How to get dynamic batch size in Onnx model from Pytorch?

Question

I have a currently working PyTorch to Onnx conversion process that I would like to enable a dynamic batch size for.

I am setting the dynamic axes like this:

    model.to_onnx(
        onnx_filepath,
        input_sample=(x, None),
        export_params=True,
        opset_version=17,
        do_constant_folding=True,
        input_names=['image'],
        output_names=['boxes', 'scores', 'labels'],
        dynamic_axes={
            "image": [0],
            "boxes": [0],
            "scores": [0],
            "labels": [0],
        }
    )

However, the onnx model that is produced only shows dynamic batch size for the input:

How do I make the output batch size dynamic? Do I need to modify the forward function of the model in some way?

score 1 · Accepted Answer · answered Aug 25 '23 at 22:41

This is just a guess, but are you by any chance processing each input image (or alternatively post-processing detections) of the batch separately inside of a for-loop? If yes, your behaviour might be due to how torch exports to ONNX, and you will need to modify your forward pass. Or, alternatively you can use torch.jit.script.

Where forwad pass could go wrong

Check your model for anything that defines a dimension of a tensor that is interpreted as a python integer during export. Setting dynamic axes will try to use variable shapes for the corresponding tensors, but will be overridden by explicit constant ones.

# WRONG - WILL EXPORT WITH STATIC BATCH SIZE
def forward(self, batch):
    bs, c, h, w = batch.shape
    # bs is saved as a constant integer during export
    for i in range(bs):
        do_something()

# WRONG - WILL EXPORT WITH STATIC BATCH SIZE
def forward(self, batch):
    # iterating over tensors is not supported for dynamic batch sizes
    # ONNX model will iterate the same amount as in batch during export
    for i in batch:
        do_something()

Potential fixes

Use tensor.size instead of tensor.shape

# CORRECT - WILL EXPORT WITH DYNAMIC AXES
def forward(self, batch):
    # This calls a function instead of getting an attribute,
    # the variable will be dynamic
    bs = batch.size(0)
    for i in range(bs):
        do_something()

Script parts of the model to preserve control flows and different input sizes

# CORRECT - WILL EXPORT WITH DYNAMIC AXES
# Script parts of the forward pass, e.g. single functions
@torch.jit._script_if_tracing
def do_something(batch):
    for i in batch:
        do_something_else()
    
def forward(self, batch):
    # function will be scripted, dynamic shapes preserved
    do_something(batch)

Export the whole module as a ScriptModule, preserving all control flows and input sizes

# CORRECT - WILL EXPORT WITH DYNAMIC AXES
script_module = torch.jit.script(model)
torch.onnx.export(
    script_module,
    ...
)

Thanks, this is very helpful info! I am using a torchvision Retinanet model, I tried some of your suggestions, but not having luck with it yet. Do you know if its possible to create a ScriptModule for this kind of model? Or will it need to be custom modified? — brad, Aug 29 '23 at 22:19
@brad If it is a `torchvision` model then this question is likely a [duplicate](https://stackoverflow.com/questions/76928530/customize-pytorch-model-export-to-onnx) — simeonovich, Aug 30 '23 at 08:19
ok thank you, it sounds like if I want a batch size larger than one I should switch away from torchvision — brad, Aug 30 '23 at 16:06

How to get dynamic batch size in Onnx model from Pytorch?

1 Answers1

Where forwad pass could go wrong

Potential fixes