# 设置路径
images_path = '../datasets/imdb_crop/'
log_file_path = '../datasets/trained_models/gender_models/gender_training.log'
trained_models_path = '../datasets/trained_models/gender_models/gender_mini_XCEPTION'
# 加载数据
data_loader = DataManager(dataset_name)
ground_truth_data = data_loader.get_data()
train_keys, val_keys = split_imdb_data(ground_truth_data, validation_split)
print('Number of training samples:', len(train_keys))
print('Number of validation samples:', len(val_keys))
image_generator = ImageGenerator(ground_truth_data,
batch_size,
input_shape[:2],
train_keys,
val_keys,
None,
path_prefix=images_path,
vertical_flip_probability=0,
grayscale=grayscale,
do_random_crop=do_random_crop)
# 模型参数
model = mini_XCEPTION(input_shape, num_classes)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# 模型回调
early_stop = EarlyStopping(monitor='val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
from utils.data_augmentation import ImageGenerator
from utils.boxes import create_prior_boxes
batch_size = 3
num_epochs = 2
image_shape = (300, 300, 3)
dataset_manager = DataManager(['VOC2007', 'VOC2012'], ['trainval', 'trainval'])
train_data = dataset_manager.load_data()
val_data = test_data = DataManager('VOC2007', 'test').load_data()
class_names = dataset_manager.class_names
num_classes = len(class_names)
prior_boxes = create_prior_boxes()
generator = ImageGenerator(train_data, val_data, prior_boxes, batch_size)
weights_path = '../trained_models/SSD300_weights.hdf5'
frozen_layers = ['input_1', 'conv1_1', 'conv1_2', 'pool1',
'conv2_1', 'conv2_2', 'pool2',
'conv3_1', 'conv3_2', 'conv3_3', 'pool3']
model = SSD300(image_shape, num_classes, weights_path, frozen_layers, True)
multibox_loss = MultiboxLoss(num_classes, neg_pos_ratio=2.0).compute_loss
model.compile(Adam(lr=3e-4), loss=multibox_loss)
# callbacks
model_path = '../trained_models/SSD_scratch/'
if not os.path.exists(model_path):
def train_gender_classifier():
# 参数设置
batch_size = 32 # 批量训练数据大小
epochs = 10000 # 训练轮数
input_shape = (64, 64, 1) # 图片矩阵
validation_split = .2 # 验证集大小
num_classes = 2 # 类数,男和女
patience = 100 # 信心值，用于后面的EarlyStopping等,在信心值个epochs过去后模型性能不再提升，就执行指定动作
dataset_name = 'imdb' # 数据集名称
do_random_crop = False
# 设为灰度图
if input_shape[2] == 1:
grayscale = True
# 设置路径
images_path = '../datasets/imdb_crop/'
log_file_path = '../datasets/trained_models/gender_models/gender_training.log'
trained_models_path = '../datasets/trained_models/gender_models/gender_mini_XCEPTION'
# 加载数据
data_loader = DataManager(dataset_name)
ground_truth_data = data_loader.get_data()
train_keys, val_keys = split_imdb_data(ground_truth_data, validation_split)
print('Number of training samples:', len(train_keys))
print('Number of validation samples:', len(val_keys))
image_generator = ImageGenerator(ground_truth_data,
batch_size,
input_shape[:2],
train_keys,
val_keys,
None,
path_prefix=images_path,
vertical_flip_probability=0,
grayscale=grayscale,
do_random_crop=do_random_crop)
# 模型参数
model = mini_XCEPTION(input_shape, num_classes)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# 模型回调
early_stop = EarlyStopping(monitor='val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=int(patience / 2),
verbose=1)
csv_logger = CSVLogger(log_file_path, append=False)
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names,
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=False)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
# 训练模型
model.fit_generator(image_generator.flow(mode='train'),
steps_per_epoch=int(len(train_keys) / batch_size),
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=image_generator.flow('val'),
validation_steps=int(len(val_keys) / batch_size))
ground_truth_data = data_manager.get_data()
train_keys, validation_keys = split_data(ground_truth_data, training_ratio=.8)
# instantiating model
model = SSD300(image_shape, num_classes, weights_path, frozen_layers)
multibox_loss = MultiboxLoss(num_classes, neg_pos_ratio=2.0).compute_loss
model.compile(optimizer, loss=multibox_loss, metrics=['acc'])
# setting parameters for data augmentation generator
prior_boxes = create_prior_boxes(model)
image_generator = ImageGenerator(ground_truth_data,
prior_boxes,
num_classes,
box_scale_factors,
batch_size,
image_shape[0:2],
train_keys,
validation_keys,
image_prefix,
vertical_flip_probability=0.5,
horizontal_flip_probability=0.5)
# instantiating callbacks
learning_rate_schedule = LearningRateScheduler(scheduler)
model_names = (trained_models_filename)
model_checkpoint = ModelCheckpoint(model_names,
monitor='val_loss',
verbose=1,
save_best_only=False,
save_weights_only=False)
def main():
# parameters
param = args()
batch_size = param.batch_size
num_epochs = param.num_epochs
validation_split = param.val_ratio
do_random_crop = False
patience = param.patience
dataset_name = param.dataset_name
grayscale = param.graymode
mode = param.mode
anno_file = param.anno_file
if mode == "gender":
num_classes = 2
elif mode == "age":
num_classes = 101
elif mode == "emotion":
num_classes = 7
else:
num_classes = 5
if grayscale:
input_shape = (64, 64, 1)
else:
input_shape = (64, 64, 3)
images_path = param.img_dir
log_file_path = '../trained_models/%s_models/%s_model/raining.log' % (
mode, dataset_name)
trained_models_path = '../trained_models/%s_models/%s_model/%s_mini_XCEPTION' % (
mode, dataset_name, mode)
pretrained_model = param.load_model
print("-------begin to load data------", input_shape)
# data loader
data_loader = DataManager(dataset_name, anno_file)
ground_truth_data = data_loader.get_data()
train_keys, val_keys = split_imdb_data(ground_truth_data, validation_split)
print('Number of training samples:', len(train_keys))
print('Number of validation samples:', len(val_keys))
train_image_generator = ImageGenerator(ground_truth_data,
batch_size,
input_shape[:2],
train_keys,
path_prefix=images_path,
grayscale=grayscale)
val_image_generator = ImageGenerator(ground_truth_data,
batch_size,
input_shape[:2],
val_keys,
path_prefix=images_path,
grayscale=grayscale)
# model parameters/compilation
if pretrained_model != None:
model = load_model(pretrained_model, compile=False)
print("pretrained model:", model.input_shape)
else:
model = mini_XCEPTION(input_shape, num_classes)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# model callbacks
early_stop = EarlyStopping('val_acc', patience=patience)
reduce_lr = ReduceLROnPlateau('val_acc',
factor=0.1,
patience=int(patience),
verbose=1,
min_lr=0.0000001)
csv_logger = CSVLogger(log_file_path, append=False)
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names,
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=False)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
# training model
print("-----begin to train model----")
model.fit_generator(
train_image_generator.flow(),
steps_per_epoch=int(np.ceil(len(train_keys) / batch_size)),
epochs=num_epochs,
verbose=1,
callbacks=callbacks,
validation_data=val_image_generator.flow(),
validation_steps=int(np.ceil(len(val_keys) / batch_size)))