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zhouchengrong
2024-03-20 11:44:15 +08:00
commit 69132570aa
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.idea/.gitignore generated vendored Normal file
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# 默认忽略的文件
/shelf/
/workspace.xml
# 基于编辑器的 HTTP 客户端请求
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml

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.idea/inspectionProfiles/Project_Default.xml generated Normal file
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.idea/inspectionProfiles/profiles_settings.xml generated Normal file
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.idea/misc.xml generated Normal file
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.idea/trinity_client_aida.iml generated Normal file
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</module>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project>

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FROM python:3.9
ENV TZ=Asia/Shanghai
RUN apt update
RUN apt install -y vim
RUN apt install -y libgl1-mesa-glx
COPY ./requirements.txt /requirements.txt
RUN pip install --upgrade pip
RUN pip install -r requirements.txt
RUN pip install gunicorn
RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
RUN pip install mmcv==1.4.2 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13/index.html
WORKDIR /app
COPY . .
ENV FLASK_APP=manage.py
LABEL maintainer="zchengrong@yeah.net" \
description="My Python 3.9 - trinity aida " \
version="1.0" \
name="trinity_aida"
CMD ["gunicorn", "-c", "gunicorn_config.py", "app.main:app"]

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文件解释
-----------
样例包括:
* README.md - 本文件
* Dockerfile - 用以自动构建 Docker 镜像的脚本
* requirements.txt - 依赖包文件
* main.py - 主 Flask 服务器端源代码
* python-version : 3.9
快速开始
---------------
如下这些引导，假定你想在自己的电脑上开发本项目。
1. 安装依赖
$ conda create -n trinity_client_aida python=3.9 -y
$ conda activate trinity_client_aida
$ pip install -r requirements.txt
$ conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -y
$ pip install mmcv==1.4.2 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13/index.html
2. 启动服务器
$ uvicorn app.main:app --host 0.0.0.0 --port 8000
3. 打开 http://127.0.0.1:8000/docs
Docker 部署
---------------
1. 构建镜像
$ cd {workspace}
$ docker build -t trinity_client_mixi
2. 使用docker-compose 启动
$ docker-compose up -d
3. 查看日志
$ docker-compose logs -f

0
app/__init__.py Normal file
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from fastapi import APIRouter
from app.api import api_test
from app.api import api_super_resolution
router = APIRouter()
router.include_router(api_test.router, tags=["test"], prefix="/test")
router.include_router(api_super_resolution.router, tags=["api_super_resolution"], prefix="/api")

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from fastapi import APIRouter
from app.schemas.super_resolution import SuperResolutionModel
from app.service.super_resolution.service import SuperResolution
router = APIRouter()
@router.post("super_resolution")
def super_resolution(request_item: SuperResolutionModel):
service = SuperResolution()
sr_result_url = service.sr_result(request_item.sr_image_url, request_item.sr_xn)
response = {"sr_result_url": sr_result_url}
return {"code": 200, "message": "ok", "data": response}

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import logging
from fastapi import APIRouter
logger = logging.getLogger()
router = APIRouter()
@router.get("")
def test():
logger.info("test")
return {"message": "ok"}

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import os
from dotenv import load_dotenv
from pydantic import BaseSettings
BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))
load_dotenv(os.path.join(BASE_DIR, '.env'))
class Settings(BaseSettings):
PROJECT_NAME = os.getenv('PROJECT_NAME', 'FASTAPI BASE')
SECRET_KEY = os.getenv('SECRET_KEY', '')
API_PREFIX = ''
BACKEND_CORS_ORIGINS = ['*']
DATABASE_URL = os.getenv('SQL_DATABASE_URL', '')
ACCESS_TOKEN_EXPIRE_SECONDS: int = 60 * 60 * 24 * 7 # Token expired after 7 days
SECURITY_ALGORITHM = 'HS256'
LOGGING_CONFIG_FILE = os.path.join(BASE_DIR, 'logging_env.py')
settings = Settings()
ckpt = 'service/super_resolution_ccsr/weights/real-world_ccsr.ckpt'
config = 'service/super_resolution_ccsr/configs/model/ccsr_stage2.yaml'
steps = 45
sr_scale = 4
repeat_times = 1
tiled = False
tile_size = 512
tile_stride = 256
color_fix_type = "adain"
t_max = 0.6667
t_min = 0.3333
show_lq = False
skip_if_exist = False
seed = 233
device = "cuda"
tile_diffusion = False #
tile_diffusion_size = 512
tile_diffusion_stride = 256
tile_vae = True
vae_decoder_tile_size = 224
vae_encoder_tile_size = 1024
strength = 1
# minio 配置
sr_bucket = "test"
MINIO_IP = "www.minio.aida.com.hk"
MINIO_PORT = 9000
MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB'
MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR'
MINIO_SECURE = True
# input = 'preprocess_img/input_x2' # 这个值需要被函数参数覆盖
# output = '/path/to/output' # 这个值将被函数参数覆盖
LOGS_PATH = "logs/errors.log"

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2024-03-12 13:03:10,034 main.py [line:43] INFO test ok

