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data.transform.MinMaxNormalization(tf.uint16.max),
There should be this
data.transform.MinMaxNormalization(),
In paper you have mentioned that you are able to perform one iteration of auto-context model so i was trying to first run one iteration first. But while implementing i got following errors
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Paddings must be non-negative: -86 -86
[[{{node compose/center_pad_3d/Pad}}]]
[[patch/IteratorGetNext]]
[[patch/IteratorGetNext/_1755]]
(1) Invalid argument: Paddings must be non-negative: -86 -86
[[{{node compose/center_pad_3d/Pad}}]]
[[patch/IteratorGetNext]]
0 successful operations.
0 derived errors ignored.
shape passed to center_pad_3d transformation function is Volume_shape : [260, 340, 360, 1]
Is preprocessing mechanism for contex-aware is different than that of unet,pixtopix one ?
The text was updated successfully, but these errors were encountered:
data.transform.MinMaxNormalization(tf.uint16.max),
There should be this
data.transform.MinMaxNormalization(),
Why?
Is preprocessing mechanism for contex-aware is different than that of unet,pixtopix one ?
Yes, it is very different. For the context-aware network you need 3d patches while the other ones use 2d slices. I was never able to achieve good results with the context-aware network. The original paper is (in my opinion) not very specific on the implementation details and you have to write a lot of custom code for your training procedure.
In provider.py at line 111
data.transform.MinMaxNormalization(tf.uint16.max),
There should be this
data.transform.MinMaxNormalization(),
In paper you have mentioned that you are able to perform one iteration of auto-context model so i was trying to first run one iteration first. But while implementing i got following errors
shape passed to center_pad_3d transformation function is Volume_shape : [260, 340, 360, 1]
Is preprocessing mechanism for contex-aware is different than that of unet,pixtopix one ?
The text was updated successfully, but these errors were encountered: