Initial commit for AI Medical project base
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ai-medical/app.py
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ai-medical/app.py
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import os
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import tempfile
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import torch
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import logging
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import boto3
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from botocore.client import Config
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from fastapi import FastAPI, HTTPException
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from contextlib import asynccontextmanager
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from pydantic_settings import BaseSettings
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# --- Logging setup ---
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logger = logging.getLogger("uvicorn")
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logging.basicConfig(level=logging.INFO)
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# --- 1. Settings ---
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class Settings(BaseSettings):
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MINIO_ENDPOINT: str = "http://localhost:9000"
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MINIO_ACCESS_KEY: str = "minio_admin"
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MINIO_SECRET_KEY: str = "minio_p@ssw0rd!"
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MODEL_BUCKET: str = "models"
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MODEL_FILE: str = "spleen_ct_spleen_model.ts"
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DEVICE: str = "cuda" if torch.cuda.is_available() else "cpu"
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model_config = {'env_file': '.env', 'env_file_encoding': 'utf-8'}
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settings = Settings()
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model = None
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# --- 2. Load Model Function ---
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def load_monai_model():
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"""โหลด TorchScript model จาก MinIO"""
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global model
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try:
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logger.info(f"Loading model '{settings.MODEL_FILE}' from MinIO...")
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s3 = boto3.client(
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"s3",
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endpoint_url=settings.MINIO_ENDPOINT,
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aws_access_key_id=settings.MINIO_ACCESS_KEY,
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aws_secret_access_key=settings.MINIO_SECRET_KEY,
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config=Config(signature_version="s3v4", connect_timeout=5, read_timeout=10)
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)
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with tempfile.TemporaryDirectory() as temp_dir:
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local_path = os.path.join(temp_dir, settings.MODEL_FILE)
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s3.download_file(settings.MODEL_BUCKET, settings.MODEL_FILE, local_path)
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model_loaded = torch.jit.load(local_path, map_location=settings.DEVICE)
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model_loaded.eval()
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model = model_loaded
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logger.info(f"Model '{settings.MODEL_FILE}' loaded successfully on {settings.DEVICE}")
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except Exception as e:
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logger.error(f"Model load failed: {e}")
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raise HTTPException(status_code=500, detail=f"Model Initialization Failed: {e}")
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# --- 3. Lifespan Event Handler (แทน @app.on_event) ---
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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global model
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# Startup
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load_monai_model()
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yield
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# Shutdown (optional cleanup)
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model = None
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logger.info("Model unloaded from memory.")
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# --- 4. Create FastAPI App ---
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app = FastAPI(
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title="MONAI Model API",
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description="FastAPI serving MONAI TorchScript model from MinIO",
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version="1.0.0",
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lifespan=lifespan
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)
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# --- 5. Root Endpoint ---
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@app.get("/")
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async def read_root():
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return {
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"status": "Service Running",
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"model_loaded": model is not None,
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"model_name": settings.MODEL_FILE,
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"device": settings.DEVICE,