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2024-03-20 11:41:28,641 decorator.py [line:11] INFO function：【read_image】,runtime：【2.3682610988616943】s
2024-03-20 11:41:28,641 decorator.py [line:11] INFO function：【read_image】,runtime：【2.3682610988616943】s
2024-03-20 11:41:28,978 decorator.py [line:11] INFO function：【sr_result】,runtime：【2.7045106887817383】s
2024-03-20 11:41:28,978 decorator.py [line:11] INFO function：【sr_result】,runtime：【2.7045106887817383】s
2024-03-20 11:41:40,123 decorator.py [line:11] INFO function：【read_image】,runtime：【6.277707099914551】s
2024-03-20 11:41:40,123 decorator.py [line:11] INFO function：【read_image】,runtime：【6.277707099914551】s
2024-03-20 11:41:40,439 decorator.py [line:11] INFO function：【sr_result】,runtime：【6.594382047653198】s
2024-03-20 11:41:40,439 decorator.py [line:11] INFO function：【sr_result】,runtime：【6.594382047653198】s
2024-03-20 11:41:41,338 decorator.py [line:11] INFO function：【read_image】,runtime：【0.16055655479431152】s
2024-03-20 11:41:41,338 decorator.py [line:11] INFO function：【read_image】,runtime：【0.16055655479431152】s
2024-03-20 11:41:41,643 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.46419310569763184】s
2024-03-20 11:41:41,643 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.46419310569763184】s
2024-03-20 11:41:42,625 decorator.py [line:11] INFO function：【read_image】,runtime：【0.15813016891479492】s
2024-03-20 11:41:42,625 decorator.py [line:11] INFO function：【read_image】,runtime：【0.15813016891479492】s
2024-03-20 11:41:42,929 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.4632871150970459】s
2024-03-20 11:41:42,929 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.4632871150970459】s
2024-03-20 11:41:48,216 decorator.py [line:11] INFO function：【read_image】,runtime：【0.1381824016571045】s
2024-03-20 11:41:48,216 decorator.py [line:11] INFO function：【read_image】,runtime：【0.1381824016571045】s
2024-03-20 11:41:48,537 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.4588344097137451】s
2024-03-20 11:41:48,537 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.4588344097137451】s
2024-03-20 11:42:48,128 decorator.py [line:11] INFO function：【read_image】,runtime：【0.15878772735595703】s
2024-03-20 11:42:48,128 decorator.py [line:11] INFO function：【read_image】,runtime：【0.15878772735595703】s
2024-03-20 11:42:48,463 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.49385905265808105】s
2024-03-20 11:42:48,463 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.49385905265808105】s
2024-03-20 11:43:24,220 decorator.py [line:11] INFO function：【read_image】,runtime：【0.16216182708740234】s
2024-03-20 11:43:24,220 decorator.py [line:11] INFO function：【read_image】,runtime：【0.16216182708740234】s
2024-03-20 11:43:24,563 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.5048878192901611】s
2024-03-20 11:43:24,563 decorator.py [line:11] INFO function：【sr_result】,runtime：【0.5048878192901611】s

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2024-03-12 13:03:10,034 main.py [line:43] INFO test ok

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import logging.config
import uvicorn
from fastapi import FastAPI
from app.api.api_route import router
from app.core.config import settings
from logging_env import LOGGER_CONFIG_DICT
logging.config.dictConfig(LOGGER_CONFIG_DICT)
from starlette.middleware.cors import CORSMiddleware
def get_application() -> FastAPI:
application = FastAPI(
title=settings.PROJECT_NAME, docs_url="/docs", redoc_url='/re-docs',
openapi_url=f"{settings.API_PREFIX}/openapi.json",
description='''
Base frame with FastAPI
- Super Resolution API
'''
)
application.add_middleware(
CORSMiddleware,
allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
application.include_router(router=router, prefix=settings.API_PREFIX)
return application
app = get_application()
if __name__ == '__main__':
uvicorn.run(app, host="0.0.0.0", port=8000)

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from pydantic import BaseModel
class SuperResolutionModel(BaseModel):
sr_image_url: str
sr_xn: int

