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
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import warnings
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import torch.nn as nn
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import torch.nn.functional as F
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def resize(input,
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size=None,
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scale_factor=None,
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mode='nearest',
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align_corners=None,
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warning=True):
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if warning:
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if size is not None and align_corners:
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input_h, input_w = tuple(int(x) for x in input.shape[2:])
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output_h, output_w = tuple(int(x) for x in size)
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if output_h > input_h or output_w > input_w:
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if ((output_h > 1 and output_w > 1 and input_h > 1
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and input_w > 1) and (output_h - 1) % (input_h - 1)
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and (output_w - 1) % (input_w - 1)):
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warnings.warn(
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f'When align_corners={align_corners}, '
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'the output would more aligned if '
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f'input size {(input_h, input_w)} is `x+1` and '
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f'out size {(output_h, output_w)} is `nx+1`')
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return F.interpolate(input, size, scale_factor, mode, align_corners)
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class Upsample(nn.Module):
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def __init__(self,
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size=None,
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scale_factor=None,
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mode='nearest',
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align_corners=None):
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super(Upsample, self).__init__()
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self.size = size
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if isinstance(scale_factor, tuple):
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self.scale_factor = tuple(float(factor) for factor in scale_factor)
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else:
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self.scale_factor = float(scale_factor) if scale_factor else None
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self.mode = mode
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self.align_corners = align_corners
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def forward(self, x):
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if not self.size:
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size = [int(t * self.scale_factor) for t in x.shape[-2:]]
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else:
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size = self.size
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return resize(x, size, None, self.mode, self.align_corners)
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