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https://github.com/qurator-spk/sbb_pixelwise_segmentation.git
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code formatting with black; typos
This commit is contained in:
cneud
parent
5f84938839
commit
02b1436f39
8 changed files with 741 additions and 768 deletions
272
train.py
272
train.py
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@ -11,12 +11,14 @@ from metrics import *
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from tensorflow.keras.models import load_model
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from tqdm import tqdm
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def configuration():
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.compat.v1.Session(config=config)
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set_session(session)
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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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@ -25,205 +27,187 @@ def get_dirs_or_files(input_data):
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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. In the case of binary classification this should be 2.
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n_epochs=1 # Number of epochs.
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input_height=224*1 # Height of model's input in pixels.
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input_width=224*1 # Width of model's input in pixels.
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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 # Set the learning rate.
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patches=False # Divides input image into smaller patches (input size of the model) when set to true. For the model to see the full image, like page extraction, set this to false.
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augmentation=False # To apply any kind of augmentation, this parameter must be set to true.
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flip_aug=False # If true, different types of flipping will be applied to the image. Types of flips are defined with "flip_index" in train.py.
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blur_aug=False # If true, different types of blurring will be applied to the image. Types of blur are defined with "blur_k" in train.py.
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scaling=False # If true, scaling will be applied to the image. The amount of scaling is defined with "scales" in train.py.
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binarization=False # If true, Otsu thresholding will be applied to augment the input with binarized images.
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dir_train=None # Directory of training dataset with subdirectories having the names "images" and "labels".
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dir_eval=None # Directory of validation dataset with subdirectories having the names "images" and "labels".
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dir_output=None # Directory where the output model will be saved.
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pretraining=False # Set to true to load pretrained weights of ResNet50 encoder.
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scaling_bluring=False # If true, a combination of scaling and blurring will be applied to the image.
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scaling_binarization=False # If true, a combination of scaling and binarization will be applied to the image.
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scaling_flip=False # If true, a combination of scaling and flipping will be applied to the image.
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thetha=[10,-10] # Rotate image by these angles for augmentation.
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blur_k=['blur','gauss','median'] # Blur image for augmentation.
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scales=[0.5,2] # Scale patches for augmentation.
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flip_index=[0,1,-1] # Flip image for augmentation.
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continue_training = False # Set to true if you would like to continue training an already trained a model.
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index_start = 0 # Index of model to continue training from. E.g. if you trained for 3 epochs and last index is 2, to continue from model_1.h5, set "index_start" to 3 to start naming model with index 3.
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dir_of_start_model = '' # Directory containing pretrained encoder to continue training the model.
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is_loss_soft_dice = False # Use soft dice as loss function. When set to true, "weighted_loss" must be false.
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weighted_loss = False # Use weighted categorical cross entropy as loss fucntion. When set to true, "is_loss_soft_dice" must be false.
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data_is_provided = False # Only set this to true when you have already provided the input data and the train and eval data are in "dir_output".
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n_classes = None # Number of classes. In the case of binary classification this should be 2.
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n_epochs = 1 # Number of epochs.
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input_height = 224 * 1 # Height of model's input in pixels.
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input_width = 224 * 1 # Width of model's input in pixels.
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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 # Set the learning rate.
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patches = False # Divides input image into smaller patches (input size of the model) when set to true. For the model to see the full image, like page extraction, set this to false.
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augmentation = False # To apply any kind of augmentation, this parameter must be set to true.
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flip_aug = False # If true, different types of flipping will be applied to the image. Types of flips are defined with "flip_index" in train.py.
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blur_aug = False # If true, different types of blurring will be applied to the image. Types of blur are defined with "blur_k" in train.py.
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scaling = False # If true, scaling will be applied to the image. The amount of scaling is defined with "scales" in train.py.
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binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
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dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels".
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dir_eval = None # Directory of validation dataset with subdirectories having the names "images" and "labels".
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dir_output = None # Directory where the output model will be saved.
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pretraining = False # Set to true to load pretrained weights of ResNet50 encoder.
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scaling_bluring = False # If true, a combination of scaling and blurring will be applied to the image.
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scaling_binarization = False # If true, a combination of scaling and binarization will be applied to the image.
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scaling_flip = False # If true, a combination of scaling and flipping will be applied to the image.
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thetha = [10, -10] # Rotate image by these angles for augmentation.
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blur_k = ['blur', 'gauss', 'median'] # Blur image for augmentation.
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scales = [0.5, 2] # Scale patches for augmentation.
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flip_index = [0, 1, -1] # Flip image for augmentation.
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continue_training = False # Set to true if you would like to continue training an already trained a model.
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index_start = 0 # Index of model to continue training from. E.g. if you trained for 3 epochs and last index is 2, to continue from model_1.h5, set "index_start" to 3 to start naming model with index 3.
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dir_of_start_model = '' # Directory containing pretrained encoder to continue training the model.
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is_loss_soft_dice = False # Use soft dice as loss function. When set to true, "weighted_loss" must be false.
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weighted_loss = False # Use weighted categorical cross entropy as loss fucntion. When set to true, "is_loss_soft_dice" must be false.
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data_is_provided = False # Only set this to true when you have already provided the input data and the train and eval data are in "dir_output".
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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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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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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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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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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.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.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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# 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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# 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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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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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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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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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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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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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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model = load_model(dir_of_start_model, compile=True,
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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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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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# 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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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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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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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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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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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))
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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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model.save(dir_output + '/' + 'model_' + str(i))
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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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