B,
C,
preprocess,
random_transform=False)
train_generator = data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=min(num_gpu * 8, 16))
val_generator = data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=min(num_gpu * 8, 16))
# This is just to check that labels are correctly encoded.
for pos in range(20):
img1, target1 = train_dataset.__getitem__(pos)
gt_color = (0, 0, 255) #red
# Get numpy image in opencv format
np_img = preprocess.post_process_image(img1)
# get bounding boxes: xyxy + class + confidence
img_h, img_w, _ = np_img.shape
target_bboxes = preprocess.decode_label(target1, img_h, img_w)
draw_all_bboxes(np_img, target_bboxes, preprocess, gt_color,
"images_transformed/gt_{}.jpg".format(pos))
import os
os._exit(1)
# Enable anomaly detection for debugging purpose
