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import einops
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from mmseg.models import build_loss
from mmpl.registry import MODELS
from mmseg.models.decode_heads.decode_head import BaseDecodeHead
import torch.nn.functional as F
@MODELS.register_module()
class UpFCNHead(BaseModule):
"""Fully Convolution Networks for Semantic Segmentation.
This head is implemented of `FCNNet <https://arxiv.org/abs/1411.4038>`_.
Args:
num_convs (int): Number of convs in the head. Default: 2.
kernel_size (int): The kernel size for convs in the head. Default: 3.
concat_input (bool): Whether concat the input and output of convs
before classification layer.
dilation (int): The dilation rate for convs in the head. Default: 1.
"""
def __init__(self,
in_channels,
mid_channels=[256, 128, 64],
num_classes=2,
kernel_size=3,
loss_decode=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
align_corners=False,
norm_cfg=None,
act_cfg=dict(type='ReLU'),
**kwargs):
super().__init__(**kwargs)
self.in_channels = in_channels
self.mid_channels = mid_channels
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.kernel_size = kernel_size
self.num_classes = num_classes
self.align_corners = align_corners
if isinstance(in_channels, list):
self.pre_layers = nn.ModuleList()
inner_channel = mid_channels[0]
for idx, channel in enumerate(in_channels):
self.pre_layers.append(
nn.Sequential(
ConvModule(
channel,
inner_channel,
kernel_size=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg
),
ConvModule(
inner_channel,
inner_channel,
kernel_size=kernel_size,
padding=kernel_size // 2,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg
),
)
)
self.pre_layers.append(
nn.Sequential(
ConvModule(
inner_channel*len(in_channels),
inner_channel,
kernel_size=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg
),
ConvModule(
inner_channel,
inner_channel,
kernel_size=kernel_size,
padding=kernel_size // 2,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg
),
)
)
input_channel = inner_channel
else:
input_channel = in_channels
convs = []
for idx, mid_channel in enumerate(mid_channels):
in_channel = input_channel if idx == 0 else mid_channels[idx-1]
convs += [
ConvModule(
in_channel,
mid_channel,
kernel_size=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg
),
ConvModule(
mid_channel,
mid_channel,
kernel_size=kernel_size,
padding=kernel_size // 2,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg
),
nn.UpsamplingBilinear2d(scale_factor=2),
]
self.convs = nn.Sequential(*convs)
if isinstance(loss_decode, dict):
self.loss_decode = MODELS.build(loss_decode)
self.conv_seg = nn.Conv2d(mid_channels[-1], num_classes, kernel_size=1)
def _forward_feature(self, img_feat, inner_states):
if hasattr(self, 'pre_layers'):
inner_states = inner_states[-len(self.in_channels):]
inner_states = [einops.rearrange(x, 'b h w c -> b c h w') for x in inner_states]
inner_states = [layer(x) for layer, x in zip(self.pre_layers[:-1], inner_states)]
img_feat = self.pre_layers[-1](torch.cat(inner_states, dim=1))
feats = self.convs(img_feat)
return feats
def forward(self, img_feat, inner_states):
"""Forward function."""
output = self._forward_feature(img_feat, inner_states)
output = self.conv_seg(output)
return output
def loss(self, img_feat, inner_states, batch_data_samples) -> dict:
"""Forward function for training.
Args:
inputs (Tuple[Tensor]): List of multi-level img features.
batch_data_samples (list[:obj:`SegDataSample`]): The seg
data samples. It usually includes information such
as `img_metas` or `gt_semantic_seg`.
train_cfg (dict): The training config.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
seg_logits = self.forward(img_feat, inner_states)
losses = self.loss_by_feat(seg_logits, batch_data_samples)
return losses
def _stack_batch_gt(self, batch_data_samples):
gt_semantic_segs = [
data_sample.gt_sem_seg.data for data_sample in batch_data_samples
]
return torch.stack(gt_semantic_segs, dim=0)
def loss_by_feat(self, seg_logits, batch_data_samples) -> dict:
"""Compute segmentation loss.
Args:
seg_logits (Tensor): The output from decode head forward function.
batch_data_samples (List[:obj:`SegDataSample`]): The seg
data samples. It usually includes information such
as `metainfo` and `gt_sem_seg`.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
seg_label = self._stack_batch_gt(batch_data_samples)
losses = dict()
seg_logits = F.interpolate(seg_logits, seg_label.shape[-2:], mode='bilinear', align_corners=self.align_corners)
seg_label = seg_label.squeeze(1)
losses['loss_ce'] = self.loss_decode(seg_logits, seg_label)
return losses
def predict(self, img_feat, inner_states):
seg_logits = self.forward(img_feat, inner_states)
return seg_logits
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