Comments (2)
It is theoretically possible to separate the PyTorch and ONNX parts, but in the current design the parts are tightly coupled:
When the model is already converted to PyTorch, ONNX dependency is not required to for inference or train, but in the current implementation, the imports of PyTorch and ONNX are in the same file -- removing ONNX will result in an ImportError
exception.
from onnx2torch.
I just tried working around this by installing onnx2torch and onnx using pip's --no-dependencies switch:
pip install onnx2torch onnx --no-dependencies
and my model loaded successfully 🍡.
from onnx2torch.
Related Issues (20)
- Failed to convert SSDLite with MobileNetV2 backbone
- Unable to successfully convert retinanet R50 HOT 1
- Circular import HOT 1
- Key error 'flow/flow.6/enc/Mul_output_0
- add piper-tts onnx to torch
- Clip convert error: Dynamic value of min/max is not implemented HOT 8
- NotImplementedError: Converter is not implemented (OperationDescription(domain='', operation_type='BatchNormalization', version=7))
- PermissionError: [Errno 13] Permission denied: 'E:\\onnx2torch\\tmptf9fub_u' HOT 3
- Optimizer got an empty parameter list.
- What is the difference with onnx2pytorch?
- running float16 model on the CPU
- NotImplementedError: Average pool operation with spatial rank == -2 is not implemented
- NotImplementedError: AvgPool with non symmetrical padding is not implemented.
- NotImplementedError: Converter is not implemented (OperationDescription(domain='', operation_type='Loop', version=13)) HOT 1
- Does onnx2torch support dynamic input shape?
- onnx2torch incorrectly omits one weight layer when converting onnx into pytorch.
- NotImplementedError: Converter is not implemented (OperationDescription(domain='', operation_type='If', version=11))
- Problem with input shape
- Not working with yolov5 6.2 and yolov8
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from onnx2torch.