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}
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# --- 6. Reload Endpoint ---
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@app.post("/reload")
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async def reload_model():
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"""รีโหลดโมเดลจาก MinIO โดยไม่ต้อง restart service"""
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try:
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load_monai_model()
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return {"message": f"Model '{settings.MODEL_FILE}' reloaded successfully"}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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ai-medical/models/spleen_ct_segmentation/.gitattributes
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ai-medical/models/spleen_ct_segmentation/.gitattributes
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models/model.ts filter=lfs diff=lfs merge=lfs -text
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201
ai-medical/models/spleen_ct_segmentation/LICENSE
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ai-medical/models/spleen_ct_segmentation/LICENSE
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Apache License
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||||||
|
Version 2.0, January 2004
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||||||
|
http://www.apache.org/licenses/
|
||||||
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|
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TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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1. Definitions.
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"License" shall mean the terms and conditions for use, reproduction,
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agreed to in writing, Licensor provides the Work (and each
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8. Limitation of Liability. In no event and under no legal theory,
|
||||||
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|
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END OF TERMS AND CONDITIONS
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||||||
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|
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||||||
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|
||||||
|
See the License for the specific language governing permissions and
|
||||||
|
limitations under the License.
|
||||||
@ -0,0 +1,76 @@
|
|||||||
|
{
|
||||||
|
"validate#dataset#cache_rate": 0,
|
||||||
|
"validate#postprocessing": {
|
||||||
|
"_target_": "Compose",
|
||||||
|
"transforms": [
|
||||||
|
{
|
||||||
|
"_target_": "Activationsd",
|
||||||
|
"keys": "pred",
|
||||||
|
"softmax": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "Invertd",
|
||||||
|
"keys": [
|
||||||
|
"pred",
|
||||||
|
"label"
|
||||||
|
],
|
||||||
|
"transform": "@validate#preprocessing",
|
||||||
|
"orig_keys": "image",
|
||||||
|
"nearest_interp": [
|
||||||
|
false,
|
||||||
|
true
|
||||||
|
],
|
||||||
|
"to_tensor": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "AsDiscreted",
|
||||||
|
"keys": [
|
||||||
|
"pred",
|
||||||
|
"label"
|
||||||
|
],
|
||||||
|
"argmax": [
|
||||||
|
true,
|
||||||
|
false
|
||||||
|
],
|
||||||
|
"to_onehot": 2
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "SaveImaged",
|
||||||
|
"_disabled_": true,
|
||||||
|
"keys": "pred",
|
||||||
|
"output_dir": "@output_dir",
|
||||||
|
"resample": false,
|
||||||
|
"squeeze_end_dims": true
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"validate#handlers": [
|
||||||
|
{
|
||||||
|
"_target_": "CheckpointLoader",
|
||||||
|
"load_path": "$@ckpt_dir + '/model.pt'",
|
||||||
|
"load_dict": {
|
||||||
|
"model": "@network"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "StatsHandler",
|
||||||
|
"iteration_log": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "MetricsSaver",
|
||||||
|
"save_dir": "@output_dir",
|
||||||
|
"metrics": [
|
||||||
|
"val_mean_dice",
|
||||||
|
"val_acc"
|
||||||
|
],
|
||||||
|
"metric_details": [
|
||||||
|
"val_mean_dice"
|
||||||
|
],
|
||||||
|
"batch_transform": "$lambda x: [xx['image'].meta for xx in x]",
|
||||||
|
"summary_ops": "*"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"run": [
|
||||||
|
"$@validate#evaluator.run()"
|
||||||
|
]
|
||||||
|
}
|
||||||
165
ai-medical/models/spleen_ct_segmentation/configs/inference.json
Normal file
165
ai-medical/models/spleen_ct_segmentation/configs/inference.json
Normal file
@ -0,0 +1,165 @@
|
|||||||
|
{
|
||||||
|
"imports": [
|
||||||
|
"$import glob",
|
||||||
|
"$import numpy",
|
||||||
|
"$import os"
|
||||||
|
],
|
||||||
|
"bundle_root": ".",
|
||||||
|
"image_key": "image",
|
||||||
|
"output_dir": "$@bundle_root + '/eval'",
|
||||||
|
"output_ext": ".nii.gz",
|
||||||
|
"output_dtype": "$numpy.float32",
|
||||||
|
"output_postfix": "trans",
|
||||||
|
"separate_folder": true,
|
||||||
|
"load_pretrain": true,
|
||||||
|
"dataset_dir": "/workspace/data/Task09_Spleen",
|
||||||
|
"datalist": "$list(sorted(glob.glob(@dataset_dir + '/imagesTs/*.nii.gz')))",
|
||||||
|
"device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
|
||||||
|
"network_def": {
|
||||||
|
"_target_": "UNet",
|
||||||
|
"spatial_dims": 3,
|
||||||
|
"in_channels": 1,
|
||||||
|
"out_channels": 2,
|
||||||
|
"channels": [
|
||||||
|
16,
|
||||||
|
32,
|
||||||
|
64,
|
||||||
|
128,
|
||||||
|
256
|
||||||
|
],
|
||||||
|
"strides": [
|
||||||
|
2,
|
||||||
|
2,
|
||||||
|
2,
|
||||||
|
2
|
||||||
|
],
|
||||||
|
"num_res_units": 2,
|
||||||
|
"norm": "batch"
|
||||||
|
},
|
||||||
|
"network": "$@network_def.to(@device)",
|
||||||
|
"preprocessing": {
|
||||||
|
"_target_": "Compose",
|
||||||
|
"transforms": [
|
||||||
|
{
|
||||||
|
"_target_": "LoadImaged",
|
||||||
|
"keys": "@image_key"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "EnsureChannelFirstd",
|
||||||
|
