[New Sample] Add squeezenet1_1 model for Paddle samples

graph_net/tests/test_squeezenet1_1_extract.py

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+import paddle
+import os
+import json
+from paddle.vision.models import squeezenet1_1
+
+
+def extract_squeezenet():
+    # 1. Environment setup
+    os.environ["FLAGS_enable_pir_api"] = "1"
+    paddle.enable_static()
+
+    # 2. Capture computation graph
+    main_program = paddle.static.Program()
+    with paddle.static.program_guard(main_program):
+        inputs = paddle.static.data(
+            name="image", shape=[1, 3, 224, 224], dtype="float32"
+        )
+        model = squeezenet1_1(pretrained=False)
+        model(inputs)
+
+    # 3. Get absolute save path
+    current_file_path = os.path.abspath(__file__)
+    project_root = os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))
+    target_dir = os.path.join(project_root, "paddle_samples/PaddleX/squeezenet1_1")
+    save_path = os.path.join(target_dir, "graph_net.json")
+
+    # 4. Standardize format: Convert program to a valid JSON string
+    # We wrap it in a dictionary to ensure valid JSON structure with double quotes
+    program_content = str(main_program)
+    json_data = {"protocol": "PIR", "program_desc": program_content}
+
+    # 5. Write to file using json.dump (this enforces double quotes for keys)
+    os.makedirs(target_dir, exist_ok=True)
+    with open(save_path, "w") as f:
+        json.dump(json_data, f, indent=4)
+
+    print(f"Extraction successful! Standard JSON generated at: {save_path}")
+
+
+if __name__ == "__main__":
+    extract_squeezenet()

paddle_samples/PaddleX/squeezenet1_1/graph_hash.txt

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+a87bc38868b688b562b67634fb432eab7051fff838290f80ec278d02c9391a80

paddle_samples/PaddleX/squeezenet1_1/input_meta.py

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+# Input metadata for SqueezeNet 1.1
+
+
+# This follows the style of existing samples in the repo
+class Program_input_tensor_image:
+    name = "image"
+    shape = [1, 3, 224, 224]
+    dtype = "float32"
+    data = None
+
+
+# The validation script needs this dict to match with model.forward(**state_dict)
+input_meta = {"image": {"shape": [1, 3, 224, 224], "dtype": "float32"}}

paddle_samples/PaddleX/squeezenet1_1/model.py

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+import paddle
+from paddle.vision.models import squeezenet1_1
+
+
+class GraphModule(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.model = squeezenet1_1(pretrained=False)
+
+    def forward(self, *args, **kwargs):
+        # 1. Try to get tensor from positional arguments
+        if args:
+            x = args[0]
+        # 2. Try to get tensor from keyword arguments
+        else:
+            x = None
+            for val in kwargs.values():
+                if isinstance(val, (paddle.Tensor, paddle.static.Variable)):
+                    x = val
+                    break
+
+            if x is None:
+                # Log keys for debugging in CI if it fails again
+                raise ValueError(
+                    f"No input tensor found. args_len: {len(args)}, kwargs_keys: {list(kwargs.keys())}"
+                )
+
+        return self.model(x)

paddle_samples/PaddleX/squeezenet1_1/weight_meta.py

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+# Weight metadata for SqueezeNet 1.1
+weight_meta = {}