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239 lines
9.4 KiB
Python
239 lines
9.4 KiB
Python
import os
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import sys
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import tensorflow as tf
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from keras.backend.tensorflow_backend import set_session
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import keras , warnings
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from keras.optimizers import *
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from sacred import Experiment
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from models import *
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from utils import *
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from metrics import *
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from keras.models import load_model
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from tqdm import tqdm
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def configuration():
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keras.backend.clear_session()
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tf.reset_default_graph()
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warnings.filterwarnings('ignore')
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os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'
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config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
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config.gpu_options.allow_growth = True
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config.gpu_options.per_process_gpu_memory_fraction=0.95#0.95
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config.gpu_options.visible_device_list="0"
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set_session(tf.Session(config=config))
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def get_dirs_or_files(input_data):
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if os.path.isdir(input_data):
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image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/')
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# Check if training dir exists
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assert os.path.isdir(image_input), "{} is not a directory".format(image_input)
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assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input)
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return image_input, labels_input
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ex = Experiment()
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@ex.config
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def config_params():
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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.
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n_epochs=1
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input_height=224*1
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input_width=224*1
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weight_decay=1e-6 # Weight decay of l2 regularization of model layers.
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n_batch=1 # Number of batches at each iteration.
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learning_rate=1e-4
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patches=False # Make patches of image in order to use all information of image. In the case of page
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# extraction this should be set to false since model should see all image.
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augmentation=False
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flip_aug=False # Flip image (augmentation).
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blur_aug=False # Blur patches of image (augmentation).
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scaling=False # Scaling of patches (augmentation) will be imposed if this set to true.
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binarization=False # Otsu thresholding. Used for augmentation in the case of binary case like textline prediction. For multicases should not be applied.
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dir_train=None # Directory of training dataset (sub-folders should be named images and labels).
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dir_eval=None # Directory of validation dataset (sub-folders should be named images and labels).
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dir_output=None # Directory of output where the model should be saved.
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pretraining=False # Set true to load pretrained weights of resnet50 encoder.
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scaling_bluring=False
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scaling_binarization=False
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scaling_flip=False
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thetha=[10,-10]
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blur_k=['blur','guass','median'] # Used in order to blur image. Used for augmentation.
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scales= [ 0.5, 2 ] # Scale patches with these scales. Used for augmentation.
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flip_index=[0,1,-1] # Flip image. Used for augmentation.
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continue_training = False # If
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index_start = 0
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dir_of_start_model = ''
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is_loss_soft_dice = False
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weighted_loss = False
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data_is_provided = False
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@ex.automain
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def run(n_classes,n_epochs,input_height,
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input_width,weight_decay,weighted_loss,
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index_start,dir_of_start_model,is_loss_soft_dice,
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n_batch,patches,augmentation,flip_aug
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,blur_aug,scaling, binarization,
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blur_k,scales,dir_train,data_is_provided,
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scaling_bluring,scaling_binarization,rotation,
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rotation_not_90,thetha,scaling_flip,continue_training,
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flip_index,dir_eval ,dir_output,pretraining,learning_rate):
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if data_is_provided:
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dir_train_flowing=os.path.join(dir_output,'train')
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dir_eval_flowing=os.path.join(dir_output,'eval')
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dir_flow_train_imgs=os.path.join(dir_train_flowing,'images')
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dir_flow_train_labels=os.path.join(dir_train_flowing,'labels')
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dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images')
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dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels')
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configuration()
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else:
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dir_img,dir_seg=get_dirs_or_files(dir_train)
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dir_img_val,dir_seg_val=get_dirs_or_files(dir_eval)
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# make first a directory in output for both training and evaluations in order to flow data from these directories.