"keys": "@image_key"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "Orientationd",
|
||||||
|
"keys": "@image_key",
|
||||||
|
"axcodes": "RAS"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "Spacingd",
|
||||||
|
"keys": "@image_key",
|
||||||
|
"pixdim": [
|
||||||
|
1.5,
|
||||||
|
1.5,
|
||||||
|
2.0
|
||||||
|
],
|
||||||
|
"mode": "bilinear"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "ScaleIntensityRanged",
|
||||||
|
"keys": "@image_key",
|
||||||
|
"a_min": -57,
|
||||||
|
"a_max": 164,
|
||||||
|
"b_min": 0,
|
||||||
|
"b_max": 1,
|
||||||
|
"clip": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "EnsureTyped",
|
||||||
|
"keys": "@image_key"
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"dataset": {
|
||||||
|
"_target_": "Dataset",
|
||||||
|
"data": "$[{'image': i} for i in @datalist]",
|
||||||
|
"transform": "@preprocessing"
|
||||||
|
},
|
||||||
|
"dataloader": {
|
||||||
|
"_target_": "DataLoader",
|
||||||
|
"dataset": "@dataset",
|
||||||
|
"batch_size": 1,
|
||||||
|
"shuffle": false,
|
||||||
|
"num_workers": 4
|
||||||
|
},
|
||||||
|
"inferer": {
|
||||||
|
"_target_": "SlidingWindowInferer",
|
||||||
|
"roi_size": [
|
||||||
|
96,
|
||||||
|
96,
|
||||||
|
96
|
||||||
|
],
|
||||||
|
"sw_batch_size": 4,
|
||||||
|
"overlap": 0.5
|
||||||
|
},
|
||||||
|
"postprocessing": {
|
||||||
|
"_target_": "Compose",
|
||||||
|
"transforms": [
|
||||||
|
{
|
||||||
|
"_target_": "Activationsd",
|
||||||
|
"keys": "pred",
|
||||||
|
"softmax": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "Invertd",
|
||||||
|
"keys": "pred",
|
||||||
|
"transform": "@preprocessing",
|
||||||
|
"orig_keys": "@image_key",
|
||||||
|
"nearest_interp": false,
|
||||||
|
"to_tensor": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "AsDiscreted",
|
||||||
|
"keys": "pred",
|
||||||
|
"argmax": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "SaveImaged",
|
||||||
|
"keys": "pred",
|
||||||
|
"output_dir": "@output_dir",
|
||||||
|
"output_ext": "@output_ext",
|
||||||
|
"output_dtype": "@output_dtype",
|
||||||
|
"output_postfix": "@output_postfix",
|
||||||
|
"separate_folder": "@separate_folder"
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"handlers": [
|
||||||
|
{
|
||||||
|
"_target_": "StatsHandler",
|
||||||
|
"iteration_log": false
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"evaluator": {
|
||||||
|
"_target_": "SupervisedEvaluator",
|
||||||
|
"device": "@device",
|
||||||
|
"val_data_loader": "@dataloader",
|
||||||
|
"network": "@network",
|
||||||
|
"inferer": "@inferer",
|
||||||
|
"postprocessing": "@postprocessing",
|
||||||
|
"val_handlers": "@handlers",
|
||||||
|
"amp": true
|
||||||
|
},
|
||||||
|
"checkpointloader": {
|
||||||
|
"_target_": "CheckpointLoader",
|
||||||
|
"load_path": "$@bundle_root + '/models/model.pt'",
|
||||||
|
"load_dict": {
|
||||||
|
"model": "@network"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"initialize": [
|
||||||
|
"$monai.utils.set_determinism(seed=123)",
|
||||||
|
"$@checkpointloader(@evaluator) if @load_pretrain else None"
|
||||||
|
],
|
||||||
|
"run": [
|
||||||
|
"$@evaluator.run()"
|
||||||
|
]
|
||||||
|
}
|
||||||
@ -0,0 +1,12 @@
|
|||||||
|
{
|
||||||
|
"imports": [
|
||||||
|
"$import glob",
|
||||||
|
"$import os",
|
||||||
|
"$import torch_tensorrt"
|
||||||
|
],
|
||||||
|
"network_def": "$torch.jit.load(@bundle_root + '/models/model_trt.ts')",
|
||||||
|
"evaluator#amp": false,
|
||||||
|
"initialize": [
|
||||||
|
"$monai.utils.set_determinism(seed=123)"
|
||||||
|
]
|
||||||
|
}
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
[loggers]
|
||||||
|
keys=root
|
||||||
|
|
||||||
|
[handlers]
|
||||||
|
keys=consoleHandler
|
||||||
|
|
||||||
|
[formatters]
|
||||||
|
keys=fullFormatter
|
||||||
|
|
||||||
|
[logger_root]
|
||||||
|
level=INFO
|
||||||
|
handlers=consoleHandler
|
||||||
|
|
||||||
|
[handler_consoleHandler]
|
||||||
|
class=StreamHandler
|
||||||
|
level=INFO
|
||||||
|
formatter=fullFormatter
|
||||||
|
args=(sys.stdout,)
|
||||||
|
|
||||||
|
[formatter_fullFormatter]
|
||||||
|
format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
|
||||||
115
ai-medical/models/spleen_ct_segmentation/configs/metadata.json
Normal file
115
ai-medical/models/spleen_ct_segmentation/configs/metadata.json
Normal file
@ -0,0 +1,115 @@
|
|||||||
|
{
|
||||||
|
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
|
||||||
|
"version": "0.6.1",
|
||||||
|
"changelog": {
|
||||||
|
"0.6.1": "enhance metadata with improved descriptions",
|
||||||
|
"0.6.0": "update to huggingface hosting",
|
||||||
|
"0.5.9": "use monai 1.4 and update large files",
|
||||||
|
"0.5.8": "update to use monai 1.3.2",
|
||||||
|
"0.5.7": "update to use monai 1.3.1",
|
||||||
|
"0.5.6": "add load_pretrain flag for infer",
|
||||||
|
"0.5.5": "add checkpoint loader for infer",
|
||||||
|
"0.5.4": "update to use monai 1.3.0",
|
||||||
|
"0.5.3": "fix the wrong GPU index issue of multi-node",
|
||||||
|
"0.5.2": "remove error dollar symbol in readme",
|
||||||
|
"0.5.1": "add RAM warning",
|
||||||
|
"0.5.0": "update the README file with the ONNX-TensorRT conversion",
|
||||||
|
"0.4.9": "update TensorRT descriptions",
|
||||||
|
"0.4.8": "update deterministic training results",
|
||||||
|
"0.4.7": "update the TensorRT part in the README file",
|
||||||
|
"0.4.6": "fix mgpu finalize issue",
|
||||||
|
"0.4.5": "enable deterministic training",
|
||||||
|
"0.4.4": "add the command of executing inference with TensorRT models",
|
||||||
|
"0.4.3": "fix figure and weights inconsistent error",
|
||||||
|
"0.4.2": "use torch 1.13.1",
|
||||||
|
"0.4.1": "update the readme file with TensorRT convert",
|
||||||
|
"0.4.0": "fix multi-gpu train config typo",
|
||||||
|
"0.3.9": "adapt to BundleWorkflow interface",
|
||||||
|
"0.3.8": "add name tag",
|
||||||
|
"0.3.7": "restructure readme to match updated template",
|
||||||
|
"0.3.6": "enhance readme with details of model training",
|
||||||
|
"0.3.5": "update to use monai 1.0.1",
|
||||||
|
"0.3.4": "enhance readme on commands example",
|
||||||
|
"0.3.3": "fix license Copyright error",
