Merge pull request #18 from virasad/develop

Release v1.0.0 stable

.gitignore

@@ -0,0 +1,218 @@
+### JetBrains template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
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+.idea/**/workspace.xml
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+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
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+# When using Gradle or Maven with auto-import, you should exclude module files,
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+dataset2/
+dataset2/images/
+
+
+/dataset2/

README.md

@@ -1 +1,3 @@
 # semantic_segment_servicev2
+
+Will be updated soon

docker-compose.yml

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+version: "3.8"
+services:
+  inference:
+    # network_mode: host
+    container_name: "semantic-segment-inference2"
+    build: "./inference"
+    command: "uvicorn main:app --host 0.0.0.0 --port 5553 --reload"
+    ports:
+      - "5553:5553"
+    volumes:
+      - ./inference:/code
+      - ./volumes/weights:/weights
+
+    environment:
+      - PORT=5556
+      - WEIGHTS_DIR=/weights
+    ipc: host
+    shm_size: 1024M
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - capabilities: [ gpu ]
+    networks:
+      - segment-network
+  #
+  train:
+    # network_mode: host
+    container_name: "semantic-segment-train2"
+    build: "./train"
+    command: "uvicorn main:app --host 0.0.0.0 --port 5554 --reload"
+    ports:
+      - "5554:5554"
+    volumes:
+      - ./train:/code
+      - ./volumes/weights:/weights
+      - ./volumes/dataset:/dataset
+    environment:
+      - RESPONSE_URL=http://web:8000/api/v1/train/done
+      - LOGGER_URL=http://127.0.0.1:8000/logger
+      - PORT=5554
+      - IS_LOGGER_ON=False
+      - WEIGHTS_DIR=/weights
+    ipc: host
+    shm_size: 1024M
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - capabilities: [ gpu]
+    networks:
+      - segment-network
+
+  redis:
+    restart: always
+    image: redis:latest
+    expose:
+      - "6379"
+    volumes:
+      - ./volumes/redis-data:/data
+    networks:
+      - segment-network
+
+networks:
+  segment-network:
+

docker-gpu-install.sh

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+distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
+      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
+      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
+            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
+            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+
+sudo apt-get update
+sudo apt-get install -y nvidia-docker2

inference/Dockerfile

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+FROM pytorch/pytorch:1.12.0-cuda11.3-cudnn8-devel
+ENV TZ=UTC
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime
+
+RUN rm /etc/apt/sources.list.d/cuda.list
+
+RUN apt-get update
+RUN apt-get install ffmpeg libsm6 libxext6  -y
+
+WORKDIR /code/
+COPY requirements.txt /code/
+RUN pip install -r requirements.txt

inference/main.py

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+import io
+
+import cv2
+import numpy as np
+from fastapi import FastAPI, File, UploadFile
+from starlette.responses import StreamingResponse
+
+from predict import InferenceSeg
+
+# import imantics
+
+
+app = FastAPI(
+    title="Segmentation Model",
+    description="Api for segmentation model training and inference",
+    version="0.1.0",
+    contact={
+        "name": "Virasad",
+        "url": "https://virasad.ir",
+        "email": "info@virasad.ir",
+    },
+    license_info={
+        "name": "Apache 2.0",
+        "url": "https://www.apache.org/licenses/LICENSE-2.0.html",
+    },
+)
+detector = InferenceSeg('mobilenet_v2', 'imagenet')
+
+
+@app.get("/")
+def root():
+    return {"message": "Welcome to the Segmentation API get documentation at /docs"}
+
+
+@app.post("/predict")
+async def predict(image: UploadFile = File(...), return_image: bool = False, return_coco: bool = False,
+                  ):
+    try:
+        if return_image and return_coco:
+            raise ValueError('return_image and return_coco cannot be True at the same time')
+
+        contents = await image.read()
+        nparr = np.fromstring(contents, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        response = detector.predict_data(img, return_image, return_coco)
+        if return_image:
+            return StreamingResponse(io.BytesIO(response), media_type='image/jpeg')
+
+        elif return_coco:
+            return {"results": response}
+        return {"message": "Success"}
+
+
+    except Exception as e:
+        return {"message": str(e)}
+
+@app.post("/n-classes")
+async def set_classes(n_classes: int):
+    detector.set_classes(n_classes)
+    return {"message": "Success"}
+
+
+@app.post("/set-size")
+async def set_size(weight: int, height: int):
+    detector.set_size(weight, height)
+    return {"message": "Success"}
+
+
+@app.post("/set-model")
+async def set_model(model_path: str):
+    detector.set_model(model_path)
+    return {"message": "Success"}