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# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
# International Conference on Computer Vision (ICCV), 2023
import io
import os
import onnx
import torch
import torch.nn as nn
from onnxsim import simplify as simplify_func
__all__ = ["export_onnx"]
def export_onnx(
model: nn.Module, export_path: str, sample_inputs: any, simplify=True, opset=11
) -> None:
"""Export a model to a platform-specific onnx format.
Args:
model: a torch.nn.Module object.
export_path: export location.
sample_inputs: Any.
simplify: a flag to turn on onnx-simplifier
opset: int
"""
model.eval()
buffer = io.BytesIO()
with torch.no_grad():
torch.onnx.export(model, sample_inputs, buffer, opset_version=opset)
buffer.seek(0, 0)
if simplify:
onnx_model = onnx.load_model(buffer)
onnx_model, success = simplify_func(onnx_model)
assert success
new_buffer = io.BytesIO()
onnx.save(onnx_model, new_buffer)
buffer = new_buffer
buffer.seek(0, 0)
if buffer.getbuffer().nbytes > 0:
save_dir = os.path.dirname(export_path)
os.makedirs(save_dir, exist_ok=True)
with open(export_path, "wb") as f:
f.write(buffer.read())
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