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move src/.../train.py to root to accomodate old PR

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import os
import sys
import json
import click
from eynollah.training.metrics import (
soft_dice_loss,
weighted_categorical_crossentropy
)
from eynollah.training.models import (
PatchEncoder,
Patches,
machine_based_reading_order_model,
resnet50_classifier,
resnet50_unet,
vit_resnet50_unet,
vit_resnet50_unet_transformer_before_cnn
)
from eynollah.training.utils import (
data_gen,
generate_arrays_from_folder_reading_order,
generate_data_from_folder_evaluation,
generate_data_from_folder_training,
get_one_hot,
provide_patches,
return_number_of_total_training_data
)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
from tensorflow.compat.v1.keras.backend import set_session
from tensorflow.keras.optimizers import SGD, Adam
from sacred import Experiment
from tensorflow.keras.models import load_model
from tqdm import tqdm
from sklearn.metrics import f1_score
from tensorflow.keras.callbacks import Callback
import numpy as np
import cv2
class SaveWeightsAfterSteps(Callback):
def __init__(self, save_interval, save_path, _config):
super(SaveWeightsAfterSteps, self).__init__()
self.save_interval = save_interval
self.save_path = save_path
self.step_count = 0
self._config = _config
def on_train_batch_end(self, batch, logs=None):
self.step_count += 1
if self.step_count % self.save_interval ==0:
save_file = f"{self.save_path}/model_step_{self.step_count}"
#os.system('mkdir '+save_file)
self.model.save(save_file)
with open(os.path.join(os.path.join(self.save_path, f"model_step_{self.step_count}"),"config.json"), "w") as fp:
json.dump(self._config, fp) # encode dict into JSON
print(f"saved model as steps {self.step_count} to {save_file}")
def configuration():
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)
set_session(session)
def get_dirs_or_files(input_data):
image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/')
if os.path.isdir(input_data):
# 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(save_git_info=False)
@ex.config
def config_params():
n_classes = None # Number of classes. In the case of binary classification this should be 2.
n_epochs = 1 # Number of epochs.
input_height = 224 * 1 # Height of model's input in pixels.
input_width = 224 * 1 # Width of model's input in pixels.
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 # Set the learning rate.
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.
augmentation = False # To apply any kind of augmentation, this parameter must be set to true.
flip_aug = False # If true, different types of flipping will be applied to the image. Types of flips are defined with "flip_index" in config_params.json.
blur_aug = False # If true, different types of blurring will be applied to the image. Types of blur are defined with "blur_k" in config_params.json.
padding_white = False # If true, white padding will be applied to the image.
padding_black = False # If true, black padding will be applied to the image.
scaling = False # If true, scaling will be applied to the image. The amount of scaling is defined with "scales" in config_params.json.
shifting = False
degrading = False # If true, degrading will be applied to the image. The amount of degrading is defined with "degrade_scales" in config_params.json.
brightening = False # If true, brightening will be applied to the image. The amount of brightening is defined with "brightness" in config_params.json.
binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
adding_rgb_background = False
adding_rgb_foreground = False
add_red_textlines = False
channels_shuffling = False
dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels".
dir_eval = None # Directory of validation dataset with subdirectories having the names "images" and "labels".
dir_output = None # Directory where the output model will be saved.
pretraining = False # Set to true to load pretrained weights of ResNet50 encoder.
scaling_bluring = False # If true, a combination of scaling and blurring will be applied to the image.
scaling_binarization = False # If true, a combination of scaling and binarization will be applied to the image.
rotation = False # If true, a 90 degree rotation will be implemeneted.
rotation_not_90 = False # If true rotation based on provided angles with thetha will be implemeneted.
scaling_brightness = False # If true, a combination of scaling and brightening will be applied to the image.
scaling_flip = False # If true, a combination of scaling and flipping will be applied to the image.
thetha = None # Rotate image by these angles for augmentation.
shuffle_indexes = None
blur_k = None # Blur image for augmentation.
scales = None # Scale patches for augmentation.
degrade_scales = None # Degrade image for augmentation.
brightness = None # Brighten image for augmentation.
flip_index = None # Flip image for augmentation.
continue_training = False # Set to true if you would like to continue training an already trained a model.
transformer_patchsize_x = None # Patch size of vision transformer patches in x direction.
transformer_patchsize_y = None # Patch size of vision transformer patches in y direction.
transformer_num_patches_xy = None # Number of patches for vision transformer in x and y direction respectively.
transformer_projection_dim = 64 # Transformer projection dimension. Default value is 64.
transformer_mlp_head_units = [128, 64] # Transformer Multilayer Perceptron (MLP) head units. Default value is [128, 64]
transformer_layers = 8 # transformer layers. Default value is 8.
transformer_num_heads = 4 # Transformer number of heads. Default value is 4.
transformer_cnn_first = True # We have two types of vision transformers. In one type, a CNN is applied first, followed by a transformer. In the other type, this order is reversed. If transformer_cnn_first is true, it means the CNN will be applied before the transformer. Default value is true.
