import os import sys import tensorflow as tf from keras.backend.tensorflow_backend import set_session import keras , warnings from keras.optimizers import * from sacred import Experiment from models import * from utils import * from metrics import * def configuration(): keras.backend.clear_session() tf.reset_default_graph() warnings.filterwarnings('ignore') os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID' config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True) config.gpu_options.allow_growth = True config.gpu_options.per_process_gpu_memory_fraction=0.95#0.95 config.gpu_options.visible_device_list="0" set_session(tf.Session(config=config)) def get_dirs_or_files(input_data): if os.path.isdir(input_data): image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/') # Check if training dir exists assert os.path.isdir(image_input), "{} is not a directory".format(image_input) assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input) return image_input, labels_input ex = Experiment() @ex.config def config_params(): n_classes=None # Number of classes. If your case study is binary case the set it to 2 and otherwise give your number of cases. n_epochs=1 input_height=224*1 input_width=224*1 weight_decay=1e-6 # Weight decay of l2 regularization of model layers. n_batch=1 # Number of batches at each iteration. learning_rate=1e-4 patches=False # Make patches of image in order to use all information of image. In the case of page # extraction this should be set to false since model should see all image. augmentation=False flip_aug=False # Flip image (augmentation). elastic_aug=False # Elastic transformation (augmentation). blur_aug=False # Blur patches of image (augmentation). scaling=False # Scaling of patches (augmentation) will be imposed if this set to true. binarization=False # Otsu thresholding. Used for augmentation in the case of binary case like textline prediction. For multicases should not be applied. dir_train=None # Directory of training dataset (sub-folders should be named images and labels). dir_eval=None # Directory of validation dataset (sub-folders should be named images and labels). dir_output=None # Directory of output where the model should be saved. pretraining=False # Set true to load pretrained weights of resnet50 encoder. weighted_loss=False # Set True if classes are unbalanced and you want to use weighted loss function. scaling_bluring=False rotation: False scaling_binarization=False blur_k=['blur','guass','median'] # Used in order to blur image. Used for augmentation. scales=[0.9 , 1.1 ] # Scale patches with these scales. Used for augmentation. flip_index=[0,1] # Flip image. Used for augmentation. @ex.automain def run(n_classes,n_epochs,input_height, input_width,weight_decay,weighted_loss, n_batch,patches,augmentation,flip_aug,blur_aug,scaling, binarization, blur_k,scales,dir_train, scaling_bluring,scaling_binarization,rotation, flip_index,dir_eval ,dir_output,pretraining,learning_rate): dir_img,dir_seg=get_dirs_or_files(dir_train) dir_img_val,dir_seg_val=get_dirs_or_files(dir_eval) # make first a directory in output for both training and evaluations in order to flow data from these directories. dir_train_flowing=os.path.join(dir_output,'train') dir_eval_flowing=os.path.join(dir_output,'eval') dir_flow_train_imgs=os.path.join(dir_train_flowing,'images') dir_flow_train_labels=os.path.join(dir_train_flowing,'labels') dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images') dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels') if os.path.isdir(dir_train_flowing): os.system('rm -rf '+dir_train_flowing) os.makedirs(dir_train_flowing) else: os.makedirs(dir_train_flowing) if os.path.isdir(dir_eval_flowing): os.system('rm -rf '+dir_eval_flowing) os.makedirs(dir_eval_flowing) else: os.makedirs(dir_eval_flowing) os.mkdir(dir_flow_train_imgs) os.mkdir(dir_flow_train_labels) os.mkdir(dir_flow_eval_imgs) os.mkdir(dir_flow_eval_labels) #set the gpu configuration configuration() #writing patches into a sub-folder in order to be flowed from directory. provide_patches(dir_img,dir_seg,dir_flow_train_imgs, dir_flow_train_labels, input_height,input_width,blur_k,blur_aug, flip_aug,binarization,scaling,scales,flip_index, scaling_bluring,scaling_binarization,rotation, augmentation=augmentation,patches=patches) provide_patches(dir_img_val,dir_seg_val,dir_flow_eval_imgs, dir_flow_eval_labels, input_height,input_width,blur_k,blur_aug, flip_aug,binarization,scaling,scales,flip_index, scaling_bluring,scaling_binarization,rotation, augmentation=False,patches=patches) if weighted_loss: weights=np.zeros(n_classes) for obj in os.listdir(dir_seg): label_obj=cv2.imread(dir_seg+'/'+obj) label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes) weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0) weights=1.00/weights weights=weights/float(np.sum(weights)) weights=weights/float(np.min(weights)) weights=weights/float(np.sum(weights)) #get our model. model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining) #if you want to see the model structure just uncomment model summary. #model.summary() if not weighted_loss: model.compile(loss='categorical_crossentropy', optimizer = Adam(lr=learning_rate),metrics=['accuracy']) if weighted_loss: model.compile(loss=weighted_categorical_crossentropy(weights), optimizer = Adam(lr=learning_rate),metrics=['accuracy']) mc = keras.callbacks.ModelCheckpoint('weights{epoch:08d}.h5', save_weights_only=True, period=1) #generating train and evaluation data train_gen = data_gen(dir_flow_train_imgs,dir_flow_train_labels, batch_size = n_batch, input_height=input_height, input_width=input_width,n_classes=n_classes ) val_gen = data_gen(dir_flow_eval_imgs,dir_flow_eval_labels, batch_size = n_batch, input_height=input_height, input_width=input_width,n_classes=n_classes ) model.fit_generator( train_gen, steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs))/n_batch), validation_data=val_gen, validation_steps=1, epochs=n_epochs) os.system('rm -rf '+dir_train_flowing) os.system('rm -rf '+dir_eval_flowing) model.save(dir_output+'/'+'model'+'.h5')