feat(detection): support Objects365 (#58)

official/vision/detection/configs/faster_rcnn_res50_objects365_1x_800size.py

+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+from official.vision.detection import models
+
+
+class CustomFasterRCNNConfig(models.FasterRCNNConfig):
+    def __init__(self):
+        super().__init__()
+
+        # ------------------------ data cfg -------------------------- #
+        self.train_dataset = dict(
+            name="objects365",
+            root="train",
+            ann_file="annotations/objects365_train_20190423.json",
+            remove_images_without_annotations=True,
+        )
+        self.test_dataset = dict(
+            name="objects365",
+            root="val",
+            ann_file="annotations/objects365_val_20190423.json",
+            remove_images_without_annotations=False,
+        )
+        self.num_classes = 365
+
+        # ------------------------ training cfg ---------------------- #
+        self.nr_images_epoch = 400000
+
+
+def faster_rcnn_res50_objects365_1x_800size(batch_size=1, **kwargs):
+    r"""
+    Faster-RCNN FPN trained from Objects365 dataset.
+    `"Faster-RCNN" <https://arxiv.org/abs/1506.01497>`_
+    `"FPN" <https://arxiv.org/abs/1612.03144>`_
+    """
+    cfg = CustomFasterRCNNConfig()
+    cfg.backbone_pretrained = False
+    return models.FasterRCNN(cfg, batch_size=batch_size, **kwargs)
+
+
+Net = models.FasterRCNN
+Cfg = CustomFasterRCNNConfig

official/vision/detection/configs/retinanet_res50_objects365_1x_800size.py

+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+from official.vision.detection import models
+
+
+class CustomRetinaNetConfig(models.RetinaNetConfig):
+    def __init__(self):
+        super().__init__()
+
+        # ------------------------ data cfg -------------------------- #
+        self.train_dataset = dict(
+            name="objects365",
+            root="train",
+            ann_file="annotations/objects365_train_20190423.json",
+            remove_images_without_annotations=True,
+        )
+        self.test_dataset = dict(
+            name="objects365",
+            root="val",
+            ann_file="annotations/objects365_val_20190423.json",
+            remove_images_without_annotations=False,
+        )
+        self.num_classes = 365
+
+        # ------------------------ training cfg ---------------------- #
+        self.nr_images_epoch = 400000
+
+
+def retinanet_res50_objects365_1x_800size(batch_size=1, **kwargs):
+    r"""
+    RetinaNet trained from Objects365 dataset.
+    `"RetinaNet" <https://arxiv.org/abs/1708.02002>`_
+    `"FPN" <https://arxiv.org/abs/1612.03144>`_
+    """
+    cfg = CustomRetinaNetConfig()
+    cfg.backbone_pretrained = False
+    return models.RetinaNet(cfg, batch_size=batch_size, **kwargs)
+
+
+Net = models.RetinaNet
+Cfg = CustomRetinaNetConfig

official/vision/detection/tools/inference.py

@@ -18,6 +18,7 @@ import megengine as mge
 from megengine import jit
 from megengine.data.dataset import COCO
 
+from official.vision.detection.tools.data_mapper import data_mapper
 from official.vision.detection.tools.utils import DetEvaluator
 
 logger = mge.get_logger(__name__)
@@ -61,7 +62,10 @@ def main():
     model.inputs["im_info"].set_value(im_info)
     pred_res = evaluator.predict(val_func)
     res_img = DetEvaluator.vis_det(
-        ori_img, pred_res, is_show_label=True, classes=COCO.class_names,
+        ori_img,
+        pred_res,
+        is_show_label=True,
+        classes=data_mapper[cfg.test_dataset["name"]].class_names,
     )
     cv2.imwrite("results.jpg", res_img)
 

official/vision/detection/tools/test.py

@@ -182,7 +182,7 @@ def worker(
         result_queue.put_nowait(
             {
                 "det_res": pred_res,
-                "image_id": int(data_dict[1][2][0].split(".")[0]),
+                "image_id": int(data_dict[1][2][0].split(".")[0].split("_")[-1]),
             }
         )
 

official/vision/detection/tools/utils.py

@@ -242,7 +242,7 @@ class DetEvaluator:
                         dataset_class.class_names[int(box[5])]
                     ]
                 else:
-                    elem["category_id"] = int(box[5])
+                    elem["category_id"] = int(box[5]) + 1
                 all_results.append(elem)
         return all_results