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-rw-r--r--text_recognizer/data/base_dataset.py2
-rw-r--r--text_recognizer/data/iam.py1
-rw-r--r--text_recognizer/data/iam_preprocessor.py28
-rw-r--r--text_recognizer/data/transforms.py6
-rw-r--r--text_recognizer/models/base.py6
-rw-r--r--text_recognizer/training/experiments/image_transformer.yaml72
-rw-r--r--text_recognizer/training/run_experiment.py201
7 files changed, 301 insertions, 15 deletions
diff --git a/text_recognizer/data/base_dataset.py b/text_recognizer/data/base_dataset.py
index d00daaf..8d644d4 100644
--- a/text_recognizer/data/base_dataset.py
+++ b/text_recognizer/data/base_dataset.py
@@ -67,7 +67,7 @@ def convert_strings_to_labels(
labels = torch.ones((len(strings), length), dtype=torch.long) * mapping["<p>"]
for i, string in enumerate(strings):
tokens = list(string)
- tokens = ["<s>", *tokens, "</s>"]
+ tokens = ["<s>", *tokens, "<e>"]
for j, token in enumerate(tokens):
labels[i, j] = mapping[token]
return labels
diff --git a/text_recognizer/data/iam.py b/text_recognizer/data/iam.py
index 01272ba..261c8d3 100644
--- a/text_recognizer/data/iam.py
+++ b/text_recognizer/data/iam.py
@@ -7,7 +7,6 @@ import zipfile
from boltons.cacheutils import cachedproperty
from loguru import logger
-from PIL import Image
import toml
from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
diff --git a/text_recognizer/data/iam_preprocessor.py b/text_recognizer/data/iam_preprocessor.py
index 3844419..d85787e 100644
--- a/text_recognizer/data/iam_preprocessor.py
+++ b/text_recognizer/data/iam_preprocessor.py
@@ -47,8 +47,6 @@ def load_metadata(
class Preprocessor:
"""A preprocessor for the IAM dataset."""
- # TODO: add lower case only to when generating...
-
def __init__(
self,
data_dir: Union[str, Path],
@@ -57,10 +55,12 @@ class Preprocessor:
lexicon_path: Optional[Union[str, Path]] = None,
use_words: bool = False,
prepend_wordsep: bool = False,
+ special_tokens: Optional[List[str]] = None,
) -> None:
self.wordsep = "▁"
self._use_word = use_words
self._prepend_wordsep = prepend_wordsep
+ self.special_tokens = special_tokens if special_tokens is not None else None
self.data_dir = Path(data_dir)
@@ -88,6 +88,10 @@ class Preprocessor:
else:
self.lexicon = None
+ if self.special_tokens is not None:
+ self.tokens += self.special_tokens
+ self.graphemes += self.special_tokens
+
self.graphemes_to_index = {t: i for i, t in enumerate(self.graphemes)}
self.tokens_to_index = {t: i for i, t in enumerate(self.tokens)}
self.num_features = num_features
@@ -115,21 +119,31 @@ class Preprocessor:
continue
self.text.append(example["text"].lower())
- def to_index(self, line: str) -> torch.LongTensor:
- """Converts text to a tensor of indices."""
+
+ def _to_index(self, line: str) -> torch.LongTensor:
+ if line in self.special_tokens:
+ return torch.LongTensor([self.tokens_to_index[line]])
token_to_index = self.graphemes_to_index
if self.lexicon is not None:
if len(line) > 0:
# If the word is not found in the lexicon, fall back to letters.
- line = [
+ tokens = [
t
for w in line.split(self.wordsep)
for t in self.lexicon.get(w, self.wordsep + w)
]
token_to_index = self.tokens_to_index
if self._prepend_wordsep:
- line = itertools.chain([self.wordsep], line)
- return torch.LongTensor([token_to_index[t] for t in line])
+ tokens = itertools.chain([self.wordsep], tokens)
+ return torch.LongTensor([token_to_index[t] for t in tokens])
+
+ def to_index(self, line: str) -> torch.LongTensor:
+ """Converts text to a tensor of indices."""
+ if self.special_tokens is not None:
+ pattern = f"({'|'.join(self.special_tokens)})"
+ lines = list(filter(None, re.split(pattern, line)))
+ return torch.cat([self._to_index(l) for l in lines])
+ return self._to_index(line)
def to_text(self, indices: List[int]) -> str:
"""Converts indices to text."""
diff --git a/text_recognizer/data/transforms.py b/text_recognizer/data/transforms.py
index 616e236..297c953 100644
--- a/text_recognizer/data/transforms.py
+++ b/text_recognizer/data/transforms.py
@@ -23,12 +23,12 @@ class ToLower:
class ToCharcters:
"""Converts integers to characters."""
- def __init__(self) -> None:
- self.mapping, _, _ = emnist_mapping()
+ def __init__(self, extra_symbols: Optional[List[str]] = None) -> None:
+ self.mapping, _, _ = emnist_mapping(extra_symbols)
def __call__(self, y: Tensor) -> str:
"""Converts a Tensor to a str."""
