sampler = build_pixel_sampler(sampler, context=self)
else:
self.sampler = None
self.conv_seg = nn.Conv2d(channels, num_classes, kernel_size=1)
if dropout_ratio > 0:
self.dropout = nn.Dropout2d(dropout_ratio)
else:
self.dropout = None
self.fp16_enabled = False
def extra_repr(self):
"""Extra repr."""
s = f'input_transform={self.input_transform}, ' \
f'ignore_index={self.ignore_index}, ' \
f'align_corners={self.align_corners}'
return s
def _init_inputs(self, in_channels, in_index, input_transform):
"""Check and initialize input transforms.
The in_channels, in_index and input_transform must match.
Specifically, when input_transform is None, only single feature map
will be selected. So in_channels and in_index must be of type int.
When input_transform
Args:
in_channels (int|Sequence[int]): Input channels.
in_index (int|Sequence[int]): Input feature index.
input_transform (str|None): Transformation type of input features.
Options: 'resize_concat', 'multiple_select', None.
'resize_concat': Multiple feature maps will be resize to the
same size as first one and than concat together.
Usually used in FCN head of HRNet.
'multiple_select': Multiple feature maps will be bundle into
a list and passed into decode head.
None: Only one select feature map is allowed.
"""
if input_transform is not None:
assert input_transform in ['resize_concat', 'multiple_select']
self.input_transform = input_transform
self.in_index = in_index
if input_transform is not None:
assert isinstance(in_channels, (list, tuple))
assert isinstance(in_index, (list, tuple))
assert len(in_channels) == len(in_index)
if input_transform == 'resize_concat':
self.in_channels = sum(in_channels)
else:
self.in_channels = in_channels
else:
assert isinstance(in_channels, int)
assert isinstance(in_index, int)
self.in_channels = in_channels
def init_weights(self):
"""Initialize weights of classification layer."""
normal_init(self.conv_seg, mean=0, std=0.01)
def _transform_inputs(self, inputs):
"""Transform inputs for decoder.
Args:
inputs (list[Tensor]): List of multi-level img features.
Returns:
Tensor: The transformed inputs
"""
if self.input_transform == 'resize_concat':
inputs = [inputs[i] for i in self.in_index]
upsampled_inputs = [
resize(
input=x,
size=inputs[0].shape[2:],
mode='bilinear',
align_corners=self.align_corners) for x in inputs
]
inputs = torch.cat(upsampled_inputs, dim=1)
elif self.input_transform == 'multiple_select':
inputs = [inputs[i] for i in self.in_index]
else:
inputs = inputs[self.in_index]
return inputs
@auto_fp16()
@abstractmethod
def forward(self, inputs):
"""Placeholder of forward function."""
pass
def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):
"""Forward function for training.
Args:
inputs (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmseg/datasets/pipelines/formatting.py:Collect`.
gt_semantic_seg (Tensor): Semantic segmentation masks
used if the architecture supports semantic segmentation task.
train_cfg (dict): The training config.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
seg_logits = self.forward(inputs)
losses = self.losses(seg_logits, gt_semantic_seg)
return losses
def forward_test(self, inputs, img_metas, test_cfg):
"""Forward function for testing.
Args:
inputs (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmseg/datasets/pipelines/formatting.py:Collect`.
test_cfg (dict): The testing config.
Returns:
Tensor: Output segmentation map.
"""
return self.forward(inputs)
def cls_seg(self, feat):
"""Classify each pixel."""
if self.dropout is not None:
feat = self.dropout(feat)
output = self.conv_seg(feat)
return output
@force_fp32(apply_to=('seg_logit', ))
def losses(self, seg_logit, seg_label):
"""Compute segmentation loss."""
loss = dict()
seg_logit = resize(
input=seg_logit,
size=seg_label.shape[2:],
mode='bilinear',
align_corners=self.align_corners)
if self.sampler is not None:
seg_weight = self.sampler.sample(seg_logit, seg_label)
else:
seg_weight = None
seg_label = seg_label.squeeze(1)
loss['loss_seg'] = self.loss_decode(
seg_logit,
seg_label,
weight=seg_weight,
ignore_index=self.ignore_index)
loss['acc_seg'] = accuracy(seg_logit, seg_label)
return loss
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