jeonghyo.k
3946ff6a25
- prediction/image/ FastAPI 서버 Docker 환경 구성 - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지 - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트 - requirements.txt: 서버 의존성 목록 - .env.example: 환경변수 템플릿 - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서 - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가 - .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가 - dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직) - api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거 - aerialRouter.ts: req.params 타입 오류 수정
132 lines
5.0 KiB
Python
132 lines
5.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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import torch.nn as nn
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from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
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from mmcv.runner import BaseModule
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from mmseg.ops import resize
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from ..builder import NECKS
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@NECKS.register_module()
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class JPU(BaseModule):
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"""FastFCN: Rethinking Dilated Convolution in the Backbone
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for Semantic Segmentation.
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This Joint Pyramid Upsampling (JPU) neck is the implementation of
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`FastFCN <https://arxiv.org/abs/1903.11816>`_.
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Args:
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in_channels (Tuple[int], optional): The number of input channels
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for each convolution operations before upsampling.
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Default: (512, 1024, 2048).
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mid_channels (int): The number of output channels of JPU.
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Default: 512.
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start_level (int): Index of the start input backbone level used to
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build the feature pyramid. Default: 0.
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end_level (int): Index of the end input backbone level (exclusive) to
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build the feature pyramid. Default: -1, which means the last level.
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dilations (tuple[int]): Dilation rate of each Depthwise
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Separable ConvModule. Default: (1, 2, 4, 8).
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align_corners (bool, optional): The align_corners argument of
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resize operation. Default: False.
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conv_cfg (dict | None): Config of conv layers.
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Default: None.
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norm_cfg (dict | None): Config of norm layers.
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Default: dict(type='BN').
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act_cfg (dict): Config of activation layers.
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Default: dict(type='ReLU').
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init_cfg (dict or list[dict], optional): Initialization config dict.
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Default: None.
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"""
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def __init__(self,
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in_channels=(512, 1024, 2048),
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mid_channels=512,
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start_level=0,
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end_level=-1,
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dilations=(1, 2, 4, 8),
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align_corners=False,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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init_cfg=None):
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super(JPU, self).__init__(init_cfg=init_cfg)
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assert isinstance(in_channels, tuple)
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assert isinstance(dilations, tuple)
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self.in_channels = in_channels
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self.mid_channels = mid_channels
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self.start_level = start_level
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self.num_ins = len(in_channels)
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if end_level == -1:
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self.backbone_end_level = self.num_ins
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else:
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self.backbone_end_level = end_level
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assert end_level <= len(in_channels)
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self.dilations = dilations
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self.align_corners = align_corners
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self.conv_layers = nn.ModuleList()
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self.dilation_layers = nn.ModuleList()
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for i in range(self.start_level, self.backbone_end_level):
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conv_layer = nn.Sequential(
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ConvModule(
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self.in_channels[i],
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self.mid_channels,
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kernel_size=3,
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padding=1,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg))
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self.conv_layers.append(conv_layer)
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for i in range(len(dilations)):
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dilation_layer = nn.Sequential(
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DepthwiseSeparableConvModule(
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in_channels=(self.backbone_end_level - self.start_level) *
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self.mid_channels,
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out_channels=self.mid_channels,
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kernel_size=3,
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stride=1,
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padding=dilations[i],
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dilation=dilations[i],
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dw_norm_cfg=norm_cfg,
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dw_act_cfg=None,
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pw_norm_cfg=norm_cfg,
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pw_act_cfg=act_cfg))
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self.dilation_layers.append(dilation_layer)
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def forward(self, inputs):
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"""Forward function."""
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assert len(inputs) == len(self.in_channels), 'Length of inputs must \
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be the same with self.in_channels!'
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feats = [
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self.conv_layers[i - self.start_level](inputs[i])
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for i in range(self.start_level, self.backbone_end_level)
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]
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h, w = feats[0].shape[2:]
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for i in range(1, len(feats)):
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feats[i] = resize(
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feats[i],
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size=(h, w),
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mode='bilinear',
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align_corners=self.align_corners)
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feat = torch.cat(feats, dim=1)
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concat_feat = torch.cat([
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self.dilation_layers[i](feat) for i in range(len(self.dilations))
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],
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dim=1)
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outs = []
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# Default: outs[2] is the output of JPU for decoder head, outs[1] is
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# the feature map from backbone for auxiliary head. Additionally,
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# outs[0] can also be used for auxiliary head.
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for i in range(self.start_level, self.backbone_end_level - 1):
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outs.append(inputs[i])
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outs.append(concat_feat)
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return tuple(outs)
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