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# Text Recognizer
Implementing the text recognizer project from the course ["Full Stack Deep Learning Course"](https://fullstackdeeplearning.com/march2019) (FSDL) in PyTorch in order to learn best practices when building a deep learning project. I have expanded on this project by adding additional feature and ideas given by Claudio Jolowicz in ["Hypermodern Python"](https://cjolowicz.github.io/posts/hypermodern-python-01-setup/).
## Setup
TBC
### Build word piece dataset
Extract text from the iam dataset:
```
poetry run 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
```
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
```
(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
- [ ] implement wandb callback for logging
- [ ] Implement CPC
- [ ] Window images
- [ ] Train backbone
- [ ] Bert training, how?
- [ ] 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
## Run Sweeps
Run the following commands to execute hyperparameter search with W&B:
```
wandb sweep training/sweep_emnist_resnet.yml
export SWEEP_ID=...
wandb agent $SWEEP_ID
```
## PyTorch Performance Guide
Tips and tricks from ["PyTorch Performance Tuning Guide - Szymon Migacz, NVIDIA"](https://www.youtube.com/watch?v=9mS1fIYj1So&t=125s):
* Always better to use `num_workers > 0`, allows asynchronous data processing
* Use `pin_memory=True` to allow data loading and computations to happen on the GPU in parallel.
* Have to tune `num_workers` to use based on the problem, too many and data loading becomes slower.
* For CNNs use `torch.backends.cudnn.benchmark=True`, allows cuDNN to select the best algorithm for convolutional computations (autotuner).
* Increase batch size to max out GPU memory.
* Use optimizer for large batch training, e.g. LARS, LAMB etc.
* Set `bias=False` for convolutions directly followed by BatchNorm.
* Use `for p in model.parameters(): p.grad = None` instead of `model.zero_grad()`.
* Careful with disable debug APIs in prod (detect_anomaly, profiler, gradcheck).
* Use `DistributedDataParallel` not `DataParallel`, uses 1 CPU core for each GPU.
* Important to load balance compute on all GPUs, if variably-sized inputs or GPUs will idle.
* Use an apex fused optimizer
* Use checkpointing to recompute memory-intensive compute-efficient ops in backward pass (e.g. activations, upsampling), `torch.utils.checkpoint`.
* Use `@torch.jit.script`, especially to fuse long sequences of pointwise operations like GELU.
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