Comments (1)
Alive files are generated throughout the training, at each step when summaries are computed. Typically, at the very beginning of the training, very few channels are pruned yet, so the alive files from early steps correspond to a very light pruning, i.e., a very small reduction in FLOPS. As training continues, the pruning becomes more aggressive. Eventually, the pruning stabilizes at some level. All of this can be seen in the FLOPS chart on Tensorboard.
The choice of the alive file can be based on the target FLOPS (or other complexity measure) of the final architecture. It is quite reasonable to try a few different point. For example one alive file can be from the end of the training when the FLOPS already reached their lowest level. Another one can be from the middle of the training where the FLOPS reduction is much more modest. Ultimately, retraining will show what quality can be reached with each structure.
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Related Issues (20)
- the morphnet can act on some layers of the model?such as act on backbone. HOT 7
- Questions about batch_normalization HOT 3
- OpRegularizerManager could not handle ops HOT 7
- Getting this Error HOT 1
- Getting error like this HOT 2
- In this case, MorphNet is getting stuck because it doesn't know how to handle the Shape op. You can try a couple options:
- Applying Morph-net while training training Yolov3 using darknet HOT 1
- Query on Morph Net Structure Exporter HOT 2
- What's the tf version this repo used?
- Can morph-net output a pruned model file? HOT 3
- Bad structure HOT 3
- MorphNet not working on google colab HOT 1
- Can I use MorphNet to slim my model trained by Pytorch? HOT 1
- Regularizer Loss remains zero / BatchToSpaceND HOT 1
- Batch Norm layers Assertion Error
- Latency regularizer for a target hardware. HOT 5
- LayerNorm not support?
- Performance issue in the definition of create_test_input, examples/slim/nets/resnet_v1_test.py HOT 1
- Tensorflow 2
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