Comments (4)
Hi @AdnanAvdagic ,
Could you please submit minimal reproducible code snippet for this. I tried with a dummy model with an input size that is not a multiple of batch_size but couldn't get any issue.Attached gist for reference.
from tensorflow.
backbone = keras_cv.models.YOLOV8Backbone.from_preset(
'yolo_v8_l_backbone',
input_shape=[512,512, 3],
)
model = keras_cv.models.YOLOV8Detector(
num_classes=1,
bounding_box_format='xyxy',
backbone=backbone,
fpn_depth=3,
)
model.prediction_decoder = keras_cv.layers.NonMaxSuppression(
bounding_box_format='xyxy',
from_logits=True,
iou_threshold=0.35,
confidence_threshold=0.53,
max_detections=50,
)
def prepare_image(self: Self, input_image_path: Path) -> np.ndarray:
try:
input_image = cv2.imread(str(input_image_path))
# input_image = cv2.bilateralFilter(input_image, 9, 75, 75)
if self.color_mode == "rgb":
input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
else:
input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
return input_image
tiles_paths = ['01.jpg', '02.jpg', ...]
image_patches = np.array(
[
prepare_image(input_image_path=tile_path)
for tile_path in tiles_paths
],
)
y_pred = model.predict(
image_patches,
batch_size=8,
)
Here we get a crash if the number of image_patches is not divisible with the batch_size it will crash at the last batch which is less than the batch_size
from tensorflow.
Hi @AdnanAvdagic ,
Since this issue seems specific to keras_cv
model. Could you please file an issue at keras_cv repo,since all keras_cv
issues are actively tracked there?
from tensorflow.
This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.
from tensorflow.
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