|
||||||
|
"0.3.2": "improve multi-gpu logging",
|
||||||
|
"0.3.1": "add multi-gpu evaluation config",
|
||||||
|
"0.3.0": "update license files",
|
||||||
|
"0.2.0": "unify naming",
|
||||||
|
"0.1.1": "disable image saving during evaluation",
|
||||||
|
"0.1.0": "complete the model package",
|
||||||
|
"0.0.1": "initialize the model package structure"
|
||||||
|
},
|
||||||
|
"monai_version": "1.4.0",
|
||||||
|
"pytorch_version": "2.4.0",
|
||||||
|
"numpy_version": "1.24.4",
|
||||||
|
"required_packages_version": {
|
||||||
|
"nibabel": "5.2.1",
|
||||||
|
"pytorch-ignite": "0.4.11",
|
||||||
|
"tensorboard": "2.17.0"
|
||||||
|
},
|
||||||
|
"supported_apps": {},
|
||||||
|
"name": "Spleen CT Segmentation",
|
||||||
|
"task": "Automated Spleen Segmentation in CT Images",
|
||||||
|
"description": "A 3D segmentation model for spleen delineation in CT images. The model processes 96x96x96 pixel patches and provides segmentation masks for spleen tissue. Trained on the Medical Segmentation Decathlon dataset.",
|
||||||
|
"authors": "MONAI team",
|
||||||
|
"copyright": "Copyright (c) MONAI Consortium",
|
||||||
|
"data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
|
||||||
|
"data_type": "nibabel",
|
||||||
|
"image_classes": "single channel data, intensity scaled to [0, 1]",
|
||||||
|
"label_classes": "single channel data, 1 is spleen, 0 is everything else",
|
||||||
|
"pred_classes": "2 channels OneHot data, channel 1 is spleen, channel 0 is background",
|
||||||
|
"eval_metrics": {
|
||||||
|
"mean_dice": 0.961
|
||||||
|
},
|
||||||
|
"intended_use": "This is an example, not to be used for diagnostic purposes",
|
||||||
|
"references": [
|
||||||
|
"Xia, Yingda, et al. '3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training. arXiv preprint arXiv:1811.12506 (2018). https://arxiv.org/abs/1811.12506.",
|
||||||
|
"Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_40"
|
||||||
|
],
|
||||||
|
"network_data_format": {
|
||||||
|
"inputs": {
|
||||||
|
"image": {
|
||||||
|
"type": "image",
|
||||||
|
"format": "hounsfield",
|
||||||
|
"modality": "CT",
|
||||||
|
"num_channels": 1,
|
||||||
|
"spatial_shape": [
|
||||||
|
96,
|
||||||
|
96,
|
||||||
|
96
|
||||||
|
],
|
||||||
|
"dtype": "float32",
|
||||||
|
"value_range": [
|
||||||
|
0,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
"is_patch_data": true,
|
||||||
|
"channel_def": {
|
||||||
|
"0": "image"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"outputs": {
|
||||||
|
"pred": {
|
||||||
|
"type": "image",
|
||||||
|
"format": "segmentation",
|
||||||
|
"num_channels": 2,
|
||||||
|
"spatial_shape": [
|
||||||
|
96,
|
||||||
|
96,
|
||||||
|
96
|
||||||
|
],
|
||||||
|
"dtype": "float32",
|
||||||
|
"value_range": [
|
||||||
|
0,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
"is_patch_data": true,
|
||||||
|
"channel_def": {
|
||||||
|
"0": "background",
|
||||||
|
"1": "spleen"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
@ -0,0 +1,31 @@
|
|||||||
|
{
|
||||||
|
"device": "$torch.device('cuda:' + os.environ['LOCAL_RANK'])",
|
||||||
|
"network": {
|
||||||
|
"_target_": "torch.nn.parallel.DistributedDataParallel",
|
||||||
|
"module": "$@network_def.to(@device)",
|
||||||
|
"device_ids": [
|
||||||
|
"@device"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"validate#sampler": {
|
||||||
|
"_target_": "DistributedSampler",
|
||||||
|
"dataset": "@validate#dataset",
|
||||||
|
"even_divisible": false,
|
||||||
|
"shuffle": false
|
||||||
|
},
|
||||||
|
"validate#dataloader#sampler": "@validate#sampler",
|
||||||
|
"validate#handlers#1#_disabled_": "$dist.get_rank() > 0",
|
||||||
|
"initialize": [
|
||||||
|
"$import torch.distributed as dist",
|
||||||
|
"$dist.is_initialized() or dist.init_process_group(backend='nccl')",
|
||||||
|
"$torch.cuda.set_device(@device)",
|
||||||
|
"$import logging",
|
||||||
|
"$@validate#evaluator.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)"
|
||||||
|
],
|
||||||
|
"run": [
|
||||||
|
"$@validate#evaluator.run()"
|
||||||
|
],
|
||||||
|
"finalize": [
|
||||||
|
"$dist.is_initialized() and dist.destroy_process_group()"
|
||||||
|
]
|
||||||
|
}
|
||||||
@ -0,0 +1,42 @@
|
|||||||
|
{
|
||||||
|
"device": "$torch.device('cuda:' + os.environ['LOCAL_RANK'])",
|
||||||
|
"network": {
|
||||||
|
"_target_": "torch.nn.parallel.DistributedDataParallel",
|
||||||
|
"module": "$@network_def.to(@device)",
|
||||||
|
"device_ids": [
|
||||||
|
"@device"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"train#sampler": {
|
||||||
|
"_target_": "DistributedSampler",
|
||||||
|
"dataset": "@train#dataset",
|
||||||
|
"even_divisible": true,
|
||||||
|
"shuffle": true
|
||||||
|
},
|
||||||
|
"train#dataloader#sampler": "@train#sampler",
|
||||||
|
"train#dataloader#shuffle": false,
|
||||||
|
"train#trainer#train_handlers": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
|
||||||
|
"validate#sampler": {
|
||||||
|
"_target_": "DistributedSampler",
|
||||||
|
"dataset": "@validate#dataset",
|
||||||
|
"even_divisible": false,
|
||||||
|
"shuffle": false
|
||||||
|
},
|
||||||
|
"validate#dataloader#sampler": "@validate#sampler",
|
||||||
|
"validate#evaluator#val_handlers": "$None if dist.get_rank() > 0 else @validate#handlers",
|
||||||
|
"initialize": [
|
||||||
|
"$import torch.distributed as dist",
|
||||||
|
"$dist.is_initialized() or dist.init_process_group(backend='nccl')",
|
||||||
|
"$torch.cuda.set_device(@device)",
|
||||||
|
"$monai.utils.set_determinism(seed=123)",
|
||||||
|
"$import logging",
|
||||||
|
"$@train#trainer.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
|
||||||
|
"$@validate#evaluator.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)"
|
||||||
|
],
|
||||||
|
"run": [
|
||||||
|
"$@train#trainer.run()"
|
||||||
|
],
|
||||||
|
"finalize": [
|
||||||
|
"$dist.is_initialized() and dist.destroy_process_group()"
|
||||||
|
]
|
||||||
|
}
|
||||||
307
ai-medical/models/spleen_ct_segmentation/configs/train.json
Normal file
307
ai-medical/models/spleen_ct_segmentation/configs/train.json
Normal file
@ -0,0 +1,307 @@
|
