feat(detection): support models with res101 backbone (#48)

README.md

@@ -70,16 +70,18 @@ export PYTHONPATH=/path/to/models:$PYTHONPATH
 | ShuffleNetV2 x1.5 |  72.806  |  90.792  |
 | ShuffleNetV2 x2.0 |  75.074  |  92.278  |
 
-
 ### 目标检测
 
 目标检测同样是计算机视觉中的常见任务，我们提供了两个经典的目标检测模型[Retinanet](./official/vision/detection/model/retinanet)和[Faster R-CNN](./official/vision/detection/model/faster_rcnn)，这两个模型在**COCO验证集**上的测试结果如下：
 
 | 模型                                     | mAP<br>@5-95 |
 | :---:                                    | :---:        |
-| retinanet-res50-1x-800size   | 36.4         |
-| faster-rcnn-res50-1x-800size | 38.8         |
-
+| retinanet-res50-coco-1x-800size          | 36.4         |
+| retinanet-res50-coco-1x-800size-syncbn   | 37.1         |
+| retinanet-res101-coco-2x-800size         | 40.8         |
+| faster-rcnn-res50-coco-1x-800size        | 38.8         |
+| faster-rcnn-res50-coco-1x-800size-syncbn | 39.3         |
+| faster-rcnn-res101-coco-2x-800size       | 43.0         |
 
 ### 图像分割
 
@@ -117,7 +119,6 @@ export PYTHONPATH=/path/to/models:$PYTHONPATH
 | chinese_L-12_H-768_A-12| [link](https://data.megengine.org.cn/models/weights/bert/chinese_L-12_H-768_A-12/vocab.txt) | [link](https://data.megengine.org.cn/models/weights/bert/chinese_L-12_H-768_A-12/bert_config.json)
 | multi_cased_L-12_H-768_A-12| [link](https://data.megengine.org.cn/models/weights/bert/multi_cased_L-12_H-768_A-12/vocab.txt) | [link](https://data.megengine.org.cn/models/weights/bert/multi_cased_L-12_H-768_A-12/bert_config.json)
 
-
 在glue_data/MRPC数据集中使用默认的超参数进行微调和评估，评估结果介于84％和88％之间。
 
 | Dataset | pretrained_bert | acc |

hubconf.py

@@ -30,8 +30,10 @@ from official.vision.classification.shufflenet.model import (
 from official.vision.detection.configs import (
     faster_rcnn_res50_coco_1x_800size,
     faster_rcnn_res50_coco_1x_800size_syncbn,
+    faster_rcnn_res101_coco_2x_800size,
     retinanet_res50_coco_1x_800size,
     retinanet_res50_coco_1x_800size_syncbn,
+    retinanet_res101_coco_2x_800size,
 )
 from official.vision.detection.models import FasterRCNN, RetinaNet
 from official.vision.detection.tools.utils import DetEvaluator

official/vision/detection/README.md

@@ -10,10 +10,12 @@
 | ---                                      | :---:        | :---:         | :---:  | :---:                     |
 | retinanet-res50-coco-1x-800size          | 36.4         | 2             | 2080Ti | 3.1(it/s)                 |
 | retinanet-res50-coco-1x-800size-syncbn   | 37.1         | 2             | 2080Ti | 1.7(it/s)                 |
+| retinanet-res101-coco-2x-800size         | 40.8         | 2             | 2080Ti | 2.1(it/s)                 |
 | faster-rcnn-res50-coco-1x-800size        | 38.8         | 2             | 2080Ti | 3.3(it/s)                 |
 | faster-rcnn-res50-coco-1x-800size-syncbn | 39.3         | 2             | 2080Ti | 1.8(it/s)                 |
+| faster-rcnn-res101-coco-2x-800size       | 43.0         | 2             | 2080Ti | 2.3(it/s)                 |
 
-* MegEngine v0.4.0
+* MegEngine v0.5.1
 
 ## 如何使用
 

official/vision/detection/configs/__init__.py

 from .faster_rcnn_res50_coco_1x_800size import faster_rcnn_res50_coco_1x_800size
 from .faster_rcnn_res50_coco_1x_800size_syncbn import faster_rcnn_res50_coco_1x_800size_syncbn
+from .faster_rcnn_res101_coco_2x_800size import faster_rcnn_res101_coco_2x_800size
 from .retinanet_res50_coco_1x_800size import retinanet_res50_coco_1x_800size
 from .retinanet_res50_coco_1x_800size_syncbn import retinanet_res50_coco_1x_800size_syncbn
+from .retinanet_res101_coco_2x_800size import retinanet_res101_coco_2x_800size
 
