ViT-B classifier added with first 30 labels

serverless/pytorch/omerferhatt/vit-b/nuclio/function-gpu.yaml

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+metadata:
+  name: pth-omerferhatt-vitb
+  namespace: cvat
+  annotations:
+    name: ViT-B
+    type: detector
+    framework: pytorch
+    spec: |
+      [
+        { "id": 0, "name": "tench", "type": "tag" },
+        { "id": 1, "name": "goldfish", "type": "tag" },
+        { "id": 2, "name": "great white shark", "type": "tag" },
+        { "id": 3, "name": "tiger shark", "type": "tag" },
+        { "id": 4, "name": "hammerhead shark", "type": "tag" },
+        { "id": 5, "name": "electric ray", "type": "tag" },
+        { "id": 6, "name": "stingray", "type": "tag" },
+        { "id": 7, "name": "cock", "type": "tag" },
+        { "id": 8, "name": "hen", "type": "tag" },
+        { "id": 9, "name": "ostrich", "type": "tag" },
+        { "id": 10, "name": "brambling", "type": "tag" },
+        { "id": 11, "name": "goldfinch", "type": "tag" },
+        { "id": 12, "name": "house finch", "type": "tag" },
+        { "id": 13, "name": "junco", "type": "tag" },
+        { "id": 14, "name": "indigo bunting", "type": "tag" },
+        { "id": 15, "name": "robin", "type": "tag" },
+        { "id": 16, "name": "bulbul", "type": "tag" },
+        { "id": 17, "name": "jay", "type": "tag" },
+        { "id": 18, "name": "magpie", "type": "tag" },
+        { "id": 19, "name": "chickadee", "type": "tag" },
+        { "id": 20, "name": "water ouzel", "type": "tag" },
+        { "id": 21, "name": "kite", "type": "tag" },
+        { "id": 22, "name": "bald eagle", "type": "tag" },
+        { "id": 23, "name": "vulture", "type": "tag" },
+        { "id": 24, "name": "great grey owl", "type": "tag" },
+        { "id": 25, "name": "European fire salamander", "type": "tag" },
+        { "id": 26, "name": "common newt", "type": "tag" },
+        { "id": 27, "name": "eft", "type": "tag" },
+        { "id": 28, "name": "spotted salamander", "type": "tag" },
+        { "id": 29, "name": "axolotl", "type": "tag" }
+      ]
+spec:
+  description: ImageNet-1k pretrained ViT-B
+  runtime: 'python:3.8'
+  handler: main:handler
+  eventTimeout: 30s
+  build:
+    image: cvat.pth.omerferhatt.vitb:latest-gpu
+    baseImage: nvidia/cuda:12.1.0-runtime-ubuntu22.04
+
+    directives:
+      preCopy:
+        - kind: RUN
+          value: |-
+            apt update \
+              && apt install -y --no-install-recommends \
+                wget \
+                git \
+                ca-certificates \
+                python-is-python3 \
+                python3 \
+                python3-pip \
+              && rm -rf /var/lib/apt/lists/*
+        - kind: WORKDIR
+          value: /opt/nuclio
+        - kind: RUN
+          value: pip install pillow pyyaml
+        - kind: RUN
+          value: |-
+            pip install torch torchvision
+
+  triggers:
+    myHttpTrigger:
+      maxWorkers: 1
+      kind: 'http'
+      workerAvailabilityTimeoutMilliseconds: 10000
+      attributes:
+        maxRequestBodySize: 33554432 # 32MB
+
+  resources:
+    limits:
+      nvidia.com/gpu: 1
+
+  platform:
+    attributes:
+      restartPolicy:
+        name: always
+        maximumRetryCount: 3
+      mountMode: volume

