Add siammask serverless function (it doesn't work, need to serialize …

…state)

serverless/pytorch/foolwood/siammask/nuclio/function.yaml

@@ -0,0 +1,57 @@
+metadata:
+  name: pth.foolwood.siammask
+  namespace: cvat
+  annotations:
+    name: SiamMask
+    type: tracker
+    spec:
+    framework: pytorch
+
+spec:
+  description: Fast Online Object Tracking and Segmentation
+  runtime: "python:3.6"
+  handler: main:handler
+  eventTimeout: 30s
+  env:
+    - name: PYTHONPATH
+      value: /opt/nuclio/SiamMask:/opt/nuclio/SiamMask/experiments/siammask_sharp
+
+  build:
+    image: cvat/pth.foolwood.siammask
+    baseImage: continuumio/miniconda3
+
+    directives:
+      preCopy:
+      - kind: WORKDIR
+        value: /opt/nuclio
+      - kind: RUN
+        value: conda create -y -n siammask python=3.6
+      - kind: RUN
+        value: source activate siammask
+      - kind: RUN
+        value: git clone https://github.com/foolwood/SiamMask.git
+      - kind: RUN
+        value: pip install -r SiamMask/requirements.txt
+      - kind: RUN
+        value: conda install -y gcc_linux-64
+      - kind: RUN
+        value: cd SiamMask && bash make.sh && cd -
+      - kind: RUN
+        value: wget -P SiamMask/experiments/siammask_sharp http://www.robots.ox.ac.uk/~qwang/SiamMask_DAVIS.pth
+
+      - kind: WORKDIR
+        value: /opt/nuclio/pysot
+
+  triggers:
+    myHttpTrigger:
+      maxWorkers: 2
+      kind: "http"
+      workerAvailabilityTimeoutMilliseconds: 10000
+      attributes:
+        maxRequestBodySize: 33554432 # 32MB
+
+  platform:
+    attributes:
+      restartPolicy:
+        name: always
+        maximumRetryCount: 3

serverless/pytorch/foolwood/siammask/nuclio/main.py

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+import json
+import base64
+from PIL import Image
+import io
+from model_handler import ModelHandler
+
+def init_context(context):
+    context.logger.info("Init context...  0%")
+
+    # Read the DL model
+    model = ModelHandler()
+    setattr(context.user_data, 'model', model)
+
+    context.logger.info("Init context...100%")
+
+def handler(context, event):
+    context.logger.info("Run SiamMask model")
+    data = event.body
+    buf = io.BytesIO(base64.b64decode(data["image"].encode('utf-8')))
+    shape = data.get("shape")
+    state = data.get("state")
+    image = Image.open(buf)
+
+    results = context.user_data.model.infer(image, shape, state)
+
+    return context.Response(body=json.dumps(results), headers={},
+        content_type='application/json', status_code=200)

serverless/pytorch/foolwood/siammask/nuclio/model_handler.py

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+# Copyright (C) 2020 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+from tools.test import *
+import os
+
+class ModelHandler:
+    def __init__(self):
+        # Setup device
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        torch.backends.cudnn.benchmark = True
+
+        base_dir = "/opt/nuclio/SiamMask/experiments/siammask_sharp"
+        class configPath:
+            config = os.path.join(base_dir, "config_davis.json")
+
+        self.config = load_config(configPath)
+        from custom import Custom
+        siammask = Custom(anchors=self.config['anchors'])
+        self.siammask = load_pretrain(siammask, os.path.join(base_dir, "SiamMask_DAVIS.pth"))
+        self.siammask.eval().to(self.device)
+
+
+    def infer(self, image, shape, state):
+        if state is None: # init tracking
+            x, y, w, h = shape
+            target_pos = np.array([x + w / 2, y + h / 2])
+            target_sz = np.array([w, h])
+            state = siamese_init(image, target_pos, target_sz, self.siammask,
+                self.config['hp'], device=self.device)
+        else: # track
+            state = siamese_track(state, image, mask_enable=True, refine_enable=True,
+                device=self.device)
+            shape = state['ploygon'].flatten()
+
+        return {"shape": shape, "state": state}
+