Save labels in output directory

maskrcnn_benchmark/data/build.py

@@ -6,6 +6,7 @@
 import torch.utils.data
 from maskrcnn_benchmark.utils.comm import get_world_size
 from maskrcnn_benchmark.utils.imports import import_file
+from maskrcnn_benchmark.utils.miscellaneous import save_labels
 
 from . import datasets as D
 from . import samplers
@@ -154,6 +155,10 @@ def make_data_loader(cfg, is_train=True, is_distributed=False, start_iter=0):
     transforms = None if not is_train and cfg.TEST.BBOX_AUG.ENABLED else build_transforms(cfg, is_train)
     datasets = build_dataset(dataset_list, transforms, DatasetCatalog, is_train)
 
+    if is_train:
+        # save category_id to label name mapping
+        save_labels(datasets, cfg.OUTPUT_DIR)
+
     data_loaders = []
     for dataset in datasets:
         sampler = make_data_sampler(dataset, shuffle, is_distributed)

maskrcnn_benchmark/data/datasets/coco.py

@@ -54,6 +54,8 @@ def __init__(
                     ids.append(img_id)
             self.ids = ids
 
+        self.categories = {cat['id']: cat['name'] for cat in self.coco.cats.values()}
+
         self.json_category_id_to_contiguous_id = {
             v: i + 1 for i, v in enumerate(self.coco.getCatIds())
         }

maskrcnn_benchmark/data/datasets/voc.py

@@ -57,6 +57,7 @@ def __init__(self, data_dir, split, use_difficult=False, transforms=None):
 
         cls = PascalVOCDataset.CLASSES
         self.class_to_ind = dict(zip(cls, range(len(cls))))
+        self.categories = dict(zip(range(len(cls)), cls))
 
     def __getitem__(self, index):
         img_id = self.ids[index]

maskrcnn_benchmark/utils/miscellaneous.py

@@ -1,6 +1,9 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 import errno
+import json
+import logging
 import os
+from .comm import is_main_process
 
 
 def mkdir(path):
@@ -9,3 +12,22 @@ def mkdir(path):
     except OSError as e:
         if e.errno != errno.EEXIST:
             raise
+
+
+def save_labels(dataset_list, output_dir):
+    if is_main_process():
+        logger = logging.getLogger(__name__)
+
+        ids_to_labels = {}
+        for dataset in dataset_list:
+            if hasattr(dataset, 'categories'):
+                ids_to_labels.update(dataset.categories)
+            else:
+                logger.warning("Dataset [{}] has no categories attribute, labels.json file won't be created".format(
+                    dataset.__class__.__name__))
+
+        if ids_to_labels:
+            labels_file = os.path.join(output_dir, 'labels.json')
+            logger.info("Saving labels mapping into {}".format(labels_file))
+            with open(labels_file, 'w') as f:
+                json.dump(ids_to_labels, f, indent=2)