serverless/pytorch/omerferhatt/vit-b/nuclio/function.yaml

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+metadata:
+  name: pth-omerferhatt-vitb
+  namespace: cvat
+  annotations:
+    name: ViT-B
+    type: detector
+    framework: pytorch
+    spec: |
+      [
+        { "id": 0, "name": "tench", "type": "tag" },
+        { "id": 1, "name": "goldfish", "type": "tag" },
+        { "id": 2, "name": "great white shark", "type": "tag" },
+        { "id": 3, "name": "tiger shark", "type": "tag" },
+        { "id": 4, "name": "hammerhead shark", "type": "tag" },
+        { "id": 5, "name": "electric ray", "type": "tag" },
+        { "id": 6, "name": "stingray", "type": "tag" },
+        { "id": 7, "name": "cock", "type": "tag" },
+        { "id": 8, "name": "hen", "type": "tag" },
+        { "id": 9, "name": "ostrich", "type": "tag" },
+        { "id": 10, "name": "brambling", "type": "tag" },
+        { "id": 11, "name": "goldfinch", "type": "tag" },
+        { "id": 12, "name": "house finch", "type": "tag" },
+        { "id": 13, "name": "junco", "type": "tag" },
+        { "id": 14, "name": "indigo bunting", "type": "tag" },
+        { "id": 15, "name": "robin", "type": "tag" },
+        { "id": 16, "name": "bulbul", "type": "tag" },
+        { "id": 17, "name": "jay", "type": "tag" },
+        { "id": 18, "name": "magpie", "type": "tag" },
+        { "id": 19, "name": "chickadee", "type": "tag" },
+        { "id": 20, "name": "water ouzel", "type": "tag" },
+        { "id": 21, "name": "kite", "type": "tag" },
+        { "id": 22, "name": "bald eagle", "type": "tag" },
+        { "id": 23, "name": "vulture", "type": "tag" },
+        { "id": 24, "name": "great grey owl", "type": "tag" },
+        { "id": 25, "name": "European fire salamander", "type": "tag" },
+        { "id": 26, "name": "common newt", "type": "tag" },
+        { "id": 27, "name": "eft", "type": "tag" },
+        { "id": 28, "name": "spotted salamander", "type": "tag" },
+        { "id": 29, "name": "axolotl", "type": "tag" }
+      ]
+spec:
+  description: ImageNet-1k pretrained ViT-B
+  runtime: 'python:3.8'
+  handler: main:handler
+  eventTimeout: 30s
+  build:
+    image: cvat.pth.omerferhatt.vitb
+    baseImage: ubuntu:22.04
+
+    directives:
+      preCopy:
+        - kind: RUN
+          value: |-
+            apt update \
+              && apt install -y --no-install-recommends \
+                wget \
+                git \
+                ca-certificates \
+                python-is-python3 \
+                python3 \
+                python3-pip \
+              && rm -rf /var/lib/apt/lists/*
+        - kind: WORKDIR
+          value: /opt/nuclio
+        - kind: RUN
+          value: pip install pillow pyyaml
+        - kind: RUN
+          value: |-
+            pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu
+
+  triggers:
+    myHttpTrigger:
+      maxWorkers: 2
+      kind: 'http'
+      workerAvailabilityTimeoutMilliseconds: 10000
+      attributes:
+        maxRequestBodySize: 33554432 # 32MB
+
+  platform:
+    attributes:
+      restartPolicy:
+        name: always
+        maximumRetryCount: 3
+      mountMode: volume

serverless/pytorch/omerferhatt/vit-b/nuclio/main.py

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+import base64
+import io
+import json
+import yaml
+
+from model_handler import ModelHandler
+from PIL import Image
+
+
+def init_context(context):
+    context.logger.info("Init context...  0%")
+    with open("/opt/nuclio/function.yaml", "rb") as function_file:
+        config_yaml = yaml.load(function_file, Loader=yaml.FullLoader)
+
+    model = ModelHandler()
+    context.user_data.labels = [
+        label["name"] for label in eval(config_yaml["metadata"]["annotations"]["spec"])
+    ]
+    context.user_data.yaml = config_yaml
+    context.user_data.model = model
+    context.logger.info("Init context...100%")
+
+def handler(context, event):
+    context.logger.info("Run ViT-B model")
+    data = event.body
+    image = Image.open(io.BytesIO(base64.b64decode(data["image"]))).convert("RGB")
+
+    class_id, _, score = context.user_data.model.infer(image)
+
+    results = [{
+        "confidence": str(score),
+        "label": context.user_data.labels[class_id],
+        "type": "tag",
+        "objectType": "tag",
+    }]
+    context.logger.info(f"Results: {results}")
+
+    return context.Response(
+        body=json.dumps(results),
+        headers={},
+        content_type="application/json",
+        status_code=200,
+    )

serverless/pytorch/omerferhatt/vit-b/nuclio/model_handler.py

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+from typing import Tuple
+
+import torch
+import torchvision as tv
+from PIL import Image
+
+
+class ModelHandler:
+    weights = tv.models.ViT_B_16_Weights.DEFAULT
+    preprocess = weights.transforms()
+
+    def __init__(self) -> None:
+        self.model = tv.models.vit_b_16(weights=self.weights)
+        self.model.eval()
+        self.device = torch.device("cpu")
+        if torch.cuda.is_available():
+            self.device = torch.device("cuda")
+        self.model = self.model.to(self.device)
+
+    def infer(self, image: Image) -> Tuple[int, str, float]:
+        with torch.inference_mode():
+            batch = self.preprocess(image).unsqueeze(0).to(self.device)
+            prediction = self.model(batch).squeeze(0).softmax(0)
+            class_id = prediction.argmax().item()
+            score = prediction[class_id].item()
+            category_name = self.weights.meta["categories"][class_id]
+        return (class_id, category_name, score)