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training.utils.data_gen: avoid repeated array allocation

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Robert Sachunsky 2026-02-08 01:25:53 +01:00
parent 514a897dd5
commit 83c2408192
1 changed files with 10 additions and 10 deletions
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20
src/eynollah/training/utils.py
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@ -600,10 +600,9 @@ def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_c
c = 0
n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images
random.shuffle(n)
img = np.zeros((batch_size, input_height, input_width, 3), dtype=float)
mask = np.zeros((batch_size, input_height, input_width, n_classes), dtype=float)
while True:
img = np.zeros((batch_size, input_height, input_width, 3)).astype('float')
mask = np.zeros((batch_size, input_height, input_width, n_classes)).astype('float')
for i in range(c, c + batch_size): # initially from 0 to 16, c = 0.
try:
filename = os.path.splitext(n[i])[0]
@ -612,21 +611,22 @@ def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_c
train_img = cv2.resize(train_img, (input_width, input_height),
interpolation=cv2.INTER_NEAREST) # Read an image from folder and resize
img[i - c] = train_img # add to array - img[0], img[1], and so on.
img[i - c, :] = train_img # add to array - img[0], img[1], and so on.
if task == "segmentation" or task=="binarization":
train_mask = cv2.imread(mask_folder + '/' + filename + '.png')
train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
n_classes)
train_mask = resize_image(train_mask, input_height, input_width)
train_mask = get_one_hot(train_mask, input_height, input_width, n_classes)
elif task == "enhancement":
train_mask = cv2.imread(mask_folder + '/' + filename + '.png')/255.
train_mask = resize_image(train_mask, input_height, input_width)
# train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
mask[i - c] = train_mask
except:
img[i - c] = np.ones((input_height, input_width, 3)).astype('float')
mask[i - c] = np.zeros((input_height, input_width, n_classes)).astype('float')
mask[i - c, :] = train_mask
except Exception as e:
print(str(e))
img[i - c, :] = 1.
mask[i - c, :] = 0.
c += batch_size
if c + batch_size >= len(os.listdir(img_folder)):