 _EXCLUDE = {}
 __all__ = [k for k in globals().keys() if k not in _EXCLUDE and not k.startswith("_")]

official/vision/detection/configs/faster_rcnn_res101_coco_2x_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 megengine import hub
+
+from official.vision.detection import models
+
+
+class CustomFasterRCNNConfig(models.FasterRCNNConfig):
+    def __init__(self):
+        super().__init__()
+
+        self.backbone = "resnet101"
+
+        # ------------------------ training cfg ---------------------- #
+        self.max_epoch = 36
+        self.lr_decay_stages = [24, 32, 34]
+
+
+@hub.pretrained(
+    "https://data.megengine.org.cn/models/weights/"
+    "faster_rcnn_res101_coco_2x_800size_43dot0_ee249359.pkl"
+)
+def faster_rcnn_res101_coco_2x_800size(batch_size=1, **kwargs):
+    r"""
+    Faster-RCNN FPN trained from COCO dataset.
+    `"Faster-RCNN" <https://arxiv.org/abs/1506.01497>`_
+    `"FPN" <https://arxiv.org/abs/1612.03144>`_
+    `"COCO" <https://arxiv.org/abs/1405.0312>`_
+    """
+    return models.FasterRCNN(CustomFasterRCNNConfig(), batch_size=batch_size, **kwargs)
+
+
+Net = models.FasterRCNN
+Cfg = CustomFasterRCNNConfig

official/vision/detection/configs/retinanet_res101_coco_2x_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 megengine import hub
+
+from official.vision.detection import models
+
+
+class CustomRetinaNetConfig(models.RetinaNetConfig):
+    def __init__(self):
+        super().__init__()
+
+        self.backbone = "resnet101"
+
+        # ------------------------ training cfg ---------------------- #
+        self.max_epoch = 36
+        self.lr_decay_stages = [24, 32, 34]
+
+
+@hub.pretrained(
+    "https://data.megengine.org.cn/models/weights/"
+    "retinanet_res101_coco_2x_800size_40dot8_661c3608.pkl"
+)
+def retinanet_res101_coco_2x_800size(batch_size=1, **kwargs):
+    r"""
+    RetinaNet trained from COCO dataset.
+    `"RetinaNet" <https://arxiv.org/abs/1708.02002>`_
+    `"FPN" <https://arxiv.org/abs/1612.03144>`_
+    `"COCO" <https://arxiv.org/abs/1405.0312>`_
+    """
+    return models.RetinaNet(CustomRetinaNetConfig(), batch_size=batch_size, **kwargs)
+
+
+Net = models.RetinaNet
+Cfg = CustomRetinaNetConfig

official/vision/detection/tools/inference.py

@@ -48,7 +48,9 @@ def main():
 
     sys.path.insert(0, os.path.dirname(args.file))
     current_network = importlib.import_module(os.path.basename(args.file).split(".")[0])
-    model = current_network.Net(current_network.Cfg(), batch_size=1)
+    cfg = current_network.Cfg()
+    cfg.backbone_pretrained = False
+    model = current_network.Net(cfg, batch_size=1)
     model.eval()
     state_dict = mge.load(args.weight_file)
     if "state_dict" in state_dict:

official/vision/detection/tools/test.py

@@ -37,9 +37,6 @@ def make_parser():
     parser.add_argument(
         "-n", "--ngpus", default=1, type=int, help="total number of gpus for testing",
     )
-    parser.add_argument(
-        "-b", "--batch_size", default=1, type=int, help="batchsize for testing",
-    )
     parser.add_argument(
         "-d", "--dataset_dir", default="/data/datasets", type=str,
     )
@@ -56,6 +53,9 @@ def main():
     parser = make_parser()
     args = parser.parse_args()
 
+    sys.path.insert(0, os.path.dirname(args.file))
+    current_network = importlib.import_module(os.path.basename(args.file).split(".")[0])
+
     if args.end_epoch == -1:
         args.end_epoch = args.start_epoch
 
@@ -75,7 +75,7 @@ def main():
             proc = Process(
                 target=worker,
                 args=(
-                    args.file,
+                    current_network,
                     model_file,
                     args.dataset_dir,
                     i,
@@ -86,10 +86,6 @@ def main():
             proc.start()
             procs.append(proc)
 
-        sys.path.insert(0, os.path.dirname(args.file))
-        current_network = importlib.import_module(
-            os.path.basename(args.file).split(".")[0]
-        )
         cfg = current_network.Cfg()
         num_imgs = dict(coco=5000, objects365=30000)
 
@@ -139,7 +135,7 @@ def main():
 
 
 def worker(
-    net_file, model_file, data_dir, worker_id, total_worker, result_queue,
+    current_network, model_file, data_dir, worker_id, total_worker, result_queue,
 ):
     """
     :param net_file: network description file
@@ -156,9 +152,9 @@ def worker(
         pred = model(model.inputs)
         return pred
 
-    sys.path.insert(0, os.path.dirname(net_file))
-    current_network = importlib.import_module(os.path.basename(net_file).split(".")[0])
-    model = current_network.Net(current_network.Cfg(), batch_size=1)
+    cfg = current_network.Cfg()
+    cfg.backbone_pretrained = False
+    model = current_network.Net(cfg, batch_size=1)
     model.eval()
     evaluator = DetEvaluator(model)
     state_dict = mge.load(model_file)