|||||||
|
{
|
||||||
|
"imports": [
|
||||||
|
"$import glob",
|
||||||
|
"$import os",
|
||||||
|
"$import ignite"
|
||||||
|
],
|
||||||
|
"bundle_root": ".",
|
||||||
|
"ckpt_dir": "$@bundle_root + '/models'",
|
||||||
|
"output_dir": "$@bundle_root + '/eval'",
|
||||||
|
"dataset_dir": "/workspace/data/Task09_Spleen",
|
||||||
|
"images": "$list(sorted(glob.glob(@dataset_dir + '/imagesTr/*.nii.gz')))",
|
||||||
|
"labels": "$list(sorted(glob.glob(@dataset_dir + '/labelsTr/*.nii.gz')))",
|
||||||
|
"val_interval": 1,
|
||||||
|
"epochs": 800,
|
||||||
|
"device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
|
||||||
|
"network_def": {
|
||||||
|
"_target_": "UNet",
|
||||||
|
"spatial_dims": 3,
|
||||||
|
"in_channels": 1,
|
||||||
|
"out_channels": 2,
|
||||||
|
"channels": [
|
||||||
|
16,
|
||||||
|
32,
|
||||||
|
64,
|
||||||
|
128,
|
||||||
|
256
|
||||||
|
],
|
||||||
|
"strides": [
|
||||||
|
2,
|
||||||
|
2,
|
||||||
|
2,
|
||||||
|
2
|
||||||
|
],
|
||||||
|
"num_res_units": 2,
|
||||||
|
"norm": "batch"
|
||||||
|
},
|
||||||
|
"network": "$@network_def.to(@device)",
|
||||||
|
"loss": {
|
||||||
|
"_target_": "DiceCELoss",
|
||||||
|
"include_background": true,
|
||||||
|
"to_onehot_y": true,
|
||||||
|
"softmax": true,
|
||||||
|
"squared_pred": true,
|
||||||
|
"batch": true,
|
||||||
|
"smooth_nr": 1e-05,
|
||||||
|
"smooth_dr": 1e-05,
|
||||||
|
"lambda_dice": 0.5,
|
||||||
|
"lambda_ce": 0.5
|
||||||
|
},
|
||||||
|
"optimizer": {
|
||||||
|
"_target_": "Novograd",
|
||||||
|
"params": "$@network.parameters()",
|
||||||
|
"lr": 0.002
|
||||||
|
},
|
||||||
|
"lr_scheduler": {
|
||||||
|
"_target_": "torch.optim.lr_scheduler.StepLR",
|
||||||
|
"optimizer": "@optimizer",
|
||||||
|
"step_size": 5000,
|
||||||
|
"gamma": 0.1
|
||||||
|
},
|
||||||
|
"train": {
|
||||||
|
"deterministic_transforms": [
|
||||||
|
{
|
||||||
|
"_target_": "LoadImaged",
|
||||||
|
"keys": [
|
||||||
|
"image",
|
||||||
|
"label"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "EnsureChannelFirstd",
|
||||||
|
"keys": [
|
||||||
|
"image",
|
||||||
|
"label"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "Orientationd",
|
||||||
|
"keys": [
|
||||||
|
"image",
|
||||||
|
"label"
|
||||||
|
],
|
||||||
|
"axcodes": "RAS"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "Spacingd",
|
||||||
|
"keys": [
|
||||||
|
"image",
|
||||||
|
"label"
|
||||||
|
],
|
||||||
|
"pixdim": [
|
||||||
|
1.5,
|
||||||
|
1.5,
|
||||||
|
2.0
|
||||||
|
],
|
||||||
|
"mode": [
|
||||||
|
"bilinear",
|
||||||
|
"nearest"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "ScaleIntensityRanged",
|
||||||
|
"keys": "image",
|
||||||
|
"a_min": -57,
|
||||||
|
"a_max": 164,
|
||||||
|
"b_min": 0,
|
||||||
|
"b_max": 1,
|
||||||
|
"clip": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "EnsureTyped",
|
||||||
|
"keys": [
|
||||||
|
"image",
|
||||||
|
"label"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"random_transforms": [
|
||||||
|
{
|
||||||
|
"_target_": "RandCropByPosNegLabeld",
|
||||||
|
"keys": [
|
||||||
|
"image",
|
||||||
|
"label"
|
||||||
|
],
|
||||||
|
"label_key": "label",
|
||||||
|
"spatial_size": [
|
||||||
|
96,
|
||||||
|
96,
|
||||||
|
96
|
||||||
|
],
|
||||||
|
"pos": 1,
|
||||||
|
"neg": 1,
|
||||||
|
"num_samples": 4,
|
||||||
|
"image_key": "image",
|
||||||
|
"image_threshold": 0
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"preprocessing": {
|
||||||
|
"_target_": "Compose",
|
||||||
|
"transforms": "$@train#deterministic_transforms + @train#random_transforms"
|
||||||
|
},
|
||||||
|
"dataset": {
|
||||||
|
"_target_": "CacheDataset",
|
||||||
|
"data": "$[{'image': i, 'label': l} for i, l in zip(@images[:-9], @labels[:-9])]",
|
||||||
|
"transform": "@train#preprocessing",
|
||||||
|
"cache_rate": 1.0,
|
||||||
|
"num_workers": 4
|
||||||
|
},
|
||||||
|
"dataloader": {
|
||||||
|
"_target_": "DataLoader",
|
||||||
|
"dataset": "@train#dataset",
|
||||||
|
"batch_size": 2,
|
||||||
|
"shuffle": true,
|
||||||
|
"num_workers": 4
|
||||||
|
},
|
||||||
|
"inferer": {
|
||||||
|
"_target_": "SimpleInferer"
|
||||||
|
},
|
||||||
|
"postprocessing": {
|
||||||
|
"_target_": "Compose",
|
||||||
|
"transforms": [
|
||||||
|
{
|
||||||
|
"_target_": "Activationsd",
|
||||||
|
"keys": "pred",
|
||||||
|
"softmax": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "AsDiscreted",
|
||||||
|
"keys": [
|
||||||
|
"pred",
|
||||||
|
"label"
|
||||||
|
],
|
||||||
|
"argmax": [
|
||||||
|
true,
|
||||||
|
false
|
||||||
|
],
|
||||||
|
"to_onehot": 2
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"handlers": [
|
||||||
|
{
|
||||||
|
"_target_": "LrScheduleHandler",
|
||||||
|
"lr_scheduler": "@lr_scheduler",
|
||||||
|
"print_lr": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "ValidationHandler",
|
||||||
|
"validator": "@validate#evaluator",
|
||||||
|
"epoch_level": true,
|
||||||
|
"interval": "@val_interval"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "StatsHandler",
|
||||||
|
"tag_name": "train_loss",
|
||||||
|
"output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "TensorBoardStatsHandler",
|
||||||
|
"log_dir": "@output_dir",
|
||||||
|
"tag_name": "train_loss",
|
||||||
|
"output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"key_metric": {
|
||||||
|
"train_accuracy": {
|
||||||
|
"_target_": "ignite.metrics.Accuracy",
|
||||||
|
"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"trainer": {
|
||||||
|
"_target_": "SupervisedTrainer",
|
||||||
|
"max_epochs": "@epochs",
|
||||||
|
"device": "@device",
|
||||||
|
"train_data_loader": "@train#dataloader",
|
||||||
|
"network": "@network",
|
||||||
|
"loss_function": "@loss",
|
||||||
|
"optimizer": "@optimizer",
|
||||||
|
"inferer": "@train#inferer",
|
||||||
|
"postprocessing": "@train#postprocessing",
|
||||||
|
"key_train_metric": "@train#key_metric",
|
||||||
|
"train_handlers": "@train#handlers",
|
||||||
|
"amp": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"validate": {
|
||||||
|
"preprocessing": {
|
||||||
|
"_target_": "Compose",
|
||||||
|
