RUNTIME_EXCEPTION : Non-zero status code returned while running Reshape node

[Priyanshu88]

Describe the issue

onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Reshape node. Name:'/Reshape_16_output_0_ReduceMax_Reshape' Status Message: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h:44 onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, onnxruntime::TensorShapeVector&, bool) input_shape_size == size was false. The input tensor cannot be reshaped to the requested shape. Input shape:{0,1}, requested shape:{1}

Urgency

Yeah urgency

Target platform

OpenMMLab

Build script

I am using the below config to convert the rtmo model to onnx model with the help of mmdeploy https://github.com/open-mmlab/mmdeploy/blob/main/configs/mmpose/pose-detection_rtmo_onnxruntime_dynamic.py

onnx_config = dict(
    output_names=['dets', 'keypoints'],
    dynamic_axes={
        'input': {
            0: 'batch',
        },
        'dets': {
            0: 'batch',
        },
        'keypoints': {
            0: 'batch'
        }
    })

codebase_config = dict(
    post_processing=dict(
        score_threshold=0.05,
        iou_threshold=0.5,
        max_output_boxes_per_class=200,
        pre_top_k=2000,
        keep_top_k=50,
        background_label_id=-1,
    ))

Error / output

Full log describing the path

When keeping the batch size as 32, am getting below failure.

onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Reshape node. Name:'/Reshape_16_output_0_ReduceMax_Reshape' Status Message: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h:44 onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, onnxruntime::TensorShapeVector&, bool) input_shape_size == size was false. The input tensor cannot be reshaped to the requested shape. Input shape:{0,1}, requested shape:{1}

When keeping the batch size as 1, am getting below failure.

[ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Reshape node. Name:'/Tile_output_0_ReduceMax_Reshape' Status Message: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h:44 onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, onnxruntime::TensorShapeVector&, bool) input_shape_size == size was false. The input tensor cannot be reshaped to the requested shape. Input shape:{1,0,1}, requested shape:{1}

Visual Studio Version

No response

GCC / Compiler Version

No response

[skottmckay]

Reshape can only change the shape of the data. It does not create/destroy data.

In both cases the input to the Reshape node has no data as one of the dimensions is 0. So we can't 'reshape' no data to data with 1 element.

Most likely part of your model has some sort of selection logic, and nothing was selected, leading to a dimension with a value of 0.

It's also slightly unusual to be using Reshape with a target shape of {1}. Squeeze may be an alternative option as it would handle dims with a value of zero as it removes dimensions with a value of 1.

[Priyanshu88]

Reshape can only change the shape of the data. It does not create/destroy data.

In both cases the input to the Reshape node has no data as one of the dimensions is 0. So we can't 'reshape' no data to data with 1 element.

Most likely part of your model has some sort of selection logic, and nothing was selected, leading to a dimension with a value of 0.

It's also slightly unusual to be using Reshape with a target shape of {1}. Squeeze may be an alternative option as it would handle dims with a value of zero as it removes dimensions with a value of 1.

But why is this different for two different batch sizes? Any possible solution will work that would handle my case of conversion, also did you take a look at onnx config specific to model and it's expected behavior ? let me know any modification if you can provide.

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