forked from aiformankind/seeing-the-world
/
train.py
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train.py
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import os, cv2
import tensorflow as tf
from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard
from keras.optimizers import Adam
from datagen import DataGenerator
from utils import setup_logging, mkdir_p
class TrainValTensorBoard(TensorBoard):
"""
Tensorboard with Validation metrics
"""
def __init__(self, log_dir='./logs', **kwargs):
training_log_dir = os.path.join(log_dir, 'training')
super(TrainValTensorBoard, self).__init__(training_log_dir, **kwargs)
self.validation_log_dir = os.path.join(log_dir, 'validation')
def set_model(self, model):
self.val_writer = tf.summary.FileWriter(self.validation_log_dir)
super(TrainValTensorBoard, self).set_model(model)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
val_logs = {k.replace('val_', ''):v for k,v in logs.items() if k.startswith('val')}
for name, value in val_logs.items():
summary = tf.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value.item()
summary_value.tag = name
self.val_writer.add_summary(summary, epoch)
self.val_writer.flush()
logs = {k:v for k,v in logs.items() if not k.startswith('val')}
super(TrainValTensorBoard, self).on_epoch_end(epoch, logs)
def on_train_end(self, logs=None):
super(TrainValTensorBoard, self).on_train_end(logs)
self.val_writer.close()
def format_training_confs(config):
"""
Set up configs for training/validation.
"""
t_config = {
'batch_size' : config['train']['batch_size'],
'num_per_class' : config['train']['num_per_class'][0],
'num_classes' : config['train']['num_classes'],
'size' : config['size']
}
v_config = {
'batch_size' : config['train']['batch_size'],
'num_per_class' : config['train']['num_per_class'][1],
'num_classes' : config['train']['num_classes'],
'size' : config['size']
}
return (t_config, v_config)
def setup_gens(data, confs):
"""
Create training/validation generators.
"""
t_config, v_config = confs
train_data = [d for d in data if d['train']==True]
val_data = [d for d in data if d['train']==False]
train_gen = DataGenerator(train_data, t_config)
val_gen = DataGenerator(val_data, v_config)
return train_gen, val_gen
def training(data, model, config):
# Setp up generators
confs = format_training_confs(config)
train_gen, val_gen = setup_gens(data, confs)
# Set up logging/checkpointing dirs
log_path = setup_logging()
early_stop = EarlyStopping(
monitor='val_loss',
min_delta=0.001,
patience=5,
mode='min',
verbose=1)
checkpoint_path = os.path.join(log_path, 'trained_weights.h5')
checkpoint = ModelCheckpoint(
checkpoint_path,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min',
period=1,
save_weights_only=True
)
tensorboard = TrainValTensorBoard(log_dir=log_path)
opt = Adam(lr=config['train']['lr'])
# Use cross entropy for sparse labels
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=opt,
metrics=['acc'])
model.fit_generator(
generator=train_gen,
steps_per_epoch=len(train_gen),
verbose=1,
validation_data=val_gen,
validation_steps=len(val_gen),
epochs=config['train']['epochs'],
callbacks=[checkpoint, tensorboard, early_stop])
def test_generator(data, config):
# Create dir to store test images
path = os.path.join(os.getcwd(), 'test_generator')
mkdir_p(path)
# Create generators
confs = format_training_confs(config)
train_gen, val_gen = setup_gens(data, confs)
# Pull small amount of images to check on augmentation
count = 0
for i in range(3):
images,_ = train_gen[i]
for im in images:
out = os.path.join(path, 'im_%s.png'%count)
cv2.imwrite(out, cv2.resize(im, (0,0), fx=2., fy=2.))
count += 1