分类部署例程

output_shape.py

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+from rknnlite.api import RKNNLite
+import numpy as np
+
+model_path = '/userdata/reenrr/inference_with_lite/mobilenetv2_640.rknn'
+
+rknn_lite = RKNNLite()
+rknn_lite.load_rknn(model_path)
+rknn_lite.init_runtime()
+
+# 通过实际推理获取输出维度
+dummy_input = np.random.randn(1, 3, 640, 640).astype(np.float32)  # 根据模型输入尺寸调整
+outputs = rknn_lite.inference(inputs=[dummy_input])
+
+print("\n输出维度信息:")
+for i, out in enumerate(outputs):
+    print(f"Output {i} shape: {out.shape}")  # 查看输出形状
+
+rknn_lite.release()
+