add support for pascal voc dataset and evaluate

README.md

@@ -83,6 +83,8 @@ ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
 ln -s /path_to_coco_dataset/train2014 datasets/coco/train2014
 ln -s /path_to_coco_dataset/test2014 datasets/coco/test2014
 ln -s /path_to_coco_dataset/val2014 datasets/coco/val2014
+# for pascal voc dataset:
+ln -s /path_to_VOCdevkit_dir datasets/voc
 ```
 
 You can also configure your own paths to the datasets.

configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_1_gpu_voc.yaml

@@ -0,0 +1,20 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN:
+    PRE_NMS_TOP_N_TEST: 6000
+    POST_NMS_TOP_N_TEST: 300
+    ANCHOR_SIZES: (128, 256, 512)
+  ROI_BOX_HEAD:
+    NUM_CLASSES: 21
+DATASETS:
+  TRAIN: ("voc_2007_trainval",)
+  TEST: ("voc_2007_test",)
+SOLVER:
+  BASE_LR: 0.001
+  WEIGHT_DECAY: 0.0001
+  STEPS: (50000, )
+  MAX_ITER: 70000
+  IMS_PER_BATCH: 1
+TEST:
+  IMS_PER_BATCH: 1

configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_4_gpu_voc.yaml

@@ -0,0 +1,20 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  RPN:
+    PRE_NMS_TOP_N_TEST: 6000
+    POST_NMS_TOP_N_TEST: 300
+    ANCHOR_SIZES: (128, 256, 512)
+  ROI_BOX_HEAD:
+    NUM_CLASSES: 21
+DATASETS:
+  TRAIN: ("voc_2007_trainval",)
+  TEST: ("voc_2007_test",)
+SOLVER:
+  BASE_LR: 0.004
+  WEIGHT_DECAY: 0.0001
+  STEPS: (12500, )
+  MAX_ITER: 17500
+  IMS_PER_BATCH: 4
+TEST:
+  IMS_PER_BATCH: 4

maskrcnn_benchmark/config/paths_catalog.py

@@ -21,6 +21,13 @@ class DatasetCatalog(object):
             "coco/val2014",
             "coco/annotations/instances_valminusminival2014.json",
         ),
+        "voc_2007_trainval": ("voc/VOC2007", 'trainval'),
+        "voc_2007_test": ("voc/VOC2007", 'test'),
+        "voc_2012_train": ("voc/VOC2012", 'train'),
+        "voc_2012_trainval": ("voc/VOC2012", 'trainval'),
+        "voc_2012_val": ("voc/VOC2012", 'val'),
+        "voc_2012_test": ("voc/VOC2012", 'test'),
+
     }
 
     @staticmethod
@@ -36,6 +43,17 @@ def get(name):
                 factory="COCODataset",
                 args=args,
             )
+        elif "voc" in name:
+            data_dir = DatasetCatalog.DATA_DIR
+            attrs = DatasetCatalog.DATASETS[name]
+            args = dict(
+                data_dir=os.path.join(data_dir, attrs[0]),
+                split=attrs[1],
+            )
+            return dict(
+                factory="PascalVOCDataset",
+                args=args,
+            )
         raise RuntimeError("Dataset not available: {}".format(name))
 
 

maskrcnn_benchmark/data/build.py

@@ -26,7 +26,8 @@ def build_dataset(dataset_list, transforms, dataset_catalog, is_train=True):
     """
     if not isinstance(dataset_list, (list, tuple)):
         raise RuntimeError(
-                "dataset_list should be a list of strings, got {}".format(dataset_list))
+            "dataset_list should be a list of strings, got {}".format(dataset_list)
+        )
     datasets = []
     for dataset_name in dataset_list:
         data = dataset_catalog.get(dataset_name)
@@ -36,6 +37,8 @@ def build_dataset(dataset_list, transforms, dataset_catalog, is_train=True):
         # during training
         if data["factory"] == "COCODataset":
             args["remove_images_without_annotations"] = is_train
+        if data["factory"] == "PascalVOCDataset":
+            args["use_difficult"] = not is_train
         args["transforms"] = transforms
         # make dataset from factory
         dataset = factory(**args)
@@ -95,7 +98,9 @@ def make_batch_data_sampler(
             sampler, images_per_batch, drop_last=False
         )
     if num_iters is not None:
-        batch_sampler = samplers.IterationBasedBatchSampler(batch_sampler, num_iters, start_iter)
+        batch_sampler = samplers.IterationBasedBatchSampler(
+            batch_sampler, num_iters, start_iter
+        )
     return batch_sampler
 
 

maskrcnn_benchmark/data/datasets/__init__.py

@@ -1,5 +1,6 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 from .coco import COCODataset
+from .voc import PascalVOCDataset
 from .concat_dataset import ConcatDataset
 
-__all__ = ["COCODataset", "ConcatDataset"]
+__all__ = ["COCODataset", "ConcatDataset", "PascalVOCDataset"]

maskrcnn_benchmark/data/datasets/coco.py

@@ -11,7 +11,6 @@ def __init__(
         self, ann_file, root, remove_images_without_annotations, transforms=None
     ):
         super(COCODataset, self).__init__(root, ann_file)
-
         # sort indices for reproducible results
         self.ids = sorted(self.ids)
 

maskrcnn_benchmark/data/datasets/evaluation/__init__.py

@@ -0,0 +1,27 @@
+from maskrcnn_benchmark.data import datasets
+
+from .coco import coco_evaluation
+from .voc import voc_evaluation
+
+
+def evaluate(dataset, predictions, output_folder, **kwargs):
+    """evaluate dataset using different methods based on dataset type.
+    Args:
+        dataset: Dataset object
+        predictions(list[BoxList]): each item in the list represents the
+            prediction results for one image.
+        output_folder: output folder, to save evaluation files or results.
+        **kwargs: other args.
+    Returns:
+        evaluation result
+    """
+    args = dict(
+        dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
+    )
+    if isinstance(dataset, datasets.COCODataset):
+        return coco_evaluation(**args)
+    elif isinstance(dataset, datasets.PascalVOCDataset):
+        return voc_evaluation(**args)
+    else:
+        dataset_name = dataset.__class__.__name__
+        raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name))

maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py

@@ -0,0 +1,21 @@
+from .coco_eval import do_coco_evaluation
+
+
+def coco_evaluation(
+    dataset,
+    predictions,
+    output_folder,
+    box_only,
+    iou_types,
+    expected_results,
+    expected_results_sigma_tol,
+):
+    return do_coco_evaluation(
+        dataset=dataset,
+        predictions=predictions,
+        box_only=box_only,
+        output_folder=output_folder,
+        iou_types=iou_types,
+        expected_results=expected_results,
+        expected_results_sigma_tol=expected_results_sigma_tol,
+    )