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training: use proper Keras callbacks and top-level loop

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This commit is contained in:
Robert Sachunsky 2026-01-22 11:25:00 +01:00
parent 3c3effcfda
commit 87d7ffbdd8
5 changed files with 84 additions and 100 deletions
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10
src/eynollah/training/build_model_load_pretrained_weights_and_save.py
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@ -1,17 +1,9 @@
import sys
import click
import tensorflow as tf
from .models import resnet50_unet
def configuration():
try:
for device in tf.config.list_physical_devices('GPU'):
tf.config.experimental.set_memory_growth(device, True)
except:
print("no GPU device available", file=sys.stderr)
@click.command()
def build_model_load_pretrained_weights_and_save():
n_classes = 2
@ -21,8 +13,6 @@ def build_model_load_pretrained_weights_and_save():
pretraining = False
dir_of_weights = 'model_bin_sbb_ens.h5'
# configuration()
model = resnet50_unet(n_classes, input_height, input_width, weight_decay, pretraining)
model.load_weights(dir_of_weights)
model.save('./name_in_another_python_version.h5')

1
src/eynollah/training/gt_gen_utils.py
View file

@ -653,6 +653,7 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
num_col = int(text_comments.split('num_col')[1])
comment_is_sub_element = True
if not comment_is_sub_element:
# FIXME: look in /Page/@custom as well
num_col = None
if num_col:

3
src/eynollah/training/models.py
View file

@ -1,3 +1,6 @@
import os
os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import *

124
src/eynollah/training/train.py
View file

@ -32,7 +32,7 @@ os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
import tensorflow as tf
from tensorflow.keras.optimizers import SGD, Adam
from tensorflow.keras.models import load_model
from tensorflow.keras.callbacks import Callback, TensorBoard
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from sacred import Experiment
from tqdm import tqdm
from sklearn.metrics import f1_score
@ -40,26 +40,28 @@ from sklearn.metrics import f1_score
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
class SaveWeightsAfterSteps(ModelCheckpoint):
def __init__(self, save_interval, save_path, _config, **kwargs):
if save_interval:
# batches
super().__init__(
os.path.join(save_path, "model_step_{batch:04d}"),
save_freq=save_interval,
verbose=1,
**kwargs)
else:
super().__init__(
os.path.join(save_path, "model_{epoch:02d}"),
save_freq="epoch",
verbose=1,
**kwargs)
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:
# overwrite tf-keras (Keras 2) implementation to get our _config JSON in
def _save_handler(self, filepath):
super()._save_handler(filepath)
with open(os.path.join(filepath, "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():
@ -396,24 +398,20 @@ def run(_config, n_classes, n_epochs, input_height,
##score_best=[]
##score_best.append(0)
callbacks = [TensorBoard(os.path.join(dir_output, 'logs'), write_graph=False)]
callbacks = [TensorBoard(os.path.join(dir_output, 'logs'), write_graph=False),
SaveWeightsAfterSteps(0, dir_output, _config)]
if save_interval:
callbacks.append(SaveWeightsAfterSteps(save_interval, dir_output, _config))
for i in tqdm(range(index_start, n_epochs + index_start)):
model.fit(
train_gen,
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
steps_per_epoch=len(os.listdir(dir_flow_train_imgs)) // n_batch - 1,
validation_data=val_gen,
validation_steps=1,
epochs=1,
#validation_steps=1, # rs: only one batch??
validation_steps=len(os.listdir(dir_flow_eval_imgs)) // n_batch - 1,
epochs=n_epochs,
callbacks=callbacks)
dir_model = os.path.join(dir_output, 'model_' + str(i))
model.save(dir_model)
with open(os.path.join(dir_model, "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)
@ -434,54 +432,49 @@ def run(_config, n_classes, n_epochs, input_height,
list_classes = list(classification_classes_name.values())
trainXY = generate_data_from_folder_training(
dir_train, n_batch, input_height, input_width, n_classes, list_classes)
testX, testY = generate_data_from_folder_evaluation(
testXY = 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=[]
callbacks = [TensorBoard(os.path.join(dir_output, 'logs'), write_graph=False)]
callbacks = [TensorBoard(os.path.join(dir_output, 'logs'), write_graph=False),
SaveWeightsAfterSteps(0, dir_output, _config,
monitor='val_f1',
save_best_only=True, mode='max')]
for i in range(n_epochs):
history = model.fit(trainXY,
steps_per_epoch=num_rows / n_batch,
#class_weight=weights)
validation_data=testXY,
verbose=1,
epochs=n_epochs,
metrics=[F1Score(average='macro', name='f1')],
callbacks=callbacks)
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)
usable_checkpoints = np.flatnonzero(np.array(history['val_f1']) > f1_threshold_classification)
if len(usable_checkpoints) >= 1:
print("averaging over usable checkpoints", usable_checkpoints)
all_weights = []
for epoch in usable_checkpoints:
cp_path = os.path.join(dir_output, 'model_{epoch:02d}'.format(epoch=epoch))
assert os.path.isdir(cp_path)
model = load_model(cp_path, compile=False)
all_weights.append(model.get_weights())
if f1score>score_best[0]:
score_best[0]=f1score
model.save(os.path.join(dir_output,'model_best'))
new_weights = []
for layer_weights in zip(*all_weights):
layer_weights = np.array([np.array(weights).mean(axis=0)
for weights in zip(*layer_weights)])
new_weights.append(layer_weights)
if f1score > f1_threshold_classification:
weights.append(model.get_weights() )
#model = tf.keras.models.clone_model(model)
model.set_weights(new_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:
cp_path = os.path.join(dir_output, 'model_ens_avg')
model.save(cp_path)
with open(os.path.join(cp_path, "config.json"), "w") as fp:
json.dump(_config, fp) # encode dict into JSON
print("ensemble model saved under", cp_path)
elif task=='reading_order':
configuration()
@ -505,7 +498,8 @@ def run(_config, n_classes, n_epochs, input_height,
optimizer=Adam(learning_rate=0.0001), # rs: why not learning_rate?
metrics=['accuracy'])
callbacks = [TensorBoard(os.path.join(dir_output, 'logs'), write_graph=False)]
callbacks = [TensorBoard(os.path.join(dir_output, 'logs'), write_graph=False),
SaveWeightsAfterSteps(0, dir_output, _config)]
if save_interval:
callbacks.append(SaveWeightsAfterSteps(save_interval, dir_output, _config))
@ -514,15 +508,11 @@ def run(_config, n_classes, n_epochs, input_height,
n_batch, input_height, input_width, n_classes,
thetha, augmentation)
for i in range(n_epochs):
history = model.fit(trainXY,
steps_per_epoch=num_rows / n_batch,
verbose=1,
epochs=n_epochs,
callbacks=callbacks)
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

2
train/requirements.txt
View file

@ -1,6 +1,6 @@
sacred
seaborn
numpy <1.24.0
numpy
tqdm
imutils
scipy