add download script and instructions

Author: holgerroth

federated_learning/breast_density_challenge/README.md

@@ -15,10 +15,15 @@ We provide a minimal example of how to implement Federated Averaging using [NVFl
 ### 1.1 Download example data
 Follow the steps described in [./data/README.md](./data/README.md) to download an example breast density mammography dataset.
 Note, the data used in the actual challenge will be different. We do however follow the same preprocessing steps and
-use the same four BI-RADS breast density classes for prediction.
+use the same four BI-RADS breast density classes for prediction, See [./code/pt/utils/preprocess_dicomdir.py](./code/pt/utils/preprocess_dicomdir.py) for details.
 
-We provide a set of random data splits as `./data/dataset_blinded_site-*.json` which follows the same format as what
-will be used in the challenge. Please do not modify the data list filenames in the configs as they will be the same during the challenge.
+We provide a set of random data splits. Please download them using
+```
+python3 ./code/pt/utils/download_datalists_and_predictions.py
+```
+After download, they will be available as `./data/dataset_blinded_site-*.json` which follows the same format as what
+will be used in the challenge. 
+Please do not modify the data list filenames in the configs as they will be the same during the challenge.
 
 Note, the location of the dataset and data lists will be given by the system.
 Do not change the locations given in [config_fed_client.json](./code/configs/mammo_fedavg/config/config_fed_client.json):
@@ -135,7 +140,8 @@ Your FL framework will also need those ports for implementing the communication.
 
 ### 3.5 Results
 Results will need to be written to `/result/predictions.json`.
-Please follow the format produced by the reference implementation at [./result_server/predictions.json](./result_server/predictions.json)
+Please follow the format produced by the reference implementation at [./result_server_example/predictions.json](./result_server_example/predictions.json) 
+(available after running `python3 ./code/pt/utils/download_datalists_and_predictions.py`)
 The code is expected to return a json file containing at least list of image names and prediction probabilities for each breast density class
 for the global model (should be named `SRV_best_FL_global_model.pt`).
 ```

federated_learning/breast_density_challenge/code/pt/utils/download_datalists_and_predictions.py

@@ -0,0 +1,16 @@
+# Copyright 2022 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from monai.apps.utils import download_url, download_and_extract
+
+
+download_and_extract(url="https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/dataset_lists.zip", output_dir="./data")
+download_url(url="https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/predictions.json", filepath="./result_server_example/predictions.json")