Comments (9)
Hello, I don't know what is the meaning of the following code? liverlist is what?
num = np.random.randint(0,6)
if num < 3 or (count in liverlist):
lines = liverlines[count]
numid = liveridx[count]
else:
lines = tumorlines[count]
numid = tumoridx[count]
from h-denseunet.
count
hi, this code is make one batch contains 50% liver image and 50% tumor image.
liverlist is the ct which can't have tumor
from h-denseunet.
hello, Thank you very much!
I don't know what is the meaning of the following code?
a = min(max(minindex[0] + deps/2, cen[0]), maxindex[0]- deps/2-1)
b = min(max(minindex[1] + rows/2, cen[1]), maxindex[1]- rows/2-1)
c = min(max(minindex[2] + cols/2, cen[2]), maxindex[2]- cols/2-1)
from h-denseunet.
from h-denseunet.
Hello, I run the train_2ddenseunet, it is StopIterators. It is relation with the pool(thread_num), fit_gerator(workers)? I want to know the relation about pool(thread_num), fit_gerator(workers).
Because my cpu and gpu is limited, I load data is by batch, then by generator to train. infinite....
1/1711 [..............................] - ETA: 547208s - loss: 0.1421 - acc: 2/1711 [..............................] - ETA: 273914s - loss: 0.1670 - acc: 0.5536(16, 224, 224, 3) (16, 224, 224, 1)
3/1711 [..............................] - ETA: 226099s - loss: 0.1711 - acc: 0.5572(16, 224, 224, 3) (16, 224, 224, 1)
4/1711 [..............................] - ETA: 182167s - loss: 0.1564 - acc: 0.5683
Traceback (most recent call last):
File "batch2.py", line 211, in
train_and_predict()
File "batch2.py", line 201, in train_and_predict
train_loss, train_acc = model.fit_generator(generate_arrays_from_file(args.b), steps_per_epoch=steps, epochs=60, verbose=1, callbacks=[model_checkpoint], max_queue_size=10, workers=3, use_multiprocessing=True)
File "/usr/local/lib/python3.6/dist-packages/Keras-2.0.8-py3.6.egg/keras/legacy/interfaces.py", line 87, in wrapper
File "/usr/local/lib/python3.6/dist-packages/Keras-2.0.8-py3.6.egg/keras/engine/training.py", line 2011, in fit_generator
StopIteration
from h-denseunet.
Hello, I run the train_2ddenseunet, it is StopIterators. It is relation with the pool(thread_num), fit_gerator(workers)? I want to know the relation about pool(thread_num), fit_gerator(workers).
Because my cpu and gpu is limited, I load data is by batch, then by generator to train. infinite....1/1711 [..............................] - ETA: 547208s - loss: 0.1421 - acc: 2/1711 [..............................] - ETA: 273914s - loss: 0.1670 - acc: 0.5536(16, 224, 224, 3) (16, 224, 224, 1)
3/1711 [..............................] - ETA: 226099s - loss: 0.1711 - acc: 0.5572(16, 224, 224, 3) (16, 224, 224, 1)
4/1711 [..............................] - ETA: 182167s - loss: 0.1564 - acc: 0.5683
Traceback (most recent call last):
File "batch2.py", line 211, in
train_and_predict()
File "batch2.py", line 201, in train_and_predict
train_loss, train_acc = model.fit_generator(generate_arrays_from_file(args.b), steps_per_epoch=steps, epochs=60, verbose=1, callbacks=[model_checkpoint], max_queue_size=10, workers=3, use_multiprocessing=True)
File "/usr/local/lib/python3.6/dist-packages/Keras-2.0.8-py3.6.egg/keras/legacy/interfaces.py", line 87, in wrapper
File "/usr/local/lib/python3.6/dist-packages/Keras-2.0.8-py3.6.egg/keras/engine/training.py", line 2011, in fit_generator
StopIteration
hi, Can you share your memory size? Because my server's memory is 64GB, it throw out of memory when run this program.
from h-denseunet.
Hello, I run the train_2ddenseunet, it is StopIterators. It is relation with the pool(thread_num), fit_gerator(workers)? I want to know the relation about pool(thread_num), fit_gerator(workers).
Because my cpu and gpu is limited, I load data is by batch, then by generator to train. infinite....
1/1711 [..............................] - ETA: 547208s - loss: 0.1421 - acc: 2/1711 [..............................] - ETA: 273914s - loss: 0.1670 - acc: 0.5536(16, 224, 224, 3) (16, 224, 224, 1)
3/1711 [..............................] - ETA: 226099s - loss: 0.1711 - acc: 0.5572(16, 224, 224, 3) (16, 224, 224, 1)
4/1711 [..............................] - ETA: 182167s - loss: 0.1564 - acc: 0.5683
Traceback (most recent call last):
File "batch2.py", line 211, in
train_and_predict()
File "batch2.py", line 201, in train_and_predict
train_loss, train_acc = model.fit_generator(generate_arrays_from_file(args.b), steps_per_epoch=steps, epochs=60, verbose=1, callbacks=[model_checkpoint], max_queue_size=10, workers=3, use_multiprocessing=True)
File "/usr/local/lib/python3.6/dist-packages/Keras-2.0.8-py3.6.egg/keras/legacy/interfaces.py", line 87, in wrapper
File "/usr/local/lib/python3.6/dist-packages/Keras-2.0.8-py3.6.egg/keras/engine/training.py", line 2011, in fit_generator
StopIterationhi, Can you share your memory size? Because my server's memory is 64GB, it throw out of memory when run this program.
hello, my server‘s memory is 62G. I want to know what I should do to solve this proplem.
from h-denseunet.
Hello, I run the train_2ddenseunet, it is StopIterators. It is relation with the pool(thread_num), fit_gerator(workers)? I want to know the relation about pool(thread_num), fit_gerator(workers).
Because my cpu and gpu is limited, I load data is by batch, then by generator to train. infinite....
1/1711 [..............................] - ETA: 547208s - loss: 0.1421 - acc: 2/1711 [..............................] - ETA: 273914s - loss: 0.1670 - acc: 0.5536(16, 224, 224, 3) (16, 224, 224, 1)
3/1711 [..............................] - ETA: 226099s - loss: 0.1711 - acc: 0.5572(16, 224, 224, 3) (16, 224, 224, 1)
4/1711 [..............................] - ETA: 182167s - loss: 0.1564 - acc: 0.5683
Traceback (most recent call last):
File "batch2.py", line 211, in
train_and_predict()
File "batch2.py", line 201, in train_and_predict
train_loss, train_acc = model.fit_generator(generate_arrays_from_file(args.b), steps_per_epoch=steps, epochs=60, verbose=1, callbacks=[model_checkpoint], max_queue_size=10, workers=3, use_multiprocessing=True)
File "/usr/local/lib/python3.6/dist-packages/Keras-2.0.8-py3.6.egg/keras/legacy/interfaces.py", line 87, in wrapper
File "/usr/local/lib/python3.6/dist-packages/Keras-2.0.8-py3.6.egg/keras/engine/training.py", line 2011, in fit_generator
StopIterationhi, Can you share your memory size? Because my server's memory is 64GB, it throw out of memory when run this program.
hello, my server‘s memory is 62G. I want to know what I should do to solve this proplem.
Same problem. Have you solved it? thx!
from h-denseunet.
hi, i am very interested in your work, but i have a question about the code in train_2ddense.py
minindex[0] = max(minindex[0] - 3, 0) minindex[1] = max(minindex[1] - 3, 0) minindex[2] = max(minindex[2] - 3, 0) maxindex[0] = min(img.shape[0], maxindex[0] + 3) maxindex[1] = min(img.shape[1], maxindex[1] + 3) maxindex[2] = min(img.shape[2], maxindex[2] + 3)
I can't understand why it's subtract and add 3, why not other number?
Me too, and how these codes roles are?
from h-denseunet.
Related Issues (20)
- keras.layers.input batch_shape 最基本問題 HOT 1
- how to predict by model denseunet-2d?
- About generate livermask HOT 1
- 请问'27386' 是怎么计算出来的呢?
- my questions about Hdenseunet
- Evaluating the results?
- Request Dataset
- np.where(livertumor == 2) HOT 1
- about preprocessing.py HOT 1
- Where is the code for slicing 3D volumes to 2d slices
- How do I get the number 27386
- 2D coarse segmentation ResNet network HOT 1
- Where is the test dataset? HOT 4
- Could you share the 3Dircadb data set? official website link is too slow HOT 1
- in train_2ddense.py, in the code ' steps = 27386 / args.b', How do I get the number 27386
- anybody who can tell me about how to calculate the number '27386' HOT 2
- about train_2ddense.py
- about model_best.hdf5
- densenet161_weights_tf.h5
- livermask HOT 2
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