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https://github.com/qurator-spk/sbb_pixelwise_segmentation.git
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continue training, losses and etc
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vahid
parent
5fb7552dbe
commit
4bea9fd535
3 changed files with 69 additions and 24 deletions
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@ -1,6 +1,6 @@
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{
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"n_classes" : 3,
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"n_epochs" : 1,
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"n_epochs" : 2,
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"input_height" : 448,
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"input_width" : 672,
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"weight_decay" : 1e-6,
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@ -8,16 +8,22 @@
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"learning_rate": 1e-4,
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"patches" : true,
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"pretraining" : true,
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"augmentation" : true,
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"augmentation" : false,
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"flip_aug" : false,
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"blur_aug" : true,
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"scaling" : false,
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"blur_aug" : false,
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"scaling" : true,
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"binarization" : false,
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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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"rotation": false,
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"rotation_not_90": false,
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"continue_training": false,
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"index_start": 0,
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"dir_of_start_model": " ",
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"weighted_loss": false,
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"is_loss_soft_dice": false,
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"data_is_provided": false,
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"dir_train": "/home/vahid/Documents/handwrittens_train/train",
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"dir_eval": "/home/vahid/Documents/handwrittens_train/eval",
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"dir_output": "/home/vahid/Documents/handwrittens_train/output"
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77
train.py
77
train.py
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@ -9,6 +9,7 @@ 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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@ -61,19 +62,24 @@ def config_params():
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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,
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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,
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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,
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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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data_is_provided = False
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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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@ -143,12 +149,43 @@ def run(n_classes,n_epochs,input_height,
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augmentation=False,patches=patches)
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continue_train = False
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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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if continue_train:
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model_dir_start = '/home/vahid/Documents/struktur_full_data/output_multi/model_0.h5'
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model = load_model (model_dir_start, compile = True, custom_objects={'soft_dice_loss': soft_dice_loss})
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index_start = 1
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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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@ -158,12 +195,16 @@ def run(n_classes,n_epochs,input_height,
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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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#model.compile(loss='categorical_crossentropy',
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#optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
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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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@ -171,7 +212,7 @@ def run(n_classes,n_epochs,input_height,
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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 range(index_start, n_epochs+index_start):
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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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@ -181,8 +222,8 @@ def run(n_classes,n_epochs,input_height,
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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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#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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2
utils.py
2
utils.py
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@ -374,9 +374,7 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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indexer=0
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for im, seg_i in tqdm(zip(imgs_cv_train,segs_cv_train)):
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#print(im, seg_i)
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img_name=im.split('.')[0]
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print(img_name,'img_name')
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if not patches:
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cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png', resize_image(cv2.imread(dir_img+'/'+im),input_height,input_width ) )
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cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png' , resize_image(cv2.imread(dir_seg+'/'+img_name+'.png'),input_height,input_width ) )
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