Lossy conversion from float64 to uint8. Range [-0.18539370596408844, 2.158207416534424]. Convert image to uint8 prior to saving to suppress this warning. Traceback (most recent call last): File "train.py", line 250, in main(args.task) File "train.py", line 106, in main X[:,:,2,np.newaxis], X[:,:,3,np.newaxis], y])#[:,:,np.newaxis]]) File "train.py", line 26, in distort_imgs fill_mode='constant') TypeError: zoom_multi() got an unexpected keyword argument 'is_random'

@zsdonghao I am getting the memory error like this, What is the solution for this error?

Traceback (most recent call last): File "train.py", line 279, in main(args.task) File "train.py", line 78, in main y_test = (y_test > 0).astype(int) MemoryError

I am running run.py but gives error:

(base) G:>cd BraTS_2018_U-Net-master

(base) G:\BraTS_2018_U-Net-master>run.py [*] creates checkpoint ... [*] creates samples/all ... finished Brats18_2013_24_1 2019-06-15 22:05:45.959220: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 Traceback (most recent call last): File "G:\BraTS_2018_U-Net-master\run.py", line 154, in

File "G:\BraTS_2018_U-Net-master\run.py", line 117, in main t_seg = tf.placeholder('float32', [1, nw, nh, 1], name='target_segment') NameError: name 'model' is not defined

• After conv: Tensor("u_net/conv8/leaky_relu:0", shape=(5, 1, 1, 512), dtype=float32, device=/device:CPU:0) Traceback (most re cent call last): File "train.py", line 250, in main(args.task) File "train.py", line 121, in main net = model.u_net_bn(t_image, is_train=True, reuse=False, n_out=1) File "/home/achi/project/u-net-brain-tumor-master/model.py", line 179, in u_net_bn padding=pad, act=None, batch_size=batch_size, W_init=w_init, b_init=b_init, name='deconv7') File "/home/achi/anaconda3/lib/python3.6/site-packages/tensorlayer/decorators/deprecated_alias.py", line 24, in wrapper return f(*args, **kwargs) TypeError: init() got an unexpected keyword argument 'out_size'

I was wondering if you would share a pre-trained model. I would need to run inference-only, and training the model is taking longer than expected.

Thanks for sharing this project!

[TL] [!] checkpoint exists ... [TL] [!] samples/all exists ... Lossy conversion from float64 to uint8. Range [-0.19753389060497284, 2.826017379760742]. Convert image to uint8 prior to saving to suppress this warning.

TypeError Traceback (most recent call last) in 239 tl.files.save_npz(net.all_params, name=save_dir+'/u_net_{}.npz'.format(task), sess=sess) 240 --> 241 main(task='all') 242 243 ##if name == "main":

in main(task) 103 for i in range(10): 104 x_flair, x_t1, x_t1ce, x_t2, label = distort_imgs([X[:,:,0,np.newaxis], X[:,:,1,np.newaxis], --> 105 X[:,:,2,np.newaxis], X[:,:,3,np.newaxis], y])#[:,:,np.newaxis]]) 106 # print(x_flair.shape, x_t1.shape, x_t1ce.shape, x_t2.shape, label.shape) # (240, 240, 1) (240, 240, 1) (240, 240, 1) (240, 240, 1) (240, 240, 1) 107 X_dis = np.concatenate((x_flair, x_t1, x_t1ce, x_t2), axis=2)

in distort_imgs(data) 23 x1, x2, x3, x4, y = tl.prepro.zoom_multi([x1, x2, x3, x4, y], 24 zoom_range=[0.9, 1.1], is_random=True, ---> 25 fill_mode='constant') 26 return x1, x2, x3, x4, y 27

TypeError: zoom_multi() got an unexpected keyword argument 'is_random'

Do you have implementation of brain tumor segmentation code directly in tensorflow without using tensorlayer? If yes, can you share the same? Thank you.

When I run "python train.py" and then have a checkpoint folder is created. What function of checkpoint folder? Thank you

And I also have another question. When we had the picture, as follows. Is that the end result? I mean we can submit them to the Brast_2018 challenge?

Thank you very much.

Novice here, i noticed the shape of the X_train arrays ended with 4. (240,240,4) Does each of those channel represent the type of the scan ( T1, t2, flair, t1ce ) ?

Hi!

I went through this repository after reading your paper. Architecture on page 6, shows the final classification layer to produce feature maps of shape (240, 240, 2) which may indicate the use of a Softmax activation (not specified in the paper). On the contrary, model used in code has a classification layer of shape (240, 240, 1) using Sigmoid activation.

Kindly clarify this ambiguity.