forked from jiuyuan/InfiniTensor
Add: shape of intermediate tensor in exported ONNX
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34ca6bf149
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2a343e240e
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@ -1,4 +1,4 @@
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import backend
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import backend
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from onnx import (
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ModelProto,
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TensorProto,
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@ -538,9 +538,6 @@ class OnnxStub:
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else:
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assert False, "Unsupported Tensor Type: {}".format(tensor.data_type)
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for output in model.graph.output:
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self.outputs[output.name] = tensors[output.name]
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return ans
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@classmethod
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@ -571,6 +568,8 @@ class OnnxStub:
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outputs: List[ValueInfoProto] = []
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# saves global input tensors
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initializers: List[TensorProto] = []
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# saves global output tensors
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value_info: List[ValueInfoProto] = []
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enable_check = False
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def __init__(self, enable_check):
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@ -586,12 +585,15 @@ class OnnxStub:
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def push_output(self, name: str, tensor: backend.Tensor) -> str:
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self.names[tensor] = name
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if not tensor.has_target():
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shape = tensor.shape()
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dtype = backend.tensor_dtype(tensor)
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value_info = make_tensor_value_info(name, dtype, shape)
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check_value_info(value_info)
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shape = tensor.shape()
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dtype = backend.tensor_dtype(tensor)
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value_info = make_tensor_value_info(name, dtype, shape)
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check_value_info(value_info)
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if not tensor.has_target(): # if this output is a global output
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self.outputs.append(value_info)
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else: # if this output is a local output
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self.value_info.append(value_info)
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return name
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def push_input(
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@ -635,7 +637,8 @@ class OnnxStub:
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def build(self, name: str) -> ModelProto:
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graph = make_graph(
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self.nodes, name, self.inputs, self.outputs, self.initializers
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self.nodes, name, self.inputs, self.outputs, self.initializers,
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value_info=self.value_info
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)
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if self.enable_check:
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check_graph(graph)
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