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import io
import logging
from io import BytesIO
import cv2
import numpy as np
import torch
import tritonclient.http as httpclient
from minio import Minio
from app.core.config import MINIO_IP, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, MINIO_PORT
from app.service.utils.decorator import RunTime
from app.service.utils.generate_uuid import generate_uuid
logger = logging.getLogger()
class SuperResolution:
def __init__(self):
self.triton_client = httpclient.InferenceServerClient(url=f"10.1.1.150:7000")
self.minio_client = Minio(
f"{MINIO_IP}:{MINIO_PORT}",
access_key=MINIO_ACCESS,
secret_key=MINIO_SECRET,
secure=MINIO_SECURE)
@RunTime
def read_image(self, image_url):
image_data = self.minio_client.get_object(image_url.split("/", 1)[0], image_url.split("/", 1)[1])
img = np.frombuffer(image_data.data, np.uint8) # 转成8位无符号整型
img = cv2.imdecode(img, cv2.IMREAD_COLOR).astype(np.float32) / 255. # 解码
return img
@RunTime
def sr_result(self, image_url, sr_xn):
sample = self.read_image(image_url)
sample = np.transpose(sample if sample.shape[2] == 1 else sample[:, :, [2, 1, 0]], (2, 0, 1))
sample = torch.from_numpy(sample).float().unsqueeze(0).numpy()
inputs = [
httpclient.InferInput("input", sample.shape, datatype="FP32")
]
inputs[0].set_data_from_numpy(sample, binary_data=True)
results = self.triton_client.infer(model_name="super_resolution", inputs=inputs)
sr_output = torch.from_numpy(results.as_numpy(f"output"))
output = sr_output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8)
output_url = self.upload_img_sr(output, generate_uuid())
return output_url
def upload_img_sr(self, image, object_name):
try:
image_bytes = cv2.imencode('.jpg', image)[1].tobytes()
image_url = f"test/{self.minio_client.put_object(f'test', f'{object_name}.jpg', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
return image_url
except Exception as e:
logger.warning(f"upload_png_mask runtime exception : {e}")
if __name__ == '__main__':
service = SuperResolution()
result_url = service.sr_result("test/128_image/11.png", 4)
print(result_url)

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import time
import cv2
import numpy as np
import torch
import tritonclient.http as httpclient
from PIL import Image
triton_client = httpclient.InferenceServerClient(url=f"10.1.1.150:7000")
sample = cv2.imread("comic2.png", cv2.IMREAD_COLOR).astype(np.float32) / 255.
sample = np.transpose(sample if sample.shape[2] == 1 else sample[:, :, [2, 1, 0]], (2, 0, 1))
sample = torch.from_numpy(sample).float().unsqueeze(0).numpy()
inputs = [
httpclient.InferInput("input", sample.shape, datatype="FP32")
]
inputs[0].set_data_from_numpy(sample, binary_data=True)
start_time = time.time()
results = triton_client.infer(model_name="super_resolution", inputs=inputs)
print(time.time() - start_time)
sr_output = torch.from_numpy(results.as_numpy(f"output"))
output = sr_output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8)
# cv2.imshow("", output)
# cv2.waitKey(0)
cv2.imwrite("comic3.png", output)

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import time
import logging
def RunTime(func):
def wrapper(*args, **kwargs):
t1 = time.time()
res = func(*args, **kwargs)
t2 = time.time()
if t2 - t1 > 0.05:
logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
return res
return wrapper

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import threading
import uuid
id_lock = threading.Lock()
def generate_uuid():
with id_lock:
unique_id = str(uuid.uuid1())
return unique_id

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version: "3"
services:
trinity_mixi:
image: "trinity_client_mixi:latest"
container_name: trinity_mixi
volumes:
- ./trinity_client_mixi:/trinity
ports:
- "10100:4562"

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# 多进程
"""gunicorn+gevent 的配置文件"""
# 预加载资源
preload_app = True
# 绑定 ip + 端口
bind = "0.0.0.0:4562"
# 进程数 = cup数量 * 2 + 1
workers = 1
# 线程数 = cup数量 * 2
threads = 2
# 等待队列最大长度,超过这个长度的链接将被拒绝连接
backlog = 2048
# 工作模式--协程
worker_class = "uvicorn.workers.UvicornWorker"
# 最大客户客户端并发数量,对使用线程和协程的worker的工作有影响
# 服务器配置设置的值 1200：中小型项目 上万并发： 中大型
# 服务器硬件：宽带+数据库+内存
# 服务器的架构：集群 主从
worker_connections = 1200
# 进程名称
proc_name = 'gunicorn.pid'
# 进程pid记录文件
pidfile = 'app_run.log'
# 日志等级
loglevel = 'info'
# 日志文件名
logfile = 'info.log'
# 访问记录
accesslog = 'access.log'
# 访问记录格式
access_log_format = '%(h)s %(t)s %(U)s %(q)s'

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from app.core.config import LOGS_PATH
LOGGER_CONFIG_DICT = {
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"simple": {"format": "%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s"}
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"level": "DEBUG",
"formatter": "simple",
"stream": "ext://sys.stdout",
},
"info_file_handler": {
"class": "logging.handlers.RotatingFileHandler",
"level": "INFO",
"formatter": "simple",
"filename": LOGS_PATH,
"maxBytes": 10485760,
"backupCount": 50,
"encoding": "utf8",
},
"error_file_handler": {
"class": "logging.handlers.RotatingFileHandler",
"level": "ERROR",
"formatter": "simple",
"filename": LOGS_PATH,
"maxBytes": 10485760,
"backupCount": 20,
"encoding": "utf8",
},
"debug_file_handler": {
"class": "logging.handlers.RotatingFileHandler",
"level": "DEBUG",
"formatter": "simple",
"filename": LOGS_PATH,
"maxBytes": 10485760,
"backupCount": 50,
"encoding": "utf8",
},
},
"loggers": {
"my_module": {"level": "INFO", "handlers": ["console"], "propagate": "no"}
},
"root": {
"level": "INFO",
"handlers": ["error_file_handler", "info_file_handler", "debug_file_handler", "console"],
},
}

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