qurator-spk/sbb_pixelwise_segmentation
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Merge pull request #18 from johnlockejrr/unifying-training-models

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Deprecations in train.py and check an argument in inference.py
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Clemens Neudecker 2025-04-17 15:43:28 +02:00 • committed by GitHub
commit d57de478eb
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2 changed files with 7 additions and 6 deletions
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1
inference.py
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@ -567,6 +567,7 @@ class sbb_predict:
img_seg_overlayed, only_layout = self.visualize_model_output(res, self.img_org, self.task) img_seg_overlayed, only_layout = self.visualize_model_output(res, self.img_org, self.task)
if self.save: if self.save:
cv2.imwrite(self.save,img_seg_overlayed) cv2.imwrite(self.save,img_seg_overlayed)
if self.save_layout:
cv2.imwrite(self.save_layout, only_layout) cv2.imwrite(self.save_layout, only_layout)
if self.ground_truth: if self.ground_truth:

12
train.py
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@ -278,16 +278,16 @@ def run(_config, n_classes, n_epochs, input_height,
if (task == "segmentation" or task == "binarization"): if (task == "segmentation" or task == "binarization"):
if not is_loss_soft_dice and not weighted_loss: if not is_loss_soft_dice and not weighted_loss:
model.compile(loss='categorical_crossentropy', model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=learning_rate), metrics=['accuracy']) optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
if is_loss_soft_dice: if is_loss_soft_dice:
model.compile(loss=soft_dice_loss, model.compile(loss=soft_dice_loss,
optimizer=Adam(lr=learning_rate), metrics=['accuracy']) optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
if weighted_loss: if weighted_loss:
model.compile(loss=weighted_categorical_crossentropy(weights), model.compile(loss=weighted_categorical_crossentropy(weights),
optimizer=Adam(lr=learning_rate), metrics=['accuracy']) optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
elif task == "enhancement": elif task == "enhancement":
model.compile(loss='mean_squared_error', model.compile(loss='mean_squared_error',
optimizer=Adam(lr=learning_rate), metrics=['accuracy']) optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
# generating train and evaluation data # generating train and evaluation data
@ -300,7 +300,7 @@ def run(_config, n_classes, n_epochs, input_height,
##score_best=[] ##score_best=[]
##score_best.append(0) ##score_best.append(0)
for i in tqdm(range(index_start, n_epochs + index_start)): for i in tqdm(range(index_start, n_epochs + index_start)):
model.fit_generator( model.fit(
train_gen, train_gen,
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1, steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
validation_data=val_gen, validation_data=val_gen,
@ -388,7 +388,7 @@ def run(_config, n_classes, n_epochs, input_height,
#f1score_tot = [0] #f1score_tot = [0]
indexer_start = 0 indexer_start = 0
opt = SGD(lr=0.01, momentum=0.9) opt = SGD(learning_rate=0.01, momentum=0.9)
opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001) opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
model.compile(loss="binary_crossentropy", model.compile(loss="binary_crossentropy",
optimizer = opt_adam,metrics=['accuracy']) optimizer = opt_adam,metrics=['accuracy'])