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.
dir_of_start_model = '' # Directory containing pretrained encoder to continue training the model.
is_loss_soft_dice = False # Use soft dice as loss function. When set to true, "weighted_loss" must be false.
weighted_loss = False # Use weighted categorical cross entropy as loss fucntion. When set to true, "is_loss_soft_dice" must be false.
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".
task = "segmentation" # This parameter defines task of model which can be segmentation, enhancement or classification.
f1_threshold_classification = None # This threshold is used to consider models with an evaluation f1 scores bigger than it. The selected model weights undergo a weights ensembling. And avreage ensembled model will be written to output.
classification_classes_name = None # Dictionary of classification classes names.
backbone_type = None # As backbone we have 2 types of backbones. A vision transformer alongside a CNN and we call it "transformer" and only CNN called "nontransformer"
save_interval = None
dir_img_bin = None
number_of_backgrounds_per_image = 1
dir_rgb_backgrounds = None
dir_rgb_foregrounds = None
@ex.automain
def run(_config, 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, padding_white, padding_black, scaling, shifting, degrading,channels_shuffling,
brightening, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, blur_k, scales, degrade_scales,shuffle_indexes,
brightness, dir_train, data_is_provided, scaling_bluring,
scaling_brightness, scaling_binarization, rotation, rotation_not_90,
thetha, scaling_flip, continue_training, transformer_projection_dim,
transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
transformer_patchsize_x, transformer_patchsize_y,
transformer_num_patches_xy, backbone_type, save_interval, flip_index, dir_eval, dir_output,
pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds, dir_rgb_foregrounds):
if dir_rgb_backgrounds:
list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)
else:
list_all_possible_background_images = None
if dir_rgb_foregrounds:
list_all_possible_foreground_rgbs = os.listdir(dir_rgb_foregrounds)
else:
list_all_possible_foreground_rgbs = None
if task == "segmentation" or task == "enhancement" or task == "binarization":
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()
imgs_list=np.array(os.listdir(dir_img))
segs_list=np.array(os.listdir(dir_seg))
imgs_list_test=np.array(os.listdir(dir_img_val))
segs_list_test=np.array(os.listdir(dir_seg_val))
# writing patches into a sub-folder in order to be flowed from directory.
provide_patches(imgs_list, segs_list, dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels, input_height, input_width, blur_k,
blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background,adding_rgb_foreground, add_red_textlines, channels_shuffling,
scaling, shifting, degrading, brightening, scales, degrade_scales, brightness,
flip_index,shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=augmentation,
patches=patches, dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds, dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs)
provide_patches(imgs_list_test, segs_list_test, dir_img_val, dir_seg_val,
dir_flow_eval_imgs, dir_flow_eval_labels, input_height, input_width,
blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, channels_shuffling,
scaling, shifting, degrading, brightening, scales, degrade_scales, brightness,
flip_index, shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=patches,dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds,dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs )
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 backbone_type=='nontransformer':
if is_loss_soft_dice and (task == "segmentation" or task == "binarization"):
model = load_model(dir_of_start_model, compile=True, custom_objects={'soft_dice_loss': soft_dice_loss})
if weighted_loss and (task == "segmentation" or task == "binarization"):
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)
elif backbone_type=='transformer':
if is_loss_soft_dice and (task == "segmentation" or task == "binarization"):
model = load_model(dir_of_start_model, compile=True, custom_objects={"PatchEncoder": PatchEncoder, "Patches": Patches,'soft_dice_loss': soft_dice_loss})
if weighted_loss and (task == "segmentation" or task == "binarization"):
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,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
else:
index_start = 0
if backbone_type=='nontransformer':
model = resnet50_unet(n_classes, input_height, input_width, task, weight_decay, pretraining)
elif backbone_type=='transformer':
num_patches_x = transformer_num_patches_xy[0]
num_patches_y = transformer_num_patches_xy[1]
num_patches = num_patches_x * num_patches_y
if transformer_cnn_first:
if input_height != (num_patches_y * transformer_patchsize_y * 32):
print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y * 32)")
sys.exit(1)
if input_width != (num_patches_x * transformer_patchsize_x * 32):
print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x * 32)")
sys.exit(1)
if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero")
sys.exit(1)
model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
else:
if input_height != (num_patches_y * transformer_patchsize_y):
print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y)")
sys.exit(1)
if input_width != (num_patches_x * transformer_patchsize_x):
print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x)")
sys.exit(1)
if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero")
sys.exit(1)
model = vit_resnet50_unet_transformer_before_cnn(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
#if you want to see the model structure just uncomment model summary.
