feat: use static capture for squeezenet1_1 json

graph_net/samples/paddle_samples/vision/squeezenet1_1/graph_hash.txt

@@ -0,0 +1 @@
+2f1fbe960d81387133cc93e530c7efa3defdb56c134a0253689cfa85f297331e

graph_net/samples/paddle_samples/vision/squeezenet1_1/input_meta.py

@@ -0,0 +1,3 @@
+import paddle
+
+input_meta = {"inputs": {"shape": [1, 3, 224, 224], "dtype": "float32"}}

graph_net/samples/paddle_samples/vision/squeezenet1_1/model.py

@@ -0,0 +1,11 @@
+import paddle
+from paddle.vision.models import squeezenet1_1
+
+
+class GraphModule(paddle.nn.Layer):
+    def __init__(self):
+        super(GraphModule, self).__init__()
+        self.model = squeezenet1_1(pretrained=False)
+
+    def forward(self, inputs):
+        return self.model(inputs)

graph_net/samples/paddle_samples/vision/squeezenet1_1/weight_meta.py

@@ -0,0 +1,3 @@
+import paddle
+
+weight_meta = {}

graph_net/tests/test_squeezenet1_1_extract.py

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+import paddle
+import os
+from paddle.vision.models import squeezenet1_1
+
+def extract_squeezenet():
+    os.environ["FLAGS_enable_pir_api"] = "1"
+    paddle.enable_static()
+    main_program = paddle.static.Program()
+    with paddle.static.program_guard(main_program):
+        inputs = paddle.static.data(name="inputs", shape=[1, 3, 224, 224], dtype="float32")
+        model = squeezenet1_1(pretrained=False)
+        model(inputs)
+
+    save_path = "graph_net/samples/paddle_samples/vision/squeezenet1_1/squeezenet1_1.json"
+    with open(save_path, "w") as f:
+        f.write(str(main_program))
+    print("Extract success")
+
+if __name__ == "__main__":
+    extract_squeezenet()