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base.py
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base.py
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# Copyright 2019 Katsuya Shimabukuro. All rights reserved.
# Licensed under the MIT License.
from typing import Tuple, List, Dict, Any, Union, Optional, Generic, TypeVar
import pathlib
import tensorflow as tf
import numpy as np
import scipy.ndimage.morphology as morphology
from dataset.base import DatasetBase, ImageClassifierDatasetBase, ObjectDitectionDatasetBase, TextDatasetBase, ImageSegmentationDatasetBase
from model.utils.losses import MaskedSparseCategoricalCrossentropy
T = TypeVar('T', bound=DatasetBase)
class ModelBase(object):
"""Learning model base."""
def __init__(self) -> None:
"""Intialize parameter and build model."""
pass
def train(self) -> Dict[str, List[Any]]:
"""Training model.
Return:
log (Dict[str, List[Any]]): training log.
"""
pass
def inference(self) -> Tuple[List[Any], List[Any], List[Any]]:
"""Inference model.
Return:
target (List[Any]): inference target.
inference (List[Any]): inference result.
gt (List[Any]): ground truth data.
"""
pass
def save(
self,
path: Union[str, pathlib.Path]) -> None:
"""Save model.
Args:
path (str or pathlib.Path): path to model save directory.
"""
pass
def load(
self,
path: Union[str, pathlib.Path]) -> None:
"""Load pre-trained model.
Args:
path (str or pathlib.Path): path to model file directory.
"""
pass
class KerasModelBase(ModelBase):
"""Keras model class."""
def __init__(self, *args: Any, **kwargs: Any) -> None:
if int(tf.__version__.split('.')[0]) < 2 and not tf.compat.v1.executing_eagerly():
tf.compat.v1.enable_eager_execution()
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
except RuntimeError:
pass
self.model: tf.keras.Model
class TerminateOnValNaN(tf.keras.callbacks.Callback):
"""Callback that terminates training when a NaN validation loss is encountered."""
def on_batch_end(self, batch, logs=None):
logs = logs or {}
loss = logs.get('loss')
if loss is not None:
if np.isnan(loss) or np.isinf(loss) or loss >= 100000:
print('Batch %d: Invalid loss, terminating training' % (batch))
self.model.stop_training = True
raise Exception()
def on_epoch_end(self, batch, logs=None):
logs = logs or {}
loss = logs.get('val_loss')
if loss is not None:
if np.isnan(loss) or np.isinf(loss) or loss >= 100000:
print('Batch %d: Invalid loss, terminating training' % (batch))
self.model.stop_training = True
raise Exception()
class KerasClassifierBase(KerasModelBase, Generic[T]):
"""Keras classification model base.
Args:
dataset (ImageClassifierDatasetBase): dataset object.
epochs (int): number of training epochs.
optimizer_name (str): optimizer class name.
lr (float): initial learning rate.
momentum (float): momentum value.
clipnorm (float): clipnorm value
lr_step_decay (bool): whether to use step learning rate decay.
lr_step_span(float): learning rate decay span.
decay (float): learning rate decay parameter.
weighted_loss (List[float]): loss weight.
restore_path (Union[str, pathlib.Path]): path to restore model file
"""
def __init__(
self,
dataset: T,
epochs: int = 5,
optimizer_name: str = "sgd",
lr: float = 0.1,
momentum: float = 0.9,
clipnorm: float = 1.0,
lr_step_decay: bool = True,
lr_step_span: float = 0.0,
decay: float = 0.0,
weighted_loss: Optional[List[float]] = None,
restore_path: Union[str, pathlib.Path] = None,
**kwargs: Any) -> None:
"""Intialize parameter and build model."""
super(KerasClassifierBase, self).__init__(**kwargs)
self.dataset = dataset
self.epochs = epochs
self.optimizer_name = optimizer_name
self.lr = lr
self.momentum = momentum
self.clipnorm = clipnorm
self.lr_step_decay = lr_step_decay
self.lr_step_span = lr_step_span
self.decay = decay
self.weighted_loss = weighted_loss
self.restore_path = restore_path
def _loss(self, label, pred):
return tf.keras.losses.categorical_crossentropy(label, pred)
@property
def metrics(self):
return [tf.keras.metrics.CategoricalAccuracy()]
def setup(self) -> None:
"""Set optimizer to model."""
config = {
'learning_rate': self.lr,
'momentum': self.momentum,
'clipnorm': self.clipnorm
}
if self.optimizer_name == 'adam':
config = {
'learning_rate': self.lr,
'beta_1': self.momentum,
'clipnorm': self.clipnorm
}
optimizer = tf.keras.optimizers.get({
'class_name': self.optimizer_name,
'config': config})
if self.restore_path is not None:
self.load(self.restore_path)
self.model.compile(
optimizer=optimizer,
loss=self._loss,
metrics=self.metrics)
def train(self) -> Dict[str, List[Any]]:
"""Training model.
Return:
log (Dict[str, List[Any]]): training log.
"""
callbacks: List = [
TerminateOnValNaN(),
tf.keras.callbacks.TensorBoard(write_graph=False, histogram_freq=1)
]
if self.lr_step_decay:
if self.lr_step_span != 0.0:
callbacks.append(tf.keras.callbacks.LearningRateScheduler(
lambda e: self.lr * tf.math.pow(self.decay, (e // self.lr_step_span))))
else:
callbacks.append(tf.keras.callbacks.LearningRateScheduler(
lambda e: self.lr if e < int(self.epochs / 2) else self.lr / 10.0 if e < int(self.epochs * 3 / 4) else self.lr / 100.0))
generator = self.dataset.training_data_generator()
eval_generator = self.dataset.eval_data_generator()
history = self.model.fit_generator(
generator,
steps_per_epoch=self.dataset.steps_per_epoch,
validation_data=eval_generator,
validation_steps=self.dataset.eval_steps_per_epoch,
epochs=self.epochs,
callbacks=callbacks)
return history.history
def inference(self) -> Tuple[List[Any], List[List[Any]], List[Any]]:
"""Inference model.
Return:
target (List[Any]): inference target.
predicts (List[List[float]]): inference result. shape is data size x category_nums.
gt (List[Any]): ground truth data.
"""
(x_test, y_test) = self.dataset.eval_data()
predicts = self.model.predict(x_test)
return x_test, predicts, y_test
def save(
self,
path: Union[str, pathlib.Path]) -> None:
"""Save model.
Args:
path (str or pathlib.Path): path to model save directory.
"""
self.model.save_weights(str(path))
def load(
self,
path: Union[str, pathlib.Path]) -> None:
"""Load pre-trained model.
Args:
path (str or pathlib.Path): path to model file directory.
"""
self.model.load_weights(str(path))
class KerasImageClassifierBase(KerasClassifierBase[ImageClassifierDatasetBase]):
"""Keras image classification model base."""
pass
class KerasObjectDetectionBase(KerasClassifierBase[ObjectDitectionDatasetBase]):
"""Keras object detection model base."""
@property
def metrics(self):
return []
class KerasImageSegmentationBase(KerasClassifierBase[ImageSegmentationDatasetBase]):
"""Keras image segmentation model base.
Args:
generarized_dice_loss (Dict): parameters for generarized dice loss. must contains `alpha`.
"""
def __init__(
self,
generarized_dice_loss: Dict = None,
**kwargs: Any) -> None:
"""Intialize parameter and build model."""
super(KerasImageSegmentationBase, self).__init__(**kwargs)
self.generarized_dice_loss = generarized_dice_loss
def setup(self) -> None:
"""Set optimizer to model."""
optimizer = tf.keras.optimizers.get({
'class_name': self.optimizer_name,
'config': {
'learning_rate': self.lr,
'momentum': self.momentum,
'clipnorm': self.clipnorm
}})
if self.restore_path is not None:
self.load(self.restore_path)
def weighted_logits(
y_true: List,
y_pred: List) -> float:
"""Return weighted loss."""
return tf.nn.weighted_cross_entropy_with_logits(
logits=y_pred,
labels=y_true,
pos_weight=tf.constant(self.weighted_loss))
def generarized_dice_loss(
y_true: tf.Tensor,
y_pred: tf.Tensor) -> float:
"""Return generarized dice loss.
Reference:
- Generarized Dice Loss: https://arxiv.org/abs/1707.03237
- Boundary Loss: https://openreview.net/pdf?id=S1gTA5VggE
"""
epsilon = tf.keras.backend.epsilon()
w = 1 / (tf.square(tf.reduce_sum(y_true, axis=(1, 2))) + epsilon)
intersection = tf.math.reduce_sum(y_true * y_pred, axis=(1, 2))
union = tf.math.reduce_sum(y_true + y_pred, axis=(1, 2))
gd_losses = 1 - 2 * (tf.math.reduce_sum(w * intersection, axis=-1) / (tf.math.reduce_sum(w * union, axis=-1) + epsilon))
distance_negative = tf.cast(
tf.py_function(morphology.distance_transform_edt, [y_true[:, :, :, 0]], tf.double),
tf.keras.backend.floatx())
distance_positive = tf.cast(
tf.py_function(morphology.distance_transform_edt, [y_true[:, :, :, 1]], tf.double),
tf.keras.backend.floatx())
boundary_losses = (
-w[:, 0] * tf.math.reduce_sum(distance_negative * y_true[:, :, :, 0] * y_pred[:, :, :, 0], axis=(1, 2)) +
w[:, 1] * tf.math.reduce_sum(distance_positive * y_true[:, :, :, 1] * y_pred[:, :, :, 0], axis=(1, 2)))
return tf.reduce_mean(gd_losses) + self.generarized_dice_loss['alpha'] * tf.reduce_mean(boundary_losses)
if self.generarized_dice_loss is not None:
loss = generarized_dice_loss
elif self.weighted_loss is None:
loss = tf.keras.losses.categorical_crossentropy
else:
loss = weighted_logits
self.model.compile(
optimizer=optimizer,
loss=loss,
metrics=(['accuracy', tf.keras.metrics.MeanIoU(num_classes=self.dataset.category_nums)]))
def train(self) -> Dict[str, List[Any]]:
"""Training model.
Return:
log (Dict[str, List[Any]]): training log.
"""
callbacks: List = []
if self.lr_step_decay:
callbacks.append(tf.keras.callbacks.LearningRateScheduler(
lambda e: self.lr if e < int(self.epochs / 2) else self.lr / 10.0 if e < int(self.epochs * 3 / 4) else self.lr / 100.0))
generator = self.dataset.training_data_generator()
(x_test, y_test) = self.dataset.eval_data()
history = self.model.fit_generator(
generator,
steps_per_epoch=self.dataset.steps_per_epoch,
validation_data=(x_test, y_test),
epochs=self.epochs,
max_queue_size=100,
callbacks=callbacks)
return history.history
class KerasLanguageModelBase(KerasClassifierBase[TextDatasetBase]):
"""Keras language model base."""
@property
def _loss(self):
return MaskedSparseCategoricalCrossentropy()
@property
def metrics(self):
return [tf.keras.metrics.SparseCategoricalAccuracy()]