if input_shape[2] == 1:
grayscale = True
images_path = '../datasets/imdb_crop/'
log_file_path = '../trained_models/gender_models/gender_training.log'
trained_models_path = '../trained_models/gender_models/gender_mini_XCEPTION'
# data loader
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)
# model parameters/compilation
model = mini_XCEPTION(input_shape, num_classes)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# model callbacks
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1,
patience=int(patience/2), verbose=1)
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)))
grayscale = True
images_path = '../datasets/imdb_crop/'
log_file_path = '../trained_models/gender_models/gender_training.log'
trained_models_path = '../trained_models/gender_models/gender_mini_XCEPTION'
# data loader
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)
# model parameters/compilation
model = mini_XCEPTION(input_shape, num_classes)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# model callbacks
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss',
image_array = load_image(image_path, input_shape)
plot_box_data(unregressed_positive_boxes, image_array,
arg_to_class, colors=colors)
plt.imshow(image_array)
plt.show()
# data augmentations
# ------------------------------------------------------------------
data_manager = DataManager(dataset_name, 'train')
train_data = data_manager.load_data()
arg_to_class = data_manager.arg_to_class
colors = get_colors(25)
val_data = DataManager(dataset_name, 'val').load_data()
# image_prefix = dataset_manager.images_path
generator = ImageGenerator(train_data, val_data, prior_boxes,
batch_size=21)
# , path_prefix=image_prefix)
generated_data = next(generator.flow('train'))
transformed_image_batch = generated_data[0]['input_1']
generated_output = generated_data[1]['predictions']
for batch_arg, transformed_image in enumerate(transformed_image_batch):
positive_mask = generated_output[batch_arg, :, 4] != 1
regressed_boxes = generated_output[batch_arg]
unregressed_boxes = unregress_boxes(regressed_boxes, prior_boxes)
unregressed_positive_boxes = unregressed_boxes[positive_mask]
plot_box_data(unregressed_positive_boxes, transformed_image,
arg_to_class, colors=colors)
plt.imshow(transformed_image.astype('uint8'))
plt.show()