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dir_train_flowing=os.path.join(dir_output,'train')
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dir_eval_flowing=os.path.join(dir_output,'eval')
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dir_flow_train_imgs=os.path.join(dir_train_flowing,'images/')
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dir_flow_train_labels=os.path.join(dir_train_flowing,'labels/')
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dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images/')
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dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels/')
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if os.path.isdir(dir_train_flowing):
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os.system('rm -rf '+dir_train_flowing)
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os.makedirs(dir_train_flowing)
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else:
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os.makedirs(dir_train_flowing)
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if os.path.isdir(dir_eval_flowing):
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os.system('rm -rf '+dir_eval_flowing)
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os.makedirs(dir_eval_flowing)
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else:
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os.makedirs(dir_eval_flowing)
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os.mkdir(dir_flow_train_imgs)
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os.mkdir(dir_flow_train_labels)
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os.mkdir(dir_flow_eval_imgs)
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os.mkdir(dir_flow_eval_labels)
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#set the gpu configuration
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configuration()
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#writing patches into a sub-folder in order to be flowed from directory.
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provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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dir_flow_train_labels,
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input_height,input_width,blur_k,blur_aug,
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flip_aug,binarization,scaling,scales,flip_index,
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scaling_bluring,scaling_binarization,rotation,
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rotation_not_90,thetha,scaling_flip,
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augmentation=augmentation,patches=patches)
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provide_patches(dir_img_val,dir_seg_val,dir_flow_eval_imgs,
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dir_flow_eval_labels,
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input_height,input_width,blur_k,blur_aug,
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flip_aug,binarization,scaling,scales,flip_index,
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scaling_bluring,scaling_binarization,rotation,
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rotation_not_90,thetha,scaling_flip,
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augmentation=False,patches=patches)
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if weighted_loss:
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weights=np.zeros(n_classes)
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if data_is_provided:
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for obj in os.listdir(dir_flow_train_labels):
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try:
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label_obj=cv2.imread(dir_flow_train_labels+'/'+obj)
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label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
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weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
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except:
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pass
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else:
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for obj in os.listdir(dir_seg):
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try:
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label_obj=cv2.imread(dir_seg+'/'+obj)
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label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
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weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
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except:
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pass
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weights=1.00/weights
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weights=weights/float(np.sum(weights))
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weights=weights/float(np.min(weights))
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weights=weights/float(np.sum(weights))
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if continue_training:
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if is_loss_soft_dice:
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model = load_model (dir_of_start_model, compile = True, custom_objects={'soft_dice_loss': soft_dice_loss})
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if weighted_loss:
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model = load_model (dir_of_start_model, compile = True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
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if not is_loss_soft_dice and not weighted_loss:
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model = load_model (dir_of_start_model, compile = True)
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else:
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#get our model.
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index_start = 0
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model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining)
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#if you want to see the model structure just uncomment model summary.
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#model.summary()
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if not is_loss_soft_dice and not weighted_loss:
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model.compile(loss='categorical_crossentropy',
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optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
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if is_loss_soft_dice:
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model.compile(loss=soft_dice_loss,
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optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
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if weighted_loss:
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model.compile(loss=weighted_categorical_crossentropy(weights),
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optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
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#generating train and evaluation data
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train_gen = data_gen(dir_flow_train_imgs,dir_flow_train_labels, batch_size = n_batch,
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input_height=input_height, input_width=input_width,n_classes=n_classes )
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val_gen = data_gen(dir_flow_eval_imgs,dir_flow_eval_labels, batch_size = n_batch,
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input_height=input_height, input_width=input_width,n_classes=n_classes )
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for i in tqdm(range(index_start, n_epochs+index_start)):
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model.fit_generator(
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train_gen,
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steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs))/n_batch)-1,
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validation_data=val_gen,
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validation_steps=1,
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epochs=1)
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model.save(dir_output+'/'+'model_'+str(i)+'.h5')
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#os.system('rm -rf '+dir_train_flowing)
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#os.system('rm -rf '+dir_eval_flowing)
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#model.save(dir_output+'/'+'model'+'.h5')
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