Fix edu challenge notebook and refactor config folder

.travis.yml

@@ -19,15 +19,15 @@ jobs:
       script: pre-commit run --all-files
     - name: "Sphinx Build"
       script:
-       - cd docs
-       - make clean html
+        - cd docs
+        - make clean html
     - stage: "Tests"
       name: "Unit Tests"
       script:  pytest --cov-report= --cov=deepreg ./test/unit/
       after_success: bash <(curl -s https://codecov.io/bash)
     - name: "Command Tests: Unpaired Unlabeled"
       script:
-        - deepreg_train -g "" --config_path deepreg/config/unpaired_labeled_ddf.yaml --log_dir test
+        - deepreg_train -g "" --config_path config/unpaired_labeled_ddf.yaml --log_dir test
         - deepreg_predict -g "" --ckpt_path logs/test/save/weights-epoch2.ckpt --mode test
         - deepreg_warp --image data/test/nifti/unit_test/moving_image.nii.gz --ddf data/test/nifti/unit_test/ddf.nii.gz --out logs/test_warp/out.nii.gz
     - name: "Output Tests: Paired Unlabeled"

deepreg/predict.py

@@ -13,9 +13,9 @@
 import numpy as np
 import tensorflow as tf
 
-import deepreg.config.parser as config_parser
 import deepreg.model.layer_util as layer_util
 import deepreg.model.optimizer as opt
+import deepreg.parser as config_parser
 from deepreg.dataset.loader.util import normalize_array
 from deepreg.model.network.build import build_model
 from deepreg.util import (

deepreg/train.py

@@ -9,8 +9,8 @@
 
 import tensorflow as tf
 
-import deepreg.config.parser as config_parser
 import deepreg.model.optimizer as opt
+import deepreg.parser as config_parser
 from deepreg.model.network.build import build_model
 from deepreg.util import build_dataset, build_log_dir
 

demos/classical_ct_headneck_affine/demo_register.py

@@ -23,7 +23,6 @@
 learning_rate = 0.01
 total_iter = int(1000)
 
-
 ## load image
 if not os.path.exists(DATA_PATH):
     raise ("Download the data using demo_data.py script")
@@ -89,12 +88,10 @@ def train_step(grid, weights, optimizer, mov, fix):
     if (step % 50) == 0:  # print info
         tf.print("Step", step, image_loss_name, loss_opt)
 
-
 ## warp the moving image using the optimised affine transformation
 grid_opt = layer_util.warp_grid(grid_ref, var_affine)
 warped_moving_image = layer_util.resample(vol=moving_image, loc=grid_opt)
 
-
 ## warp the moving labels using the optimised affine transformation
 warped_moving_labels = tf.stack(
     [
@@ -104,7 +101,6 @@ def train_step(grid, weights, optimizer, mov, fix):
     axis=4,
 )
 
-
 ## save output to files
 SAVE_PATH = "output_" + datetime.now().strftime("%Y%m%d-%H%M%S")
 os.mkdir(SAVE_PATH)
@@ -137,5 +133,4 @@ def train_step(grid, weights, optimizer, mov, fix):
             gray=True,
         )
 
-
 os.chdir(MAIN_PATH)

demos/classical_mr_prostate_nonrigid/demo_register.py

@@ -88,7 +88,6 @@ def train_step(warper, weights, optimizer, mov, fix):
             loss_deform_opt,
         )
 
-
 # warp the moving image using the optimised ddf
 warped_moving_image = warping(inputs=[var_ddf, moving_image])
 
@@ -127,5 +126,4 @@ def train_step(warper, weights, optimizer, mov, fix):
         save_dir=SAVE_PATH, arr=arr, name=arr_name, gray=True, save_png=False
     )
 
-
 os.chdir(MAIN_PATH)

demos/grouped_mask_prostate_longitudinal/demo_data.py

@@ -28,7 +28,6 @@
 
 print("\nMask data downloaded: %s." % os.path.abspath(DATA_PATH))
 
-
 ## now read the data and convert to train/val/test
 ratio_val = 0.1
 ratio_test = 0.2
@@ -98,7 +97,6 @@
 
 print("Done. \n")
 
-
 ## now download the pre-trained model
 MODEL_PATH = os.path.join(DATA_PATH, "pre-trained")
 if os.path.exists(MODEL_PATH):

demos/paired_ct_lung/demo_data.py

@@ -34,7 +34,6 @@
 
 print("The file ", fname, " has successfully been downloaded!")
 