- return "".join([self.mapping(int(i)) for i in y]).strip("<p>").replace(" ", "▁")
+ return "".join([self.mapping[int(i)] for i in y]).replace(" ", "▁")
class WordPieces:
diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index 3c1919e..0928e6c 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -49,7 +49,7 @@ class LitBaseModel(pl.LightningModule):
optimizer_class = getattr(torch.optim, self._optimizer.type)
return optimizer_class(params=self.parameters(), **args)
- def _configure_lr_scheduler(self) -> Dict[str, Any]:
+ def _configure_lr_scheduler(self, optimizer: Type[torch.optim.Optimizer]) -> Dict[str, Any]:
"""Configures the lr scheduler."""
scheduler = {"monitor": self.monitor}
args = {} or self._lr_scheduler.args
@@ -59,13 +59,13 @@ class LitBaseModel(pl.LightningModule):
scheduler["scheduler"] = getattr(
torch.optim.lr_scheduler, self._lr_scheduler.type
- )(**args)
+ )(optimizer, **args)
return scheduler
def configure_optimizers(self) -> Tuple[List[type], List[Dict[str, Any]]]:
"""Configures optimizer and lr scheduler."""
optimizer = self._configure_optimizer()
- scheduler = self._configure_lr_scheduler()
+ scheduler = self._configure_lr_scheduler(optimizer)
return [optimizer], [scheduler]
diff --git a/text_recognizer/training/experiments/image_transformer.yaml b/text_recognizer/training/experiments/image_transformer.yaml
new file mode 100644
index 0000000..bedcbb5
--- /dev/null
+++ b/text_recognizer/training/experiments/image_transformer.yaml
@@ -0,0 +1,72 @@
+seed: 4711
+
+network:
+ desc: null
+ type: ImageTransformer
+ args:
+ encoder:
+ type: null
+ args: null
+ num_decoder_layers: 4
+ hidden_dim: 256
+ num_heads: 4
+ expansion_dim: 1024
+ dropout_rate: 0.1
+ transformer_activation: glu
+
+model:
+ desc: null
+ type: LitTransformerModel
+ args:
+ optimizer:
+ type: MADGRAD
+ args:
+ lr: 1.0e-2
+ momentum: 0.9
+ weight_decay: 0
+ eps: 1.0e-6
+ lr_scheduler:
+ type: CosineAnnealingLR
+ args:
+ T_max: 512
+ criterion:
+ type: CrossEntropyLoss
+ args:
+ weight: None
+ ignore_index: -100
+ reduction: mean
+ monitor: val_loss
+ mapping: sentence_piece
+
+data:
+ desc: null
+ type: IAMExtendedParagraphs
+ args:
+ batch_size: 16
+ num_workers: 12
+ train_fraction: 0.8
+ augment: true
+
+callbacks:
+ - type: ModelCheckpoint
+ args:
+ monitor: val_loss
+ mode: min
+ - type: EarlyStopping
+ args:
+ monitor: val_loss
+ mode: min
+ patience: 10
+
+trainer:
+ desc: null
+ args:
+ stochastic_weight_avg: true
+ auto_scale_batch_size: binsearch
+ gradient_clip_val: 0
+ fast_dev_run: false
+ gpus: 1
+ precision: 16
+ max_epochs: 512
+ terminate_on_nan: true
+ weights_summary: true
diff --git a/text_recognizer/training/run_experiment.py b/text_recognizer/training/run_experiment.py
new file mode 100644
index 0000000..ed1a947
--- /dev/null
+++ b/text_recognizer/training/run_experiment.py
@@ -0,0 +1,201 @@
+"""Script to run experiments."""
+from datetime import datetime
+import importlib
+from pathlib import Path
+from typing import Dict, List, Optional, Type
+
+import click
+from loguru import logger
+from omegaconf import DictConfig, OmegaConf
+import pytorch_lightning as pl
+import torch
+from torch import nn
+from torchsummary import summary
+from tqdm import tqdm
+import wandb
+
+
+SEED = 4711
+EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
+
+
+def _configure_logging(log_dir: Optional[Path], verbose: int = 0) -> None:
+ """Configure the loguru logger for output to terminal and disk."""
+
+ def _get_level(verbose: int) -> str:
+ """Sets the logger level."""
+ levels = {0: "WARNING", 1: "INFO", 2: "DEBUG"}
+ verbose = min(verbose, 2)
+ return levels[verbose]
+
+ # Remove default logger to get tqdm to work properly.
+ logger.remove()
+
+ # Fetch verbosity level.
+ level = _get_level(verbose)
+
+ logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True, level=level)
+ if log_dir is not None:
+ logger.add(
+ str(log_dir / "train.log"),
+ format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
+ )
+
+
+def _load_config(file_path: Path) -> DictConfig:
+ """Return experiment config."""
+ logger.info(f"Loading config from: {file_path}")
+ if not file_path.exists():
+ raise FileNotFoundError(f"Experiment config not found at: {file_path}")
+ return OmegaConf.load(file_path)
+
+
+def _import_class(module_and_class_name: str) -> type:
+ """Import class from module."""
