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#!/usr/bin/env python3
# Scene Text Recognition Model Hub
# Copyright 2022 Darwin Bautista
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import math
import os
import shutil
from pathlib import Path
import hydra
import numpy as np
from hydra.core.hydra_config import HydraConfig
from omegaconf import DictConfig, open_dict
from ray import air, train, tune
from ray.tune import CLIReporter
from ray.tune.integration.pytorch_lightning import TuneReportCheckpointCallback
from ray.tune.schedulers import MedianStoppingRule
from ray.tune.search.ax import AxSearch
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.loggers import TensorBoardLogger
from strhub.data.module import SceneTextDataModule
from strhub.models.base import BaseSystem
log = logging.getLogger(__name__)
class MetricTracker(tune.Stopper):
"""Tracks the trend of the metric. Stops downward/stagnant trials. Assumes metric is being maximized."""
def __init__(self, metric, max_t, patience: int = 3, window: int = 3) -> None:
super().__init__()
self.metric = metric
self.trial_history = {}
self.max_t = max_t
self.training_iteration = 0
self.eps = 0.01 # sensitivity
self.patience = patience # number of consecutive downward/stagnant samples to trigger early stoppage.
self.kernel = self.gaussian_pdf(np.arange(window) - window // 2, sigma=0.6)
# Extra samples to keep in order to have better MAs + gradients for the middle p samples.
self.buffer = 2 * (len(self.kernel) // 2) + 2
@staticmethod
def gaussian_pdf(x, sigma=1.0):
return np.exp(-((x / sigma) ** 2) / 2) / (sigma * np.sqrt(2 * np.pi))
@staticmethod
def moving_average(x, k):
return np.convolve(x, k, 'valid') / k.sum()
def __call__(self, trial_id, result):
self.training_iteration = result['training_iteration']
if np.isnan(result['loss']) or self.training_iteration >= self.max_t:
try:
del self.trial_history[trial_id]
except KeyError:
pass
return True
history = self.trial_history.get(trial_id, [])
# FIFO queue of metric values.
history = history[-(self.patience + self.buffer - 1) :] + [result[self.metric]]
# Only start checking once we have enough data. At least one non-zero sample is required.
if len(history) == self.patience + self.buffer and sum(history) > 0:
smooth_grad = np.gradient(self.moving_average(history, self.kernel))[1:-1] # discard edge values.
# Check if trend is downward or stagnant
if (smooth_grad < self.eps).all():
log.info(f'Stopping trial = {trial_id}, hist = {history}, grad = {smooth_grad}')
try:
del self.trial_history[trial_id]
except KeyError:
pass
return True
self.trial_history[trial_id] = history
return False
def stop_all(self):
return False
class TuneReportCheckpointPruneCallback(TuneReportCheckpointCallback):
def _handle(self, trainer: Trainer, pl_module: LightningModule):
super()._handle(trainer, pl_module)
# Prune older checkpoints
trial_dir = train.get_context().get_trial_dir()
for old in sorted(Path(trial_dir).glob('checkpoint_epoch=*-step=*'), key=os.path.getmtime)[:-1]:
log.info(f'Deleting old checkpoint: {old}')
shutil.rmtree(old)
def trainable(hparams, config):
with open_dict(config):
config.model.lr = hparams['lr']
# config.model.weight_decay = hparams['wd']
model: BaseSystem = hydra.utils.instantiate(config.model)
datamodule: SceneTextDataModule = hydra.utils.instantiate(config.data)
tune_callback = TuneReportCheckpointPruneCallback({
'loss': 'val_loss',
'NED': 'val_NED',
'accuracy': 'val_accuracy',
})
if checkpoint := train.get_checkpoint():
with checkpoint.as_directory() as checkpoint_dir:
ckpt_path = os.path.join(checkpoint_dir, 'checkpoint')
else:
ckpt_path = None
trainer: Trainer = hydra.utils.instantiate(
config.trainer,
enable_progress_bar=False,
enable_checkpointing=False,
logger=TensorBoardLogger(save_dir=train.get_context().get_trial_dir(), name='', version='.'),
callbacks=[tune_callback],
)
trainer.fit(model, datamodule=datamodule, ckpt_path=ckpt_path)
@hydra.main(config_path='configs', config_name='tune', version_base='1.2')
def main(config: DictConfig):
# Special handling for PARseq
if config.model.get('perm_mirrored', False):
assert config.model.perm_num % 2 == 0, 'perm_num should be even if perm_mirrored = True'
# Modify config
with open_dict(config):
# Use mixed-precision training
if config.trainer.get('gpus', 0):
config.trainer.precision = 16
# Resolve absolute path to data.root_dir
config.data.root_dir = hydra.utils.to_absolute_path(config.data.root_dir)
hparams = {
'lr': tune.loguniform(config.tune.lr.min, config.tune.lr.max),
# 'wd': tune.loguniform(config.tune.wd.min, config.tune.wd.max),
}
steps_per_epoch = len(hydra.utils.instantiate(config.data).train_dataloader())
val_steps = steps_per_epoch * config.trainer.max_epochs / config.trainer.val_check_interval
max_t = round(0.75 * val_steps)
warmup_t = round(config.model.warmup_pct * val_steps)
scheduler = MedianStoppingRule(time_attr='training_iteration', grace_period=warmup_t)
# Always start by evenly diving the range in log scale.
lr = hparams['lr']
start = np.log10(lr.lower)
stop = np.log10(lr.upper)
num = math.ceil(stop - start) + 1
initial_points = [{'lr': np.clip(x, lr.lower, lr.upper).item()} for x in reversed(np.logspace(start, stop, num))]
search_alg = AxSearch(points_to_evaluate=initial_points)
reporter = CLIReporter(parameter_columns=['lr'], metric_columns=['loss', 'accuracy', 'training_iteration'])
out_dir = Path(HydraConfig.get().runtime.output_dir if config.tune.resume_dir is None else config.tune.resume_dir)
resources_per_trial = {
'cpu': 1,
'gpu': config.tune.gpus_per_trial,
}
wrapped_trainable = tune.with_parameters(tune.with_resources(trainable, resources_per_trial), config=config)
if config.tune.resume_dir is None:
tuner = tune.Tuner(
wrapped_trainable,
param_space=hparams,
tune_config=tune.TuneConfig(
mode='max',
metric='NED',
search_alg=search_alg,
scheduler=scheduler,
num_samples=config.tune.num_samples,
),
run_config=air.RunConfig(
name=out_dir.name,
stop=MetricTracker('NED', max_t),
progress_reporter=reporter,
local_dir=str(out_dir.parent.absolute()),
),
)
else:
tuner = tune.Tuner.restore(config.tune.resume_dir, wrapped_trainable)
results = tuner.fit()
best_result = results.get_best_result()
print('Best hyperparameters found were:', best_result.config)
print('with result:\n', best_result)
if __name__ == '__main__':
main()
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