-
 data_folder_name = "data"
 
 if os.path.exists(os.path.join(main_path, project_dir, data_folder_name)) is not True:
@@ -84,7 +83,6 @@ def moveFilesIntoCorrectPath(
 
 
 if os.path.exists(path_to_images_and_labels):
-
     moveFilesIntoCorrectPath(
         images_fnames, path_to_images_and_labels, path_to_train, "images", "scans"
     )
@@ -228,7 +226,6 @@ def moveTestCasesIntoCorrectPath(test_cases, path_to_train, path_to_test):
                         )
                     nib.save(img, os.path.join(data_dir, folder, subfolder, file))
 
-
 ######## DOWNLOAD MODEL CKPT FROM MODEL ZOO ########
 
 url = "https://github.com/DeepRegNet/deepreg-model-zoo/raw/master/paired_ct_lung_demo_logs.zip"

demos/paired_ct_lung/demo_predict.py

@@ -46,7 +46,6 @@
 path_to_moving_image = os.path.join(path_to_test, "pair_0", "moving_image")
 path_to_moving_label = os.path.join(path_to_test, "pair_0", "label_0", "moving_label")
 
-
 # change inds_to_plot if different images need to be plotted instead
 
 inds_to_plot = [144, 145, 184, 140, 150, 180]

demos/paired_mrus_prostate/demo_data.py

@@ -26,7 +26,6 @@
 
 print("\nMR and ultrasound data downloaded: %s." % os.path.abspath(DATA_PATH))
 
-
 ## now split the data in to num_part partitions
 num_part = 11
 
@@ -62,7 +61,6 @@
 
 print("All data are partitioned into %d folders." % num_part)
 
-
 ## now download the pre-trained model
 MODEL_PATH = os.path.join(DATA_PATH, "pre-trained")
 if os.path.exists(MODEL_PATH):

demos/unpaired_ct_abdomen/demo_train.py

@@ -7,7 +7,6 @@
     by following the instructions in readme.md"
 )
 
-
 """
 from deepreg.train import train
 

demos/unpaired_ct_lung/demo_data.py

@@ -35,10 +35,8 @@
 
 get_file(os.path.join(os.getcwd(), fname), url)
 
-
 print("The file ", fname, " has successfully been downloaded!")
 
-
 data_folder_name = "data"
 
 if os.path.exists(os.path.join(main_path, project_dir, data_folder_name)) is not True:
@@ -88,7 +86,6 @@ def moveFilesIntoCorrectPath(
 
 
 if os.path.exists(path_to_images_and_labels):
-
     moveFilesIntoCorrectPath(
         images_fnames, path_to_images_and_labels, path_to_train, "images", "scans"
     )

demos/unpaired_ct_lung/demo_predict.py

@@ -49,7 +49,6 @@
 path_to_moving_image = os.path.join(path_to_test, pair_n, "moving_image")
 path_to_moving_label = os.path.join(path_to_test, pair_n, "label_0", "moving_label")
 
-
 # change inds_to_plot if different images need to be plotted instead
 
 inds_to_plot = [20, 60, 100, 140, 160, 200]

demos/unpaired_mr_brain/demo_data.py

@@ -115,7 +115,6 @@
 
 print("Files succesfully copied to " + path_to_train + " and " + path_to_test)
 
-
 #################
 # Preprocessing #
 #################

demos/unpaired_mr_brain/demo_train.py

@@ -4,7 +4,6 @@
 
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
-
 gpu = "0"
 gpu_allow_growth = False
 ckpt_path = ""  # To load pre-trained weights

demos/unpaired_us_prostate_cv/demo_data.py

@@ -19,7 +19,6 @@
 with zipfile.ZipFile(ZIP_PATH, "r") as zf:
     zf.extractall()
 
-
 if os.path.exists(DATA_PATH):
     shutil.rmtree(DATA_PATH)
 shutil.move(DATA_REPO, DATA_PATH)