updated readme to reflect folder structure changes
@@ -18,7 +18,7 @@ NiftyNet was developed by the [Centre for Medical Image Computing][cmic] at
* Nibabel 2.1
* Numpy 1.12
* Scipy 0.19
-* medpy-0.2.2
+* medpy 0.2.2
### Usage
@@ -33,7 +33,8 @@ After the training process, to do segmentation with a trained "toynet":
``` sh
cd NiftyNet/
python run_application.py inference --net_name toynet \
---eval_data_dir ./example_volumes/monomodal_parcellation --save_seg_dir ./seg_output \
+ --eval_data_dir ./example_volumes/monomodal_parcellation \
+ --save_seg_dir ./seg_output \
--image_size 64 --label_size 64 --batch_size 4
```
*Commandline parameters override the default settings defined in `config/default_config.txt`.*
@@ -66,16 +67,18 @@ Only nifty files (extension .nii or .nii.gz) are supported.
### Structure
The basic picture of training procedure (data parallelism) is:
```
-<Multi-GPU training> (training.py)
+<Multi-GPU training>
+ (nn/training.py)
|>----------------------+
|>---------------+ |
|^| | |
- (queue.py) |^| sync GPU_1 GPU_2 ...
+ (nn/input_queue.py) |^| sync GPU_1 GPU_2 ...
|^| +----> model model (network/*.py)
+ |^| | | |
with multiple threads: |^| | | |
- (preprocess.py) |^| CPU v v (loss.py)
-image&label -->> (sampler.py) -->> | model <----+------+
-(*.nii.gz) (data_augmentation.py) update stochastic gradients
+ (nn/preprocess.py) |^| CPU v v (nn/loss.py)
+image&label ->> (nn/sampler.py) ->> | model <----+------+
+(*.nii.gz) (nn/data_augmentation.py)| update stochastic gradients
```
### Citation