"transforms": "%train#deterministic_transforms"
|
||||||
|
},
|
||||||
|
"dataset": {
|
||||||
|
"_target_": "CacheDataset",
|
||||||
|
"data": "$[{'image': i, 'label': l} for i, l in zip(@images[-9:], @labels[-9:])]",
|
||||||
|
"transform": "@validate#preprocessing",
|
||||||
|
"cache_rate": 1.0
|
||||||
|
},
|
||||||
|
"dataloader": {
|
||||||
|
"_target_": "DataLoader",
|
||||||
|
"dataset": "@validate#dataset",
|
||||||
|
"batch_size": 1,
|
||||||
|
"shuffle": false,
|
||||||
|
"num_workers": 4
|
||||||
|
},
|
||||||
|
"inferer": {
|
||||||
|
"_target_": "SlidingWindowInferer",
|
||||||
|
"roi_size": [
|
||||||
|
96,
|
||||||
|
96,
|
||||||
|
96
|
||||||
|
],
|
||||||
|
"sw_batch_size": 4,
|
||||||
|
"overlap": 0.5
|
||||||
|
},
|
||||||
|
"postprocessing": "%train#postprocessing",
|
||||||
|
"handlers": [
|
||||||
|
{
|
||||||
|
"_target_": "StatsHandler",
|
||||||
|
"iteration_log": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "TensorBoardStatsHandler",
|
||||||
|
"log_dir": "@output_dir",
|
||||||
|
"iteration_log": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_target_": "CheckpointSaver",
|
||||||
|
"save_dir": "@ckpt_dir",
|
||||||
|
"save_dict": {
|
||||||
|
"model": "@network"
|
||||||
|
},
|
||||||
|
"save_key_metric": true,
|
||||||
|
"key_metric_filename": "model.pt"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"key_metric": {
|
||||||
|
"val_mean_dice": {
|
||||||
|
"_target_": "MeanDice",
|
||||||
|
"include_background": false,
|
||||||
|
"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_metrics": {
|
||||||
|
"val_accuracy": {
|
||||||
|
"_target_": "ignite.metrics.Accuracy",
|
||||||
|
"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"evaluator": {
|
||||||
|
"_target_": "SupervisedEvaluator",
|
||||||
|
"device": "@device",
|
||||||
|
"val_data_loader": "@validate#dataloader",
|
||||||
|
"network": "@network",
|
||||||
|
"inferer": "@validate#inferer",
|
||||||
|
"postprocessing": "@validate#postprocessing",
|
||||||
|
"key_val_metric": "@validate#key_metric",
|
||||||
|
"additional_metrics": "@validate#additional_metrics",
|
||||||
|
"val_handlers": "@validate#handlers",
|
||||||
|
"amp": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"initialize": [
|
||||||
|
"$monai.utils.set_determinism(seed=123)"
|
||||||
|
],
|
||||||
|
"run": [
|
||||||
|
"$@train#trainer.run()"
|
||||||
|
]
|
||||||
|
}
|
||||||
152
ai-medical/models/spleen_ct_segmentation/docs/README.md
Normal file
152
ai-medical/models/spleen_ct_segmentation/docs/README.md
Normal file
@ -0,0 +1,152 @@
|
|||||||
|
# Model Overview
|
||||||
|
A pre-trained model for volumetric (3D) segmentation of the spleen from CT images.
|
||||||
|
|
||||||
|
This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## Data
|
||||||
|
The training dataset is the Spleen Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/.
|
||||||
|
|
||||||
|
- Target: Spleen
|
||||||
|
- Modality: CT
|
||||||
|
- Size: 61 3D volumes (41 Training + 20 Testing)
|
||||||
|
- Source: Memorial Sloan Kettering Cancer Center
|
||||||
|
- Challenge: Large-ranging foreground size
|
||||||
|
|
||||||
|
## Training configuration
|
||||||
|
The segmentation of spleen region is formulated as the voxel-wise binary classification. Each voxel is predicted as either foreground (spleen) or background. And the model is optimized with gradient descent method minimizing Dice + cross entropy loss between the predicted mask and ground truth segmentation.
|
||||||
|
|
||||||
|
The training was performed with the following:
|
||||||
|
|
||||||
|
- GPU: at least 12GB of GPU memory
|
||||||
|
- Actual Model Input: 96 x 96 x 96
|
||||||
|
- AMP: True
|
||||||
|
- Optimizer: Novograd
|
||||||
|
- Learning Rate: 0.002
|
||||||
|
- Loss: DiceCELoss
|
||||||
|
- Dataset Manager: CacheDataset
|
||||||
|
|
||||||
|
### Memory Consumption Warning
|
||||||
|
|
||||||
|
If you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate `cache_rate` in the configurations within range [0, 1] to minimize the System RAM requirements.
|
||||||
|
|
||||||
|
### Input
|
||||||
|
One channel
|
||||||
|
- CT image
|
||||||
|
|
||||||
|
### Output
|
||||||
|
Two channels
|
||||||
|
- Label 1: spleen
|
||||||
|
- Label 0: everything else
|
||||||
|
|
||||||
|
## Performance
|
||||||
|
Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.961.
|
||||||
|
|
||||||
|
#### Training Loss
|
||||||
|
|
||||||
|
|
||||||
|
#### Validation Dice
|
||||||
|
|
||||||
|
|
||||||
|
#### TensorRT speedup
|
||||||
|
The `spleen_ct_segmentation` bundle supports acceleration with TensorRT through the ONNX-TensorRT method. The table below displays the speedup ratios observed on an A100 80G GPU.
|
||||||
|
|
||||||
|
| method | torch_fp32(ms) | torch_amp(ms) | trt_fp32(ms) | trt_fp16(ms) | speedup amp | speedup fp32 | speedup fp16 | amp vs fp16|
|
||||||
|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|
||||||
|
| model computation | 6.46 | 4.48 | 2.52 | 1.96 | 1.44 | 2.56 | 3.30 | 2.29 |
|
||||||
|
| end2end | 1268.03 | 1152.40 | 1137.40 | 1114.25 | 1.10 | 1.11 | 1.14 | 1.03 |
|
||||||
|
|
||||||
|
Where:
|
||||||
|
- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
|
||||||
|
- `end2end` means run the bundle end-to-end with the TensorRT based model.
|
||||||
|
- `torch_fp32` and `torch_amp` are for the PyTorch models with or without `amp` mode.
|
||||||
|
- `trt_fp32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.
|
||||||
|
- `speedup amp`, `speedup fp32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model
|
||||||
|
- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.
|
||||||
|
|
||||||
|
Currently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.
|
||||||
|
|
||||||
|
This result is benchmarked under:
|
||||||
|
- TensorRT: 8.5.3+cuda11.8
|
||||||
|
- Torch-TensorRT Version: 1.4.0
|
||||||
|
- CPU Architecture: x86-64
|
||||||
|
- OS: ubuntu 20.04
|
||||||
|
- Python version:3.8.10
|
||||||
|
- CUDA version: 12.1
|
||||||
|
- GPU models and configuration: A100 80G
|
||||||
|
|
||||||
|
## MONAI Bundle Commands
|
||||||
|
In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.
|
||||||
|
|
||||||
|
For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).
|
||||||
|
|
||||||
|
#### Execute training:
|
||||||
|
|
||||||
|
```
|
||||||
|
python -m monai.bundle run --config_file configs/train.json
|
||||||
|
```
|
||||||
|
|
||||||
|
Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:
|
||||||
|
|
||||||
|
```
|
||||||
|
python -m monai.bundle run --config_file configs/train.json --dataset_dir <actual dataset path>
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Override the `train` config to execute multi-GPU training:
|
||||||
|
|
||||||
|
```
|
||||||
|
torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"
|
||||||
|
```
|
||||||
|
|
||||||
|
Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
|
||||||
|
|
||||||
|
#### Override the `train` config to execute evaluation with the trained model:
|
||||||
|
|
||||||
|
```
|
||||||
|
python -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json']"
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Override the `train` config and `evaluate` config to execute multi-GPU evaluation:
|
||||||
|
|
||||||
|
```
|
||||||
|
torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']"
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Execute inference:
|
||||||
|
|
||||||
|
```
|
||||||
|
python -m monai.bundle run --config_file configs/inference.json
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Export checkpoint to TensorRT based models with fp32 or fp16 precision:
|
||||||
|
|
||||||
|
```
|
||||||
|
python -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json --precision <fp32/fp16> --dynamic_batchsize "[1, 4, 8]" --use_onnx "True" --use_trace "True"
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Execute inference with the TensorRT model:
|
||||||
|
|
||||||
|
```
|
||||||
|
python -m monai.bundle run --config_file "['configs/inference.json', 'configs/inference_trt.json']"
|
||||||
|
```
|
||||||
|
|
||||||
|
# References
|
||||||
|
[1] Xia, Yingda, et al. "3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training." arXiv preprint arXiv:1811.12506 (2018). https://arxiv.org/abs/1811.12506.
|
||||||
|
|
||||||
|
[2] Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_40
|
||||||
|
|
||||||
|
# License
|
||||||
|
Copyright (c) MONAI Consortium
|
||||||
|
|
||||||
|
Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
you may not use this file except in compliance with the License.
|
||||||
|
You may obtain a copy of the License at
|
||||||
|
|
||||||
|
http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
|
||||||
|
Unless required by applicable law or agreed to in writing, software
|
||||||
|
distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
See the License for the specific language governing permissions and
|
||||||
|
limitations under the License.
|
||||||
@ -0,0 +1,6 @@
|
|||||||
|
Third Party Licenses
|
||||||
|
-----------------------------------------------------------------------
|
||||||
|
|
||||||
|
/*********************************************************************/
|
||||||
|
i. Medical Segmentation Decathlon
|
||||||
|
http://medicaldecathlon.com/
|
||||||
BIN
ai-medical/models/spleen_ct_segmentation/models/model.pt
(Stored with Git LFS)
Normal file
BIN
ai-medical/models/spleen_ct_segmentation/models/model.pt
(Stored with Git LFS)
Normal file
Binary file not shown.
BIN
ai-medical/models/spleen_ct_segmentation/models/model.ts
(Stored with Git LFS)
Normal file
BIN
ai-medical/models/spleen_ct_segmentation/models/model.ts
(Stored with Git LFS)
Normal file
Binary file not shown.
13
ai-medical/requirements.ai.txt
Normal file
13
ai-medical/requirements.ai.txt
Normal file
@ -0,0 +1,13 @@
|
|||||||
|
# สำหรับ Model Serving Framework
|
||||||
|
uvicorn[standard]
|
||||||
|
fastapi[standard]
|
||||||
|
# สำหรับ MONAI และ Dependencies หลัก
|
||||||
|
torchvision
|
||||||
|
torchaudio
|
||||||
|
torch
|
||||||
|
pydicom # สำหรับจัดการไฟล์ภาพทางการแพทย์ DICOM
|
||||||
|
numpy
|
||||||
|
scipy
|
||||||
|
monai[all]
|
||||||
|
boto3
|
||||||
|
pydantic-settings
|
||||||
@ -13,6 +13,12 @@ https://docs.djangoproject.com/en/5.2/ref/settings/
|
|||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
import os
|
import os
|
||||||
|
|
||||||
|
# ใน core/settings.py (ด้านบนสุด)
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
load_dotenv() # โหลดตัวแปรจาก .env
|
||||||
|
|
||||||
|
DB_HOST = os.getenv("DB_HOST", "cockroach-1")
