Comments (1)
Hi @Johnson-yue, indeed TensorFlow is used quite a bit for the metrics implementation, mainly to make sure the scores can be reproduced as closely to original as possible. For example as mentioned in #7, inception score can be quite sensitive to weight differences when using a different model, and the tensorflow inception model most people use for research seems to be using a very particular implementation (e.g. the output node has >1k outputs).
However, indeed the tensorflow use seems to be quite bulky compared to a pure pytorch approach. Recently the move to TF2 also has some breaking changes that I'm hoping to update and wait to see if it these changes remain stable in the long run. Hopefully there won't be a need to update too much whenever TF updates!
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Related Issues (20)
- Cannot import sagan when I use pip install. Please fix. HOT 1
- Some cons when using metric... HOT 3
- Evaluation settings question HOT 4
- Pretrained Discriminators HOT 3
- Difference of spectral norm conv2d in Dblock/DBlockOptimized
- Evaluation on the test set HOT 1
- How to judge the training process is correct HOT 1
- The gap of FID on LSUN dataset. HOT 1
- Could you add the SAGAN for 256x256 size image? HOT 2
- Could you add the support for torch >= 1.8? HOT 1
- LSUN bedroom 128x128 HOT 1
- bugs in README.md and Documentation about evaluate HOT 2
- dtype bugs on windows10 HOT 1
- How to calculate the FID IS after the experiments? HOT 4
- fid_score() got an unexpected keyword argument 'dataset_name' HOT 1
- ImportError: cannot import name 'PY3' from 'torch._six' HOT 1
- size mismatch for block4.c2.weight
- In which file should I modify the structure of a specific model?
- [CelebA]RuntimeError: The daily quota of the file img_align_celeba.zip is exceeded and it can't be downloaded. This is a limitation of Google Drive and can only be overcome by trying again later. HOT 1
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