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@taigw
I tried for the code of training i am getting the assertion error. What will be the solution for this?
Thank you.
ujjwal@ujjwal-precision-t1700:~/Desktop/ALL_TRIES/brats17-master_NIFTYNET$ python train.py config17/train_wt_ax.txt
data data_root /home/ujjwal/Desktop/ALL_TRIES/brats17-master_NIFTYNET/data_root/Brats17TrainingData /home/ujjwal/Desktop/ALL_TRIES/brats17-master_NIFTYNET/data_root/Brats17TrainingData
data data_names config17/train_names_all.txt config17/train_names_all.txt
data modality_postfix [flair, t1, t1ce, t2] ['flair', 't1', 't1ce', 't2']
data label_postfix seg seg
data file_postfix nii.gz nii.gz
data with_ground_truth True True
data batch_size 5 5
data data_shape [19, 144, 144, 4] [19, 144, 144, 4]
data label_shape [11, 144, 144, 1] [11, 144, 144, 1]
data label_convert_source [0, 1, 2, 4] [0, 1, 2, 4]
data label_convert_target [0, 1, 1, 1] [0, 1, 1, 1]
data batch_slice_direction axial axial
data train_with_roi_patch False False
data label_roi_mask None
data roi_patch_margin None
network net_type MSNet MSNet
network net_name MSNet_WT32 MSNet_WT32
network downsample_twice True True
network class_num 2 2
training learning_rate 1e-3 0.001
training decay 1e-7 1e-07
training maximal_iteration 20000 20000
training snapshot_iteration 5000 5000
training start_iteration 0 0
training test_iteration 100 100
training test_step 10 10
training model_pre_trained None
training model_save_prefix model17/msnet_wt32 model17/msnet_wt32
size of predicty: Tensor("MSNet_WT32/final_pred/conv:0", shape=(5, 11, 144, 144, 2), dtype=float32)
2018-06-05 09:23:03.159216: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2018-06-05 09:23:03.159247: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2018-06-05 09:23:03.159253: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-06-05 09:23:03.159256: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-06-05 09:23:03.159260: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
Data load, 49.0196078431% finished
Data load, 98.0392156863% finished
Traceback (most recent call last):
File "train.py", line 124, in
train(config_file)
File "train.py", line 83, in train
dataloader.load_data()
File "/home/ujjwal/Desktop/ALL_TRIES/brats17-master_NIFTYNET/util/data_loader.py", line 91, in load_data
volume, volume_name = self.__load_one_volume(self.patient_names[i], self.modality_postfix[mod_idx])
File "/home/ujjwal/Desktop/ALL_TRIES/brats17-master_NIFTYNET/util/data_loader.py", line 69, in __load_one_volume
assert(volume_name is not None)
AssertionError
The text was updated successfully, but these errors were encountered:
line 82, in __load_one_volume
assert (volume_name is not None)
AssertionError
-I ran rename_crop_BRATS.py on the BraTS '17 training data set
-Subsequently, I had to change the patient_dir variable in line 62 in data_loader.py
I suspect that rename_crop_BRATS.py is somehow responsible for this and causing the load_one_volume function in data_loader.py to be unable to find the images/volumes?
@taigw
I tried for the code of training i am getting the assertion error. What will be the solution for this?
Thank you.
ujjwal@ujjwal-precision-t1700:~/Desktop/ALL_TRIES/brats17-master_NIFTYNET$ python train.py config17/train_wt_ax.txt
data data_root /home/ujjwal/Desktop/ALL_TRIES/brats17-master_NIFTYNET/data_root/Brats17TrainingData /home/ujjwal/Desktop/ALL_TRIES/brats17-master_NIFTYNET/data_root/Brats17TrainingData
data data_names config17/train_names_all.txt config17/train_names_all.txt
data modality_postfix [flair, t1, t1ce, t2] ['flair', 't1', 't1ce', 't2']
data label_postfix seg seg
data file_postfix nii.gz nii.gz
data with_ground_truth True True
data batch_size 5 5
data data_shape [19, 144, 144, 4] [19, 144, 144, 4]
data label_shape [11, 144, 144, 1] [11, 144, 144, 1]
data label_convert_source [0, 1, 2, 4] [0, 1, 2, 4]
data label_convert_target [0, 1, 1, 1] [0, 1, 1, 1]
data batch_slice_direction axial axial
data train_with_roi_patch False False
data label_roi_mask None
data roi_patch_margin None
network net_type MSNet MSNet
network net_name MSNet_WT32 MSNet_WT32
network downsample_twice True True
network class_num 2 2
training learning_rate 1e-3 0.001
training decay 1e-7 1e-07
training maximal_iteration 20000 20000
training snapshot_iteration 5000 5000
training start_iteration 0 0
training test_iteration 100 100
training test_step 10 10
training model_pre_trained None
training model_save_prefix model17/msnet_wt32 model17/msnet_wt32
size of predicty: Tensor("MSNet_WT32/final_pred/conv:0", shape=(5, 11, 144, 144, 2), dtype=float32)
2018-06-05 09:23:03.159216: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2018-06-05 09:23:03.159247: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2018-06-05 09:23:03.159253: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-06-05 09:23:03.159256: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-06-05 09:23:03.159260: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
Data load, 49.0196078431% finished
Data load, 98.0392156863% finished
Traceback (most recent call last):
File "train.py", line 124, in
train(config_file)
File "train.py", line 83, in train
dataloader.load_data()
File "/home/ujjwal/Desktop/ALL_TRIES/brats17-master_NIFTYNET/util/data_loader.py", line 91, in load_data
volume, volume_name = self.__load_one_volume(self.patient_names[i], self.modality_postfix[mod_idx])
File "/home/ujjwal/Desktop/ALL_TRIES/brats17-master_NIFTYNET/util/data_loader.py", line 69, in __load_one_volume
assert(volume_name is not None)
AssertionError
The text was updated successfully, but these errors were encountered: