Created
January 11, 2021 17:47
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import torch, torchvision | |
from onnxruntime import InferenceSession | |
model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True) | |
x = torch.rand(1, 3, 360, 640) | |
torch.onnx.export( | |
model, | |
x, # ONNX requires fixed input size | |
"test-mask-rcnn-export.onnx", | |
do_constant_folding=True, | |
dynamic_axes={ | |
"images_tensors": [0, 1, 2, 3], | |
"boxes": [0, 1], | |
"labels": [0], | |
"scores": [0], | |
"masks": [0, 1, 2, 3], | |
}, | |
# dynamic_axes={"input_image": {0: "sequence"}, "output": {0: "sequence"}}, | |
input_names=["input_image"], | |
output_names=["bbox_coords", "bbox_labels", "bbox_scores", "bbox_masks"], | |
opset_version=11, # opset_version 11 required for Mask R-CNN | |
) | |
session = InferenceSession("test-mask-rcnn-export.onnx") | |
session.run(None, {"input_image": x.detach().cpu().numpy()} ) |
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