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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2021-05-02 22:57:17 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2021-05-02 22:57:17 +0200
commit5108e57aad2427c7e47061f7251ebfdc4bb6eedc (patch)
treecb33d220204d2171132b63c188d020c69392929a /README.md
parent9167fa2cf2967fcc4288cb8432f62a69bd2dde35 (diff)
Updated todos in readme
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Extract text from the iam dataset:
```
-poetry run extract-iam-text --use_words --save_text train.txt --save_tokens letters.txt
+poetry run python extract-iam-text --use_words --save_text train.txt --save_tokens letters.txt
```
Create word pieces from the extracted training text:
```
-poetry run make-wordpieces --output_prefix iamdb_1kwp --text_file train.txt --num_pieces 100
+poetry run python make-wordpieces --output_prefix iamdb_1kwp --text_file train.txt --num_pieces 100
```
Optionally, build a transition graph for word pieces:
```
-poetry run build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kwp_lex_1000.txt --blank optional --self_loops --save_path 1kwp_prune_0_10_optblank.bin --prune 0 10
+poetry run python build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kwp_lex_1000.txt --blank optional --self_loops --save_path 1kwp_prune_0_10_optblank.bin --prune 0 10
```
(TODO: Not working atm, needed for GTN loss function)
## Todo
-- [x] create wordpieces
- - [x] make_wordpieces.py
- - [x] build_transitions.py
- - [x] transform that encodes iam targets to wordpieces
- - [x] transducer loss function
-- [ ] Train with word pieces
- - [ ] Pad word pieces index to same length
-- [ ] Local attention in first layer of transformer
-- [ ] Halonet encoder
-- [ ] Implement CPC
- - [ ] https://arxiv.org/pdf/1905.09272.pdf
- - [ ] https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html?highlight=byol
-
-
-- [ ] Predictive coding
- - https://arxiv.org/pdf/1807.03748.pdf
- - https://arxiv.org/pdf/1904.05862.pdf
- - https://arxiv.org/pdf/1910.05453.pdf
- - https://blog.evjang.com/2016/11/tutorial-categorical-variational.html
-
-
-
-
+- [ ] Reimplement transformer from scratch
+- [ ] Implement Nyström attention (for efficient attention)
+- [ ] Dino
+- [ ] Efficient-net b0 + transformer decoder
+- [ ] Test encoder pre-training ViT (CvT?) with Dino, then train decoder in a separate step
## Run Sweeps