[New Sample] Add vovnet39a model computational graph and extraction script

graph_net/test/extract_vovnet39a.py

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+import os
+
+import timm
+import torch
+from graph_net.torch.extractor import extract
+
+# 强制注入环境变量，防止丢失
+os.environ["GRAPH_NET_EXTRACT_WORKSPACE"] = "/home/aistudio/graphnet_workspace"
+os.makedirs(os.environ["GRAPH_NET_EXTRACT_WORKSPACE"], exist_ok=True)
+
+
+def main():
+    name = "vovnet39a"
+    device_str = "cpu"
+    device = torch.device(device_str)
+    print(f"开始测试模型: {name} on {device_str}")
+
+    try:
+        model = timm.create_model(name, pretrained=False)
+    except Exception as e:
+        print(f"[FAIL] 实例化模型失败 - {e}")
+        return
+
+    # VoVNet 标准输入分辨率是 224
+    input_data = torch.rand(1, 3, 224, 224, device=device)
+
+    model = model.to(device).eval()
+    wrapped = extract(name=name)(model).eval()
+
+    try:
+        with torch.no_grad():
+            wrapped(input_data)
+        print(f"[OK] {name} 计算图抽取成功！")
+    except Exception as e:
+        print(f"[FAIL] {name} 抽取发生错误 - {e}")
+
+
+if __name__ == "__main__":
+    main()

samples/timm/vovnet39a/graph_hash.txt

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+59ec37f6d64ba344a84c2ac69fcb427273851fb0eee47fa4f2a755b30c277913

samples/timm/vovnet39a/graph_net.json

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+{
+    "framework": "torch",
+    "num_devices_required": 1,
+    "num_nodes_required": 1,
+    "dynamic": true,
+    "model_name": "vovnet39a"
+}