+ module_name, class_name = module_and_class_name.rsplit(".", 1)
+ module = importlib.import_module(module_name)
+ return getattr(module, class_name)
+
+
+def _configure_callbacks(
+ callbacks: List[DictConfig],
+) -> List[Type[pl.callbacks.Callback]]:
+ """Configures lightning callbacks."""
+ pl_callbacks = [
+ getattr(pl.callbacks, callback.type)(**callback.args) for callback in callbacks
+ ]
+ return pl_callbacks
+
+
+def _configure_logger(
+ network: Type[nn.Module], args: Dict, use_wandb: bool
+) -> Type[pl.loggers.LightningLoggerBase]:
+ """Configures lightning logger."""
+ if use_wandb:
+ pl_logger = pl.loggers.WandbLogger()
+ pl_logger.watch(network)
+ pl_logger.log_hyperparams(vars(args))
+ return pl_logger
+ return pl.logger.TensorBoardLogger("training/logs")
+
+
+def _save_best_weights(
+ callbacks: List[Type[pl.callbacks.Callback]], use_wandb: bool
+) -> None:
+ """Saves the best model."""
+ model_checkpoint_callback = next(
+ callback
+ for callback in callbacks
+ if isinstance(callback, pl.callbacks.ModelCheckpoint)
+ )
+ best_model_path = model_checkpoint_callback.best_model_path
+ if best_model_path:
+ logger.info(f"Best model saved at: {best_model_path}")
+ if use_wandb:
+ logger.info("Uploading model to W&B...")
+ wandb.save(best_model_path)
+
+
+def _load_lit_model(
+ lit_model_class: type, network: Type[nn.Module], config: DictConfig
+) -> Type[pl.LightningModule]:
+ """Load lightning model."""
+ if config.load_checkpoint is not None:
+ logger.info(
+ f"Loading network weights from checkpoint: {config.load_checkpoint}"
+ )
+ return lit_model_class.load_from_checkpoint(
+ config.load_checkpoint, network=network, **config.model.args
+ )
+ return lit_model_class(network=network, **config.model.args)
+
+
+def run(
+ filename: str,
+ train: bool,
+ test: bool,
+ tune: bool,
+ use_wandb: bool,
+ verbose: int = 0,
+) -> None:
+ """Runs experiment."""
+
+ _configure_logging(None, verbose=verbose)
+ logger.info("Starting experiment...")
+
+ # Seed everything in the experiment.
+ logger.info(f"Seeding everthing with seed={SEED}")
+ pl.utilities.seed.seed_everything(SEED)
+
+ # Load config.
+ file_path = EXPERIMENTS_DIRNAME / filename
+ config = _load_config(file_path)
+
+ # Load classes.
+ data_module_class = _import_class(f"text_recognizer.data.{config.data.type}")
+ network_class = _import_class(f"text_recognizer.networks.{config.network.type}")
+ lit_model_class = _import_class(f"text_recognizer.models.{config.model.type}")
+
+ # Initialize data object and network.
+ data_module = data_module_class(**config.data.args)
+ network = network_class(**data_module.config(), **config.network.args)
+
+ # Load callback and logger.
+ callbacks = _configure_callbacks(config.callbacks)
+ pl_logger = _configure_logger(network, config, use_wandb)
+
+ # Load ligtning model.
+ lit_model = _load_lit_model(lit_model_class, network, config)
+
+ trainer = pl.Trainer(
+ **config.trainer.args,
+ callbacks=callbacks,
+ logger=pl_logger,
+ weigths_save_path="training/logs",
+ )
+
+ if tune:
+ logger.info(f"Tuning learning rate and batch size...")
+ trainer.tune(lit_model, datamodule=data_module)
+
+ if train:
+ logger.info(f"Training network...")
+ trainer.fit(lit_model, datamodule=data_module)
+
+ if test:
+ logger.info(f"Testing network...")
+ trainer.test(lit_model, datamodule=data_module)
+
+ _save_best_weights(callbacks, use_wandb)
+
+
+@click.command()
+@click.option("-f", "--experiment_config", type=str, help="Path to experiment config.")
+@click.option("--use_wandb", is_flag=True, help="If true, do use wandb for logging.")
+@click.option(
+ "--tune", is_flag=True, help="If true, tune hyperparameters for training."
+)
+@click.option("--train", is_flag=True, help="If true, train the model.")
+@click.option("--test", is_flag=True, help="If true, test the model.")
+@click.option("-v", "--verbose", count=True)
+def cli(
+ experiment_config: str,
+ use_wandb: bool,
+ tune: bool,
+ train: bool,
+ test: bool,
+ verbose: int,
+) -> None:
+ """Run experiment."""
+ run(
+ filename=experiment_config,
+ train=train,
+ test=test,
+ tune=tune,
+ use_wandb=use_wandb,
+ verbose=verbose,
+ )
+
+
+if __name__ == "__main__":
+ cli()
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-11-10 06:34:37 +0000