Comments (2)
If you are using DSNT at the output, one option is to save the model up until (but excluding) the DSNT-related operations. Since those layers don't have weights anyway, saving them is not important. Kind of like how you don't need to save the loss function.
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This is effectively a duplicate of #9
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Related Issues (20)
- Get confidence of prediciton per regressed coordinate HOT 8
- RuntimeError: expected flip dims axis >= 0, but got min flip dims=-1 HOT 1
- dsnt in testing phase HOT 8
- Working with 3D HOT 8
- Suggestion: Define X,Y grid so that they include -1 and 1 HOT 6
- DSNT support only 1 point in 1 heatmap? HOT 2
- Question when I use dsnt in my net HOT 8
- A question about the input and target HOT 4
- how to get the confidence score from the output result? HOT 2
- Is Frobenius computed correctly? HOT 2
- 3 dimension coordinate regression HOT 1
- increase batch size 1 to 16, it made wrong result. HOT 7
- For the normalized_linspace function HOT 2
- Pip install fails HOT 1
- Question re. occluded or missing points in training data HOT 2
- Use generated 2d-guassion heatmap as the regularization. HOT 2
- output coords are negative floats HOT 15
- Values outside (-1,1) HOT 1
- Trace warnings when trying to jit.trace a model HOT 11
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