model.summary()
if task == "segmentation" or task == "binarization":
if not is_loss_soft_dice and not weighted_loss:
model.compile(loss='categorical_crossentropy',
optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
if is_loss_soft_dice:
model.compile(loss=soft_dice_loss,
optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
if weighted_loss:
model.compile(loss=weighted_categorical_crossentropy(weights),
optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
elif task == "enhancement":
model.compile(loss='mean_squared_error',
optimizer=Adam(learning_rate=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, task=task)
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, task=task)
##img_validation_patches = os.listdir(dir_flow_eval_imgs)
##score_best=[]
##score_best.append(0)
if save_interval:
save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config)
for i in tqdm(range(index_start, n_epochs + index_start)):
if save_interval:
model.fit(
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, callbacks=[save_weights_callback])
else:
model.fit(
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(os.path.join(dir_output,'model_'+str(i)))
with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp:
json.dump(_config, fp) # encode dict into JSON
#os.system('rm -rf '+dir_train_flowing)
#os.system('rm -rf '+dir_eval_flowing)
#model.save(dir_output+'/'+'model'+'.h5')
elif task=='classification':
configuration()
model = resnet50_classifier(n_classes, input_height, input_width, weight_decay, pretraining)
opt_adam = Adam(learning_rate=0.001)
model.compile(loss='categorical_crossentropy',
optimizer = opt_adam,metrics=['accuracy'])
list_classes = list(classification_classes_name.values())
testX, testY = generate_data_from_folder_evaluation(dir_eval, input_height, input_width, n_classes, list_classes)
y_tot=np.zeros((testX.shape[0],n_classes))
score_best= [0]
num_rows = return_number_of_total_training_data(dir_train)
weights=[]
for i in range(n_epochs):
history = model.fit( generate_data_from_folder_training(dir_train, n_batch , input_height, input_width, n_classes, list_classes), steps_per_epoch=num_rows / n_batch, verbose=1)#,class_weight=weights)
y_pr_class = []
for jj in range(testY.shape[0]):
y_pr=model.predict(testX[jj,:,:,:].reshape(1,input_height,input_width,3), verbose=0)
y_pr_ind= np.argmax(y_pr,axis=1)
y_pr_class.append(y_pr_ind)
y_pr_class = np.array(y_pr_class)
f1score=f1_score(np.argmax(testY,axis=1), y_pr_class, average='macro')
print(i,f1score)
if f1score>score_best[0]:
score_best[0]=f1score
model.save(os.path.join(dir_output,'model_best'))
if f1score > f1_threshold_classification:
weights.append(model.get_weights() )
if len(weights) >= 1:
new_weights=list()
for weights_list_tuple in zip(*weights):
new_weights.append( [np.array(weights_).mean(axis=0) for weights_ in zip(*weights_list_tuple)] )
new_weights = [np.array(x) for x in new_weights]
model_weight_averaged=tf.keras.models.clone_model(model)
model_weight_averaged.set_weights(new_weights)
model_weight_averaged.save(os.path.join(dir_output,'model_ens_avg'))
with open(os.path.join( os.path.join(dir_output,'model_ens_avg'), "config.json"), "w") as fp:
json.dump(_config, fp) # encode dict into JSON
with open(os.path.join( os.path.join(dir_output,'model_best'), "config.json"), "w") as fp:
json.dump(_config, fp) # encode dict into JSON
elif task=='reading_order':
configuration()
model = machine_based_reading_order_model(n_classes,input_height,input_width,weight_decay,pretraining)
dir_flow_train_imgs = os.path.join(dir_train, 'images')
dir_flow_train_labels = os.path.join(dir_train, 'labels')
classes = os.listdir(dir_flow_train_labels)
if augmentation:
num_rows = len(classes)*(len(thetha) + 1)
else:
num_rows = len(classes)
#ls_test = os.listdir(dir_flow_train_labels)
#f1score_tot = [0]
indexer_start = 0
# opt = SGD(learning_rate=0.01, momentum=0.9)
opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
model.compile(loss="binary_crossentropy",
optimizer = opt_adam,metrics=['accuracy'])
if save_interval:
save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config)
for i in range(n_epochs):
if save_interval:
history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes, thetha, augmentation), steps_per_epoch=num_rows / n_batch, verbose=1, callbacks=[save_weights_callback])
else:
history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes, thetha, augmentation), steps_per_epoch=num_rows / n_batch, verbose=1)
model.save( os.path.join(dir_output,'model_'+str(i+indexer_start) ))
with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp:
json.dump(_config, fp) # encode dict into JSON
'''
if f1score>f1score_tot[0]:
f1score_tot[0] = f1score
model_dir = os.path.join(dir_out,'model_best')
model.save(model_dir)
'''