| |
| from typing import Dict, List |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from mmcv.cnn import ConvModule |
| from mmengine.model import BaseModule, ModuleList, Sequential |
| from torch import Tensor |
|
|
|
|
| class DAPPM(BaseModule): |
| """DAPPM module in `DDRNet <https://arxiv.org/abs/2101.06085>`_. |
| |
| Args: |
| in_channels (int): Input channels. |
| branch_channels (int): Branch channels. |
| out_channels (int): Output channels. |
| num_scales (int): Number of scales. |
| kernel_sizes (list[int]): Kernel sizes of each scale. |
| strides (list[int]): Strides of each scale. |
| paddings (list[int]): Paddings of each scale. |
| norm_cfg (dict): Config dict for normalization layer. |
| Default: dict(type='BN'). |
| act_cfg (dict): Config dict for activation layer in ConvModule. |
| Default: dict(type='ReLU', inplace=True). |
| conv_cfg (dict): Config dict for convolution layer in ConvModule. |
| Default: dict(order=('norm', 'act', 'conv'), bias=False). |
| upsample_mode (str): Upsample mode. Default: 'bilinear'. |
| """ |
|
|
| def __init__(self, |
| in_channels: int, |
| branch_channels: int, |
| out_channels: int, |
| num_scales: int, |
| kernel_sizes: List[int] = [5, 9, 17], |
| strides: List[int] = [2, 4, 8], |
| paddings: List[int] = [2, 4, 8], |
| norm_cfg: Dict = dict(type='BN', momentum=0.1), |
| act_cfg: Dict = dict(type='ReLU', inplace=True), |
| conv_cfg: Dict = dict( |
| order=('norm', 'act', 'conv'), bias=False), |
| upsample_mode: str = 'bilinear'): |
| super().__init__() |
|
|
| self.num_scales = num_scales |
| self.unsample_mode = upsample_mode |
| self.in_channels = in_channels |
| self.branch_channels = branch_channels |
| self.out_channels = out_channels |
| self.norm_cfg = norm_cfg |
| self.act_cfg = act_cfg |
| self.conv_cfg = conv_cfg |
|
|
| self.scales = ModuleList([ |
| ConvModule( |
| in_channels, |
| branch_channels, |
| kernel_size=1, |
| norm_cfg=norm_cfg, |
| act_cfg=act_cfg, |
| **conv_cfg) |
| ]) |
| for i in range(1, num_scales - 1): |
| self.scales.append( |
| Sequential(*[ |
| nn.AvgPool2d( |
| kernel_size=kernel_sizes[i - 1], |
| stride=strides[i - 1], |
| padding=paddings[i - 1]), |
| ConvModule( |
| in_channels, |
| branch_channels, |
| kernel_size=1, |
| norm_cfg=norm_cfg, |
| act_cfg=act_cfg, |
| **conv_cfg) |
| ])) |
| self.scales.append( |
| Sequential(*[ |
| nn.AdaptiveAvgPool2d((1, 1)), |
| ConvModule( |
| in_channels, |
| branch_channels, |
| kernel_size=1, |
| norm_cfg=norm_cfg, |
| act_cfg=act_cfg, |
| **conv_cfg) |
| ])) |
| self.processes = ModuleList() |
| for i in range(num_scales - 1): |
| self.processes.append( |
| ConvModule( |
| branch_channels, |
| branch_channels, |
| kernel_size=3, |
| padding=1, |
| norm_cfg=norm_cfg, |
| act_cfg=act_cfg, |
| **conv_cfg)) |
|
|
| self.compression = ConvModule( |
| branch_channels * num_scales, |
| out_channels, |
| kernel_size=1, |
| norm_cfg=norm_cfg, |
| act_cfg=act_cfg, |
| **conv_cfg) |
|
|
| self.shortcut = ConvModule( |
| in_channels, |
| out_channels, |
| kernel_size=1, |
| norm_cfg=norm_cfg, |
| act_cfg=act_cfg, |
| **conv_cfg) |
|
|
| def forward(self, inputs: Tensor): |
| feats = [] |
| feats.append(self.scales[0](inputs)) |
|
|
| for i in range(1, self.num_scales): |
| feat_up = F.interpolate( |
| self.scales[i](inputs), |
| size=inputs.shape[2:], |
| mode=self.unsample_mode) |
| feats.append(self.processes[i - 1](feat_up + feats[i - 1])) |
|
|
| return self.compression(torch.cat(feats, |
| dim=1)) + self.shortcut(inputs) |
|
|
|
|
| class PAPPM(DAPPM): |
| """PAPPM module in `PIDNet <https://arxiv.org/abs/2206.02066>`_. |
| |
| Args: |
| in_channels (int): Input channels. |
| branch_channels (int): Branch channels. |
| out_channels (int): Output channels. |
| num_scales (int): Number of scales. |
| kernel_sizes (list[int]): Kernel sizes of each scale. |
| strides (list[int]): Strides of each scale. |
| paddings (list[int]): Paddings of each scale. |
| norm_cfg (dict): Config dict for normalization layer. |
| Default: dict(type='BN', momentum=0.1). |
| act_cfg (dict): Config dict for activation layer in ConvModule. |
| Default: dict(type='ReLU', inplace=True). |
| conv_cfg (dict): Config dict for convolution layer in ConvModule. |
| Default: dict(order=('norm', 'act', 'conv'), bias=False). |
| upsample_mode (str): Upsample mode. Default: 'bilinear'. |
| """ |
|
|
| def __init__(self, |
| in_channels: int, |
| branch_channels: int, |
| out_channels: int, |
| num_scales: int, |
| kernel_sizes: List[int] = [5, 9, 17], |
| strides: List[int] = [2, 4, 8], |
| paddings: List[int] = [2, 4, 8], |
| norm_cfg: Dict = dict(type='BN', momentum=0.1), |
| act_cfg: Dict = dict(type='ReLU', inplace=True), |
| conv_cfg: Dict = dict( |
| order=('norm', 'act', 'conv'), bias=False), |
| upsample_mode: str = 'bilinear'): |
| super().__init__(in_channels, branch_channels, out_channels, |
| num_scales, kernel_sizes, strides, paddings, norm_cfg, |
| act_cfg, conv_cfg, upsample_mode) |
|
|
| self.processes = ConvModule( |
| self.branch_channels * (self.num_scales - 1), |
| self.branch_channels * (self.num_scales - 1), |
| kernel_size=3, |
| padding=1, |
| groups=self.num_scales - 1, |
| norm_cfg=self.norm_cfg, |
| act_cfg=self.act_cfg, |
| **self.conv_cfg) |
|
|
| def forward(self, inputs: Tensor): |
| x_ = self.scales[0](inputs) |
| feats = [] |
| for i in range(1, self.num_scales): |
| feat_up = F.interpolate( |
| self.scales[i](inputs), |
| size=inputs.shape[2:], |
| mode=self.unsample_mode, |
| align_corners=False) |
| feats.append(feat_up + x_) |
| scale_out = self.processes(torch.cat(feats, dim=1)) |
| return self.compression(torch.cat([x_, scale_out], |
| dim=1)) + self.shortcut(inputs) |
|
|
