omagdy/3drdn-cyclegan

Deep CNN for performing 3D super resolution on CT/MRI scans

3drdn-cyclegan's Issues

How to obtain the data set?

if I just want to generate thin slice CTs，how can I do?

Just remove the SCALING_FACTOR for x and y, and keep the SCALING_FACTOR for z, Will it work if I just want to generate thin slice CTs?
lr_image = tf.image.resize(blurred_image, [x//SCALING_FACTOR, y//SCALING_FACTOR], method=interpolation_method).numpy()
lr_image = np.rot90(lr_image, axes=(1,2))
lr_image = tf.image.resize(lr_image, [x//SCALING_FACTOR, z//SCALING_FACTOR], method=interpolation_method).numpy()
ups_lr_image = tf.image.resize(lr_image, [x//SCALING_FACTOR, z], method=interpolation_method).numpy()
ups_lr_image = np.rot90(ups_lr_image, axes=(1,2))

Inference

Could you pleaes help me with the inference problem, I have no idea of how to inference the model which I have trained well. I could not find the ckpt.meta file.

Thank you !

Error in valid_batch = [next(valid_dataset)]

Error:
how to solve this problem,
#Initial Random Slice Image Generation
valid_batch = [next(valid_dataset)]

Traceback (most recent call last):
File "main.py", line 26, in
main()
File "main.py", line 22, in main
LAMBDA_CYC, LAMBDA_IDT, CRIT_ITER, TRAIN_ONLY, MODEL)
File "F:\mazhiqiang\3DRDN-CycleGAN-main\training.py", line 86, in main_loop
valid_batch = [next(valid_dataset)]
File "C:\Users\user\anaconda3\envs\tensorflow-gpu2.6.0\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 4692, in next
return nest.map_structure(to_numpy, next(self._iterator))
File "C:\Users\user\anaconda3\envs\tensorflow-gpu2.6.0\lib\site-packages\tensorflow\python\data\ops\iterator_ops.py", line 761, in next
return self._next_internal()
File "C:\Users\user\anaconda3\envs\tensorflow-gpu2.6.0\lib\site-packages\tensorflow\python\data\ops\iterator_ops.py", line 747, in _next_internal
output_shapes=self._flat_output_shapes)
File "C:\Users\user\anaconda3\envs\tensorflow-gpu2.6.0\lib\site-packages\tensorflow\python\ops\gen_dataset_ops.py", line 2727, in iterator_get_next
_ops.raise_from_not_ok_status(e, name)
File "C:\Users\user\anaconda3\envs\tensorflow-gpu2.6.0\lib\site-packages\tensorflow\python\framework\ops.py", line 6941, in raise_from_not_ok_status
six.raise_from(core._status_to_exception(e.code, message), None)
File "", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: Need minval < maxval, got 50 >= 14
[[{{node StatefulPartitionedCall/StatefulPartitionedCall/random_uniform_2}}]] [Op:IteratorGetNext]

Can you provide pretrained weights?

My hardware is insufficient for training and I want to try inference before I consider using it for research.
Is it possible to share your model weights or provide a pretrained model?