|
||||||
|
|
||||||
# Build paths inside the project like this: BASE_DIR / 'subdir'.
|
# Build paths inside the project like this: BASE_DIR / 'subdir'.
|
||||||
BASE_DIR = Path(__file__).resolve().parent.parent
|
BASE_DIR = Path(__file__).resolve().parent.parent
|
||||||
|
|
||||||
@ -192,15 +198,18 @@ DJOSER = {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
REDIS_HOST = os.getenv("REDIS_HOST", "redis")
|
||||||
|
REDIS_PORT = os.getenv("REDIS_PORT", "6379")
|
||||||
|
|
||||||
# 1. ตั้งค่า Redis Cache
|
# 1. ตั้งค่า Redis Cache
|
||||||
CACHES = {
|
CACHES = {
|
||||||
"default": {
|
"default": {
|
||||||
"BACKEND": "django_redis.cache.RedisCache",
|
"BACKEND": "django_redis.cache.RedisCache",
|
||||||
# 'redis' คือ Hostname ของ Service ใน Docker Compose
|
# ใช้ตัวแปร REDIS_HOST ที่ดึงมาจาก .env (localhost) หรือ Docker (redis)
|
||||||
"LOCATION": "redis://redis:6379/1",
|
"LOCATION": f"redis://{REDIS_HOST}:{REDIS_PORT}/1",
|
||||||
"OPTIONS": {
|
"OPTIONS": {
|
||||||
"CLIENT_CLASS": "django_redis.client.DefaultClient",
|
"CLIENT_CLASS": "django_redis.client.DefaultClient",
|
||||||
"IGNORE_EXCEPTIONS": True # ป้องกันการ Crash ถ้า Redis ล่ม
|
"IGNORE_EXCEPTIONS": True
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
@ -214,8 +223,8 @@ SESSION_CACHE_ALIAS = "default"
|
|||||||
# CACHE_MIDDLEWARE_KEY_PREFIX = 'auth_cache'
|
# CACHE_MIDDLEWARE_KEY_PREFIX = 'auth_cache'
|
||||||
|
|
||||||
# CELERY CONFIGURATION
|
# CELERY CONFIGURATION
|
||||||
CELERY_BROKER_URL = 'redis://redis:6379/0' # ใช้ Redis เป็น Broker
|
CELERY_BROKER_URL = f'redis://{REDIS_HOST}:{REDIS_PORT}/0'
|
||||||
CELERY_RESULT_BACKEND = 'redis://redis:6379/0' # ใช้ Redis ในการเก็บผลลัพธ์ของ Task
|
CELERY_RESULT_BACKEND = f'redis://{REDIS_HOST}:{REDIS_PORT}/0'
|
||||||
CELERY_ACCEPT_CONTENT = ['json']
|
CELERY_ACCEPT_CONTENT = ['json']
|
||||||
CELERY_TASK_SERIALIZER = 'json'
|
CELERY_TASK_SERIALIZER = 'json'
|
||||||
CELERY_RESULT_SERIALIZER = 'json'
|
CELERY_RESULT_SERIALIZER = 'json'
|
||||||
|
|||||||
34
backend/create_db.py
Normal file
34
backend/create_db.py
Normal file
@ -0,0 +1,34 @@
|
|||||||
|
import psycopg
|
||||||
|
import os
|
||||||
|
|
||||||
|
# --- ตั้งค่าการเชื่อมต่อ (สำคัญ: ต้องตรงกับค่าใน .env) ---
|
||||||
|
DB_HOST = os.environ.get("DB_HOST", "localhost") # ใช้ localhost
|
||||||
|
DB_PORT = os.environ.get("DB_PORT", 26257)
|
||||||
|
DB_NAME = os.environ.get("DB_NAME", "my_db")
|
||||||
|
DB_USER = os.environ.get("DB_USER", "root")
|
||||||
|
DB_PASSWORD = os.environ.get("DB_PASSWORD", "")
|
||||||
|
|
||||||
|
print(f"Attempting to connect to CockroachDB at {DB_HOST}:{DB_PORT} to create database '{DB_NAME}'...")
|
||||||
|
|
||||||
|
try:
|
||||||
|
# ต้องเชื่อมต่อไปยัง Database มาตรฐาน (defaultdb) ก่อน เพื่อให้มีสิทธิ์สร้าง Database ใหม่
|
||||||
|
conn = psycopg.connect(
|
||||||
|
host=DB_HOST,
|
||||||
|
port=DB_PORT,
|
||||||
|
dbname="defaultdb", # ใช้ defaultdb เพื่อสร้าง my_db
|
||||||
|
user=DB_USER,
|
||||||
|
password=DB_PASSWORD
|
||||||
|
)
|
||||||
|
conn.autocommit = True
|
||||||
|
cur = conn.cursor()
|
||||||
|
|
||||||
|
# คำสั่งสร้าง Database
|
||||||
|
cur.execute(f"CREATE DATABASE IF NOT EXISTS {DB_NAME};")
|
||||||
|
|
||||||
|
cur.close()
|
||||||
|
conn.close()
|
||||||
|
print(f"Database '{DB_NAME}' created or already exists successfully.")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"ERROR: Failed to connect to or create database: {e}")
|
||||||
|
print("Ensure Docker Compose is running and environment variables (DB_HOST, DB_PORT) are set correctly.")
|
||||||
@ -10,3 +10,5 @@ djangorestframework-simplejwt # สำหรับสร้าง JWT (JSON Web
|
|||||||
django-redis # สำหรับเชื่อมต่อ Django กับ Redis
|
django-redis # สำหรับเชื่อมต่อ Django กับ Redis
|
||||||
redis # ไคลเอนต์ Python สำหรับ Redis
|
redis # ไคลเอนต์ Python สำหรับ Redis
|
||||||
celery # ตัว Worker
|
celery # ตัว Worker
|
||||||
|
boto3
|
||||||
|
python-dotenv
|
||||||
@ -87,7 +87,41 @@ services:
|
|||||||
DB_USER: root
|
DB_USER: root
|
||||||
DB_PASSWORD: ''
|
DB_PASSWORD: ''
|
||||||
|
|
||||||
|
# AI Model Serving Service (MONAI Inference)
|
||||||
|
ai_model_server:
|
||||||
|
build:
|
||||||
|
context: ../ # อ้างอิงจาก Root Monorepo
|
||||||
|
dockerfile: infra/docker/Dockerfile.ai
|
||||||
|
container_name: ai_model_server
|
||||||
|
volumes:
|
||||||
|
- ../ai-medical:/app/ai-medical # Map โฟลเดอร์โค้ด AI
|
||||||
|
# - /path/to/gpu/device:/dev/nvidia0 # Uncomment ถ้าใช้ GPU
|
||||||
|
ports:
|
||||||
|
- "8001:8001" # Port สำหรับ API Model Serving
|
||||||
|
depends_on:
|
||||||
|
- backend # ให้มั่นใจว่า Backend พร้อมใช้งานก่อน
|
||||||
|
environment:
|
||||||
|
# กำหนดตัวแปรสภาพแวดล้อมที่ AI Service ต้องใช้
|
||||||
|
MODEL_STORAGE_URL: http://minio:9000/models/
|
||||||
|
MODEL_FILE_NAME: monai_model_v1.pth
|
||||||
|
|
||||||
|
# MinIO Service (S3-Compatible Object Storage)
|
||||||
|
minio:
|
||||||
|
image: minio/minio
|
||||||
|
container_name: minio
|
||||||
|
ports:
|
||||||
|
- "9000:9000" # API Port
|
||||||
|
- "9001:9001" # Console/Web UI Port
|
||||||
|
volumes:
|
||||||
|
- minio_data:/data
|
||||||
|
environment:
|
||||||
|
MINIO_ROOT_USER: minio_admin
|
||||||
|
MINIO_ROOT_PASSWORD: minio_p@ssw0rd!
|
||||||
|
command: server /data --console-address ":9001"
|
||||||
|
restart: always
|
||||||
|
|
||||||
volumes:
|
volumes:
|
||||||
cockroach1:
|
cockroach1:
|
||||||
cockroach2:
|
cockroach2:
|
||||||
cockroach3:
|
cockroach3:
|
||||||
|
minio_data:
|
||||||
Loading…
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Reference in New Issue
Block a user