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
76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from torch import nn
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from torch.nn import functional as F
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class Encoding(nn.Module):
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"""Encoding Layer: a learnable residual encoder.
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Input is of shape (batch_size, channels, height, width).
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Output is of shape (batch_size, num_codes, channels).
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Args:
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channels: dimension of the features or feature channels
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num_codes: number of code words
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"""
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def __init__(self, channels, num_codes):
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super(Encoding, self).__init__()
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# init codewords and smoothing factor
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self.channels, self.num_codes = channels, num_codes
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std = 1. / ((num_codes * channels)**0.5)
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# [num_codes, channels]
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self.codewords = nn.Parameter(
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torch.empty(num_codes, channels,
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dtype=torch.float).uniform_(-std, std),
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requires_grad=True)
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# [num_codes]
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self.scale = nn.Parameter(
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torch.empty(num_codes, dtype=torch.float).uniform_(-1, 0),
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requires_grad=True)
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@staticmethod
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def scaled_l2(x, codewords, scale):
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num_codes, channels = codewords.size()
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batch_size = x.size(0)
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reshaped_scale = scale.view((1, 1, num_codes))
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expanded_x = x.unsqueeze(2).expand(
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(batch_size, x.size(1), num_codes, channels))
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reshaped_codewords = codewords.view((1, 1, num_codes, channels))
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scaled_l2_norm = reshaped_scale * (
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expanded_x - reshaped_codewords).pow(2).sum(dim=3)
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return scaled_l2_norm
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@staticmethod
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def aggregate(assignment_weights, x, codewords):
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num_codes, channels = codewords.size()
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reshaped_codewords = codewords.view((1, 1, num_codes, channels))
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batch_size = x.size(0)
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expanded_x = x.unsqueeze(2).expand(
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(batch_size, x.size(1), num_codes, channels))
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encoded_feat = (assignment_weights.unsqueeze(3) *
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(expanded_x - reshaped_codewords)).sum(dim=1)
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return encoded_feat
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def forward(self, x):
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assert x.dim() == 4 and x.size(1) == self.channels
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# [batch_size, channels, height, width]
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batch_size = x.size(0)
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# [batch_size, height x width, channels]
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x = x.view(batch_size, self.channels, -1).transpose(1, 2).contiguous()
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# assignment_weights: [batch_size, channels, num_codes]
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assignment_weights = F.softmax(
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self.scaled_l2(x, self.codewords, self.scale), dim=2)
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# aggregate
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encoded_feat = self.aggregate(assignment_weights, x, self.codewords)
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return encoded_feat
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def __repr__(self):
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repr_str = self.__class__.__name__
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repr_str += f'(Nx{self.channels}xHxW =>Nx{self.num_codes}' \
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f'x{self.channels})'
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return repr_str
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