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import argparse
import json
import logging
import os
import pandas as pd
from typing import Any, Callable, Dict, List, Tuple
import tensorflow as tf
from tensorflow_neuroimaging.preprocessing import center_crop_or_pad
from tensorflow_neuroimaging.loaders.mgh import load_mgh
from pyment.configurations import DatasetConfiguration, FinetuningConfiguration
from pyment.factories import loss_factory, metric_factory, optimizer_factory
from pyment.models.sfcn import sfcn_factory
from pyment.models.utils.load_select_pretrained_weights import (
load_select_pretrained_weights
)
from pyment.utils.json_serialize import json_serialize
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s: %(message)s',
level=logging.DEBUG
)
logger = logging.getLogger(__name__)
def _create_tensorflow_dataset(
df: pd.DataFrame, *,
target: str,
input_shape: Tuple[int, int, int],
batch_size: str,
shuffle: bool = False
) -> tf.data.Dataset:
input_shape = tf.constant(input_shape)
df = df.copy()
df = df.sample(frac=1.)
dataset = tf.data.Dataset.from_tensor_slices((df['path'], df[target]))
if shuffle:
dataset = dataset.shuffle(buffer_size=5*batch_size)
dataset = dataset.map(
lambda path, label: (load_mgh(path), label),
num_parallel_calls=tf.data.AUTOTUNE
)
dataset = dataset.map(
lambda image, label: (center_crop_or_pad(image, input_shape), label),
num_parallel_calls=tf.data.AUTOTUNE
)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(tf.data.AUTOTUNE)
return dataset
def _create_checkpointing_callback(
destination: str,
metrics: List[tf.keras.metrics.Metric] = None
):
os.mkdir(destination)
train_metrics = []
val_metrics = []
if metrics is not None:
for metric in metrics:
name = metric.name.replace('_', '-')
train_metrics.append(f'{name}={{{metric.name}:.2f}}')
val_metrics.append(f'val-{name}={{val_{metric.name}:.2f}}')
terms = [
'epoch={epoch:03d}',
'loss={loss:.2f}'
] + train_metrics + [
'val-loss={val_loss:.2f}'
] + val_metrics
filename = '_'.join(terms) + '.hdf5'
filepath = os.path.join(destination, filename)
return tf.keras.callbacks.ModelCheckpoint(
filepath,
monitor='val_loss',
save_best_only=True,
save_weights_only=True
)
def finetune(
model_type: str,
model_constructor_arguments: Dict[str, Any],
weights: str,
input_shape: Tuple[int, int, int],
target: str,
loss: Callable[[tf.Tensor, tf.Tensor], tf.Tensor],
metrics: List[tf.keras.metrics.Metric],
optimizer: tf.optimizers.Optimizer,
learning_rate_scheduler: tf.keras.callbacks.Callback,
training: pd.DataFrame,
validation: pd.DataFrame,
batch_size: int,
epochs: int,
destination: str
):
if destination is not None:
if os.path.isdir(destination):
raise ValueError(f'Destination {destination} already exists')
logger.info('Creating destination folder %s', destination)
os.mkdir(destination)
model_class = sfcn_factory(model_type)
model = model_class(
input_shape=input_shape,
**model_constructor_arguments
)
load_select_pretrained_weights(model, weights, target=target)
model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
training_dataset = _create_tensorflow_dataset(
training,
input_shape=input_shape,
target=target,
batch_size=batch_size,
shuffle=True
)
validation_dataset = _create_tensorflow_dataset(
validation,
input_shape=input_shape,
target=target,
batch_size=batch_size,
shuffle=False
)
callbacks = [
_create_checkpointing_callback(
os.path.join(destination, 'checkpoints'),
metrics=metrics
),
learning_rate_scheduler
]
history = model.fit(
training_dataset,
validation_data=validation_dataset,
epochs=epochs,
callbacks=callbacks
)
with open(os.path.join(destination, 'history.json'), 'w') as f:
json.dump(json_serialize(history.history), f)
def finetune_from_configuration(configuration: str):
with open(configuration, 'r') as f:
configuration = json.load(f)
configuration = FinetuningConfiguration.model_validate(configuration)
training, validation = DatasetConfiguration.parse(
configuration.data,
target=configuration.training.target
)
loss_cls = loss_factory(configuration.training.loss)
loss = loss_cls()
optimizer_cls = optimizer_factory(configuration.training.optimizer)
optimizer = optimizer_cls(configuration.training.learning_rate)
metrics = None
if configuration.training.metrics is not None:
metrics = [
metric_factory(metric)
for metric in configuration.training.metrics
]
learning_rate_scheduler = None
if configuration.training.learning_rate_schedule:
learning_rate_scheduler = (
configuration.training.learning_rate_schedule.instantiate()
)
finetune(
model_type=configuration.model.type,
model_constructor_arguments=configuration.model.hyperparameters,
weights=configuration.model.weights,
input_shape=configuration.data.input_shape,
target=configuration.training.target,
loss=loss,
metrics=metrics,
optimizer=optimizer,
learning_rate_scheduler=learning_rate_scheduler,
training=training,
validation=validation,
batch_size=configuration.training.batch_size,
epochs=configuration.training.epochs,
destination=configuration.training.destination
)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
'Finetunes a multi-task SFCN according to the given configuration'
)
parser.add_argument('configuration', help='Path to configuration JSON')
args = parser.parse_args()
finetune_from_configuration(args.configuration) |