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Enable dedicated MMSeg full mmcv runtime

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admin
2026-06-30 13:23:32 +08:00
parent 4d80ec4d75
commit 2d7d54ba13
10 changed files with 121 additions and 35 deletions
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README.md
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@@ -27,8 +27,9 @@ Seg_Data_Server_Net/
cd Seg_Data_Server_Net
cp .env.example .env
# Backend. The deployment env is seg_smp so the API and task wrappers share
# the same segmentation dependency stack.
# Backend. The deployment env is seg_smp so the API and most task wrappers
# share the same segmentation dependency stack. MMSeg jobs default to the
# separate SEG_MMSEG_CONDA_ENV because full mmcv wheels must match torch/CUDA.
conda run -n seg_smp uvicorn app.main:app --app-dir backend --host 0.0.0.0 --port 8010
# Frontend.
@@ -57,12 +58,23 @@ artifact API, runs a mock job, checks SSE log streaming, and executes one
legacy image/label overlay job on tiny generated PNGs. It also runs model
family readiness checks: a SegModel/SMP forward pass, a YOLO segmentation
prediction on a tiny image, MMSeg config parsing, and local MMSeg pretrained
weight discovery.
weight discovery. MMSeg full-model readiness is validated in
`SEG_MMSEG_CONDA_ENV` by importing `mmcv._ext` and building a local MMSeg
`EncoderDecoder` from the existing config tree.
Current `seg_smp` uses `mmcv-lite` because no `torch 2.6/cu124` full `mmcv`
wheel is available on this machine and `nvcc` is not installed for source
builds. The acceptance smoke reports MMSeg full model construction as a
warning until a full `mmcv` build with `mmcv._ext` is installed.
builds. A dedicated `seg_mmcv` environment is used for MMSeg tasks and has
`torch 2.1.2+cu121`, `mmcv 2.1.0`, `mmsegmentation 1.2.2`, and NumPy 1.26.
If rebuilding the environment, keep these versions aligned:
```bash
conda create -n seg_mmcv python=3.10 -y
conda run -n seg_mmcv python -m pip install -U pip
conda run -n seg_mmcv python -m pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121
conda run -n seg_mmcv python -m pip install mmengine==0.10.7 mmsegmentation==1.2.2 'mmcv==2.1.0' -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.1/index.html
conda run -n seg_mmcv python -m pip install 'numpy<2' 'opencv-python<4.12' ftfy regex matplotlib pandas scikit-learn scipy seaborn tqdm tensorboard
```
## Weight Sync
@@ -118,7 +130,8 @@ Run the local evaluation and validation agents before publishing changes:
PYTHONPATH=backend conda run -n seg_smp python scripts/run_agents.py --build
```
The validation agent checks catalog coverage, the new `seg_smp` env, GPU
visibility, no-weight Git safety, backend tests, frontend build, and live
backend/frontend endpoints when the services are running. With live validation
enabled it also runs the lightweight acceptance smoke above.
The validation agent checks catalog coverage, the `seg_smp` task env, the
`seg_mmcv` MMSeg env, GPU visibility, no-weight Git safety, backend tests,
frontend build, and live backend/frontend endpoints when the services are
running. With live validation enabled it also runs the lightweight acceptance
smoke above.