Open M-Zubair10 opened 1 year ago
It is a good question, but we have not explored it yet. The straightest way is to deploy the model like ONNX.
@SlongLiu should the operations in the model already support ONNX export? e.g.
code vaguely similar to the below should work?
# Export the model
torch.onnx.export](https://pytorch.org/docs/stable/onnx.html#torch.onnx.export)(torch_model, # model being run
x, # model input (or a tuple for multiple inputs)
"super_resolution.onnx", # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
opset_version=10, # the ONNX version to export the model to
do_constant_folding=True, # whether to execute constant folding for optimization
input_names = ['input'], # the model's input names
output_names = ['output'], # the model's output names
dynamic_axes={'input' : {0 : 'batch_size'}, # variable length axes
'output' : {0 : 'batch_size'}})
I'll look in to optimising it with tools like OpenVINO if it does.
i try to use torch.onnx.export to transfer grounding-dino to onnx, it seems like some problem , such as several logical operators are not supported by onnx. for example: torch.onnx.symbolic_registry.UnsupportedOperatorError: Exporting the operator ::_ior to ONNX opset version 13 is not supported do you know which part of model make this problem. @GeorgePearse
i try to use torch.onnx.export to transfer grounding-dino to onnx, it seems like some problem , such as several logical operators are not supported by onnx. for example: torch.onnx.symbolic_registry.UnsupportedOperatorError: Exporting the operator ::_ior to ONNX opset version 13 is not supported do you know which part of model make this problem. @GeorgePearse
Have you solve it please
@SlongLiu should the operations in the model already support ONNX export? e.g.
code vaguely similar to the below should work?
# Export the model torch.onnx.export](https://pytorch.org/docs/stable/onnx.html#torch.onnx.export)(torch_model, # model being run x, # model input (or a tuple for multiple inputs) "super_resolution.onnx", # where to save the model (can be a file or file-like object) export_params=True, # store the trained parameter weights inside the model file opset_version=10, # the ONNX version to export the model to do_constant_folding=True, # whether to execute constant folding for optimization input_names = ['input'], # the model's input names output_names = ['output'], # the model's output names dynamic_axes={'input' : {0 : 'batch_size'}, # variable length axes 'output' : {0 : 'batch_size'}})
I'll look in to optimising it with tools like OpenVINO if it does.
am also interested in onx export code of GDINO
Thanks for this awesome model, it does evaluate good with pretrained model
Right now i am getting 15s average inference time, any way to reduce it to 2-3s