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 * from keras.models import load_model from tqdm import tqdm 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). 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. scaling_bluring=False scaling_binarization=False scaling_flip=False thetha=[10,-10] blur_k=['blur','guass','median'] # Used in order to blur image. Used for augmentation. scales= [ 0.5, 2 ] # Scale patches with these scales. Used for augmentation. flip_index=[0,1,-1] # Flip image. Used for augmentation. continue_training = False # If index_start = 0 dir_of_start_model = '' is_loss_soft_dice = False weighted_loss = False data_is_provided = False @ex.automain def run(n_classes,n_epochs,input_height, input_width,weight_decay,weighted_loss, index_start,dir_of_start_model,is_loss_soft_dice, n_batch,patches,augmentation,flip_aug ,blur_aug,scaling, binarization, blur_k,scales,dir_train,data_is_provided, scaling_bluring,scaling_binarization,rotation, rotation_not_90,thetha,scaling_flip,continue_training, flip_index,dir_eval ,dir_output,pretraining,learning_rate): if data_is_provided: 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') configuration() else: 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, rotation_not_90,thetha,scaling_flip, 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, rotation_not_90,thetha,scaling_flip, augmentation=False,patches=patches) if weighted_loss: weights=np.zeros(n_classes) if data_is_provided: for obj in os.listdir(dir_flow_train_labels): try: label_obj=cv2.imread(dir_flow_train_labels+'/'+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) except: pass else: for obj in os.listdir(dir_seg): try: 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) except: pass weights=1.00/weights weights=weights/float(np.sum(weights)) weights=weights/float(np.min(weights)) weights=weights/float(np.sum(weights)) if continue_training: if is_loss_soft_dice: model = load_model (dir_of_start_model, compile = True, custom_objects={'soft_dice_loss': soft_dice_loss}) if weighted_loss: model = load_model (dir_of_start_model, compile = True, custom_objects={'loss': weighted_categorical_crossentropy(weights)}) if not is_loss_soft_dice and not weighted_loss: model = load_model (dir_of_start_model, compile = True) else: #get our model. index_start = 0 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 is_loss_soft_dice and not weighted_loss: model.compile(loss='categorical_crossentropy', optimizer = Adam(lr=learning_rate),metrics=['accuracy']) if is_loss_soft_dice: model.compile(loss=soft_dice_loss, optimizer = Adam(lr=learning_rate),metrics=['accuracy']) if weighted_loss: model.compile(loss=weighted_categorical_crossentropy(weights), optimizer = Adam(lr=learning_rate),metrics=['accuracy']) #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 ) for i in tqdm(range(index_start, n_epochs+index_start)): model.fit_generator( train_gen, steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs))/n_batch)-1, validation_data=val_gen, validation_steps=1, epochs=1) model.save(dir_output+'/'+'model_'+str(i)+'.h5') #os.system('rm -rf '+dir_train_flowing) #os.system('rm -rf '+dir_eval_flowing) #model.save(dir_output+'/'+'model'+'.h5')