From a548e421314908771ce9e413d9fa4e205943cceb Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 11 Apr 2021 19:00:55 +0200
Subject: Changed project layout again
---
.../training/experiments/image_transformer.yaml | 72 --------
text_recognizer/training/run_experiment.py | 201 ---------------------
2 files changed, 273 deletions(-)
delete mode 100644 text_recognizer/training/experiments/image_transformer.yaml
delete mode 100644 text_recognizer/training/run_experiment.py
(limited to 'text_recognizer')
diff --git a/text_recognizer/training/experiments/image_transformer.yaml b/text_recognizer/training/experiments/image_transformer.yaml
deleted file mode 100644
index bedcbb5..0000000
--- a/text_recognizer/training/experiments/image_transformer.yaml
+++ /dev/null
@@ -1,72 +0,0 @@
-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
deleted file mode 100644
index ed1a947..0000000
--- a/text_recognizer/training/run_experiment.py
+++ /dev/null
@@ -1,201 +0,0 @@
-"""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()
--
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