feat: 导出未初始化的张量

Signed-off-by: YdrMaster <ydrml@hotmail.com>

include/core/graph_handler.h

@@ -90,20 +90,14 @@ class GraphHandlerObj {
     inline void data_malloc() { g->dataMalloc(); }
 
     inline void copy_int32(Tensor tensor, std::vector<int32_t> list) {
-        std::cout << "copy " << list.size() << " ints to (" << tensor->size()
-                  << ")" << std::endl;
         tensor->copyData(list);
     }
 
     inline void copy_int64(Tensor tensor, std::vector<int64_t> list) {
-        std::cout << "copy " << list.size() << " ints to (" << tensor->size()
-                  << ")" << std::endl;
         tensor->copyData(list);
     }
 
     inline void copy_float(Tensor tensor, std::vector<float> list) {
-        std::cout << "copy " << list.size() << " floats to (" << tensor->size()
-                  << ")" << std::endl;
         tensor->copyData(list);
     }
 

pyinfinitensor/src/pyinfinitensor/onnx.py

@@ -32,7 +32,9 @@ def cuda_runtime():
     return backend.cuda_runtime()
 
 
-def from_onnx(model: ModelProto, runtime) -> backend.GraphHandler:
+def from_onnx(
+    model: ModelProto, runtime
+) -> Tuple[Dict[str, backend.Tensor], backend.GraphHandler]:
     model = infer_shapes(model)
     handler = backend.GraphHandler(runtime)
 
@@ -330,12 +332,13 @@ def from_onnx(model: ModelProto, runtime) -> backend.GraphHandler:
 
     handler.data_malloc()
 
-    inputs = []
+    inputs: Dict[str, backend.Tensor] = {}
     for name, obj in tensors.items():
+        print("{}: {}".format(name, obj))
         tensor = data.get(name)
         if tensor == None:
             if any(input.name == name for input in model.graph.input):
-                inputs.append((name, tensor))
+                inputs[name] = obj
         else:
             if tensor.data_type == TensorProto.INT32:
                 handler.copy_int32(obj, [int(i) for i in tensor.int32_data])
@@ -346,6 +349,8 @@ def from_onnx(model: ModelProto, runtime) -> backend.GraphHandler:
             else:
                 assert False, "Unsupported Tensor Type: {}".format(tensor.data_type)
 
+    return inputs, handler
+
 
 def to_onnx(graph: backend.GraphHandler, name: str) -> ModelProto:
     class Context: