正如this git issue中所描述的（非常完整的描述），我试图在openvino后端加载一个.onnx模型。然而，在设置track_running_stats=False时，BatchNorm层被考虑在训练模式中。下面是我在将我的torch模型转换为onnx之前如何做到这一点：

model.eval()

for child in model.children():
    if type(child)==nn.BatchNorm2d:
        child.track_running_stats = False
        child.running_mean = None
        child.running_var = None

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然后，我将模型导出到onnx：

dummy_input = torch.randn(1, 3, 200, 200, requires_grad=True)  
torch.onnx.export(model, dummy_input, model_path,  export_params=True, opset_version=16, training=torch.onnx.TrainingMode.PRESERVE)

型
最后，我在openvino中加载它时得到了这个错误：

Error: Check '(node.get_outputs_size() == 1)' failed at src/frontends/onnx/frontend/src/op/batch_norm.cpp:67:
While validating ONNX node '<Node(BatchNormalization): BatchNormalization_10>':
Training mode of BatchNormalization is not supported.

型
正如在git issue中提到的，我尝试查看BatchNorm输入/输出：

for node in onnx_model.graph.node:
    if any(("BatchNorm" in s or "bn" in s) for s in node.input) or any(("BatchNorm" in s or "bn" in s) for s in node.output):
        print('Node:',node.name)
        print(node)

型
所以你可以看到这些节点与BN相关：

Node: ReduceMean_5
input: "onnx::ReduceMean_22"
output: "onnx::BatchNormalization_23"
name: "ReduceMean_5"
op_type: "ReduceMean"
attribute {
  name: "axes"
  ints: 0
  ints: 1
  type: INTS
}
attribute {
  name: "keepdims"
  i: 0
  type: INT
}

Node: ReduceMean_9
input: "onnx::ReduceMean_26"
output: "onnx::BatchNormalization_27"
name: "ReduceMean_9"
op_type: "ReduceMean"
attribute {
  name: "axes"
  ints: 0
  ints: 1
  type: INTS
}
attribute {
  name: "keepdims"
  i: 0
  type: INT
}

Node: BatchNormalization_10
input: "input"
input: "bn1.weight"
input: "bn1.bias"
input: "onnx::BatchNormalization_23"
input: "onnx::BatchNormalization_27"
output: "input.4"
output: "29"
output: "30"
name: "BatchNormalization_10"
op_type: "BatchNormalization"
attribute {
  name: "epsilon"
  f: 9.999999747378752e-06
  type: FLOAT
}
attribute {
  name: "momentum"
  f: 0.8999999761581421
  type: FLOAT
}
attribute {
  name: "training_mode"
  i: 1
  type: INT
}

Node: BatchNormalization_13
input: "input.8"
input: "bn2.0.weight"
input: "bn2.0.bias"
input: "bn2.0.running_mean"
input: "bn2.0.running_var"
output: "input.12"
name: "BatchNormalization_13"
op_type: "BatchNormalization"
attribute {
  name: "epsilon"
  f: 9.999999747378752e-06
  type: FLOAT
}
attribute {
  name: "momentum"
  f: 0.8999999761581421
  type: FLOAT
}
attribute {
  name: "training_mode"
  i: 0
  type: INT
}

Node: ReduceMean_18
input: "onnx::ReduceMean_37"
output: "onnx::BatchNormalization_38"
name: "ReduceMean_18"
op_type: "ReduceMean"
attribute {
  name: "axes"
  ints: 0
  ints: 1
  type: INTS
}
attribute {
  name: "keepdims"
  i: 0
  type: INT
}

Node: ReduceMean_22
input: "onnx::ReduceMean_41"
output: "onnx::BatchNormalization_42"
name: "ReduceMean_22"
op_type: "ReduceMean"
attribute {
  name: "axes"
  ints: 0
  ints: 1
  type: INTS
}
attribute {
  name: "keepdims"
  i: 0
  type: INT
}

Node: BatchNormalization_23
input: "input.16"
input: "bn3.weight"
input: "bn3.bias"
input: "onnx::BatchNormalization_38"
input: "onnx::BatchNormalization_42"
output: "43"
output: "44"
output: "45"
name: "BatchNormalization_23"
op_type: "BatchNormalization"
attribute {
  name: "epsilon"
  f: 9.999999747378752e-06
  type: FLOAT
}
attribute {
  name: "momentum"
  f: 0.8999999761581421
  type: FLOAT
}
attribute {
  name: "training_mode"
  i: 1
  type: INT
}

型
你确实可以看到2/3 BN层处于训练模式= 1（-> True）。如何处理它，以便onnx在eval模式下考虑它们，同时保持track_running_stats=False？
我不是很熟悉Onnx和更多的全球DL初学者，所以我喜欢任何建议！