TaylorHere commented on issue #11783:
URL: https://github.com/apache/tvm/issues/11783#issuecomment-1163402246
```python
import numpy as np
import onnx
from tvm.driver import tvmc
def create_initializer_tensor(
name: str,
tensor_array: np.ndarray,
data_type: onnx.TensorProto = onnx.TensorProto.FLOAT,
) -> onnx.TensorProto:
return onnx.helper.make_tensor(
name=name,
data_type=data_type,
dims=tensor_array.shape,
vals=tensor_array.flatten().tolist(),
)
input_4001_np = np.ones(shape=(1,)).astype(np.int64)
input_4001 = create_initializer_tensor(
name="input_4001", tensor_array=input_4001_np,
data_type=onnx.TensorProto.INT64
)
attr_value = onnx.helper.make_tensor("input_value", onnx.TensorProto.INT64,
[1], [1])
output_892 = onnx.helper.make_tensor_value_info(
"output_892", onnx.TensorProto.INT64, [1]
)
ConstantOfShape_704 = onnx.helper.make_node(
name="ConstantOfShape_704",
op_type="ConstantOfShape",
inputs=["input_4001"],
outputs=["output_892"],
value=attr_value,
)
input_4000_np = np.ones(shape=(3, 2)).astype(np.int64)
input_4000 = create_initializer_tensor(
name="input_4000", tensor_array=input_4000_np,
data_type=onnx.TensorProto.INT64
)
output_893 = onnx.helper.make_tensor_value_info(
"output_893", onnx.TensorProto.INT64, [3, 2]
)
Expand_705 = onnx.helper.make_node(
name="Expand_705",
op_type="Expand",
inputs=["input_4000", "output_892"],
outputs=["output_893"],
)
# Create the graph (GraphProto)
graph_def = onnx.helper.make_graph(
nodes=[ConstantOfShape_704, Expand_705],
name="constantOfShape->Expand",
inputs=[],
outputs=[output_893],
initializer=[input_4001, input_4000],
)
# Create the model (ModelProto)
model_def = onnx.helper.make_model(graph_def, producer_name="onnx-example")
model_def.opset_import[0].version = 13
model_def = onnx.shape_inference.infer_shapes(model_def)
onnx.checker.check_model(model_def)
onnx.save(model_def, "convnet.onnx")
mod = tvmc.load('convnet.onnx')
tvmc.compile(mod, 'llvm')
```
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