fcn_vgg16 = FCN(input_shape=(224, 224, 3),
classes=21,
weight_decay=3e-3,
weights='imagenet',
trainable_encoder=True)
optimizer = keras.optimizers.Adam(1e-4)
fcn_vgg16.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
fcn_vgg16.fit_generator(
datagen.flow_from_imageset(class_mode='categorical',
classes=21,
batch_size=1,
shuffle=True,
image_set_loader=train_loader),
steps_per_epoch=1112,
epochs=100,
validation_data=datagen.flow_from_imageset(class_mode='categorical',
classes=21,
batch_size=1,
shuffle=True,
image_set_loader=val_loader),
validation_steps=1111,
verbose=1,
callbacks=[
lr_reducer, early_stopper, csv_logger, checkpointer, nan_terminator
])
patience=100)
csv_logger = CSVLogger('output/{}_fcn_vgg16.csv'.format(
datetime.datetime.now().isoformat()))
datagen = PascalVocGenerator(**init_args['pascal_voc_generator']['train'])
train_loader = ImageSetLoader(**init_args['image_set_loader']['train'])
val_loader = ImageSetLoader(**init_args['image_set_loader']['val'])
fcn_vgg16 = FCN(basenet='vgg16', input_shape=(500, 500, 3), num_output=21)
fcn_vgg16.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy', 'categorical_accuracy'])
flow_args = init_args['pascal_voc_generator']['flow_from_imageset']
train_flow_args = flow_args.copy()
train_flow_args['image_set_loader'] = train_loader
val_flow_args = flow_args.copy()
val_flow_args['image_set_loader'] = val_loader
fcn_vgg16.fit_generator(
datagen.flow_from_imageset(**train_flow_args),
steps_per_epoch=1112,
epochs=100,
validation_data=datagen.flow_from_imageset(**val_flow_args),
validation_steps=1111,
verbose=1,
max_q_size=100,
callbacks=[lr_reducer, early_stopper, csv_logger])
fcn_vgg16.save('output/fcn_vgg16.h5')
def main():
'''
Main function
'''
# Define common arguments
checkpointer = ModelCheckpoint(filepath='output/fcn_vgg16_weights_tmp.h5',
verbose=1,
save_best_only=True)
lr_reducer = ReduceLROnPlateau(monitor='val_loss',
factor=np.sqrt(0.1),
cooldown=0,
patience=10,
min_lr=1e-12)
early_stopper = EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=30)
nan_terminator = TerminateOnNaN()
csv_logger = CSVLogger('output/tmp_fcn_vgg16.csv')
# Set data generator
datagen = PascalVocGenerator(image_shape=[224, 224, 3],
image_resample=True,
pixelwise_center=True,
pixel_mean=[115.85100, 110.50989, 102.16182],
pixelwise_std_normalization=True,
pixel_std=[70.30930, 69.41244, 72.60676])
# Define training set and validation set
train_loader = ImageSetLoader(*arg_gen('train'))
val_loader = ImageSetLoader(*arg_gen('val'))
# Construct model
fcn_vgg16 = model.fcn_vgg16(input_shape=(224, 224, 3),
classes=21,
weight_decay=3e-3,
weights='imagenet',
trainable_encoder=False)
# Set optimizer
optimizer = Adam(1e-4)
# Compile model
fcn_vgg16.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
# Fit model with the above generators
fcn_vgg16.fit_generator(
datagen.flow_from_imageset(class_mode='categorical',
classes=21,
batch_size=1,
shuffle=True,
image_set_loader=train_loader),
steps_per_epoch=1112,
epochs=40,
validation_data=datagen.flow_from_imageset(
class_mode='categorical',
classes=21,
batch_size=1,
shuffle=True,
image_set_loader=val_loader),
validation_steps=1111,
verbose=1,
callbacks=[
lr_reducer, early_stopper, csv_logger, checkpointer, nan_terminator
])
# Save weights
fcn_vgg16.save('output/fcn_vgg16.h5')