Add transform to handle training data with empty ground truth boxes (Project-MONAI#1256)

Fixes Project-MONAI#1197 .

### Description
Add transform to handle training data with empty ground truth boxes
Add verbose choice for training script

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Avoid including large-size files in the PR.
- [ ] Clean up long text outputs from code cells in the notebook.
- [x] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder
- [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

---------

Author: Can-Zhao

detection/README.md

@@ -88,6 +88,8 @@ python3 luna16_training.py \
     -e ./config/environment_luna16_fold${i}.json \
     -c ./config/config_train_luna16_16g.json
 ```
+If you are tuning hyper-parameters, please also add `--verbose` flag.
+Details about matched anchors during training will be printed out.
 
 For each fold, 95% of the training data is used for training, while the rest 5% is used for validation and model selection.
 The training and validation curves for 300 epochs of 10 folds are shown below. The upper row shows the training losses for box regression and classification. The bottom row shows the validation mAP and mAR for IoU ranging from 0.1 to 0.5.

detection/generate_transforms.py

@@ -37,6 +37,7 @@
     RandRotateBox90d,
     RandZoomBoxd,
     ConvertBoxModed,
+    StandardizeEmptyBoxd,
 )
 
 
@@ -70,7 +71,6 @@ def generate_detection_train_transform(
     Return:
         training transform for detection
     """
-    amp = True
     if amp:
         compute_dtype = torch.float16
     else:
@@ -82,6 +82,7 @@ def generate_detection_train_transform(
             EnsureChannelFirstd(keys=[image_key]),
             EnsureTyped(keys=[image_key, box_key], dtype=torch.float32),
             EnsureTyped(keys=[label_key], dtype=torch.long),
+            StandardizeEmptyBoxd(box_keys=[box_key], box_ref_image_keys=image_key),
             Orientationd(keys=[image_key], axcodes="RAS"),
             intensity_transform,
             EnsureTyped(keys=[image_key], dtype=torch.float16),
@@ -216,7 +217,6 @@ def generate_detection_val_transform(
     Return:
         validation transform for detection
     """
-    amp = True
     if amp:
         compute_dtype = torch.float16
     else:
@@ -228,6 +228,7 @@ def generate_detection_val_transform(
             EnsureChannelFirstd(keys=[image_key]),
             EnsureTyped(keys=[image_key, box_key], dtype=torch.float32),
             EnsureTyped(keys=[label_key], dtype=torch.long),
+            StandardizeEmptyBoxd(box_keys=[box_key], box_ref_image_keys=image_key),
             Orientationd(keys=[image_key], axcodes="RAS"),
             intensity_transform,
             ConvertBoxToStandardModed(box_keys=[box_key], mode=gt_box_mode),
@@ -272,7 +273,6 @@ def generate_detection_inference_transform(
     Return:
         validation transform for detection
     """
-    amp = True
     if amp:
         compute_dtype = torch.float16
     else:

detection/luna16_training.py

@@ -57,6 +57,13 @@ def main():
         default="./config/config_train.json",
         help="config json file that stores hyper-parameters",
     )
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        default=False,
+        action="store_true",
+        help="whether to print verbose detail during training, recommand True when you are not sure about hyper-parameters",
+    )
     args = parser.parse_args()
 
     set_determinism(seed=0)
@@ -188,7 +195,7 @@ def main():
     )
 
     # 3) build detector
-    detector = RetinaNetDetector(network=net, anchor_generator=anchor_generator, debug=False).to(device)
+    detector = RetinaNetDetector(network=net, anchor_generator=anchor_generator, debug=args.verbose).to(device)
 
     # set training components
     detector.set_atss_matcher(num_candidates=4, center_in_gt=False)